**Meet the editors**

Cláudia do Rosario Vaz Morgado has been associate professor at the Polytechnic School of Rio de Janeiro Federal University (UFRJ) since 1997. She received a D.Sc. in Industrial Engineering (1994) from COPPE/ UFRJ. She has been the head of the human resources program in Environmental Engineering of the National Petroleum Agency since 2010. She was the founder of

the Program in Environmental Engineering of the Polytechnic School and Chemistry School at UFRJ (2007-2011). She was the former head of the Environmental Committee of the Regional Council of Architecture, Engineering and Agronomy of Rio de Janeiro/CREA-RJ. Prof. Morgado also founded GESTORE, a leading group that acts in training and research in life cycle assessment, governance and sustainability.

Victor Paulo Peçanha Esteves is assistant professor of Information Technology and Environment with the Professional Master's Program of UFRJ. He has been a lecturer and senior researcher in Department of Electronic Engineering at UFRJ since 1987. He holds a B.Sc. in Electronic Engineering (1987), Executive MBA in Environmental Management (2008) and M.Sc. in Envi-

ronmental Engineering (2011), all from UFRJ. He is also a civilian speaker and instructor for the Brazilian Navy. He has carried out research projects for Petrobras and other companies and public sector. His recent research interests include a georeferenced and ecological approach for carbon sequestration and biofuels.

## Contents

### **Preface XI**


E. Gbur, Nathan A. Slaton and Michelle A. Evans-White


Margarita J. Ramírez-Moreno, Issis C. Romero-Ibarra, José Ortiz-Landeros and Heriberto Pfeiffer

Chapter 15 **Predicting the Phase Equilibria of Carbon Dioxide Containing Mixtures Involved in CCS Processes Using the PPR78 Model 443**

Romain Privat and Jean-Noël Jaubert

## Preface

Chapter 8 **Carbon Sequestration in Central European Forest**

Robert Jandl and Andreas Schindlbacher

**Bioenergy Cropping Systems 251**

N. Pawlowski and Susan E. Crow

Chapter 9 **The Role of Simulation Models in Monitoring Soil Organic**

Chapter 10 **Pre-Injection Phase: Site Selection and Characterization 281** B. Llamas, M. Arribas, E. Hernandez and L.F. Mazadiego

Chapter 11 **Numerical Simulation of CO2 Sequestration in Large Saline**

Chapter 13 **Optimization of CO2 Sequestration in Saline Aquifers 365**

Chapter 14 **Alkaline and Alkaline-Earth Ceramic Oxides for CO2 Capture, Separation and Subsequent Catalytic Chemical**

Chapter 15 **Predicting the Phase Equilibria of Carbon Dioxide Containing Mixtures Involved in CCS Processes Using the**

Margarita J. Ramírez-Moreno, Issis C. Romero-Ibarra, José Ortiz-

Zheming Zhang and Ramesh K. Agarwal

Claudia L. Ravazzoli and Julián L. Gómez

Ramesh K. Agarwal and Zheming Zhang

Landeros and Heriberto Pfeiffer

Romain Privat and Jean-Noël Jaubert

Chapter 12 **Seismic Reflectivity in Carbon Dioxide Accumulations:**

**Carbon Storage and Greenhouse Gas Mitigation Potential in**

Manyowa N. Meki, James R. Kiniry, Kathrine D. Behrman, Meghan

**Ecosystems 225**

**VI** Contents

**Aquifers 305**

**A Review 343**

**Conversion 403**

**PPR78 Model 443**

It is well established that the principal causes of the increasing concentration of CO2 in the atmosphere are human activities, mainly the burning of fossil fuels for transportation and electricity generation.

Notwithstanding the huge efforts to obtain a cleaner energy mix, human society has far to go to achieve the oft-expressed goal of sustainable development. The reconciliation of eco‐ nomic development, social justice and reduction of greenhouse gas emissions is one of the main political challenges of the moment.

Strategies for large-scale mitigation of CO2 emissions using technologies for sequestration, storage and utilization of carbon, along with increasing the carbon stock in plant cover (natural or not), are priorities on the agenda of research centers and governments around the world.

Readers will find in this book fifteen chapters that reflect on, propose, study and contribute to new technologies, strategies and policies for sustainability of the planet.

The four classes of carbon sequestration presented are: natural sequestration, which uses mechanisms to maintain carbon stocks in natural plant cover; biological sequestration, which employs management techniques in agriculture and silviculture; sequestration by storage in the ocean or in geological sites by capture and injection in rock formations; and sequestration by reuse of carbon on a large scale – chemical and/or biochemical sequestra‐ tion – where the effort is to achieve a balance between inputs and products in the various productive chains that use carbon as a raw material.

The first three chapters cover new strategies and technological solutions proposed for sus‐ tainability in relation to atmospheric emissions. Reducing emissions from deforestation and forest degradation (REDD) combined with payment for environmental services, use of CO2 as a raw material for chemical products and fuels within a perspective for regional syner‐ gies, and the feasibility and public acceptance of carbon sequestration in the oceans are the themes addressed.

The next six chapters present studies, simulations and proposals for management models to maintain and monitor the carbon stocks in the soil, forests and cultivated areas.

The last six chapters examine the systems and subsystems for carbon capture and geological storage (CCGS) in the phases of site characterization and selection, capture, separation and injection of CO2 and various models for thermodynamic, multicriteria and seismic simulation.

The editors would like to extend special thanks to Professor Ofélia Araújo of Rio de Janeiro Federal University and the coordinators of the PROCLIMA and PRO-CO2 programs at the Petrobras Research Center (CENPES), especially the engineers Paulo Cunha, Paulo Negrais and Wilson Grava, for their encouragement, stimulus and support for our research efforts into carbon sequestration.

The editors kindly acknowledge financial support from the Civil Construction Department of the Federal University Rio de Janeiro.

We also thank InTech Publisher for the invitation to edit this book on such an important theme to the welfare of humanity, today and in the future.

Finally, we acknowledge the contribution of the chapters' authors by providing a better un‐ derstanding of the latest research in this important theme.

Research into carbon sequestration is a key to solving one of the greatest economic, environ‐ mental and social problems of this century – a complex question that requires a multidisci‐ plinary approach involving science and technology along with collaborative policies among nations. This challenge makes this book an important source of information for researchers, policymakers and anyone with an inquiring mind on this subject.

> **Cláudia do R. Vaz Morgado and Victor P. P. Esteves** Polytechnic School Rio de Janeiro Federal University

#### **REDD Roses for a Green Lady – Target Setting for Deforestation in the Brazilian Amazon** 2 **REDD Roses for a Green Lady – Target Setting for** 3 **Deforestation in the Brazilian Amazon**

Valny Giacomelli Sobrinho 4 Valny Giacomelli Sobrinho

Additional information is available at the end of the chapter 5 Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57288

7 **1. Introduction**

and Wilson Grava, for their encouragement, stimulus and support for our research efforts

The editors kindly acknowledge financial support from the Civil Construction Department

We also thank InTech Publisher for the invitation to edit this book on such an important

Finally, we acknowledge the contribution of the chapters' authors by providing a better un‐

Research into carbon sequestration is a key to solving one of the greatest economic, environ‐ mental and social problems of this century – a complex question that requires a multidisci‐ plinary approach involving science and technology along with collaborative policies among nations. This challenge makes this book an important source of information for researchers,

**Cláudia do R. Vaz Morgado and Victor P. P. Esteves**

Polytechnic School

Rio de Janeiro Federal University

into carbon sequestration.

VIII Preface

of the Federal University Rio de Janeiro.

theme to the welfare of humanity, today and in the future.

derstanding of the latest research in this important theme.

policymakers and anyone with an inquiring mind on this subject.

 In the lyrics written by Sid Tepper and Roy C. Bennett of a popular 1948 song, a broken-hearted guy, who had the day before argued with his girlfriend, rushed to the florist to buy some "red roses for a blue lady". His hope was that those pretty flowers could chase her blues away. In short, he wished some red flowers could compensate for the damage he had caused to his lover's heart.

Likewise, climate policy has recently1 13 come up with the REDD mechanism (Reduced Emissions from Deforestation and forest Degradation) to protect natural standing forests before they are damaged by deforestation or degradation. Within the REDD framework, carbon credits can be earned for deforestation avoidance, rather than as in forestry CDM (Clean Development Mechanism), for afforestation or reforestation.

18 Since deforestation gives off nearly one-fifth (1.6 Gt) of global carbon emissions [2], avoiding 19 it is claimed to be the most effective and cheapest way of control. However, until recently 20 deforestation avoidance had been kept out of international climate accords, mainly because of

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

<sup>1</sup> The first time REDD came into the UNFCCC (United Nations Framework Convention on Climate Change) agenda was in 2005, when a mechanism for reducing deforestation and forest degradation was proposed by Papua New Guinea, Costa Rica and eight other countries. Two years earlier, Brazilian researchers had already come up with a similar tool backed by international payments for reduced deforestation. Between the Conference of the Parties (COP) in Bali, Indonesia, in 2007 (COP-13), and in Copenhagen, Denmark, in 2009 (COP-15), different REDD proposals emerged. As the scope of the mechanism was getting wider, its abbreviation was getting longer with the addition of "D's" and plus (+) signs. Chronologically, it started with RED — short for Reducing Emissions from Deforestation; next, it became REDD — when forest Degradation was added; then, it turned out to be REDD+ — so as to encompass conservation and enhancement of forest stocks through Sustainable Forest Management (SFM) techniques; at present, it has often been labelled REDD++, including reforestation and afforestation — that is, reforestation of non-forested areas [1]. Throughout this chapter, simply REDD will be employed, regardless of its several chronological meanings. However, afforestation and reforestation will be considered to fall into forestry-CDM — the branch of the Clean Development Mechanism suited to unnatural forests.

 the fear that credits supplied in return could flood carbon markets, thereby lowering the price of carbon credits [2]. But, with the closing, in 2012, of the first commitment period of the Kyoto Protocol (2008-2012), avoiding deforestation has been taken up as a post-2012 proposal for an international agreement that might bring in developing (non-Annex I) countries also to comply with emission targets.

 Although the forest conservation feature of REDD means a step forward regarding carbon offsets from either energy or forestry-CDM [3], the credits arising from avoiding deforestation sound like roses offered as rewards for *not* causing damages to any "green lady" (forest). However, public incentive systems seldom pay people for not doing something — in the case of REDD, for not deforesting or not degrading. Hence, since a reward means a payment due for not doing something, it might encourage people to do what is already forbidden. In addition, it could make room for compensation seekers, whose anti-social and imprudent behaviour would otherwise have been inhibited by moral or legal censure [4].

 Anyway, such a reward largely rests upon the creation of a market for REDD credits, in which Payments for Ecosystem Services (PES) are made to landowners and users, to compensate them for keeping forests — which are worth more alive than dead. PES schemes are defined as voluntary, conditional transactions — the payment is only made if the service is delivered — with at least one seller, one buyer, and a well-defined environmental service [2]. However, none of these conditions are yet in place in most REDD countries: the commodity is hard to quantify, the sellers are not well defined, big buyers do not exist and the rules of the game are not well established [5].

 Besides these practical hindrances, REDD architecture is not without ideological critiques. Polanyi's [6] followers have charged not only REDD with having a privatising and marketing nature, but also PES with allowing elites and large-scale commercial actors to profit from what is called "forest capitalism". The gist of the criticism is that the largest share of deforestation is carried out by larger and wealthier players than by the poor, who REDD notwithstanding claims to be the greatest beneficiaries of the payments for avoiding deforestation [5]. Thus, of course, the bulk of these reward payments is expected to go to the players whose monetary foregone benefits are the greatest, when deforestation is halted.

 Another pitfall of REDD and PES is that they rely too much on financial support. Although forests are assigned a high value in public debate, the relatively low returns accruing to forest management are due to the many and diverse non-financial benefits that forests can provide [7]. Both global REDD regimes and national REDD strategies call for the provision of financial resources — in the form of compensation or incentive payments — to alter the political economy of commercial access to forest resources [2]. Yet, REDD finance hinges on political will, institutional settings and long bureaucratic chains to work properly [2], [5].

 At the national level, REDD policies demand the removal of support and subsidies for companies or activities that increase the pressure towards forest conversion — agricultural expansion, destructive logging, settlement schemes, plantation and road development in forest areas [2]. At the international level, a large proportion of finance will need to come from developed countries. Therefore, the scale of such finance will depend on the political will  within these countries [5]. Nevertheless, developed countries are not likely to transfer payments to fragile states, where long-term efforts would be required to create or reform institutions, strengthen governance processes and build capacity to deal with the new models of forest management underlying REDD policies [2].

 the fear that credits supplied in return could flood carbon markets, thereby lowering the price of carbon credits [2]. But, with the closing, in 2012, of the first commitment period of the Kyoto Protocol (2008-2012), avoiding deforestation has been taken up as a post-2012 proposal for an international agreement that might bring in developing (non-Annex I) countries also to comply

 Although the forest conservation feature of REDD means a step forward regarding carbon offsets from either energy or forestry-CDM [3], the credits arising from avoiding deforestation sound like roses offered as rewards for *not* causing damages to any "green lady" (forest). However, public incentive systems seldom pay people for not doing something — in the case of REDD, for not deforesting or not degrading. Hence, since a reward means a payment due for not doing something, it might encourage people to do what is already forbidden. In addition, it could make room for compensation seekers, whose anti-social and imprudent

 Anyway, such a reward largely rests upon the creation of a market for REDD credits, in which Payments for Ecosystem Services (PES) are made to landowners and users, to compensate them for keeping forests — which are worth more alive than dead. PES schemes are defined as voluntary, conditional transactions — the payment is only made if the service is delivered — with at least one seller, one buyer, and a well-defined environmental service [2]. However, none of these conditions are yet in place in most REDD countries: the commodity is hard to quantify, the sellers are not well defined, big buyers do not exist and the rules of the game are

 Besides these practical hindrances, REDD architecture is not without ideological critiques. Polanyi's [6] followers have charged not only REDD with having a privatising and marketing nature, but also PES with allowing elites and large-scale commercial actors to profit from what is called "forest capitalism". The gist of the criticism is that the largest share of deforestation is carried out by larger and wealthier players than by the poor, who REDD notwithstanding claims to be the greatest beneficiaries of the payments for avoiding deforestation [5]. Thus, of course, the bulk of these reward payments is expected to go to the players whose monetary

 Another pitfall of REDD and PES is that they rely too much on financial support. Although forests are assigned a high value in public debate, the relatively low returns accruing to forest management are due to the many and diverse non-financial benefits that forests can provide [7]. Both global REDD regimes and national REDD strategies call for the provision of financial resources — in the form of compensation or incentive payments — to alter the political economy of commercial access to forest resources [2]. Yet, REDD finance hinges on political

 At the national level, REDD policies demand the removal of support and subsidies for companies or activities that increase the pressure towards forest conversion — agricultural expansion, destructive logging, settlement schemes, plantation and road development in forest areas [2]. At the international level, a large proportion of finance will need to come from developed countries. Therefore, the scale of such finance will depend on the political will

will, institutional settings and long bureaucratic chains to work properly [2], [5].

behaviour would otherwise have been inhibited by moral or legal censure [4].

foregone benefits are the greatest, when deforestation is halted.

with emission targets.

2 Carbon Sequestration CO2 Sequestration and Valorization

not well established [5].

 In any event, disturbances of this sort rest not only upon the promise to serve different actors and interests, but also upon that to bridge the environment and development agendas. While this proposal sounds so appealing and distinct from past efforts in the forestry sector, it not only has turned REDD into a successful idea, but also made it move from single (carbon) to multiple objectives. Such a move, though, is now threatening and overshadowing the main characteristic of REDD, which comes down to large-scale funding and performance-based support. So far, the vast majority of both developed and developing countries lack concrete strategies on how to implement REDD. Therefore, REDD finance remains unresolved, because the cost of reducing emissions from deforestation also depends on the strategy chosen [5].

 Arguably, new strategies must emphasise carbon-stock protection [2]. Many studies have indicated that, after wood production, carbon sequestration is the most valuable output from forests [7]. However, a conservation market (REDD credits) for forest protection should draw on a form of compensation for producing something additional (new carbon stored), rather than on a reward for "not doing something" (not deforesting or not degrading) [4]. To begin with, this is supposed to help the political economy of REDD build the argument for its public support [4], thus allaying the mistrust that lurks over it of rewarding the wealthiest forest users.

 A further step towards that shift lies in emphasising a *stock maintenance* rather than an *emissions avoidance* approach. This turns carbon conservation strategies from output (performance) into input-driven ones. After all, forest sequestration of carbon emissions is primarily a matter of forestland availability rather than of emissions avoided. If forest stocks are maintained or even increased, they do not emit carbon, which is kept there. Although this might sound like a "two- sides-of-the-same-coin" problem, the stock maintenance approach highlights the positive side of conservation — the stewardship of carbon stocks — whereas the emissions avoidance approach stresses its negative side — the discouragement or closure of activities causing emissions to rise in spite of delivering economic benefits. Quite often, the latter is seen as an unproductive strategy while the former might well evoke the production of some useful commodity.

 The commodity at stake is carbon storage rather than money. Yet, REDD finance claims that economic and monetary incentives can, through price signals, alter the decisions of individual land users and compensate them for foregone benefits from not converting or degrading the forest [2]. Since carbon storage is increasingly needed, the demand for carbon credits is expected to go up, thereby generating finance for forest conservation [5]. At present, however, there is deep uncertainty as to whether and how a future international climate agreement would value carbon sequestration provided by forests. In addition, when standing forests compete with high-value agricultural and mining commodities, no one can ensure anymore whether and how REDD funding — particularly output, results-based finance — would be available in the future. So far, from current REDD finance, it is widely recognised that more  money alone cannot solve the deforestation problem and that the expectations of more money can even make it worse [8].

 As argued elsewhere [3], [9], [10], since the ascent of money in modern societies, a commun‐ ity's wealth now has two components: real goods, accumulated through real investments, and fiat or paper "goods", made of nothing. Unlike the former, the latter kind of wealth is not subject to the natural law of decrease, the entropy law. Whereas the accumulation of real goods, which hold use value, does meet physical limits, there is no limit at all to the accumulation of "virtual wealth" [10], used for exchange only [9]. So, in this world, where the substance is exchanged for the shadow, what determines the value of money is the amount of wealth people prefer to do without, and that is the same as the amount of credit they retain as money [10]. In monetary economies, the greater this "virtual wealth" is, the higher the price of the real goods it can afford; conversely, the smaller the former, the lower the latter2 . It is puzzling how the protection of environmental goods and services should be commanded by such a virtual wealth rationale [3], [9].

 In this regard, a non-monetary Bio-Economic model for carbon Sequestration by Forests (BESF) [3] is applied to a deforestation scenario taking place in the Brazilian "Legal Amazon" region (*Amazônia Legal*, Figure 1), to show how forest stocks can be prevented from being degraded. After all, if these stocks wither away, they will no longer be able to store carbon emissions. The model assumes that the growth of natural stocks follows an upper limited path. Therefore, if emissions from economic growth (*k*) have to be taken in by standing natural forests, their stocks could not fall below a certain limit. Those forest storage and tree growth constraints cap emissions.

 Building upon fishery and forest bio-economics, in the BESF model emitters play the catchers, whereas natural forests supply the catch — namely, the environmental service of emissions storage. But unlike in fishery and forest models, the "catch", in this case, is an input rather than an output. As emissions increase, so does the demand for their storage, and the supply of forest stocks goes down. Thus, forest stocks turn out to be priced biophysically rather than monetarily.

 Such a biophysically set price is called the *bio-economic exchange rate* (ε). Found by dynamic optimisation methods, it works as a *shadow price* measuring the shortage of the environmental input (carbon removal) on demand. At any given time, the more (less) this service is demanded, the less (more) of it is carried over into the future. However, the supply of current removal stocks varies across the carbon sinks. Such spatial differences, measured by the *bio-diversity ratio* λ, are called "exports" (*Z*) of carbon removal to elsewhere and correspond to *conservation*

<sup>2</sup> In monetary economics, this relationship is known as the "quantity theory of money". "The quantity theory is a mechanistic proposition strangely alien to the assumptions of rational maximising behaviour on which classical and neoclassical theories generally rely ... It ignores the effects of the returns to holding money on the amounts economic agents choose to hold ... Money holdings depend ... on the opportunity costs, the expected changes in the value of money and the real yields of other assets into which the same funds could be placed" [11]. Of course, these remarks follow a Keynesian theoretical tradition, within which money is thought to be an asset rather than a token of bank liabilities to current account holders. Taking money as liabilities dates back to the days when bank-notes were "promises to pay", handed over at once as a receipt to depositors who had voluntarily given up gold to the bank, which, in turn, promised to repay them on demand [10].

1 *loans* (REDD credits) between carbon sinks. On the other hand, the provision of such an 2 environmental service in the future is "imported" (borrowed) from the savings of removal 3 stocks that happened in the past. Therefore, these transfers of carbon removal over time are 4 called "imports" (*M*) and are referred to as *compensation loans* (CDM offsets) between the sinks. "exports" (*Z*) of carbon removal to elsewhere and correspond to *conservation loans* (REDD credits) between carbon sinks. On the other hand, the provision of such an environmental service in the future is "imported" (borrowed) from the savings of removal stocks that happened in the past. Therefore,

these transfers of carbon removal over time are called "imports" (*M*) and are referred to as

*compensation loans* (CDM offsets) between the sinks.

1 money alone cannot solve the deforestation problem and that the expectations of more money

 As argued elsewhere [3], [9], [10], since the ascent of money in modern societies, a commun‐ ity's wealth now has two components: real goods, accumulated through real investments, and fiat or paper "goods", made of nothing. Unlike the former, the latter kind of wealth is not subject to the natural law of decrease, the entropy law. Whereas the accumulation of real goods, which hold use value, does meet physical limits, there is no limit at all to the accumulation of "virtual wealth" [10], used for exchange only [9]. So, in this world, where the substance is exchanged for the shadow, what determines the value of money is the amount of wealth people prefer to do without, and that is the same as the amount of credit they retain as money [10]. In monetary economies, the greater this "virtual wealth" is, the higher the price of the real goods it can afford; conversely, the smaller the former, the lower the latter2 13 . It is puzzling how the protection of environmental goods and services should be

 In this regard, a non-monetary Bio-Economic model for carbon Sequestration by Forests (BESF) [3] is applied to a deforestation scenario taking place in the Brazilian "Legal Amazon" region (*Amazônia Legal*, Figure 1), to show how forest stocks can be prevented from being degraded. After all, if these stocks wither away, they will no longer be able to store carbon emissions. The model assumes that the growth of natural stocks follows an upper limited path. Therefore, if emissions from economic growth (*k*) have to be taken in by standing natural forests, their stocks could not fall below a certain limit. Those forest storage and tree growth constraints cap

 Building upon fishery and forest bio-economics, in the BESF model emitters play the catchers, whereas natural forests supply the catch — namely, the environmental service of emissions storage. But unlike in fishery and forest models, the "catch", in this case, is an input rather than an output. As emissions increase, so does the demand for their storage, and the supply of forest stocks goes down. Thus, forest stocks turn out to be priced biophysically rather than

 Such a biophysically set price is called the *bio-economic exchange rate* (ε). Found by dynamic optimisation methods, it works as a *shadow price* measuring the shortage of the environmental input (carbon removal) on demand. At any given time, the more (less) this service is demanded, the less (more) of it is carried over into the future. However, the supply of current removal stocks varies across the carbon sinks. Such spatial differences, measured by the *bio-diversity ratio* λ, are called "exports" (*Z*) of carbon removal to elsewhere and correspond to *conservation*

2 In monetary economics, this relationship is known as the "quantity theory of money". "The quantity theory is a mechanistic proposition strangely alien to the assumptions of rational maximising behaviour on which classical and neoclassical theories generally rely ... It ignores the effects of the returns to holding money on the amounts economic agents choose to hold ... Money holdings depend ... on the opportunity costs, the expected changes in the value of money and the real yields of other assets into which the same funds could be placed" [11]. Of course, these remarks follow a Keynesian theoretical tradition, within which money is thought to be an asset rather than a token of bank liabilities to current account holders. Taking money as liabilities dates back to the days when bank-notes were "promises to pay", handed over at once as a receipt to depositors who had voluntarily given up gold to the bank,

2 can even make it worse [8].

4 Carbon Sequestration 4 CO2 Sequestration and Valorization

22 emissions.

28 monetarily.

14 commanded by such a virtual wealth rationale [3], [9].

which, in turn, promised to repay them on demand [10].

Source: [12] Figure 1. Deforestation accumulated until 2012 in the Brazilian Legal Amazon 6 (\*) Prodes Project — Satellite-borne monitoring of the Brazilian Amazon Rainforest [23]; Source: [12]

(\*) Prodes Project — Satellite-borne monitoring of the Brazilian Amazon Rainforest [23]

Whereas *Z* means that the ecological burden of removing carbon emissions is "exported" to 7 **Figure 1.** Deforestation accumulated until 2012 in the Brazilian Legal Amazon

elsewhere, *M* implies carrying the ecological burden within an economy's boundaries over time [3]. The former translates into an ecological credit (0 < ε < 1) and the latter into an ecological debt (ε > 1). When conservation is low (high), compensation is supposed to decrease (increase), unless ε depreciates (appreciates). Since ε exchanges future carbon removal stocks (*M*) for current ones (*Z*), its rise (depreciation) means that *Z* is relatively deteriorating, whereas its fall (appreciation) means that *Z* is relatively increasing. Otherwise, for a given ε, *Z* and *M* vary positively with it (Figure 2.b). 8 Whereas *Z* means that the ecological burden of removing carbon emissions is "exported" to 9 elsewhere, *M* implies carrying the ecological burden within an economy's boundaries over 10 time [3]. The former translates into an ecological credit (0 < ε < 1) and the latter into an ecological 11 debt (ε > 1).

Just like bond markets are grounded in existing loan supply (savings) and demand (investment), removal loans for either carbon conservation or compensation are backed up by the biological growth of actual stocks (removal supply) of forest sinks set aside for curbing emissions from economic growth (removal demand). Forest-wide, *Z* grows with λ, whose growth, in turn, causes ε to fall (appreciate). On the other hand, the faster (slower) the speed *k* of economic activity, the greater (smaller) the demand for *M* and the depreciation (rise) of ε should be. Hence, at this forestland level 12 When conservation is low (high), compensation is supposed to decrease (increase), unless ε 13 depreciates (appreciates). Since ε exchanges future carbon removal stocks (*M*) for current ones 14 (*Z*), its rise (depreciation) means that *Z* is relatively deteriorating, whereas its fall (appreciation) 15 means that *Z* is relatively increasing. Otherwise, for a given ε, *Z* and *M* vary positively with it 16 (Figure 2.b).

 Just like bond markets are grounded in existing loan supply (savings) and demand (invest‐ ment), removal loans for either carbon conservation or compensation are backed up by the biological growth of actual stocks (removal supply) of forest sinks set aside for curbing emissions from economic growth (removal demand). Forest-wide, *Z* grows with λ, whose growth, in turn, causes ε to fall (appreciate). On the other hand, the faster (slower) the speed *k* of economic activity, the greater (smaller) the demand for *M* and the depreciation (rise) of ε should be. Hence, at this forestland level of aggregation, ε is a negative function of the spatial distribution of carbon sinks (λ) and a positive function of emissions given off by the growth of the economy over time (*k*) (Figure 2.a).

 The objective of probing these relationships is to demonstrate that the macro-scale determi‐ nation of ε (Figure 2.a) can help find: a) the optimal supply of conservation and compensation in the loan market for carbon removal (Figure 2.b); b) biophysically attainable deforestation targets at the micro-scale (Figure 2.c), according to the rates at which emissions from economic growth (ln *k*) are given off and the ratio of forest to deforested land— the bio-diversity ratio — varies (ln λ).This analysis will be carried out for three scenarios:


 To start with, the methodology sections will describe the BESF model, its geometry, basic assumptions, parameters, functions and variables. After that, empirical data on deforestation in Brazilian Amazonia will be used to account for the model equilibrium points — both at the micro and macro-scale. Finally, the aforementioned three scenarios will be assessed to determine, for either of them, how much forestland would have to be used for conservation (REDD) and for offsetting carbon emissions (CDM). According to this allocation, carbon sequestration provided by forests could be "paid" at its real, biophysical value, rather than accordingtothevirtualmonetarybenefits suchanenvironmental service is supposedtodeliver.

## **2. Research question and analytical framework**

 The creation of a market for carbon is based on the assumption that monetary payments for carbon storage might make economic agents opt for forest conservation rather than forest conversion [4]. Although such payments might sound like a working solution, the heavier any forest-related decision falls back on them, the lighter it is supposed to lean on its biophysical and environmental footings. Thus, how should a market fit into turning this logic upside- down? Put differently, how could ecosystem services, such as carbon sequestration, be priced biophysically rather than monetarily? What does that mean? How would it work?

 The answers to whether prices can be equated with value or considered only indirect means of measuring values [13] rest upon the proposition that economic value should not be reduced to an ultimate one dimensional gauge, as held by the labour, utility and energy theories of value3 . Economic value should be thought of as not bearing a single substance out of which it should be drawn or within which it should be found. In a biophysical sense, value is limited to the degree to which an item contributes to an objective or condition in an ecosystem [16]. For instance, the biophysical value of a tree species could come from its contribution to controlling soil erosion in steeply sloped areas [16]. Elsewhere, the same tree species might be worth for, say, sequestering carbon. So, although the tree species has not changed, the substance or content from which its value is drawn has.

 Just like bond markets are grounded in existing loan supply (savings) and demand (invest‐ ment), removal loans for either carbon conservation or compensation are backed up by the biological growth of actual stocks (removal supply) of forest sinks set aside for curbing emissions from economic growth (removal demand). Forest-wide, *Z* grows with λ, whose growth, in turn, causes ε to fall (appreciate). On the other hand, the faster (slower) the speed *k* of economic activity, the greater (smaller) the demand for *M* and the depreciation (rise) of ε should be. Hence, at this forestland level of aggregation, ε is a negative function of the spatial distribution of carbon sinks (λ) and a positive function of emissions given off by the growth

 The objective of probing these relationships is to demonstrate that the macro-scale determi‐ nation of ε (Figure 2.a) can help find: a) the optimal supply of conservation and compensation in the loan market for carbon removal (Figure 2.b); b) biophysically attainable deforestation targets at the micro-scale (Figure 2.c), according to the rates at which emissions from economic growth (ln *k*) are given off and the ratio of forest to deforested land— the bio-diversity ratio

 To start with, the methodology sections will describe the BESF model, its geometry, basic assumptions, parameters, functions and variables. After that, empirical data on deforestation in Brazilian Amazonia will be used to account for the model equilibrium points — both at the micro and macro-scale. Finally, the aforementioned three scenarios will be assessed to determine, for either of them, how much forestland would have to be used for conservation (REDD) and for offsetting carbon emissions (CDM). According to this allocation, carbon sequestration provided by forests could be "paid" at its real, biophysical value, rather than accordingtothevirtualmonetarybenefits suchanenvironmental service is supposedtodeliver.

 The creation of a market for carbon is based on the assumption that monetary payments for carbon storage might make economic agents opt for forest conservation rather than forest conversion [4]. Although such payments might sound like a working solution, the heavier any forest-related decision falls back on them, the lighter it is supposed to lean on its biophysical and environmental footings. Thus, how should a market fit into turning this logic upside-down? Put differently, how could ecosystem services, such as carbon sequestration, be priced

 The answers to whether prices can be equated with value or considered only indirect means of measuring values [13] rest upon the proposition that economic value should not be reduced to an ultimate one dimensional gauge, as held by the labour, utility and energy theories of

biophysically rather than monetarily? What does that mean? How would it work?

— varies (ln λ).This analysis will be carried out for three scenarios:

**i.** BAU (Business-As-Usual), in which ln *k* > ln λ;

**2. Research question and analytical framework**

of the economy over time (*k*) (Figure 2.a).

6 Carbon Sequestration CO2 Sequestration and Valorization

**ii.** REDD, in which ln *k* < ln λ;

**iii.** CDM, in which ln *k* = ln λ.

 Furthermore, just like man cannot reach too deep into the material microcosm [13], he is likely unable to search too deeply for the one and only source of economic value. According to the Heisenberg principle of indeterminacy in quantum physics, and given the high com‐ plexity of microstructures, the probability of, say, building a living cell from scratch is ex‐ tremely small [13]. By the same token, the probability of drawing economic value out of a single underlying source is very low.

 Within the economy's biophysical realms, useful goods and services should be valued by their usefulness4 rather than by their exchange properties. What is meant by "usefulness" builds on Daly's [18] notion of *ultimate efficiency*, defined as the ratio of service to throughput. The services yielded by the stocks of artefacts are the ultimate benefit of economic activity. The throughput is the inevitable cost of maintaining the stocks of people and artefacts. Thus, in the

<sup>3</sup> Dating back to classical times, the labour theory of value paradoxically stated that only when land (natural resources) is (are) running out is the maximum value reached. Therefore, any natural resource that gets into the market and thus acquires exchange value is dying out or close to extinction. So, exchange value lays bare that there is less of that natural resource than there was when it held no value at all! Later on, the neoclassical utility theory displaced the economic value to an even odder container: consumption. In the "utility world", the economic value was placed in the individual preferences for commodities. As preferences were a subjective matter, the economic value then turned out to rest upon the abstract basis of utility. From then on, the economic analysis has been cut off from its biophysical roots [14]. But as early as 1883, S. Podolinsky pioneered the idea of associating energy with value, as claimed by energy theories of value. These theories aimed at substituting energy for money as the only source of value. However, money is not particularly correlated with energy content, because there is no direct equivalence between low entropy and economic value [13][15]. For instance, the monetary value per unit energy content of a diamond is extremely large compared to the monetary value per unit energy content of a lump of coal. Nonetheless, if all indirect energy flows were to be tracked down and accounted for, the discrepancy between diamonds and coal might not be so great [15]. By and large, economists have rebutted these energy evaluation methods because of the fear that economics might end up turning into a branch of thermodynamics [14]. Furthermore, as Georgescu-Roegen [13] once pointed out, the economic process has only two flows: an input flow of low entropy and an output flow of high entropy, namely, waste. If the balance sheet of value should be set on the basis of these inputs and outputs, one "would arrive at the absurd conclusion that the value of the low entropy flow on which the maintenance of life itself depends is equal to the value of the flow of waste, that is, zero" (p. 284). This paradox only vanishes by acknowledging that the true "product" of the economic process is not a material flow, but a psychic flux — the enjoyment of life. Thus, the economic value has both psychic (neoclassical) and physical (classical) roots. An entropy-oriented, energy theory of value would only account for the supply side of the process and neglect the corresponding demand side [9]. That is why the proposition of a shadow price for natural resource inputs, on one hand, and for the waste sinking capacity of the environment, on the other, results from an economic rather than from an energy theory of value [14].

<sup>4</sup> Although nature might well have other values — existence and bequeath — than use ones, non-use values are harder to estimate. Obviously, the use value is the one arising from the real — direct, indirect or optional — use of a given resource, whether in the present or in the future. On the other hand, the existence value is simply related to the existence of specific riches. The bequeath value measures the benefit that any individual obtains from knowing that, in the future, other people will also be able to benefit from the resource they have been left [17]. First and foremost, both non-use values essentially depend on estimating the preferences of future generations, which is not that easy to foreshadow. Moreover, non-use values resemble very much the intrinsic value of nature, which was claimed by Deep Ecology followers. According to them, nature had to be preserved for itself, rather than for satisfying the well-being of present and future generations. The intrinsic value is fully separated from any use value, even in the future [14]. Once these non-use concepts are difficult to apply, environmental goods and services are taken up thereafter in their usefulness sense.

1 final analysis, the stock of physical wealth is an accumulated flow of throughput, which is a 2 cost to be minimised [18].

 Likewise, if carbon removal is the service to be used, then it must be valued by the biophysical cost of delivering carbon removal stocks. Yet if not technically estimating price or value, a method that estimates costs should at least be a fairly good approximation to price and value, when markets are in equilibrium [16]. As known, whenever a commodity has a much higher value than its cost of production, its profits will be higher. The commodity will then be produced with increasing marginal costs until cost just equals price and profits are zero. The opposite happens when the commodity cost is much higher than its value. Since the com‐ modity profits are lower, it will not be produced. The marginal costs then decrease until cost and price are equal.

12 As real markets are seldom in equilibrium, cost and price are expected to diverge, so that 13 commodities could become overpriced or underpriced. The *shadow price* ε = *M* ÷ *Z*, calculated 14 by the BESF model, is the biophysical sign of that distortion. It informs how far or close the 15 growth rates of the economy's emissions (ln k) and of the forest stocks to remove them (ln λ) 16 are from each other (Figure 2.a). As explained before, the greater λ is, the larger *Z* will be, 17 which, *ceteris paribus*, makes ε go down (appreciate); on the other hand, the greater *k* is, the 18 larger *M* will be, which, *ceteris paribus*, makes ε go up (depreciate). The optimal balance between *k* and λ defines not only ε\* 19 , in Figure 2.a, but also the optimal level of carbon conser‐ vation (*Z*\* ) and compensation (*M*\* ), in Figure 2.b, as well as the optimal growth (*G*(*Xt* 20 )) of and demand (*ht* ) for carbon removal stocks (*Xt* 21 ), in Figure 2.c. Hence, any bio-economic distortion 22 in carbon prices is communicated by the bio-economic exchange rate ε. Figure 2.a, but also the optimal level o carbon conservation (Z\* ) and compensation (M\* ), in Figure 2.b, as well as the optimal growth (G(Xt)) of and demand (ht) for carbon removal stocks (Xt), in

Figure 2.c. Hence, any bio-economic distortion in carbon prices is communicated by the bio-

Figure 2.c. It is these curves that set optimal deforestation targets and rates if forestland is split into

)to be greater than carbon compensation (M\*

was the outcome of a specific value taken on by λ and k, this bio-diversity

), in

) will then be applied to yield the curves

For instance, given the hypothetical functional forms shown in Figure 2, ε\* 24 **Figure 2.** Loan and removal markets for carbon

ratio (λ\*

depicted by

Figure 2. Loan and removal markets for carbon

) and deforestation (economic growth) rate (k\*

, found in Figure 2.a, requires carbon conservation (Z\*

deforested (u) and conserved (v) patches.

Figure 2.b. Similarly, as ε\*

Model assumptions

23

economic exchange rate ε.

For instance, given the hypothetical functional forms shown in Figure 2, ε\* 1 , found in Figure 2.a, requires carbon conservation (*Z*\* ) to be greater than carbon compensation (*M*\* 2 ), in Figure 2.b. Similarly, as ε 3 \* was the outcome of a specific value taken on by λ and *k*, this bio-diversity ratio (*λ\** ) and economic growth rate (*k\** 4 ) will then be applied to yield the curves depicted by 5 Figure 2.c. It is these curves that set optimal deforestation targets and rates if forestland is split 6 into deforested (u) and conserved (v) patches.

## 7 **3. Model assumptions**

1 final analysis, the stock of physical wealth is an accumulated flow of throughput, which is a

 Likewise, if carbon removal is the service to be used, then it must be valued by the biophysical cost of delivering carbon removal stocks. Yet if not technically estimating price or value, a method that estimates costs should at least be a fairly good approximation to price and value, when markets are in equilibrium [16]. As known, whenever a commodity has a much higher value than its cost of production, its profits will be higher. The commodity will then be produced with increasing marginal costs until cost just equals price and profits are zero. The opposite happens when the commodity cost is much higher than its value. Since the com‐ modity profits are lower, it will not be produced. The marginal costs then decrease until cost

 As real markets are seldom in equilibrium, cost and price are expected to diverge, so that commodities could become overpriced or underpriced. The *shadow price* ε = *M* ÷ *Z*, calculated by the BESF model, is the biophysical sign of that distortion. It informs how far or close the growth rates of the economy's emissions (ln k) and of the forest stocks to remove them (ln λ) are from each other (Figure 2.a). As explained before, the greater λ is, the larger *Z* will be, which, *ceteris paribus*, makes ε go down (appreciate); on the other hand, the greater *k* is, the larger *M* will be, which, *ceteris paribus*, makes ε go up (depreciate). The optimal balance between *k* and λ defines not only ε\* 19 , in Figure 2.a, but also the optimal level of carbon conser‐

), in Figure 2.b, as well as the optimal growth (*G*(*Xt* 20 )) of and

) for carbon removal stocks (*Xt* 21 ), in Figure 2.c. Hence, any bio-economic distortion

ε

lnk

Z

M

ε

<sup>h</sup><sup>t</sup>(a) Removal market for carbon

Figure 2.b, as well as the optimal growth (G(Xt)) of and demand (ht) for carbon removal stocks (Xt), in Figure 2.c. Hence, any bio-economic distortion in carbon prices is communicated by the bio-

) and compensation (M\*

)to be greater than carbon compensation (M\*

G(Xt)

was the outcome of a specific value taken on by λ and k, this bio-diversity

Figure 2.c. It is these curves that set optimal deforestation targets and rates if forestland is split into

), in

ht

G(Xt)

) will then be applied to yield the curves

X 0 <sup>t</sup>

(c)

), in

22 in carbon prices is communicated by the bio-economic exchange rate ε. Figure 2.a, but also the optimal level o carbon conservation (Z\*

(b) Loan market for carbon

ε0 ε ε1 \*

2 cost to be minimised [18].

8 Carbon Sequestration 8 CO2 Sequestration and Valorization

11 and price are equal.

vation (*Z*\*

demand (*ht*

) and compensation (*M*\*

Z, M

Z1= M<sup>1</sup>

Z0= M<sup>0</sup>

Z\*

M\*

lnλ lnk

lnλ<sup>0</sup>

lnλ\* = lnk\*

0

24 **Figure 2.** Loan and removal markets for carbon

23

Figure 2, ε\*

ratio (λ\*

depicted by

Figure 2. Loan and removal markets for carbon

ε \*

0 ε0 ε<sup>1</sup>

lnk0 lnλ

, found in Figure 2.a, requires carbon conservation (Z\*

deforested (u) and conserved (v) patches.

Figure 2.b. Similarly, as ε\*

Model assumptions

For instance, given the hypothetical functional forms shown in

) and deforestation (economic growth) rate (k\*

economic exchange rate ε.

 Figure 2.c draws on bio-economic models, such as forestry and fishery models, which are concerned with the age and size of their biomass stocks (trees and fish) [3][19]. As the growth of a forest depends on the age of its trees, forestry models consider the time at which the maximum biological growth — or Maximum Sustainable Yield (MSY) — will be reached. In contrast, since fishgrowthdependsonthe size insteadofonthe ageoffishstocks,fisherymodels involve the stock size at which the Maximum Economic Yield (MEY) will be attained [3].

 Despite their different biological and economic emphasis, both such models are concerned with the output (trees and fish) provided by the corresponding natural source (forests and oceans). Neither of them, though, cares about the *input* — environmental service — supplied by the natural *sink* upon which the corresponding resource stock grows. Therefore, in the BESF model, the biomass stock is an *input* rendering an environmental service (carbon removal), instead of an *output* yielding biological (MSY) and economic (MEY) gains [3].

In this case, the stock harvested (*h* ^ *<sup>t</sup>* 20 ) stands for the *throughput* of emissions removal — or the 21 environmental cost of storing in the forest biomass the emissions given off by the production 22 and consumption of the economy's output. Provided that emissions from economic growth have to be removed by natural standing forests, the supply of removal forest stocks (*G* ^ (*Xt* 23 )) need to meet an upper boundary (*Kh* 24 ) somewhere, since tree growth typically has an upper 25 bound [3].

## 26 **4. Model parameters and variables**

Such an upper bound (*Kh* 27 ) depends both on *space* (λ) and *time*-related (*k*) variables (Table 1 and Table 2). On one hand, *Kh* is a function of λ*<sup>t</sup>* 28 (Eq. (3)) — the spatial distribution of forest biomass *Xt* 29 (Eq. (1)), in GtC, between *j* = 1,..., *n* conserved (**v**) and deforested (**u**) sinks at each time period *t* = 1,..., *m*, where **u** = (*u*1, *u*2,..., *um*) = (*x*11, *x*21,..., *xm*1); **v** = (*v*1, *v*2,..., *vm*) = (*x*1*<sup>n</sup>*, *x*2*<sup>n</sup>* 30 ,..., *xmn*); **u** < **v**; *xtj* 31 = carbon-equivalent emissions by sources at time *t* to be stored (removed) by 32 sink *j.* On the other hand, the distribution of forest biomass is highly affected by economic activity and the rate at which land-use changes take place. In other words, λ*<sup>t</sup>* 33 depends on *kj* 34 (Eq. (12)) — which measures emissions from deforestation or carbon emissions of sink *j* 35 given off by its economic growth over time.

 As time goes by, the ratio of energy changes (ln *k*) to land changes (ln λ), measured by ε (Eq. (14)), indicates in each period how much emissions from economic growth can be removed by each hectare of forestland [20]. Whereas economic growth (*k*) demands increasing removal of stocks, the supply of rising stocks is constrained by biophysical limits on growing and maintaining standing forests (λ).

6 Building on standard bio-economic (forestry and fishery) models, it is assumed that such a 7 biological growth constraint follows a logistic pattern [3]. So as to hook the economy to its natural strings, emission flows (*h* ^ *<sup>t</sup>*) must be capped (*Kh* 8 ) rather than the growth of removal forest stocks (*G* ^ (*Xt* 9 )), which already are by nature. Capping emissions from deforestation, 10 though, implies that, at some former time *T*, when deforestation was negligible, there was a maximum level of removal stocks, *XK*, associated with that least emission release (*Kh* 11 ).


( \* 12 ) *j* = **<sup>u</sup>** = the smallest carbon biomass stock sink or the *deforested* sink; *j* = **<sup>v</sup>** = the largest carbon biomass stock sink or 13 the *conserved* sink. It is required that ε > 0, because, in biophysical terms, there cannot be a negative accountancy [10]. Therefore, so that *Z* > 0, *j* sinks must be displayed on an increasing biomass stock order. Likewise, so that *M* > 0, *Mj* 14 = | *xmj* – *x*1*<sup>j</sup>* 15 |.

16 Source: [3]

17 **Table 1.** Algebraic emission-removal matrix

$$\mathbf{X}\_t = \sum\_{j=1}^{n} \mathbf{x}\_{tj} \tag{1}$$

$$\mathbf{X}\_t = \boldsymbol{\upsilon}\_t \text{ - } \boldsymbol{\mu}\_t = \boldsymbol{\chi}\_{\text{tr}} \text{ - } \boldsymbol{\chi}\_{t\mathbf{1}} \tag{2}$$

$$\mathbf{u} \cdot \mathbf{u}\_t = \upsilon\_t / \mathbf{u}\_t = \mathbf{x}\_{tn} / \mathbf{x}\_{t1} \tag{3}$$

$$
\begin{aligned}
\lambda &= 1 + \left(Z \mid V\_1\right) & \text{(a)}\\\ln \lambda &= Z \mid V\_1 & \text{(b)}\end{aligned}
\tag{4}
$$

$$V = \sum\_{j=1}^{n} V\_{\ j} \tag{5}$$


(†) *kt* = variable *k* (Eqs. (13)a-c) at time *t*; *k*\* 51 = optimal value for *k*.

52 Source: [3]

 As time goes by, the ratio of energy changes (ln *k*) to land changes (ln λ), measured by ε (Eq. (14)), indicates in each period how much emissions from economic growth can be removed by each hectare of forestland [20]. Whereas economic growth (*k*) demands increasing removal of stocks, the supply of rising stocks is constrained by biophysical limits on growing and

6 Building on standard bio-economic (forestry and fishery) models, it is assumed that such a 7 biological growth constraint follows a logistic pattern [3]. So as to hook the economy to its

*<sup>t</sup>*) must be capped (*Kh* 8 ) rather than the growth of removal

(*Xt* 9 )), which already are by nature. Capping emissions from deforestation, 10 though, implies that, at some former time *T*, when deforestation was negligible, there was a maximum level of removal stocks, *XK*, associated with that least emission release (*Kh* 11 ).

> 1 *x* <sup>11</sup> *x* <sup>12</sup> *X* <sup>1</sup> *Z* <sup>1</sup> λ<sup>1</sup> ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ *m xm1 xm2 Xm Zm* λ*<sup>m</sup> V V* <sup>1</sup> *V* <sup>2</sup> *V* = *X Z* λ

(**u** < **v**) *X Z* λ

*xtj* (1)

*Zt* =*vt* - *ut* = *xtn* - *xt*<sup>1</sup> (2)

*λ<sup>t</sup>* =*vt* / *ut* = *xtn* / *xt*<sup>1</sup> (3)

*Z V* (4)

*V <sup>j</sup>* (5)

^

*j* = 1,..., 2 removal sinks\*

**u v**

*k k* <sup>1</sup> *k* <sup>2</sup> *k*

*M M* <sup>1</sup> *M* <sup>2</sup> *M* ε

\* 12 ) *j* = **<sup>u</sup>** = the smallest carbon biomass stock sink or the *deforested* sink; *j* = **<sup>v</sup>** = the largest carbon biomass stock sink or 13 the *conserved* sink. It is required that ε > 0, because, in biophysical terms, there cannot be a negative accountancy [10]. Therefore, so that *Z* > 0, *j* sinks must be displayed on an increasing biomass stock order. Likewise, so that *M* > 0, *Mj* 14 = |

> *Xt* = ∑ *j*=1 *n*

( <sup>1</sup> ) 1 1 / (a) ln (/ b)

*Z V*

*V* = ∑ *j*=1 *n*

l

l

= + =

5 maintaining standing forests (λ).

10 Carbon Sequestration 10 CO2 Sequestration and Valorization

forest stocks (*G*

*t* periods (emission sources)

17 **Table 1.** Algebraic emission-removal matrix

(

*xmj* – *x*1*<sup>j</sup>* 15 |. 16 Source: [3]

natural strings, emission flows (*h*

^

53 **Table 2.** Variables and equations of the BESF model

$$\Delta M = \sum\_{j=1}^{n} M\_{\;j} = \Delta X = \parallel X\_m - \parallel X\_1 \tag{6}$$

$$\left| \psi\_t = k\_t \right| k \tag{7}$$

$$X = \sum\_{t=1}^{m} X\_t \tag{8}$$

$$\Delta Z = \sum\_{t=1}^{m} Z\_t = \Delta V = \big|\, V\_m \text{ - } V\_1\big| \tag{9}$$

$$\mathbf{V}\_{\;j} = \sum\_{t=1}^{m} \mathbf{x}\_{t\!\!j} \tag{10}$$

$$\|\mathbf{M}\_{\,\,j} = \|\mathbf{x}\_{m\,\,j} - \mathbf{x}\_{1\,\,j}\,\|\tag{11}$$

$$k\_{\vec{j}} = \mathbf{x}\_{m\vec{j}} / \mathbf{x}\_{1\vec{j}} \tag{12}$$

$$k = 1 + \left(M \mid X\_1\right) \qquad \quad \text{(a)}$$

$$\begin{aligned} \ln k &= M \, / \, X\_1 & \quad \text{(b)}\\ k &= X\_{t-1} \, / \, X\_t & \quad \text{(c)} \end{aligned} \tag{13}$$

$$\varepsilon\_{\mathcal{E}} = \frac{M}{Z} = \frac{\ln k \times X\_1}{\ln \lambda \times V\_1} = \frac{\Delta X}{\Delta V} \tag{14}$$

$$
\Delta \psi = \sqrt[m]{\prod\_{t=1}^{m} \psi\_t} \tag{15}
$$

The parameter *Kh* 1 is an algebraically found macro-scale bound to emissions. Actually, it is the value taken on by the emission demand function *h* ^ *<sup>t</sup>* when *Xt* = *XK* 2 . Hence, the first step to set *Kh* is to find *XK* 3 , which is arrived at through vector algebra (Eq. (16)). Eq. (16) fulfils a twofold 4 ideal requirement for sustainability:


 Theoretically, these conditions not only allow the source-sink system to simultaneously reach its economic and ecological sustainability, but also require it to remain sustainably stable. Therefore, the stock level *XK* 11 represents the "bio-economic cost" of achieving a stable state of sustainability. Rather than a target to be complied with, it translates into the space-time needed to make *k* stable (*kj = k*) and ε = 1 [3].

$$2\begin{pmatrix} 1 & \frac{\bar{k}^{-1} + \Lambda^{-1}}{2} \\ \frac{\bar{k} + \Lambda}{2} & 1 \end{pmatrix} \begin{pmatrix} -\bar{\mathcal{U}}\_{t} \\ \bar{\mathcal{v}}\_{t} \end{pmatrix} = \begin{pmatrix} 0 \\ 0 \end{pmatrix} \tag{16}$$

14 In Eq. (16), the bar over the symbols stands for the corresponding mean values in the last row 15 of Table 4. However, since the distribution of forestland (λ) is known for every period and the release of emissions from deforestation (*k*) instead is to be limited, *k* ¯ 16 must be found by taking *<sup>λ</sup>*¯ = 7.975, *u*¯*t*= 24 and *v*¯*t* = 182.40 out of Table 4. By so doing, it turns out that *<sup>k</sup>* ¯ ≈ 7.23 and *k* ¯-1 17 ≈ 0.1383. Next, by substituting these values into Eq. (17), in which, again, *u*¯*t*= 24 and *<sup>X</sup>*¯ *<sup>t</sup>* 18 = 206.40 come from Table 4, *vK* = 1319.20 and *XK* 19 = 1492.78 are finally found.

REDD Roses for a Green Lady – Target Setting for Deforestation in the Brazilian Amazon 13 http://dx.doi.org/10.5772/57288 13


18 (a) According to [21], until 1997, and thereafter calculated from the previous deforested area plus the yearly deforestation rate. (b) Figures in this column are obtained by dividing the deforested area by estimates that consider 4483.972 × 10 19 <sup>3</sup> km2 = 4.483972 × 106 km2 as, approximately, the original forested area of Brazil's 5 × 106 km2 20 Legal Amazon [22]. (c) Figures obtained by calculating 4483.972 × 103 km2 – deforested area × 103 km2 21 . (d) "For the region's forests as a whole, 22 the mean biomass loading (t/ha) for pre-logging biomass (weighted by the area of each eco-region present) is estimated at 464 t/ha" [22]. So the figures in this column result from 464 t/ha × forest remnant × 103 km2 = 464 t/10-2 km2 23 × forest remnant × 103 km2 = (464 × forest remnant × 105) t = (464 × forest remnant × 105) 10-9 24 Gt. (e) The loading (biomass per 25 hectare) for pre-logging biomass of forests cleared in 1990 (weighted by the deforestation rate in each state) is calculated at 434 t/ha. As before, the figures in this column result from 434 t/ha × forest remnant × 103 km2 = 434 t/10-2 km2 26 × forest remnant × 103 km2 = (434 × forest remnant × 105) t = (434 × forest remnant × 105) 10-9 27 Gt [22].

28 Source: [23]

*M <sup>j</sup>* =|*xmj* - *x*<sup>1</sup> *<sup>j</sup>*

( <sup>1</sup> ) 1

*<sup>Z</sup>* <sup>=</sup> ln *<sup>k</sup>* <sup>×</sup> *<sup>X</sup>*<sup>1</sup>

*ψ*= ∏ *t*=1 *m ψt*

The parameter *Kh* 1 is an algebraically found macro-scale bound to emissions. Actually, it is the

 = *XK* 2 . Hence, the first step to set *Kh* is to find *XK* 3 , which is arrived at through vector algebra (Eq. (16)). Eq. (16) fulfils a twofold

5 **a.** *Maximum economic efficiency* (*kj = k*), so that the rates of economic growth or return even

7 **b.** *Perfect ecological efficiency* (ε = 1), so that neither further reallocation nor redistribution of

 Theoretically, these conditions not only allow the source-sink system to simultaneously reach its economic and ecological sustainability, but also require it to remain sustainably stable. Therefore, the stock level *XK* 11 represents the "bio-economic cost" of achieving a stable state of sustainability. Rather than a target to be complied with, it translates into the space-time needed

> ¯ -1 + *λ*¯-1 2

14 In Eq. (16), the bar over the symbols stands for the corresponding mean values in the last row 15 of Table 4. However, since the distribution of forestland (λ) is known for every period and the

¯ 16 must be found by taking

¯-1 17 ≈ 0.1383. Next, by substituting these values into Eq. (17), in which, again, *u*¯*t*= 24 and *<sup>X</sup>*¯ *<sup>t</sup>* 18 = 206.40


<sup>2</sup> <sup>1</sup> )(

^

*<sup>t</sup>* when *Xt*

1 / (a) ln / (b)

/ (c) -

ln *<sup>λ</sup>* <sup>×</sup> *<sup>V</sup>*<sup>1</sup> <sup>=</sup> <sup>∆</sup> *<sup>X</sup>*

1

*k MX k MX kX X*

= + = = *t t*

*<sup>ε</sup>* <sup>=</sup> *<sup>M</sup>*

value taken on by the emission demand function *h*

8 emissions removal takes place across the sinks [3][15][25].

2( <sup>1</sup> *<sup>k</sup>*

*k* ¯ + *λ*¯

release of emissions from deforestation (*k*) instead is to be limited, *k*

come from Table 4, *vK* = 1319.20 and *XK* 19 = 1492.78 are finally found.

*<sup>λ</sup>*¯ = 7.975, *u*¯*t*= 24 and *v*¯*t* = 182.40 out of Table 4. By so doing, it turns out that *<sup>k</sup>*

4 ideal requirement for sustainability:

6 out across the sinks [3][24];

12 Carbon Sequestration 12 CO2 Sequestration and Valorization

13 to make *k* stable (*kj = k*) and ε = 1 [3].


<sup>∆</sup> *<sup>V</sup>* (14)

) (16)

¯ ≈ 7.23 and *k*

*<sup>m</sup>* (15)

(13)

*k <sup>j</sup>* = *xmj* / *x*<sup>1</sup> *<sup>j</sup>* (12)

29 **Table 3.** Forest conservation and deforestation in the Brazilian Legal Amazon (1988-2012)

$$
\begin{pmatrix} 1 & \bar{k}^{-1} \\ \bar{k} & 1 \end{pmatrix} \begin{pmatrix} \bar{\boldsymbol{\mu}}\_{t} \\ \boldsymbol{\upsilon}\_{K} \end{pmatrix} = \begin{pmatrix} \overline{\boldsymbol{X}}\_{t} \\ \boldsymbol{X}\_{K} \end{pmatrix} \tag{17}
$$

#### **5. Removal demand function (***h* **^** *<sup>t</sup>* 1 **)**

 In an emissions-saving (low carbon) economy, a seesaw balance between deforestation and conservation of carbon stocks is expected to hold. At lower deforestation rates, the savings (conservation) of biomass stocks outstrip their consumption. But as economic growth increas‐ es, conservation savings of biomass stocks decline, while their consumption by deforestation goes up. Thus, in order to account for these offsetting effects at any time, the observed removal demands for deforestation and conservation are respectively determined by:

$$h\_t(\boldsymbol{\mu}\_t) = \bar{\boldsymbol{\mu}}\_t \text{ -- } \boldsymbol{\mu}\_t \tag{18}$$

$$h\_t(\upsilon\_t) = \bar{\upsilon}\_t \text{ - } \upsilon\_{t'} \tag{19}$$

where *u*¯*t* and *v*¯*<sup>t</sup>* 8 are mean values for either biomass stock, displayed in the last row of Table 4.

9 By summing Eqs. (18) and (19), the total observed demand for removal per period is obtained:

$$
\lambda h\_t = \bar{\mathbf{x}}\_t \text{ - } \mathbf{x}\_{t\prime} \tag{20}
$$

where xt = ut + vt and *x*¯*<sup>t</sup>* <sup>=</sup>*u*¯*<sup>t</sup>* <sup>+</sup> *<sup>v</sup>*¯*<sup>t</sup>* 10 .

 The underlying assumption of Eqs. (18)-(20) is the *Permanent Income Hypothesis* (PIH), put forward by the American economist Milton Friedman in the late 1950. According to this hypothesis, consumption is smoothed in the long run, so as to handle short-run fluctuations in income. Income fluctuates because of its transitory component, which consists of unforeseen additions or subtractions likely to cancel out in the long run [26][27]. In an emissions avoiding world, where carbon storage sounds like the economy's earnings, conservation of carbon stocks adds to income, whereas deforestation lessens it. So, Eq. (20) amounts to the permanent component of income in the long run, which accounts for a smoother path based on consump‐ tion expectations (*x*¯*t*) rather than on current consumption (*xt* ). The regression5 19 of the results for Eq. (20) on the observed values for *Xt* 20 , in Table 5, yields the estimated long-run mean of removal demand (Eq. (21)).

$$\begin{aligned} \hat{h}\_t &= 206.121 - 0.999 \,\text{X}\_t \\ \text{t-stat.} \, 497.034 & \, -496.808 \\ \text{sig. t.} \, 0.0000 & 0.0000 \end{aligned} \tag{21}$$

<sup>5</sup> All statistical estimations were performed in SPSS Statistics 17.0.


(<sup>1</sup> *<sup>k</sup>* ¯-1

7 demands for deforestation and conservation are respectively determined by:

¯ <sup>1</sup> )( *<sup>u</sup>*¯*<sup>t</sup> vK*

**^**

 In an emissions-saving (low carbon) economy, a seesaw balance between deforestation and conservation of carbon stocks is expected to hold. At lower deforestation rates, the savings (conservation) of biomass stocks outstrip their consumption. But as economic growth increas‐ es, conservation savings of biomass stocks decline, while their consumption by deforestation goes up. Thus, in order to account for these offsetting effects at any time, the observed removal

where *u*¯*t* and *v*¯*<sup>t</sup>* 8 are mean values for either biomass stock, displayed in the last row of Table 4.

9 By summing Eqs. (18) and (19), the total observed demand for removal per period is obtained:

 The underlying assumption of Eqs. (18)-(20) is the *Permanent Income Hypothesis* (PIH), put forward by the American economist Milton Friedman in the late 1950. According to this hypothesis, consumption is smoothed in the long run, so as to handle short-run fluctuations in income. Income fluctuates because of its transitory component, which consists of unforeseen additions or subtractions likely to cancel out in the long run [26][27]. In an emissions avoiding world, where carbon storage sounds like the economy's earnings, conservation of carbon stocks adds to income, whereas deforestation lessens it. So, Eq. (20) amounts to the permanent component of income in the long run, which accounts for a smoother path based on consump‐

). The regression5 19 of the results for Eq. (20) on the observed values for *Xt* 20 , in Table 5, yields the estimated long-run mean of

> 206.121 0.999 t- stat. 497.034 496.808

*t t h X* = -

0.0000

sig. t. 0.0000

ˆ

5 All statistical estimations were performed in SPSS Statistics 17.0.

tion expectations (*x*¯*t*) rather than on current consumption (*xt*

) =( *X*¯ *t X <sup>K</sup>*

) (17)

*ht*(*ut*) <sup>=</sup>*u*¯*<sup>t</sup>* - *ut* (18)

*ht*(*vt*) =*v*¯*<sup>t</sup>* - *vt*, (19)

*ht* <sup>=</sup> *<sup>x</sup>*¯*<sup>t</sup>* - *xt*, (20)


*k*

**5. Removal demand function (***h*

*<sup>t</sup>* 1 **)**

14 Carbon Sequestration 14 CO2 Sequestration and Valorization

where xt

 = ut + vt and *x*¯*<sup>t</sup>* <sup>=</sup>*u*¯*<sup>t</sup>* <sup>+</sup> *<sup>v</sup>*¯*<sup>t</sup>* 10 .

21 removal demand (Eq. (21)).

1 **Table 4.** Observed deforested, conserved and demanded biomass in the Brazilian Legal Amazon (1988-2012)


**Table 5.** Observed forest data and estimated demand (*h* ^ *<sup>t</sup>*) and supply (*G* ^(*Xt* 1 )) functions of the BESF model

#### **6. Removal supply function (***G* **^ (***Xt* 2 **))**

By substituting, in Eq. (21), *XK* = 1492.78, found by Eqs. (16) and (17), for *Xt* 3 , the least level of 4 emissions demand *Kh* = -1286.16 comes out. This value is used as the lower limit of a logistic 5 function (Eq. (23)) in the following constrained optimisation problem:

1 Objective function:

$$\min\_{X\_t} \sum\_t S\_t = \min\_{X\_t} \sum\_t \mathbb{E} \left\{ \mathbb{g} \left( \mathbf{v} \{ X\_t \} \right) \cdot \hat{h}\_t \right\} \tag{22}$$

2 Constraints:

Time periods

Years

16 Carbon Sequestration 16 CO2 Sequestration and Valorization

Xt (GtC)

**Table 5.** Observed forest data and estimated demand (*h*

**6. Removal supply function (***G*

**(***Xt* 2 **))**

*ht* (GtC)

*h* ^ *t* (GtC)

^

By substituting, in Eq. (21), *XK* = 1492.78, found by Eqs. (16) and (17), for *Xt* 3 , the least level of 4 emissions demand *Kh* = -1286.16 comes out. This value is used as the lower limit of a logistic

^(*Xt* 1 )) functions of the BESF model

**^**

5 function (Eq. (23)) in the following constrained optimisation problem:

*<sup>t</sup>*) and supply (*G*

(*t*) Eq.(20) Eq.(21) Eq.(23) Eq.(25) Eq.(26) Eq.(27) *T* ? 1492.78 — -1285.17 0.0000 0.00 -1492.78 1.25×107 1988 206.93 -0.53 -0.5983 0.5370 1431.25 1224.33 1215.00 1989 206.85 -0.45 -0.5242 0.5376 1252.76 1045.91 1039.41 1990 206.81 -0.41 -0.4829 0.5379 1153.21 946.40 941.33 1991 206.78 -0.38 -0.4493 0.5382 1072.51 865.73 861.74 1992 206.74 -0.34 -0.4080 0.5386 973.18 766.45 763.70 1993 206.74 -0.34 -0.4080 0.5386 973.18 766.45 763.70 1994 206.65 -0.25 -0.3187 0.5393 759.12 552.47 552.05 1995 206.57 -0.17 -0.2374 0.5400 564.94 358.38 359.66 1996 206.51 -0.11 -0.1775 0.5405 421.94 215.44 217.72 1997 206.47 -0.07 -0.1385 0.5409 329.13 122.67 125.48 1998 206.4139 -0.02 -0.0864 0.5413 205.20 -1.22 2.17 1999 206.36 0.04 -0.0347 0.5418 82.34 -124.02 -120.23 2000 206.31 0.09 0.0199 0.5422 -47.19 -253.50 -249.45 2001 206.25 0.14 0.0744 0.5427 -176.08 -382.33 -378.21 2002 206.19 0.21 0.1385 0.5433 -327.61 -533.80 -529.81 2003 206.11 0.28 0.2141 0.5439 -506.05 -712.16 -708.66 2004 206.03 0.37 0.2963 0.5446 -699.41 -905.44 -902.84 2005 205.97 0.42 0.3528 0.5451 -832.02 -1037.99 -1036.24 2006 205.93 0.47 0.3951 0.5455 -931.14 -1137.07 -1136.09 2007 205.90 0.50 0.4296 0.5458 -1012.07 -1217.97 -1217.69 2008 205.86 0.54 0.4683 0.5461 -1102.58 -1308.43 -1309.03 2009 205.84 0.56 0.4907 0.5463 -1154.85 -1360.68 -1361.82 2010 205.82 0.58 0.5117 0.5465 -1203.84 -1409.66 -1411.33 2011 205.80 0.60 0.5309 0.5466 -1248.73 -1454.53 -1456.72 2012 205.78 0.62 0.5446 0.5468 -1280.69 -1486.47 -1489.04

*g(v(Xt))* (GtC)

*F* ^ (*Xt*) (GtC)

*G*(*Xt*) (GtC) *G* ^(*Xt*) (GtC)

3 Constraint I:

$$
\stackrel\frown{h}\_t = 206.121 \text{ - } 0.999 \,\text{X}\_t \tag{21}
$$

4 Constraint II:

$$
\mathcal{G}\{\boldsymbol{v}(\mathcal{K}\_t)\} = \frac{1}{\left(1 \mid \boldsymbol{\kappa}\_h \right) + \boldsymbol{\alpha}\_b \boldsymbol{\alpha}\_t} \tag{23}
$$

$$
\log\left\{\boldsymbol{v}\left(\boldsymbol{X}\_t\right)\right\} = \frac{1}{\left(1 \; \text{\$\cdot\$-1286.16\$}\right) + \left.07075414 \times 1.01593216\right\}^{\boldsymbol{\kappa}\_t}} \tag{23}
$$

5 Constraint III:

$$\log(v(X\_t)) \ge \hat{h}\_{t\nu} \tag{24}$$

6 where *St* stands for the instantaneous surplus arising from the gap between removal growth rates (*g*(*v*(*Xt* ))) and removal consumption rates (*h* ^ *<sup>t</sup>*). Because of Eq. (24), *St* 7 ≥ 0, that is, at any 8 time, the rate at which removal stocks are supplied must be greater than or equal to the rate at which they are demanded. In Eq. (23), the parameter *Kh* 9 is found by Eqs. (16), (17) and (21), whereas the parameters *b*<sup>0</sup> 10 and *b*<sup>1</sup> are provided by GAMS-IDE (General Algebraic Modelling 11 System – Integrated Development Environment), version 24.1.2 (http://www.gams.com/ 12 download/), in which the optimisation programme described by Eqs. (21)-(24) was run.

The optimal values for *g*(*v*(*Xt* 13 )) are displayed in Table 5. They are now employed to estimate the future supply of removal stocks (*F* ^ (*Xt*)) given the existing ones (*Xt* 14 ), used up in the present. The estimated variable *F* ^ (*Xt* 15 ) stands for an *outflow-inflow ratio*, defined by logistically con‐ 16 strained rates of demand (numerator) and supply (denominator) of removal stocks (Eq. (25)). The difference between *F* ^ (*Xt*) and *Xt* is the removal supply per time period (*G*(*Xt* 17 )). Starting from the values for *G*(*Xt* 18 ), found by Eq. (26) and displayed in Table 5, it is possible to estimate 19 the removal supply function (Eq. (27)), whose values are also displayed in Table 5.

$$\overset{\frown}{F}\{X\_t\} = \frac{\overset{\frown}{\partial \mathring{h}}\_t / \partial X\_t}{\operatorname{g}\{\upsilon\{X\_t\}\}} = \frac{\overset{\frown}{h}\_t \{K\_h \stackrel{\frown}{\to} \hat{h}\_t\}}{\operatorname{g}\{\upsilon\{X\_t\}\}}\tag{25}$$

$$\mathbf{G}\left(\mathbf{X}\_{t}\right) = \overset{\wedge}{F}\left(\mathbf{X}\_{t}\right) \cdot \mathbf{X}\_{t} \tag{26}$$

$$\begin{aligned} \overset{\circ}{G} \begin{pmatrix} X\_t \\ \end{pmatrix} &= 5.727X\_t^2 - 244006.276 \\ \text{t-stat.} & \quad 588.143 & -588.763 \\ \text{sig.t.} & 0.0000 & 0.0000 \\ \end{aligned} \tag{27}$$

## **7. Avoiding deforestation** *versus* **stock maintenance approach**

 All removal market functions are drawn in Figure 3. They look like those in Figure 2.c, and their interplay shows how long emissions from deforestation can be removed by natural forest stocks. In deforestation-conservation settings, the cost of maintaining a high economic growth rate (ln *k*) would be an abrupt fall in the rate at which natural removal stocks grow (ln λ). As demonstrated by Eq. (3), in Table 2, and by the figures in the last column of Table 4, the value of λ indirectly defines the quantities of removal consumption (*ht* ) from deforestation (*ut* ) or removal savings from conservation (*vt* ). Thus, *h* ^ *<sup>t</sup>* (Eq. (21)) hinges on that sink distribution, and so does *G* ^ (*Xt* ) (Eq. (27)), whose estimation ultimately relies on *Kh* — the lower bound of removal demand. As a matter of fact, λ is the critical variable drawing the bottom line in carbon removal markets.

 The impacts of λ on the equilibrium of removal markets are shown in Table 6. The equilibrium scenarios checked, but the BESF one, follow the standard fishery analysis [28]. In the context of deforestation and forest conservation, though, there are important remarks regarding equilibrium conditions. To begin with, although the MSY equilibrium might apply to unnatu‐ ral, even-aged stands, it is not likely to suit the dynamics of conservation of natural forests and deforestation. It is known that in natural forests, the wide frequency and age range of tree species points to a biomass yielding function that does not reach a maximum sustainable yield. Mathematically, this is translated by taking the first derivative of Eq. (27) and making it equal to zero. Thus, it is found that *X*MSY = 21303.15 GtC, which is a prohibitively high level of removal stocks, provided their logistic upper bound is *XK* = 1492.78. The same holds for the restricted access (RA) stock level, which is found to be slightly lower (*X*RA = 21303.06 GtC).

 Although in standard bio-economic (fishery) analysis, the latter is expected to be higher than the former, it must be borne in mind that, in a *compensation approach* — such as *avoiding deforestation* —, the demand for removal stocks grows with deforestation (Figure 4.a). There‐ fore, conservation requires *X*MSY > *X*RA, since a smaller *compensating* stock means that less deforestation occurred.

 On the other hand, Figure 3 shows that the greater the stock, the smaller its demand is, because the stock can only grow when its depletion is low. Thus, as is clear so far, Figure 3 illustrates a rather different standpoint, namely, a *stock maintenance approach* (Figure 4.b). It has been argued that such a *conservation approach* favours the largest forest countries, like Brazil. Unlike in small forest countries, with only tiny remnants of forest left, in countries where large expanses of forest remain standing, stock maintenance represents a much greater carbon service than does avoiding deforestation [21].

1 In this regard, the equilibrium conditions in Table 6 had to be adjusted to fall within a stock maintenance rationale. Since, in Figure 3, the slopes of the functions *G* ^ (*Xt*) (Eq. (27)) and *h* ^ <sup>2</sup> *<sup>t</sup>* 3 (Eq. (21)) are never expected to be equal — as the columns labelled "rate of return" and "rate of depletion" in Table 6 also show —within the feasible region (*X*0A ≤ *Xt* \* ≤ *XK* 4 ), RA equilibrium only calls for maximising some positive level of *Yt* 5 , which could otherwise be warranted if the functions *G* ^ (*Xt*) and *h* ^ *<sup>t</sup>* 6 were to have the same slopes somewhere. However, as the outcomes in Table 6 show, a positive *Yt* can be accomplished with a negative value for *G* ^ (*Xt* 7 ), provided that, in absolute terms, this is smaller than that for *h* ^ *<sup>t</sup>*. Since *G* ^ (*Xt* 8 ) < 0 is environmentally 9 threatening, BESF equilibrium becomes a more stringent condition, because it calls both for positive *Yt* and *G* ^ (*Xt*). By minimising this positive level of *Yt* 10 , then, the BESF equilibrium ensures that, for a removal demand function like *ht* , in Figure 4.b, and *h* ^ *<sup>t</sup>* 11 , in Figure 3, the stock level satisfying this will lie slightly beyond (greater than) *K* — the stock level at which *G* ^ (*Xt* 12 ) = 0 — and further beyond (greater than) *X*OA — where *G* ^ (*Xt*) becomes equal to *h* ^ *<sup>t</sup>* 13 , but turns 14 out to be negative. stock can only grow when its depletion is low. Thus, as is clear so far, Figure 3 illustrates a rather different standpoint, namely, a *stock maintenance approach* (Figure 4.b). It has been argued that such a *conservation approach* favours the largest forest countries, like Brazil. Unlike in small forest countries, with only tiny remnants of forest left, in countries where large expanses of forest remain standing, stock maintenance represents a much greater carbon service than does avoiding deforestation [21]. In this regard, the equilibrium conditions in Table 6 had to be adjusted to fall within a stock maintenance rationale. Since, in Figure 3, the slopes of the functions ܩሺܺ௧ሻ (Eq. (27)) and ݄௧ (Eq. (21)) are never expected to be equal — as the columns labelled "rate of return" and "rate of depletion" in Table 6 also show —within the feasible region (*X*0A ≤ *Xt* \* ≤ *XK*), RA equilibrium only calls for maximising some positive level of *Yt*, which could otherwise be warranted if the functions ܩሺܺ௧ሻ and ݄௧ were to have the same slopes somewhere. However, as the outcomes in Table 6 show, a positive *Yt* can be accomplished with a negative value for ܩሺܺ௧ሻ, provided that, in absolute terms, this is smaller than that for ݄௧. Since ܩሺܺ௧ሻ < 0 is environmentally threatening, BESF equilibrium becomes a more stringent condition, because it calls both for positive *Yt* and ܩሺܺ௧ሻ. By minimising this positive level of *Yt*, then, the BESF equilibrium ensures that, for a removal demand function like *ht*, in Figure 4.b, and ݄௧, in Figure 3, the stock level satisfying this will lie slightly before *K* — the stock level at which ܩሺܺ௧ሻ = 0 — and further before *X*OA — where ܩሺܺ௧ሻ becomes equal to ݄௧, but

On the other hand, Figure 3 shows that the greater the stock, the smaller its demand is, because the

The open access stock level (*X*OA) is the smallest, yet just slightly smaller than the others. As expected 16 **Figure 3.** Removal market functions

Figure 3. Removal market functions

turns out to be negative.

15

( ) ˆ <sup>2</sup> 5.727 244006.276 t- stat. 588.143 588.763 sig. t. 0.0000 0.0000

 All removal market functions are drawn in Figure 3. They look like those in Figure 2.c, and their interplay shows how long emissions from deforestation can be removed by natural forest stocks. In deforestation-conservation settings, the cost of maintaining a high economic growth rate (ln *k*) would be an abrupt fall in the rate at which natural removal stocks grow (ln λ). As demonstrated by Eq. (3), in Table 2, and by the figures in the last column of Table 4, the value

) from deforestation (*ut* 7 ) or

). Thus, *h* ^ *<sup>t</sup>* 8 (Eq. (21)) hinges on that sink distribution,

(*Xt* 9 ) (Eq. (27)), whose estimation ultimately relies on *Kh* — the lower bound of 10 removal demand. As a matter of fact, λ is the critical variable drawing the bottom line in carbon

 The impacts of λ on the equilibrium of removal markets are shown in Table 6. The equilibrium scenarios checked, but the BESF one, follow the standard fishery analysis [28]. In the context of deforestation and forest conservation, though, there are important remarks regarding equilibrium conditions. To begin with, although the MSY equilibrium might apply to unnatu‐ ral, even-aged stands, it is not likely to suit the dynamics of conservation of natural forests and deforestation. It is known that in natural forests, the wide frequency and age range of tree species points to a biomass yielding function that does not reach a maximum sustainable yield. Mathematically, this is translated by taking the first derivative of Eq. (27) and making it equal to zero. Thus, it is found that *X*MSY 20 = 21303.15 GtC, which is a prohibitively high level of removal stocks, provided their logistic upper bound is *XK* = 1492.78. The same holds for the restricted

22 access (RA) stock level, which is found to be slightly lower (*X*RA = 21303.06 GtC).

 Although in standard bio-economic (fishery) analysis, the latter is expected to be higher than the former, it must be borne in mind that, in a *compensation approach* — such as *avoiding deforestation* —, the demand for removal stocks grows with deforestation (Figure 4.a). There‐ fore, conservation requires *X*MSY 26 > *X*RA, since a smaller *compensating* stock means that less

 On the other hand, Figure 3 shows that the greater the stock, the smaller its demand is, because the stock can only grow when its depletion is low. Thus, as is clear so far, Figure 3 illustrates a rather different standpoint, namely, a *stock maintenance approach* (Figure 4.b). It has been argued that such a *conservation approach* favours the largest forest countries, like Brazil. Unlike in small forest countries, with only tiny remnants of forest left, in countries where large expanses of forest remain standing, stock maintenance represents a much greater carbon


*GX X t t* = -

1 **7. Avoiding deforestation** *versus* **stock maintenance approach**

of λ indirectly defines the quantities of removal consumption (*ht*

removal savings from conservation (*vt*

^

18 Carbon Sequestration 18 CO2 Sequestration and Valorization

and so does *G*

11 removal markets.

27 deforestation occurred.

34 service than does avoiding deforestation [21].

from standard theory on renewable resources, the economic rent at this level is zero. At any other equilibrium point, it is non-zero and positive. But it is the highest at BESF, which minimises the throughput of maintaining stocks by requiring the supply of their emissions removal services (ܩሺܺ௧ሻ) always to be positive. Finally, by comparing the optimal stock levels (*Xt* \* ), in Table 6, with the observed ones (*Xt*), in Table 5, it can be inferred when each equilibrium scenario must have occurred. It is worrying to ascertain 17 The open access stock level (*X*OA) is the smallest, yet just slightly smaller than the others. As 18 expected from standard theory on renewable resources, the economic rent at this level is zero. 19 At any other equilibrium point, it is non-zero and positive. But it is the highest at BESF, which 20 minimises the throughput of maintaining stocks by requiring the supply of their emissions removal services (*G* ^ (*Xt* 21 )) always to be positive.

Finally, by comparing the optimal stock levels (*Xt* \* ), in Table 6, with the observed ones (*Xt* 22 ), in 23 Table 5, it can be inferred when each equilibrium scenario must have occurred. It is worrying 24 to ascertain that all of them are already gone somewhere between 1998 and 1999.

that all of them are already gone somewhere between 1998 and 1999.

1

2 **Figure 4.** The BESF model functions and the REDD approaches

Figure 4. The BESF model functions and the REDD approaches

Optimalc


Removal supply

Removal demand

Rate of

Rate of deple-

Economic

3 (a) BESF = Bio-Economic carbon Sequestration by Forests; K = steady-state equilibrium; MSY = Maximum Sustainable 4 Yield; RA = Restricted Access equilibrium; OA = Open Access equilibrium. (b) In standard fishery models, however, *X*RA is found where *dG* ^(*Xt*) / *<sup>d</sup> Xt* <sup>=</sup>*dh* ^ *<sup>t</sup>* / *<sup>d</sup> Xt*. (c) Provided by GAMS-IDE 24.1.2. (d) First derivative of Eq. (27) = 11.454*Xt* 5 . However, so that *G* ^(*Xt*) and *<sup>h</sup>* ^ *<sup>t</sup>* can be plotted together, the barter ratio between them is 10 GtC of *G* ^(*Xt* 6 ) per 10 MtC of *h* ^ *<sup>t</sup>* 7 , as the vertical axis of the graph in Figure 3 indicates. More simply, this barter ratio can be expressed as 1 GtC : 1 MtC, which means 10 8 <sup>3</sup> MtC : 1 MtC. Therefore, so that rates of return and depletion can be compared with one anoth‐ er, the former must be multiplied by 10-3 9 . (e) First derivative of Eq. (21).

10 **Table 6.** Removal market equilibrium analysis

#### **8. Aggregate emissions demand or removal supply function (***λ* **^(***ε* **^** <sup>1</sup> **))**

 Perhaps these worries could have been dismissed before, if the variable regulating land-use changes (λ) had not been overlooked. As seen, ε (Eq. (14)) — the bio-economic exchange rate — is defined as the ratio of ecological debt — excess demand for removal services (supply of emissions) — to ecological credit — excess supply of removal services (demand for emissions). Although it is an underlying variable, it stands for the *shadow price* measuring, along an optimal path through time, the marginal bio-economic value of the forestland asset [19]. When the speed *k* of economic activity drives deforestation, forestland shrinks and thus ε is expected to rise (depreciate).

10 Since ε critically and ultimately depends on λ and *k*, it must, to begin with, be expressed in terms of them. Methodologically, this can be first accomplished by fixing *k* =*k* ¯ 11 =1.00023, given 12 in the last row of Table 4. Then this rate is assumed to hold for every year according to the 13 following rule:

$$\left|X\_{t-1}\right| \left|X\_t\right| = \bar{k} = 1.00023,\tag{28}$$

where *Xt* ' is the stock level that would be observed in column *Xt* of Table 5, if *k* =*k* ¯ 14 = 1.0023.

Figure 4. The BESF model functions and the REDD approaches

*X*RA *X*MSY *X*OA

(a) Emissions avoidance approach (REDD) *ht* = *f*(*Xt*)

> Optimalc stock (*Xt* \* )

> > stock

Optimalc

(Xt \* ) Removal supply (����� ∗�)

Compensating stock (*Xt*)

*G*(*Xt*)

*ht*

*K*

Conserved stock (*Xt*)

*G*(*Xt*)

(in GtC) (in GtC) (in GtC)

(in GtC) (in GtC) (in GtC)

Removal supply (*G* ^(*Xt \** ))

K ������ � � 206.412933 0.000 -0.086 2.364 0.999 0.086

OA ������ � ��� 206.412898 -0.08 -0.08 2.364 0.999 0.000 (a) BESF = Bio-Economic carbon Sequestration by Forests; K = steady-state equilibrium; MSY = Maximum Sustainable Yield; RA = Restricted Access equilibrium; OA = Open Access equilibrium. (b) In standard fishery models, however, *X*RA is found where �������⁄��� � ����⁄���. (c) Provided by GAMS-IDE 24.1.2. (d) First derivative of Eq. (27) = 11.454*Xt*. However, so that ������ and ��� can be plotted together, the barter ratio between them is 10 GtC of ������ per 10 MtC of ���, as the vertical axis of the graph in Figure 3 indicates. More simply, this barter ratio can be expressed as 1 GtC : 1 MtC, which means 10-3 MtC : 1 MtC. Therefore, so that rates of return and depletion can be compared with one another, the former must be multiplied by 10-3. (e) First derivative of Eq.

3 (a) BESF = Bio-Economic carbon Sequestration by Forests; K = steady-state equilibrium; MSY = Maximum Sustainable 4 Yield; RA = Restricted Access equilibrium; OA = Open Access equilibrium. (b) In standard fishery models, however, *X*RA

*<sup>t</sup>* / *<sup>d</sup> Xt*. (c) Provided by GAMS-IDE 24.1.2. (d) First derivative of Eq. (27) = 11.454*Xt* 5 .

^(*Xt* 6 ) per 10 MtC

*<sup>t</sup>* 7 , as the vertical axis of the graph in Figure 3 indicates. More simply, this barter ratio can be expressed as 1 GtC : 1 MtC, which means 10 8 <sup>3</sup> MtC : 1 MtC. Therefore, so that rates of return and depletion can be compared with one anoth‐

^(*Xt*)=0 206.412933 0.000 -0.086 2.364 0.999 0.086

Removal demand (�� ∗)

Removal demand (*ht*

*ht*

206.412933 → 0.000 -0.086 2.364 0.999 > 0.086

— — — — — —

206.412933 → 0.000 -0.086 2.364 0.999 > 0.086

206.412933 -3.49×10-10 -0.086 2.364 0.999 < 0.086

— — — — — —

206.412933 -3.49×10-10 -0.086 2.364 0.999 < 0.086

*<sup>t</sup>* 206.412898 -0.08 -0.08 2.364 0.999 0.000

*<sup>t</sup>* can be plotted together, the barter ratio between them is 10 GtC of *G*

Rate of return<sup>d</sup>

*K*

(b) Stock maintenance approach (REDD+) *ht* = *f* -1(*Xt*)

*<sup>X</sup>*OA 0

*G*(*Xt*) *ht*

∗� Eq. (27) Eq. (21)

Rate of returnd ( *dG* ^ (*Xt \**) *d Xt \** )

) Eq. (27) Eq. (21)

Rate of depletione

> Rate of depletione ( *dh* ^ *t \* d Xt \** )

Economic rent ��� � ����� ∗� � ���

> *G* ^(*Xt \** ) - *h* ^ *t \**

Economic rent (*Yt* =

� ���� ∗ ��� ∗�

� ������ ∗� ��� <sup>∗</sup> �

*\** )

Equilibria<sup>a</sup>

BESF

MSY

1

Equilibrium conditionsb

2 **Figure 4.** The BESF model functions and the REDD approaches

0

*G*(*Xt*) *ht*

20 Carbon Sequestration 20 CO2 Sequestration and Valorization

������ � � *X*OA *≤ Xt* \* ≤ *XK*

conditionsb

� �

Table 6. Removal market equilibrium analysis

^

er, the former must be multiplied by 10-3 9 . (e) First derivative of Eq. (21).

���

min *Yt* > 0 *G* ^(*Xt*)>0 *X* OA ≤ X*<sup>t</sup>* \* ≤ *XK*

*X*OA *≤ Xt* \* ≤ *XK*

BESF min *Yt* > 0

Equilibriaa Equilibrium

MSY �������

K *G*

(21).

OA *G*

is found where *dG*

However, so that *G*

of *h* ^ RA max *Yt* > 0 *X* OA ≤ X*<sup>t</sup>* \* ≤ *XK*

RA max *Yt* > 0 *X*OA *≤ Xt* \* ≤ *XK*

> *dG* ^ (*Xt*) *<sup>d</sup> Xt* =0 *X* OA ≤ X*<sup>t</sup>* \* ≤ *XK*

> > ^(*Xt*)=*<sup>h</sup>* ^

> > > ^(*Xt*) / *<sup>d</sup> Xt* <sup>=</sup>*dh*

^(*Xt*) and *<sup>h</sup>* ^

10 **Table 6.** Removal market equilibrium analysis

 As λ changes, so will ε — whose calculation follows Eqs. (11) and (6), for *M* (imports); (2) and (9), for *Z* (exports); and (14), for ε proper. These outcomes must be ordered pair-wise, according to increasing values of λ. The objective of this disposition is to check how ε is affected by changes in λ. Next, an equation for ε, as a long-run function (thus, bearing no *t* index) of λ, is estimated:

$$\begin{aligned} \hat{\varepsilon}(\lambda) &= \exp\left( -0.131 + \frac{1.096}{\ln \ln \lambda} \right) \\ \text{t-stat.} & \quad -243.522 \quad 1048.581 \\ \text{sig.t.} & \quad 0.0000 \quad 0.0000 \end{aligned} \tag{29}$$

Finally, the results for *ε* ^ 20 (*λ*), in Eq. (29), are used as inputs (independent variables) to arrive at 21 an equation for λ as a long-run function of ε:

$$\begin{aligned} \ln \hat{\mathcal{L}} \{ \hat{\mathcal{z}} \} &= \exp \left( -1.127 + \frac{2.766}{\hat{\mathcal{z}}} \right) \\ \text{t-stat.} & \quad -133.522 \quad 214.563 \\ \text{sig. t.} & \quad 0.0000 \quad 0.0000 \end{aligned} \tag{30}$$

22 Eq. (30) stands for the *aggregate emissions demand* or *removal supply* function in the long run.

#### **9. Aggregate emissions supply or removal demand function (***k* **^ (***ε* **^** <sup>1</sup> **))**

 By a similar procedure, the functional relationship between ε and *k*, as well as between *k* and <sup>ε</sup>, can then be calculated. This time, though, the variable made fixed is *<sup>λ</sup>* <sup>=</sup>*λ*¯= 7.975, given in the last row of Table 4. This value is kept unchanged for each two consecutive years, to either of which Eqs. (1) and (3) apply:

$$\mathbf{x}\_{t,j} + \mathbf{x}\_{t,j+1} = \mathbf{X}\_t \tag{1}$$

$$\mathbf{x}\_{t,j+1}/\mathbf{x}\_{t,j} = \overline{\lambda} = 7.975 \tag{3}$$

6 By substituting Eq. (3)a into Eq. (1)a, it turns out that:

$$\mathbf{x} \, X\_t = (\mathbf{1} + \boldsymbol{\mathcal{X}}) \mathbf{x}\_{t,j'} \, \tag{1}$$

 which can be correspondingly replaced in Eq. (13)c to find the new value of *k*, at each two consecutive periods, when λ = *λ*¯ = 7.975 and thence remains constant. Again, the resulting <sup>ε</sup> requires Eqs. (11), (6), (2), (9) and (14). Also, like before, so as to inquire into the effects of changing *k* on ε, these variables are taken pair-wise on an increasing order of *k* values. Lastly, an equation for ε, as a long-run function (thus, bearing no *t* index) of *k*, can be estimated:

$$
\begin{aligned}
\hat{\varepsilon}\left(k\right) &= & 6434.098 \ln \ln k \\
\text{t-stat. } 3921.720 \\
\text{sig.t.} & 0.0000
\end{aligned}
\tag{31}
$$

Now, the outcomes for *ε* ^ 12 (*k*), in Eq. (31), are used as inputs (independent variables) to obtain 13 an equation for *k* as a long-run function of ε:

$$\begin{aligned} \hat{k}\left(\hat{\varepsilon}\right) &= 0.99999998 + 1.554575 \times 10^{-4} \,\hat{\varepsilon} \\ \text{t-stat.} \quad & - & - \\ \text{sig. t.} \quad & - \\ \end{aligned} \tag{32}$$

14 Eq. (32) stands for the *aggregate emissions supply* or *removal demand* function in the long run.

#### 15 **10. Macro-bio-economic scenarios**

16 Based on Eqs. (30) and (32), REDD, CDM and BAU scenarios are tested to understand how 17 well conservation (REDD) and compensation (CDM) strategies can do as compared with

 business-as-usual (BAU) ones (Table 7 and Table 8). The most useful results shown by Table 8 are those displayed in its last two columns. They make clear how much the natural forest and the economy are expected to grow annually, through 25 years, in each scenario. Eq. (32) stands for the *aggregate emissions supply* or *removal demand* function in the long run.

4 It is noteworthy that a stringent conservation scenario, such as REDD1, requires an optimal value for λ (*λ* ^ REDD1 *\** 5 = 5.787) that is not too far from its observed mean value, displayed in the 6 last row and column of Table 4 (*λ*¯= 7.975). Thus, the allowed annual deforestation rate through 7 25 years (Eq. (33)) is 3.18% p.a. (last column of Table 8). This figure might sound startling when 8 compared, for instance, with the deforestation rate in the Brazilian Legal Amazon for a single 9 year: just between August 2012 and August 2013, this rate reached 20% [31]! However, neither 10 would more "economic growth-driven" strategies (CDM and BAU's) stand such a high annual 11 deforestation rate. Nearly all of them (last four rows and last column of Table 8) would allow 12 for a yearly deforestation rate of about 7.8%. On the other hand, a 100% rate of deforestation 13 reduction, even spread over 25 years (scenario REDD2), would render no more than an 14 economic growth rate as low as 0.00149% (last column of Table 8). These numbers help shed 15 some light on the feasibility of the targets set by deforestation reduction programmes [29] (Figure 7). Figure 5 shows that the supply of emissions (*k* ^ <sup>16</sup> ) is nearly *perfectly inelastic* to the shadow price ε, whereas the removal of them (*λ* ^ 17 ) dramatically falls with the rise of ε. Although 18 at some high value of ε, the demand for removal also becomes almost inelastic to price changes, this only happens at very low levels of existing removal stocks, when thus *<sup>λ</sup>* 19 ^→ 1 (Eq. (3)) and 20 the share of forest conservation approaches that of deforestation (v → u) or becomes even 21 smaller (v < u). **10. Macro-bio-economic scenarios**  Based on Eqs. (30) and (32), REDD, CDM and BAU scenarios are tested to understand how well conservation (REDD) and compensation (CDM) strategies can do as compared with business-as-usual (BAU) ones (Table 7 and Table 8). The most useful results shown by Table 8 are those displayed in its last two columns. They make clear how much the natural forest and the economy are expected to grow annually, through 25 years, in each scenario. It is noteworthy that a stringent conservation scenario, such as REDD1, requires an optimal value for λ (�� REDD1 <sup>∗</sup> = 5.787) that is not too far from its observed mean value, displayed in the last line and column of Table 4 (�̅ = 7.975). Thus, the allowed annual deforestation rate through 25 years (Eq. (33)) is 3.18% p.a. (last column of Table 8). This figure might sound startling when compared, for instance, with the deforestation rate in the Brazilian Legal Amazon for a single year: just between August 2012 and August 2013, this rate reached 20% [31]! However, neither would more "economic growth-driven" strategies (CDM and BAU's) stand such a high annual deforestation rate. Nearly all of them (last four rows and last column of Table 8) would allow for a yearly deforestation rate of about 7.8%. On the other hand, a 100% rate of deforestation reduction, even spread over 25 years (scenario REDD2), would render no more than an economic growth rate as low as 0.00149% (last column of Table 8). These numbers help shed some light on the feasibility of the targets set by deforestation reduction programmes [29] (Figure 7). Figure 5 shows that the supply of emissions (��) is nearly *perfectly inelastic* to the shadow price ε, whereas the removal of them (��) dramatically falls with the rise of ε. Although at some high value of ε, the demand for removal also becomes almost inelastic to price changes, this only happens at very low levels of existing removal stocks, when thus �� → 1 (Eq. (3)) and the share of forest conservation approaches


Table 9. Scenario analysis (a) Because, as shown by Table 4, *k* is much smaller than λ, the greater of them must be scaled down through logarithms to make them comparable. (b) Growth rate for λ that would smooth, over 25 years (1988-2012), the accumulated deforestation reduction rate defined by a 7-year programme, from 2007 to 2015, for reducing deforestation in the Brazilian Amazon [29]. The annual reduction rates for every period *t* = 1,..., 7 are, respectively, 25%, 25%, 30%, 40%, 50%, 75% and 100%. The "capitalisation" (multiplication) of all these rates yields 7.4648438, which amounts to the full figure to be reached in 7 years. This 7-year time is factored into a 25-year 22 (a) Because, as shown by Table 4, *<sup>k</sup>* is much smaller than λ, the greater of them must be scaled down through logarithms 23 to make them comparable. (b) Growth rate for λ that would smooth, over 25 years (1988-2012), the accumulated 24 deforestation reduction rate defined by a 7-year programme, from 2007 to 2015, for reducing deforestation in the 25 Brazilian Amazon [29]. The annual reduction rates for every period *t* = 1,..., 7 are, respectively, 25%, 25%, 30%, 40%, 26 50%, 75% and 100%. The "capitalisation" (multiplication) of all these rates yields 7.4648438, which amounts to the full 27 figure to be reached in 7 years. This 7-year time is factored into a 25-year period, thereby yielding 3.5714286 sub-periods, 28 over which the deforestation reduction rate accumulated during 7 years is spread according to its geometric mean 25

period, thereby yielding 3.5714286 sub-periods, over which the deforestation reduction rate accumulated during 7 years is spread according to its geometric mean � √7.4648438 �� � �. (c) Growth rate for λ that would amount to a 100% deforestation reduction in 25 years. It is calculated by √2 �� , where 2 is a rate of growth that is worth 100%. (d) Value needed to yield an annual rate of economic ( 7.4648438 <sup>7</sup> ) 29 . (c) Growth rate for λ that would amount to a 100% deforestation reduction in 25 years. It is calculated by 2 <sup>25</sup> 30 , where 2 is a rate of growth that is worth 100%. (d) Value needed to yield an annual rate of economic growth of 31 2.40% during 25 years. This rate is the one projected for Brazil's GDP growth in 2013 by the Brazilian Central Bank, in its 32 latest Focus Report [30]. (e) Value needed to yield an annual rate of economic growth of 4.00% during 25 years. (f) Value 33 needed to yield an annual economic growth rate of 5.00% during 25 years.

**p.a. †**

��∗ **% p.a.†** 

**Allowed def. rate**  �<sup>∗</sup>**% p.a. (Eq.(33))** 

**(× 10-4)** �� <sup>∗</sup> �� � � <sup>∗</sup> ��∗ �� ��∗ ��∗**%** 

REDD1 1.637 5.7874 **1.755682** 1.000254 0.000254 7.27520 0.00102 3.1804 REDD2 2.395 2.7958 **1.028114** 1.000372 0.000372 4.19819 0.00149 6.7997 �� � � � �� 2.453 2.7193 **1.000381 1.000381** 0.000381 4.08267 0.00153 6.8623 CDM 2464.70 1.3832 **0.324368** 1.383156 **0.324368 1.30592 1.30592** 7.8350 BAU1 5205.61 1.3829 0.324176 **1.809251** 0.592913 1.30515 **2.40000** 7.8352 BAU2 10715.70 1.3828 0.324087 **2.665836** 0.980518 1.30479 **4.00000** 7.8353 BAU3 15350.53 1.3827 0.324062 **3.386355** 1.219754 1.30469 **5.00000** 7.8353

**Scenario** ��∗

Table 10. Optimal (\*) results‡ from scenario analysis

yield an annual economic growth rate of 5.00% during 25 years.

that of deforestation (**v** → **u)** or becomes even smaller (**v** < **u**).

 ˆ <sup>4</sup> 0.99999998 1.554575 10 t- stat. — —

sig. t. — —

*k* 

ˆ ˆ

(32)

**9. Aggregate emissions supply or removal demand function (***k*

5 of which Eqs. (1) and (3) apply:

22 Carbon Sequestration 22 CO2 Sequestration and Valorization

Now, the outcomes for *ε*

13 an equation for *k* as a long-run function of ε:

15 **10. Macro-bio-economic scenarios**

*k* e

6 By substituting Eq. (3)a into Eq. (1)a, it turns out that:

2 By a similar procedure, the functional relationship between ε and *k*, as well as between *k* and 3 <sup>ε</sup>, can then be calculated. This time, though, the variable made fixed is *<sup>λ</sup>* <sup>=</sup>*λ*¯= 7.975, given in 4 the last row of Table 4. This value is kept unchanged for each two consecutive years, to either

*Xt* =(1 <sup>+</sup> *<sup>λ</sup>*¯)*xt*, *<sup>j</sup>*

 which can be correspondingly replaced in Eq. (13)c to find the new value of *k*, at each two consecutive periods, when λ = *λ*¯ = 7.975 and thence remains constant. Again, the resulting <sup>ε</sup> requires Eqs. (11), (6), (2), (9) and (14). Also, like before, so as to inquire into the effects of changing *k* on ε, these variables are taken pair-wise on an increasing order of *k* values. Lastly, an equation for ε, as a long-run function (thus, bearing no *t* index) of *k*, can be estimated:

> ˆ( ) 6434.098lnln t- stat. 3921.720 sig. t. 0.0000

*k k* =

^ 12 (*k*), in Eq. (31), are used as inputs (independent variables) to obtain

( ) ˆ <sup>4</sup> 0.99999998 1.554575 10 t- stat. — —

14 Eq. (32) stands for the *aggregate emissions supply* or *removal demand* function in the long run.

16 Based on Eqs. (30) and (32), REDD, CDM and BAU scenarios are tested to understand how 17 well conservation (REDD) and compensation (CDM) strategies can do as compared with

sig. t. — —

ˆ ˆ


 e

e

**^** <sup>1</sup> **))**

**^ (***ε*

*xt*, *<sup>j</sup>* + *xt*, *<sup>j</sup>*+1 = *Xt* (1)a *xt*, *<sup>j</sup>*+1 / *xt*, *<sup>j</sup>* <sup>=</sup>*λ*¯ =7.975 (3)a

, (1)b

(31)

(32)

∗

Scenario �̂


REDD2 2.395 2.7958 **1.028114** 1.000372 0.000372 4.19819 0.00149 6.7997 �� � � � �� 2.453 2.7193 **1.000381 1.000381** 0.000381 4.08267 0.00153 6.8623

p.a. †

�� <sup>∗</sup>% p.a.†

Allowed def. rate �∗% p.a. (Eq.(33))

(× 10-4) ��∗ �� � � <sup>∗</sup> �� <sup>∗</sup> �� �� <sup>∗</sup> ��∗%

(†) Growth rates per annum (p.a.). Figures under these column captions respectively come from ( λ ^*\** <sup>25</sup> ) 1 - <sup>1</sup> ×100 and ( *k* ^*\** <sup>25</sup> ) 2 - <sup>1</sup> ×100. (‡) Provided by GAMS-IDE 24.1.2. about 7.8%. On the other hand, a 100% rate of deforestation reduction, even spread over 25 years (scenario REDD2), would render no more than an economic growth rate as low as 0.00149% (last column of Table 8). These numbers help shed some light on the feasibility of the targets set by

**Table 8.** Optimal (\* ) results‡ 3 from scenario analysis deforestation reduction programmes [29] (Figure 7). Figure 5 shows that the supply of emissions (��) is nearly *perfectly inelastic* to the shadow price ε, whereas the removal of them (��) dramatically falls

Figure 5. Aggregate equilibrium for conservation and deforestation in the Brazilian Legal Amazon 5 **Figure 5.** Aggregate equilibrium for conservation and deforestation in the Brazilian Legal Amazon

#### 6 **11. Removal trade**

4

 Now, the optimal values for ε brought to light in Table 8 can be used in Table 9 to define, as in Figure 2.b, the amount of removal loans across the space (*Z*) and over time (*M*). Of course, this previously requires that both *Z* and *M* are estimated as long-run functions of ε. However, as shown in Table 4, it is precisely ε that arises from observable *Z* and *M*; not the other way 1 around. So, what must be estimated first and foremost is an equation in which ε works as a 2 long-run function of observable *Z* and *M*.

Scenario <sup>ε</sup>

<sup>100</sup> and �� ��� <sup>∗</sup> � �� <sup>⁄</sup>

Table 8. Optimal (\*

Scenario �̂

ln <sup>λ</sup>^ <sup>=</sup>*<sup>k</sup>*

( *k*

4

**Table 8.** Optimal (\*

^*\** (× 10-4)

∗

24 Carbon Sequestration 24 CO2 Sequestration and Valorization

^*\** <sup>25</sup> ) 2 - <sup>1</sup> ×100. (‡) Provided by GAMS-IDE 24.1.2.

) results‡ 3 from scenario analysis

becomes even smaller (**v** < **u**).

Economic growth rate (k^)

6 **11. Removal trade**

Conservation rate (ln λ^)

0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9 *δ* \*

= (*λ*¯ - *λ* ^\*) <sup>25</sup>

λ

) results‡

*^\* ln* <sup>λ</sup> *^ \* <sup>k</sup>*

� � 1� � 100. (‡) Provided by GAMS-IDE 24.1.2.

�<sup>∗</sup> � � ���̅� ��∗� � �� <sup>⁄</sup>

from scenario analysis

(× 10-4) ��∗ �� � � <sup>∗</sup> �� <sup>∗</sup> �� �� <sup>∗</sup> ��∗%

*^\* ln <sup>k</sup>*

REDD1 1.637 5.7874 **1.755682** 1.000254 0.000254 7.27520 0.00102 3.1804 REDD2 2.395 2.7958 **1.028114** 1.000372 0.000372 4.19819 0.00149 6.7997

growth-driven" strategies (CDM and BAU's) stand such a high annual deforestation rate. Nearly all of them (last four rows and last column of Table 8) would allow for a yearly deforestation rate of about 7.8%. On the other hand, a 100% rate of deforestation reduction, even spread over 25 years (scenario REDD2), would render no more than an economic growth rate as low as 0.00149% (last column of Table 8). These numbers help shed some light on the feasibility of the targets set by deforestation reduction programmes [29] (Figure 7). Figure 5 shows that the supply of emissions (��) is nearly *perfectly inelastic* to the shadow price ε, whereas the removal of them (��) dramatically falls with the rise of ε. Although at some high value of ε, the demand for removal also becomes almost inelastic to price changes, this only happens at very low levels of existing removal stocks, when thus �� → 1 (Eq. (3)) and the share of forest conservation approaches that of deforestation (**v** → **u)** or

REDD1 1.637 5.7874 **1.755682** 1.000254 0.000254 7.27520 0.00102 3.1804 REDD2 2.395 2.7958 **1.028114** 1.000372 0.000372 4.19819 0.00149 6.7997 �� � � � �� 2.453 2.7193 **1.000381 1.000381** 0.000381 4.08267 0.00153 6.8623 CDM 2464.70 1.3832 **0.324368** 1.383156 **0.324368 1.30592 1.30592** 7.8350 BAU1 5205.61 1.3829 0.324176 **1.809251** 0.592913 1.30515 **2.40000** 7.8352 BAU2 10715.70 1.3828 0.324087 **2.665836** 0.980518 1.30479 **4.00000** 7.8353 BAU3 15350.53 1.3827 0.324062 **3.386355** 1.219754 1.30469 **5.00000** 7.8353 (†) Growth rates per annum (p.a.). Figures under these column captions respectively come from �� ���∗ � �� <sup>⁄</sup>

(†) Growth rates per annum (p.a.). Figures under these column captions respectively come from ( λ ^*\** <sup>25</sup> ) 1 - <sup>1</sup> ×100 and

^ 2.453 2.7193 **1.000381 1.000381** 0.000381 4.08267 0.00153 6.8623 CDM 2464.70 1.3832 **0.324368** 1.383156 **0.324368 1.30592 1.30592** 7.8350 BAU1 5205.61 1.3829 0.324176 **1.809251** 0.592913 1.30515 **2.40000** 7.8352 BAU2 10715.70 1.3828 0.324087 **2.665836** 0.980518 1.30479 **4.00000** 7.8353 BAU3 15350.53 1.3827 0.324062 **3.386355** 1.219754 1.30469 **5.00000** 7.8353


Figure 5. Aggregate equilibrium for conservation and deforestation in the Brazilian Legal Amazon

 Now, the optimal values for ε brought to light in Table 8 can be used in Table 9 to define, as in Figure 2.b, the amount of removal loans across the space (*Z*) and over time (*M*). Of course, this previously requires that both *Z* and *M* are estimated as long-run functions of ε. However, as shown in Table 4, it is precisely ε that arises from observable *Z* and *M*; not the other way

0 5 10 15 20 25 30 35 40 45 50

Bio-economic exchange rate (ε^) (× 10-4)

5 **Figure 5.** Aggregate equilibrium for conservation and deforestation in the Brazilian Legal Amazon

*^\** λ

^*\** % p.a. †

� 1� � 100� �with �̅� ��∗ (33)

p.a. †

*k* ^*\** % p.a.†

�� <sup>∗</sup>% p.a.†

^\* (33)

k^

ln lambda^

Allowed def. rate δ *\**

� � 1� �

% p.a. (Eq.(33))

Allowed def. rate �∗% p.a. (Eq.(33))

$$\begin{aligned} \hat{\varepsilon}\{Z\} &= \exp\{1.428 \times Z\} \\ \text{t-stat.} &\quad 26.544 \\ \text{sig.t.} &\quad 0.0000 \\\\ \hat{\varepsilon}\{M\} &= 6.643M^{0.926} \\ \text{t-stat.} &\quad 67.364 \text{ 35.514} \\ \text{sig.t.} &\quad 0.0000 \text{ 0.0000} \end{aligned} \tag{35}$$

Then, the outcomes for *ε* ^(*Z*) and *<sup>ε</sup>* ^ 3 (*<sup>M</sup>* ), in Eqs. (34) and (35), are used as inputs (independent 4 variables) to obtain the equations for *Z* and *M* as long-run functions of ε:

$$\begin{aligned} \hat{Z}(\hat{\varepsilon}) &= \exp\left(0.895 - \frac{4.154}{\hat{\varepsilon}}\right) \\ \text{t-stat.} &= 259.616 \quad -133.481 \\ \text{sig. t.} &= 0.0000 \quad 0.0000 \end{aligned} \tag{36}$$

$$\begin{aligned} \hat{M}\{\hat{\varepsilon}\} &= 0.129 \hat{\varepsilon}^{1.08} \\ \text{t-stat.} & \quad - & - \\ \text{sig. t.} & \quad - & - \end{aligned} \tag{37}$$


5 (a) Actually, these figures are in hundreds of GtC, so that, as indicated by Table 4, their order of magnitude can be compared to that of *M* ^ 6 . (b) Results provided by GAMS-IDE 24.1.2.

**Table 9.** Scenario analysisb 7 for conservation and compensation loans

(†) Determined by GAMS-IDE 24.1.2. 2 (†) Determined by GAMS-IDE 24.1.2.

1

Figure 6. Optimal path† for conservation (ܼመ) — REDD — and compensation (ܯ — (CDM — loans (a) and rates (b) in the long run Eqs. (36) and (37) define, respectively, the optimal path of *conservation* (REDD) and *compensation* **Figure 6.** Optimal path† for conservation (*Z* ^) — REDD — and compensation (*<sup>M</sup>* ^ 3 ) — CDM — loans (a) and rates (b) in 4 the long run

(CDM) to be loaned out in the long run (Figure 6.a). Since conservation is the surplus of carbon removal services, they can be *exported* (lent) to somewhere else. On the other hand, when these missing services have nevertheless been used, some compensation is due. However, compensating for environmental services that were already lacking before implies *importing* (borrowing) them from somewhere. In this sense, *conservation* (exports) defines an *excess supply* of removal services, while *compensation* (imports) is caused by an *excess demand* for removal services. If these services are to be loaned out, the sink yielding conservation *lends* them, whereas the sink owing compensation *borrows* them. In any event, the demand for these loans, as shown in Figure 2, lies behind disaggregate (Figure 3) periodical — and aggregate (Figure 5) — long run — removal needs. As long as *B* (last column of Table 9) stands for the balance of carbon trading loans, it is interesting to note that a stringent scenario, such as REDD1 (Table 7), yields more ecological debt (ܯ (than ecological credit (ܼመ). This 5 Eqs. (36) and (37) define, respectively, the optimal path of *conservation* (REDD) and *compensa‐* 6 *tion* (CDM) to be loaned out in the long run (Figure 6.a). Since conservation is the surplus of 7 carbon removal services, they can be *exported* (lent) to somewhere else. On the other hand, 8 when these missing services have nevertheless been already used, some compensation is due. 9 However, compensating for environmental services that were already lacking before implies 10 *importing* (borrowing) them from somewhere. In this sense, *conservation* (exports) defines an 11 *excess supply* of removal services, while *compensation* (imports) is caused by an *excess demand* 12 for removal services. If these services are to be loaned out, the sink yielding conservation *lends* 13 them, whereas the sink owing compensation *borrows* them.

14 In any event, the demand for these loans, as shown in Figure 2, lies behind disaggregate (Figure 15 3) — periodical — and aggregate (Figure 5) — long run — removal needs. As long as *B* (last 16 column of Table 9) stands for the balance of carbon trading loans, it is interesting to note that a stringent scenario, such as REDD1 (Table 7), yields more ecological debt (*M* ^ <sup>17</sup> ) than ecological credit (*Z* ^ ). This balance only turns out to be positive in the interval where 1.799 ≤ *<sup>ε</sup>* ^ 18 ≤ 10.621. *<sup>B</sup>* reaches its maximum in this interval, when *ε* ^ 19 = 5.454. Later, it starts dropping, reaching zero 1 again. From then on, it becomes increasingly negative, and neither the CDM nor BAU scenario 2 can turn it over anymore.

3 Actually, CDM and BAU1 are but very loose scenarios. When they come into play, the ecological credit region (1.799 ≤*ε* ^ 4 <sup>≤</sup> 10.621) has already been left behind. Therefore, in spite of the high values for *ε* ^ 5 set by CDM and BAU scenarios, these prices appear to be too low to 6 prevent B < 0. Although CDM requires that ln *λ* =ln *k* (Table 7 and Table 8), existing ecological 7 conditions demand, rather, that ln *λ* >ln *k*.

If these conditions are overlooked, no further rise in *ε* ^ 8 , as in the BAUs scenarios, can prevent *<sup>B</sup>* 9 from keep falling. That likely occurs because the environmental capacity of providing remov‐ al services has been already overshot. As it turns out, *ε* ^ 10 cannot be set like usual prices can; 11 instead, it is set by ecological conditions, as though ecology were guiding the economy [32].

## 12 **12. Target setting**

(†) Determined by GAMS-IDE 24.1.2. Figure 6. Optimal path†

Rate of change of conservation Z^ (in

hundreds of GtC) and compensation M^

(in GtC)


13 them, whereas the sink owing compensation *borrows* them.

reaches its maximum in this interval, when *ε*


Conservation Z^ (in hundreds of GtC)

26 Carbon Sequestration 26 CO2 Sequestration and Valorization

Compensation M^ (in GtC)

(a) and rates (b) in the long run

**Figure 6.** Optimal path† for conservation (*Z*

*borrows* them.

2 (†) Determined by GAMS-IDE 24.1.2.

1

4 the long run

credit (*Z*

for conservation (ܼመ) — REDD — and compensation (ܯ — (CDM — loans

Eqs. (36) and (37) define, respectively, the optimal path of *conservation* (REDD) and *compensation* (CDM) to be loaned out in the long run (Figure 6.a). Since conservation is the surplus of carbon removal services, they can be *exported* (lent) to somewhere else. On the other hand, when these missing services have nevertheless been used, some compensation is due. However, compensating for environmental services that were already lacking before implies *importing* (borrowing) them from somewhere. In this sense, *conservation* (exports) defines an *excess supply* of removal services, while *compensation* (imports) is caused by an *excess demand* for removal services. If these services are to be loaned out, the sink yielding conservation *lends* them, whereas the sink owing compensation

 Eqs. (36) and (37) define, respectively, the optimal path of *conservation* (REDD) and *compensa‐ tion* (CDM) to be loaned out in the long run (Figure 6.a). Since conservation is the surplus of carbon removal services, they can be *exported* (lent) to somewhere else. On the other hand, when these missing services have nevertheless been already used, some compensation is due. However, compensating for environmental services that were already lacking before implies *importing* (borrowing) them from somewhere. In this sense, *conservation* (exports) defines an *excess supply* of removal services, while *compensation* (imports) is caused by an *excess demand* for removal services. If these services are to be loaned out, the sink yielding conservation *lends*

Bio-economic exchange rate (ε^) (× 10-4)

^) — REDD — and compensation (*<sup>M</sup>* ^ 3 ) — CDM — loans (a) and rates (b) in

0 2 4 6 8 10 12 14 16

Z^ M^

(a)

ln Z^ ln M^

(b)

Bio-economic exchange rate (ε^) (× 10-4)

0 3 5 8 10 13 15

In any event, the demand for these loans, as shown in Figure 2, lies behind disaggregate (Figure 3) periodical — and aggregate (Figure 5) — long run — removal needs. As long as *B* (last column of Table 9) stands for the balance of carbon trading loans, it is interesting to note that a stringent scenario, such as REDD1 (Table 7), yields more ecological debt (ܯ (than ecological credit (ܼመ). This

14 In any event, the demand for these loans, as shown in Figure 2, lies behind disaggregate (Figure 15 3) — periodical — and aggregate (Figure 5) — long run — removal needs. As long as *B* (last 16 column of Table 9) stands for the balance of carbon trading loans, it is interesting to note that

^ <sup>17</sup> ) than ecological

^ 19 = 5.454. Later, it starts dropping, reaching zero

^ ). This balance only turns out to be positive in the interval where 1.799 ≤ *<sup>ε</sup>* ^ 18 ≤ 10.621. *<sup>B</sup>*

a stringent scenario, such as REDD1 (Table 7), yields more ecological debt (*M*

 Table 10 focuses on scenario REDD1, from Table 8, in order to demonstrate how an economy can be ecologically guided. Of course, the figures in Table 10 do not account for the real picture. Rather, they relate to a 25-year deforestation period (1988-2012), to show how things would look if it had followed out the optimal path suggested by the conservation scenario REDD1. The percentages in the last column of Table 10 were merged in such a way that the 25-year period is divided into 7 time lags. By so doing, REDD1 reduction path, although applying to a different time period (1988-2012), can be compared to the same time length of the defores‐ tation reduction programme tailored by [29]. The comparison is shown in Figure 7.

 Nevertheless, it can still be asked why the percentage deforestation rate along the optimal path in Table 10 (15.59%) does not match that in the last column of Table 8 (3.1804%). Even though the former is referred to in deforested area terms, in km2 23 , while the latter, in carbon biomass, in GtC, the proper calculation (Eq. (39)) shows that the values would be equal. Accordingly, the percentage deforestation rates displayed in Table 10 can be reckoned either in carbon biomass (GtC) or in area units (km2 26 ). Actually, the underlying reason for the mismatch between deforestation rates in Table 10 and in Table 8 is that the rates in the former are *bounded* (Eq. (33)), whereas those in the latter are *unbounded*. This difference can be grasped from combining Eqs. (1) and (3) to yield:

$$X\_t = \mu\_t + \lambda\_t \mu\_t = (1 + \lambda\_t)\mu\_t \tag{38}$$

From Eq. (38) and from the definition of the deforestation rate (δ*<sup>t</sup>* 30 ) given in note *i* of Table 10, 31 it turns out that:

$$\mathcal{S}\_t = \frac{u\_t}{X\_t} = \frac{u\_t}{(1 + \lambda\_t)u\_t} = \frac{1}{1 + \lambda\_t} \tag{39}$$

If λ*<sup>t</sup>* 1 , in Eq. (39), takes on whatever optimal value displayed in Table 8, then this equation can be compared with Eq. (33). Yet now, unlike in the former equation, in the latter *λ* ^*\** 2 has an upper 3 bound (*λ*¯= 7.975). Since this ceiling stands for a mean value for the whole period (1988-2012), it is not surprising that δ\* 4 in Eq. (33) is lower than that arising from Eq. (39) — displayed in the 5 antepenult column of Table 10.

 The deforested area in Table 10 is given by Eq. (39). Its size follows closely — yet throughout a 25-year period — the size of forestland that, according to Table 3, has been cut down since 2005 (*t* = 18). However, while in the observed data of Table 3, the size of deforested land keeps on getting bigger, in the optimal path of Table 10, it increasingly shrinks.



(a) *ut* = *Xt* ÷ (1 +λ ^*\** ). (b) *vt* = *Xt* – *ut*. (c) Eq. (2). (d) Eq. (14): *Mt* = ε × *Zt*. (e) (*ut* ÷ 434) × 10-4, according to note *e* in Table 3. (f) (*vt* ÷ 464) × 10-4 1 , according to note *d* in Table 3. (g) *Xt* (in km2) = *ut* (in km2) + *vt* (in km2 2 ). However unrealistic it may sound, the total area "shrinks" to ac‐ 3 count for the loss of forestland implied by substituting the carbon stored in deforested sites for that stored in natural forests. (h) For *t* = 1 (1988), Σ*tut* (in km2) = 17508.67 – *u*1 (in km2); for *<sup>t</sup>* > 1, (remaining deforestation)*t* – 1 – *ut* (in km2). (i) *ut* (in km2) ÷ *Xt* (in km2). (j) (*ut* – 1 – *ut* 4 ) (in km2) × 103. (k) For *t* = 1 (1988), *ut* (in km2) ÷ Σ*tut* (in km2); for *t* > 1, *ut* (in km2) ÷ (remaining deforestation)*t* – 1 5 .

**Table 10.** Deforestation targets and rates from optimal results in the REDD1 scenario (λ ^*\** 6 =5.7874) (Table 8)

8 (\*) During 1988-2012. (\*\*) From 2007 to 2015 [29].

9 **Figure 7.** Targets for reducing deforestation in the Brazilian Legal Amazon

## 10 **13. Conclusion**

7

If λ*<sup>t</sup>* 1 , in Eq. (39), takes on whatever optimal value displayed in Table 8, then this equation can

6 The deforested area in Table 10 is given by Eq. (39). Its size follows closely — yet throughout 7 a 25-year period — the size of forestland that, according to Table 3, has been cut down since 8 2005 (*t* = 18). However, while in the observed data of Table 3, the size of deforested land keeps

be compared with Eq. (33). Yet now, unlike in the former equation, in the latter *λ* ^*\** 2 has an upper 3 bound (*λ*¯= 7.975). Since this ceiling stands for a mean value for the whole period (1988-2012), it is not surprising that δ\* 4 in Eq. (33) is lower than that arising from Eq. (39) — displayed in the

9 on getting bigger, in the optimal path of Table 10, it increasingly shrinks.

*Mt*

**(GtC)**

**ε**

**(×10-4)**

 1988 30.49 176.44 206.93 145.95 0.023890 1.637 702.46 3802.57 4505.03 16806.21 15.59 — 4.01 1989 30.48 176.38 206.85 145.90 0.023881 1.637 702.21 3801.21 4503.42 16104.00 15.59 251.55 4.18 1990 30.47 176.34 206.81 145.87 0.023877 1.637 702.07 3800.45 4502.52 15401.93 15.59 140.54 4.36 1991 30.46 176.31 206.78 145.85 0.023873 1.637 701.96 3799.83 4501.79 14699.98 15.59 114.06 4.56 1992 30.46 176.28 206.74 145.82 0.023868 1.637 701.82 3799.07 4500.89 13998.16 15.59 140.54 4.77 1993 30.46 176.28 206.74 145.82 0.023868 1.637 701.82 3799.07 4500.89 13296.35 15.59 0.00 5.01 1994 30.45 176.20 206.65 145.76 0.023858 1.637 701.51 3797.43 4498.94 12594.83 15.59 303.49 5.28 1995 30.43 176.13 206.57 145.70 0.023848 1.637 701.24 3795.93 4497.17 11893.60 15.59 275.99 5.57 1996 30.42 176.08 206.51 145.66 0.023841 1.637 701.03 3794.83 4495.86 11192.57 15.59 203.68 5.89 1997 30.42 176.05 206.47 145.63 0.023837 1.637 700.90 3794.12 4495.02 10491.67 15.59 132.40 6.26 1998 30.41 176.00 206.41 145.59 0.023831 1.637 700.72 3793.16 4493.88 9790.94 15.59 177.03 6.68 1999 30.40 175.96 206.36 145.55 0.023825 1.637 700.55 3792.21 4492.75 9090.40 15.59 175.77 7.16 2000 30.40 175.91 206.31 145.52 0.023818 1.637 700.36 3791.20 4491.56 8390.04 15.59 185.62 7.70 2001 30.39 175.87 206.25 145.48 0.023812 1.637 700.18 3790.20 4490.38 7689.86 15.59 185.00 8.35 2002 30.38 175.81 206.19 145.43 0.023805 1.637 699.96 3789.02 4488.98 6989.90 15.59 217.88 9.10 2003 30.37 175.75 206.11 145.38 0.023796 1.637 699.70 3787.63 4487.33 6290.20 15.59 257.12 10.01 2004 30.35 175.68 206.03 145.32 0.023786 1.637 699.42 3786.12 4485.54 5590.78 15.59 279.28 11.12 2005 30.35 175.63 205.97 145.28 0.023780 1.637 699.23 3785.08 4484.31 4891.55 15.59 191.93 12.51 2006 30.34 175.59 205.93 145.25 0.023775 1.637 699.09 3784.30 4483.39 4192.46 15.59 143.69 14.29 2007 30.34 175.56 205.90 145.23 0.023771 1.637 698.97 3783.66 4482.63 3493.49 15.59 117.44 16.67 2008 30.33 175.53 205.86 145.20 0.023767 1.637 698.84 3782.95 4481.79 2794.65 15.59 131.49 20.00 2009 30.33 175.51 205.84 145.18 0.023764 1.637 698.76 3782.54 4481.30 2095.89 15.59 76.02 25.00 2010 30.32 175.49 205.82 145.17 0.023762 1.637 698.69 3782.15 4480.84 1397.20 15.59 71.29 33.34

**u e**

**(103 km2)**

**v f**

**(103 km2)**

*Xt*

**(103km2)**

**Remaining deforest.h**

**(103 km2)**

**Def. ratei(%)**

**Def. ratej**

**(km2)**

**Deforest. targetsk (%)**

*g*

*d*

5 antepenult column of Table 10.

28 Carbon Sequestration 28 CO2 Sequestration and Valorization

**t**

**Year**

**u a**

**(GtC)**

**v b**

**(GtC)**

*Xt*

**(GtC)**

*Zt*

**(GtC)**

*c*

 The analysis carried out so far has demonstrated that, where policy climate and deforestation are concerned, carbon conservation (REDD) and compensation (CDM) entail a trade-off that cannot be overcome by monetary mechanisms. Instead of money, the underlying variable which forest value rests upon is the bio-diversity ratio (λ). Although it is typically a space- based measure, this ratio also accounts for forestland distribution over time. When λ is affected by the demand of removal stocks (*h*) set off by the emissions growth from the economy, the  amount of compensation (*M*) for these emissions over time is assumed to feed on the conser‐ vation (*Z*) of carbon savings carried out at each period.

 Conservation surplus can be loaned out to afford compensation demands from an economy producing growing emissions. However, such demands must be halted somewhere, otherwise the supply of removal forest stocks (*G*(*Xt* )) will hit a ceiling and fall short of delivering enough conservation. As shown by Figure 6 and Table 9, it is likely there also is a biophysical limit to conservation, which should prevent this environmental service from being further encouraged by incentives such as monetary payments (PES). When conservation is driven too far, as in the REDD1 scenario (Table 8 and Table 9), it might end up turning the balance of carbon trading loans negative. On the other hand, when it is traded off against compensation, as in the CDM scenario, it deteriorates the balance of carbon trading loans even further. Quite often, economy-wise price setting and policy-making grow apart from ecological conditions.

 Figure 2, Figure 3, Figure 5 and Figure 6 show how ecology could economically guide the economy [32]. If this linkage should be fully accomplished, setting targets for reducing deforestation would be grounded in biophysical, rather than in economic or political bearings.

 Shifting from a deforestation avoidance approach to a forest stock maintenance one would certainly be a step forward. While the former carries a misleadingly uneconomic meaning, the latter brings forest conservation to the economic foreground. As avoiding deforestation usually implies forgoing profit-making activities, it mistakenly underlies monetary rewards. Thus, a great deal of the REDD mechanism draws heavily upon them. However, from a bio- economic standpoint, they are most likely to become romantic red roses, whose purchase will, rather than mend a broken heart, make money melt into thin air.

## **Author details**

Valny Giacomelli Sobrinho\*

Address all correspondence to: giacomelliv@yahoo.com.br

 Department of Economics, Federal University of Santa Maria (UFSM), Santa Maria, Rio Grande do Sul, Brazil

## **References**

 [1] Linhares-Juvenal T. REDD and the Challenge of Protecting the Global Forest Cover. In: Serôa da Motta R., Hargrave J., Luedemann G., Gutierrez MBS. (eds.) Climate Change in Brazil — Economic, Social and Regulatory Aspects. Brasília: IPEA; 2011. p319-328.

 [2] Kanninen M., Murdiyarso D., Seymour F, Angelsen A., Wunder S., German L. Do Trees Grow on Money? The Implications of Deforestation Research for Policies to Promote REDD. Bogor, Indonesia: CIFOR; 2007.

amount of compensation (*M*) for these emissions over time is assumed to feed on the conser‐

 Conservation surplus can be loaned out to afford compensation demands from an economy producing growing emissions. However, such demands must be halted somewhere, otherwise the supply of removal forest stocks (*G*(*Xt* )) will hit a ceiling and fall short of delivering enough conservation. As shown by Figure 6 and Table 9, it is likely there also is a biophysical limit to conservation, which should prevent this environmental service from being further encouraged by incentives such as monetary payments (PES). When conservation is driven too far, as in the REDD1 scenario (Table 8 and Table 9), it might end up turning the balance of carbon trading loans negative. On the other hand, when it is traded off against compensation, as in the CDM scenario, it deteriorates the balance of carbon trading loans even further. Quite often, economy-

 Figure 2, Figure 3, Figure 5 and Figure 6 show how ecology could economically guide the economy [32]. If this linkage should be fully accomplished, setting targets for reducing deforestation would be grounded in biophysical, rather than in economic or political bearings.

 Shifting from a deforestation avoidance approach to a forest stock maintenance one would certainly be a step forward. While the former carries a misleadingly uneconomic meaning, the latter brings forest conservation to the economic foreground. As avoiding deforestation usually implies forgoing profit-making activities, it mistakenly underlies monetary rewards. Thus, a great deal of the REDD mechanism draws heavily upon them. However, from a bio-economic standpoint, they are most likely to become romantic red roses, whose purchase will,

Department of Economics, Federal University of Santa Maria (UFSM), Santa Maria, Rio

 [1] Linhares-Juvenal T. REDD and the Challenge of Protecting the Global Forest Cover. In: Serôa da Motta R., Hargrave J., Luedemann G., Gutierrez MBS. (eds.) Climate Change in Brazil — Economic, Social and Regulatory Aspects. Brasília: IPEA; 2011.

wise price setting and policy-making grow apart from ecological conditions.

rather than mend a broken heart, make money melt into thin air.

Address all correspondence to: giacomelliv@yahoo.com.br

**Author details**

Grande do Sul, Brazil

**References**

p319-328.

Valny Giacomelli Sobrinho\*

vation (*Z*) of carbon savings carried out at each period.

30 Carbon Sequestration CO2 Sequestration and Valorization


 [30] Banco Central do Brasil. BCB. Sistema de Metas para a Inflação: Sala do Investidor: Focus: Focus — Relatório de Mercado. http://www.bcb.gov.br/pec/GCI/PORT/read‐ out/R20130920.pdf (accessed 25 September 2013).

[16] Costanza R. Value Theory and Energy. Encyclopedia of Energy 2004; 6: 337-346.

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tember 2013).

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New York: Addison Wesley Longman; 1996.

logical Economics 1999; 29: 473-483.

nomic Journal 2004; 114: F293-F306.

view 1958; 48: 396-404.

[17] Pearce D., Moran D. O Valor Económico da Biodiversidade. Lisboa: Instituto Piaget;

[18] Daly HE. The Economics of the Steady State. The American Economic Review 1974;

[19] Clark CW. Mathematical Bio-Economics — the Mathematics of Conservation. 3rd.

[20] Wackernagel M., Rees W. Our Ecological Footprint — Reducing Human Impact on

 [21] Fearnside PM. Forests and Global Warming Mitigation in Brazil: Opportunities in the Brazilian Forest Sector for Responses to Global Warming under the "Clean Develop‐

[22] Fearnside PM. Greenhouse Gases from Deforestation in Brazilian Amazonia: Net

 [23] Instituto Nacional de Pesquisas Espaciais. INPE: Amazonia: PRODES: Taxas Anuais 1988 a 2012. http://www.obt.inpe.br/prodes/prodes\_1988\_2012.htm (accessed 20 Sep‐

[24] Common M. Environmental and Resource Economics — an Introduction. 2nd. ed.

[25] Ayres RU. The Second Law, the Fourth Law, Recycling and Limits to Growth. Eco‐

[26] Houthakker HS. The Permanent Income Hypothesis. The American Economic Re‐

[27] Meghir C. A Retrospective on Friedman's Theory of Permanent Income. The Eco‐

 [28] Perman R., Ma Y., McGilvray J., Common M. Natural Resource and Environmental Economics. 3rd. ed. Harlow, England: Pearson Education; 2003. http://rapidli‐ brary.com/files/ perman-natural-resource-and-environ mental-economics-3rd-edi‐

 [29] Instituto Socioambiental. ISA, Greenpeace, Instituto Centro de Vida. ICV, The Nature Conservancy. TNT, Conservação Internacional, Amigos da Terra – Amazônia Brasi‐ leira, Instituto do Homem e Meio Ambiente na Amazônia. IMAZON, WWF-Brasil. Pacto pela Valorização da Floresta e pelo Fim do Desmatamento na Amazônia Brasi‐ leira. http://www.socioambiental. org/banco\_imagens/pdfs/doc-pacto%20desmata‐ mento%20zero%20SUM%20ONGs%20FINAL. pdf (accessed 25 September 2013)

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#### CO2 Sequestration and Valorization

## **CO2 Utilization: A Process Systems Engineering Vision**

Ofélia de Queiroz F. Araújo, José Luiz de Medeiros and Rita Maria B. Alves

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57560

## **1. Introduction**

34 CO2 Sequestration and Valorization

The development of economies results in increased energy consumptions, as observed in Figure 1. In the coming decades, the supply of such expanding demand will remain based on fossil fuels technologies. Expanding the share of renewable energy (*e.g.*, biofuels in the case of transportation fuels) would require massive investments in creating a new infrastructure, which would eventually raise the standards to a new economic order entirely based on renewables in the near future. On the other hand, the current scenario involves the announce‐ ment of large proven reserves of non-conventional gas and oil and expansion of installed infrastructure of production and refining.

**Figure 1.** Economic development and electricity consumption. [graph constructed with data available at hdr.undp.org]

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Fossil fuel based energy is recognized as carbon emitter. The challenge of the century is thus to expand energy supply in a carbon-constrained economy. According to the World Economic Forum (King, 2010), no truly low-carbon technology will be able to penetrate the mass market in the short term. The use of installed fossil processing infrastructure, with co-processing of biomass and fossil feedstock and capturing and utilization of emitted CO2 is the "escape route" for a moderate transition from the present to a long-term sustainable future. In this context, putting a price on carbon will gradually build the road to a greener tomorrow. Meanwhile, bio-based products are a realistic supplement to fossil-based products, but they cannot mitigate the rising demand for fossil fuels.

According to an IEA Technology Roadmap, the manufacture of only 18 chemicals account for 80% of the energy demand in the chemical industry and 75% of its greenhouse gas (GHG) emissions. The study concludes that, "in the short to medium term (to 2025), steady progress in implementing incremental improvements and deploying best practice technologies (BPT) could provide substantial energy savings and emissions reductions compared to business as usual". "A step change in the sector's energy consumption and GHG emissions would require the development of 'game changer' technologies, such as sustainable biomass feedstocks and hydrogen from renewable energy sources which have not yet reached commercial maturity." (IEA, 2013)

With this prospect, CO2 utilization in the short term should allow parallel production routes based on BPT, driven by emission-capture-utilization synergies. In this sense, production and conversion of synthesis gas (SYNGAS) exhibits the highest potential for medium term of commercial success. Nevertheless, it is worth noting that, while the utilization of CO2 has potential to reduce greenhouse gas emissions into the atmosphere, CO2 has disadvantages as a chemical reactant due to its relative significant chemical inertness. This inertness is the underlying reason why CO2 has broad industrial application as solvent (supercritical CO2), as fire and pest extinguisher, and as a non-toxic amusement additive in the food industry. From the standpoint of building a low-carbon economy, each potential use of CO2 as reactant has a customarily high energy requirement, entailing associated energy-related GHG emissions. Obviously, such GHG emissions should not exceed the yield of chemical conversion of CO2. Reverse water gas shift (RWGS) and dry reforming to yield SYNGAS and CO2 hydrogenation to methanol are the most prominent CO2 conversion alternatives to high volume chemical commodities. On the other hand, it is reasonable to expect that CO2 conversion and biomassbased processing alternatives will coexist for a while with fossil-based technologies. Figure 2 illustrates the concept of co-existing fossil and biomass feedstock, having SYNGAS generation as the integration phase. In this scenario, SYNGAS can be obtained from coexisting fossil and green feedstock via gasification (of biomass, coal and heavy residues), steam reform and dry (CO2) reform of natural gas, which, in turn, will also coexist with downstream nonconventional conversion routes – e.g., Fischer-Tropsch (FT) and methanol to olefins (MTO) – to fuels and chemical intermediates to supply the installed petrochemical industries.

In countries with large bioresources as well as significant oil and natural gas production infrastructure(e.g.,USA,Canada,Mexico,China,Russia,Australia,ArgentinaandBrazil),fossil feedstock (mainly nonconventional oil and gas) will present the greatest challenge to bio‐ mass. In Brazil, for instance, gas supply capacity in Santos Basin increased from 600 thousand m3 /d, in 2009, to 22.2 million m3 /d in 2013 (PETROBRAS, 2013). The identified risks associated to such expansion are, beside depletion of natural resources, increased CO2 emissions.

Fossil fuel based energy is recognized as carbon emitter. The challenge of the century is thus to expand energy supply in a carbon-constrained economy. According to the World Economic Forum (King, 2010), no truly low-carbon technology will be able to penetrate the mass market in the short term. The use of installed fossil processing infrastructure, with co-processing of biomass and fossil feedstock and capturing and utilization of emitted CO2 is the "escape route" for a moderate transition from the present to a long-term sustainable future. In this context, putting a price on carbon will gradually build the road to a greener tomorrow. Meanwhile, bio-based products are a realistic supplement to fossil-based products, but they cannot

According to an IEA Technology Roadmap, the manufacture of only 18 chemicals account for 80% of the energy demand in the chemical industry and 75% of its greenhouse gas (GHG) emissions. The study concludes that, "in the short to medium term (to 2025), steady progress in implementing incremental improvements and deploying best practice technologies (BPT) could provide substantial energy savings and emissions reductions compared to business as usual". "A step change in the sector's energy consumption and GHG emissions would require the development of 'game changer' technologies, such as sustainable biomass feedstocks and hydrogen from renewable energy sources which have not yet reached commercial maturity."

With this prospect, CO2 utilization in the short term should allow parallel production routes based on BPT, driven by emission-capture-utilization synergies. In this sense, production and conversion of synthesis gas (SYNGAS) exhibits the highest potential for medium term of commercial success. Nevertheless, it is worth noting that, while the utilization of CO2 has potential to reduce greenhouse gas emissions into the atmosphere, CO2 has disadvantages as a chemical reactant due to its relative significant chemical inertness. This inertness is the underlying reason why CO2 has broad industrial application as solvent (supercritical CO2), as fire and pest extinguisher, and as a non-toxic amusement additive in the food industry. From the standpoint of building a low-carbon economy, each potential use of CO2 as reactant has a customarily high energy requirement, entailing associated energy-related GHG emissions. Obviously, such GHG emissions should not exceed the yield of chemical conversion of CO2. Reverse water gas shift (RWGS) and dry reforming to yield SYNGAS and CO2 hydrogenation to methanol are the most prominent CO2 conversion alternatives to high volume chemical commodities. On the other hand, it is reasonable to expect that CO2 conversion and biomassbased processing alternatives will coexist for a while with fossil-based technologies. Figure 2 illustrates the concept of co-existing fossil and biomass feedstock, having SYNGAS generation as the integration phase. In this scenario, SYNGAS can be obtained from coexisting fossil and green feedstock via gasification (of biomass, coal and heavy residues), steam reform and dry (CO2) reform of natural gas, which, in turn, will also coexist with downstream nonconventional conversion routes – e.g., Fischer-Tropsch (FT) and methanol to olefins (MTO) – to fuels and

chemical intermediates to supply the installed petrochemical industries.

In countries with large bioresources as well as significant oil and natural gas production infrastructure(e.g.,USA,Canada,Mexico,China,Russia,Australia,ArgentinaandBrazil),fossil feedstock (mainly nonconventional oil and gas) will present the greatest challenge to bio‐

mitigate the rising demand for fossil fuels.

36 CO2 Sequestration and Valorization

(IEA, 2013)

Therefore, CO2 capture, transportation and utilization must be included in the scene since CO2 stands conceptually as a renewable feedstock. In this sense, Figure 3 illustrates CO2 emissions capture and utilization associated to the production and refining of fossil fuel. It is worth noting that, in addition to the variety of alternative technologies, other factors influence the conception of capture and utilization of CO2 for the production of chemicals.

Rostrup-Nielsen and Christiansen (2011) present four recognizable trends in next generation successful plants for producing chemical commodities: (a) location of cheap raw materials; (b) economy of scale; (c) highly integrated process plants; and (d) low CO2 footprint (t CO2/t product) Consequently, the production chain and the associated GHG emissions are complex and require a system view for optimal decision making on technologies for production of energy and chemicals taking into account the CO2 perspective.

**Figure 2.** Coexistence of fossil and biomass feedstock uses: CO2 capture and utilization dropped into existing produc‐ tion infrastructure

Chemical Process Systems Engineering (cPSE) deals with the set of basic unit operations involved in turning raw materials into products via chemical and bio-chemical processes. In a wider definition, PSE is concerned with the optimization of decision-making process for creation and operation of chemical supply chains. Such integrated framework embraces product and process design, manufacture and distribution of chemical products with multiple and conflicting technical, economic, energetic, environmental and social objectives.

With this vision, c-PSE of industrial use and reuse of CO2 supply chain is assessed in a life cycle approach of technological alternatives involving integration of CO2 capture (separation), CO2 transportation and CO2 chemical/biochemical utilization. Apart the conventional utiliza‐ tions of CO2 listed above, there are three new potential chains of CO2 utilizations, namely: (i) production of chemicals via chemical conversion (CC); (ii) enhanced oil recovery (EOR) and carbon geological storage (CGS); and (iii) conversion via algae and microalgae to biomass or biochemical conversion (BC).

**Figure 3.** CO2 capture and utilization in the oil & gas supply chain

EOR and CGS constitute, under present conditions, the only CO2 processing chain that has some steps ready to be put into operation at high scales. These encompass some separation technologies for CO2 post-combustion capture, CO2 compression and CO2 transportation via long pipelines. EOR and CGS can be reinforced if oxy-fuel technologies finally attain industrial maturity, which may occur within the next 5 decades. Pre-combustion technologies are also potential powerful contributors to EOR-CGS systems, which should be fully developed within the next 5 decades. Pre-combustion relies on some separation technologies also present in postcombustion alternatives.

BC via solar photosynthesis is a promising but still incipient package of technologies that have yet to be proven feasible in large scales. The main drawback is the impressive footprint and liquid hold-up of algae cultivation and processing plants, which have to comply with the high dispersion and periodicity of solar light and small concentration of biomass in growing medium (always below 10g/L). Besides, the use of non-solar light energy is totally out of question due to the photosynthesis efficiency limitations of green life forms (always below 10%, i.e., only a maximum of 10% of the incident light, already with appropriate wavelength, is biochemically usable to bio-convert CO2).

CC context encompasses novel approaches extending the technology scenario for CO2 supply chain via chemical conversion to benign, stable compounds for long-term storage or to valueadded products like plastics, chemical intermediates and oxygenated octane enhancers like DMC – dimethyl-carbonate (Souza et al., 2013) for reuse.

The cPSE vision of CO2 treats routes of CC and BC (and BC integrated with CC, i.e., BCC) to useable products and fuels, not as substitute, but rather as a complement to EOR and CGS. The present text is oriented to analyze CO2 as a profitable feedstock, i.e., its potential use as feedstock to chemicals and fuels within economic applications, besides its relevant use in EOR. CGS is not considered an economic application; instead, it constitutes only a plausible, secure, destination of the excess of carbon, i.e., all CO2 that has to be discarded because it is not dispatched to any economic use.

Among the routes for CO2 utilization, BC is a natural choice as photosynthesis yields biomass, allowing the production of bioproducts, biofuels and chemicals through downstream CC processing routes. In fact, biomass gasification is the most flexible technology for dropping into conventional downstream CC routes. This integrated option configures BCC – biochem‐ ical and chemical conversion of CO2.

The beneficial use of alkaline wastes or metallic ions to convert CO2 via neutralization of alkaline wastes, or reaction of CO2 with metallic ions to form less soluble carbonates that can be removed from produced water (oil & gas industry) is also a relevant CC application of CO2. Lastly, this study presents the EOR use of CO2, i.e., by injection into depleting oil or gas fields to maximize hydrocarbon production.

Finally, analysis of routes that undertake CO2 reduction must take into account the life cycle of the processes in order to assess whether additional CO2 production occurs beyond the amount abated from atmospheric emissions. This is precisely the case of CC of CO2 into fuels and chemicals that always requires high energy input, normally derived from burning fossil fuels, entailing further associated GHG emissions. In this context, new or mature cPSE solutions should comply with the triple objective of sustainability, namely: economically feasible, environmentally benign and socially beneficial, in a supply chain approach. In connection with this, the chapter presents the CO2 Capture Cycle as well as promising alternatives of its reutilization.

## **2. The thermodynamics of pure CO2**

Chemical Process Systems Engineering (cPSE) deals with the set of basic unit operations involved in turning raw materials into products via chemical and bio-chemical processes. In a wider definition, PSE is concerned with the optimization of decision-making process for creation and operation of chemical supply chains. Such integrated framework embraces product and process design, manufacture and distribution of chemical products with multiple

With this vision, c-PSE of industrial use and reuse of CO2 supply chain is assessed in a life cycle approach of technological alternatives involving integration of CO2 capture (separation), CO2 transportation and CO2 chemical/biochemical utilization. Apart the conventional utiliza‐ tions of CO2 listed above, there are three new potential chains of CO2 utilizations, namely: (i) production of chemicals via chemical conversion (CC); (ii) enhanced oil recovery (EOR) and carbon geological storage (CGS); and (iii) conversion via algae and microalgae to biomass or

BIOETANOL

DOWNSTREAM OIL & GAS

EOR and CGS constitute, under present conditions, the only CO2 processing chain that has some steps ready to be put into operation at high scales. These encompass some separation technologies for CO2 post-combustion capture, CO2 compression and CO2 transportation via long pipelines. EOR and CGS can be reinforced if oxy-fuel technologies finally attain industrial maturity, which may occur within the next 5 decades. Pre-combustion technologies are also potential powerful contributors to EOR-CGS systems, which should be fully developed within the next 5 decades. Pre-combustion relies on some separation technologies also present in post-

BC via solar photosynthesis is a promising but still incipient package of technologies that have yet to be proven feasible in large scales. The main drawback is the impressive footprint and liquid hold-up of algae cultivation and processing plants, which have to comply with the high

SYNGAS

BIOCHEMICAL CONVERSION OF CO2

GASIFICATION UNIT

BIOMASS (MICROALGA)

CHEMICAL CONVERSION OF CO2

> BASE & INTERMEDIATE CHEMICALS

BIO-PRODUCTS

and conflicting technical, economic, energetic, environmental and social objectives.

biochemical conversion (BC).

38 CO2 Sequestration and Valorization

CANE BIOETHANOL

OIL & GAS

combustion alternatives.

PLANT

CO2 CAPTURE

CO2

UPSTREAM OIL & GAS

**Figure 3.** CO2 capture and utilization in the oil & gas supply chain

CO2-EOR

CO2 PIPELINE

SUGAR

The phase behavior of pure CO2 (Figure 4) exhibits particularities when compared with common light species of natural gas (NG) like CH4, C2H6 and C3H8. In general grounds, CO2  has a fluid phase behavior similar to ethane (C2H6) with a very similar Critical Point (CP) temperature. The pronounced distinctive characteristic is its high Triple Point (TP) (Figure 4) temperature comparatively with light hydrocarbons. This means that solid CO2 (dry ice) can be easily encountered below -56.6o C and above 5.2bar, if the original processing stream is rich enough in CO2. This freeze-out of solid is not observed with the light hydrocarbon species unless below -182o C, which is about 20o C below the lowest temperature that can be achieved in LNG processing (i.e., the Normal Boiling Point of CH4). In other words, without CO2, the coldest NG processing (LNG plant) does not have solid formation. The phase behavior of pure CO2 (Figure 4) is characterized by two larger (and infinite) continents corresponding to gas and solid states, and a finite intermediate liquid continent extending between the TP and CP temperatures. The three continents are two-dimensional (2D) objects due to the Phase Rule, which stipulates two degrees of freedom for a pure species at one-phase condition. Onedimensional (1D) equilibrium boundaries – SLE, SVE and VLE lines – are positioned between two neighboring continents. Their 1D nature is also a consequence of the phase Rule, which stipulates one degree of freedom for a pure species at a two-phase condition. SLE – solid-liquid (or solid-fluid) equilibrium line – is an endless line extending from the TP to indefinitely high pressures, characterized by the coexistence of solid and liquid CO2. SVE – solid-vapor equilibrium line – is characterized by coexistence of solid and gas CO2 ending at the TP and lower bounded by the absolute 0K. VLE – vapor-liquid equilibrium line – is a finite locus between TP and CP where liquid and gas CO2 coexist. Just above the liquid continent there is a somewhat indefinite supercritical fluid zone (SCF). The SCF is a fusion of gas and liquid behaviors at high pressures above the CP pressure and high temperatures above the CP temperature. The SCF is characterized by high densities and high compressibility.

The contact point for the three continents is the Triple Point TP, an invariant three-phase point (zero degrees of freedom) by the Phase Rule. The VLE locus ends at the Critical Point CP where the differences between liquid and vapor vanish. The CP is also an invariant point with zero degrees of freedom, but, contrary to the TP, the CP is a single phase point satisfying two extra criticality conditions. The phase behavior of CO2 depicted in Figure 4 can be enriched if density (kg/m3 ) is put on the third axis as shown in Figure 5. In Figure 5, densities of gas phase are calculated via the Peng-Robinson (PR) equation of state (EOS), whereas liquid and solid phase densities (saturated or not) are calculated via the correlations presented by Span and Wagner (1996) and Trusler (2011), respectively. In Figure 5, the saturation lines SLE, SVE and VLE are also depicted, where the VLE line is presented with its gas and liquid branches merging at the CP. Figure 5 reveals that CO2 can be found with densities well above the density of water (1000 kg/m3 ), either as a solid or as high pressure dense liquid or SCF fluid.

The PR-EOS is a very simple thermodynamic relationship that can be used to predict gas and liquid properties of pure CO2 and of CO2 rich mixtures. The PR-EOS is certainly not the most accurate state relationship to address the fluid properties of CO2 (Genesis, 2011; see also the EOS presented by Span and Wagner, 1996), but it does represent the best compromise between simplicity of use and accuracy for CO2 rich systems without water, either in single fluid phase or in two coexisting fluid phases (VLE), at low or high pressures and densities (Li, 2008; Li and Yan, 2009; Genesis, 2011). According to Genesis (2011), the PR-EOS produces errors for

**Figure 4.** Phase behavior of pure CO2 [*TC, PC* critical point; *TT, PT* triple point]

 has a fluid phase behavior similar to ethane (C2H6) with a very similar Critical Point (CP) temperature. The pronounced distinctive characteristic is its high Triple Point (TP) (Figure 4) temperature comparatively with light hydrocarbons. This means that solid CO2 (dry ice) can

enough in CO2. This freeze-out of solid is not observed with the light hydrocarbon species

in LNG processing (i.e., the Normal Boiling Point of CH4). In other words, without CO2, the coldest NG processing (LNG plant) does not have solid formation. The phase behavior of pure CO2 (Figure 4) is characterized by two larger (and infinite) continents corresponding to gas and solid states, and a finite intermediate liquid continent extending between the TP and CP temperatures. The three continents are two-dimensional (2D) objects due to the Phase Rule, which stipulates two degrees of freedom for a pure species at one-phase condition. Onedimensional (1D) equilibrium boundaries – SLE, SVE and VLE lines – are positioned between two neighboring continents. Their 1D nature is also a consequence of the phase Rule, which stipulates one degree of freedom for a pure species at a two-phase condition. SLE – solid-liquid (or solid-fluid) equilibrium line – is an endless line extending from the TP to indefinitely high pressures, characterized by the coexistence of solid and liquid CO2. SVE – solid-vapor equilibrium line – is characterized by coexistence of solid and gas CO2 ending at the TP and lower bounded by the absolute 0K. VLE – vapor-liquid equilibrium line – is a finite locus between TP and CP where liquid and gas CO2 coexist. Just above the liquid continent there is a somewhat indefinite supercritical fluid zone (SCF). The SCF is a fusion of gas and liquid behaviors at high pressures above the CP pressure and high temperatures above the CP

temperature. The SCF is characterized by high densities and high compressibility.

), either as a solid or as high pressure dense liquid or SCF fluid.

The contact point for the three continents is the Triple Point TP, an invariant three-phase point (zero degrees of freedom) by the Phase Rule. The VLE locus ends at the Critical Point CP where the differences between liquid and vapor vanish. The CP is also an invariant point with zero degrees of freedom, but, contrary to the TP, the CP is a single phase point satisfying two extra criticality conditions. The phase behavior of CO2 depicted in Figure 4 can be enriched if density

The PR-EOS is a very simple thermodynamic relationship that can be used to predict gas and liquid properties of pure CO2 and of CO2 rich mixtures. The PR-EOS is certainly not the most accurate state relationship to address the fluid properties of CO2 (Genesis, 2011; see also the EOS presented by Span and Wagner, 1996), but it does represent the best compromise between simplicity of use and accuracy for CO2 rich systems without water, either in single fluid phase or in two coexisting fluid phases (VLE), at low or high pressures and densities (Li, 2008; Li and Yan, 2009; Genesis, 2011). According to Genesis (2011), the PR-EOS produces errors for

) is put on the third axis as shown in Figure 5. In Figure 5, densities of gas phase are calculated via the Peng-Robinson (PR) equation of state (EOS), whereas liquid and solid phase densities (saturated or not) are calculated via the correlations presented by Span and Wagner (1996) and Trusler (2011), respectively. In Figure 5, the saturation lines SLE, SVE and VLE are also depicted, where the VLE line is presented with its gas and liquid branches merging at the CP. Figure 5 reveals that CO2 can be found with densities well above the density of water (1000

C and above 5.2bar, if the original processing stream is rich

C below the lowest temperature that can be achieved

be easily encountered below -56.6o

C, which is about 20o

unless below -182o

40 CO2 Sequestration and Valorization

(kg/m3

kg/m3

**Figure 5.** Density x Pressure x Temperature of Pure CO2 [with TP and CP points, VLE, SVE & SLE lines and SCF domain]

compressibility factor (Z), molar enthalpy (H) and sound speed (C) below 1% for CO2 subcritical gas phases and between 5% and 20% for subcritical liquid phases. Near the CP, the errors in Z, H, C can be higher but the uncertainties of experimental values are also higher. In the SCF domain, the PR errors for Z, H, C fall between 1% and 15%. In all the aforementioned cases, errors are relative to Span-Wagner EOS for fluid CO2. The PR-EOS also seems adequate to describe the supercritical fluid (SCF) domain of CO2 and its rich mixtures near critical transitions. This means that PR-EOS can be used to address any property (density, enthalpy, entropy, exergy, etc) of liquid and vapor phases of CO2 and its mixtures with NG species under moderate errors, which are compatible with engineering applications (Li, 2008 and Li and Yan, 2009; Genesis, 2011). The PR-EOS is presented in Eq. (1), while its classical mixing rules follow in Eqs. (2) and (3). PR-EOS component parameters are given in Eq. (4) to (6) where *Tci* , *Pci* , *ω<sup>I</sup>* and *nc* are, respectively, critical temperature, critical pressure, acentric factor for component *i*, and the number of components. *Kij* represents the binary interaction parameter (BIP) for species *i* and *j* which is symmetric, can be used as zero in some cases, and is not necessary for pure CO2. In Eqs. (1) to (3), *V, T,P,R* and *Ni* represent volume (m3 ), temperature (K), pressure (bar), ideal gas constant (8.314.10-5 bar.m3 /mol.K) and the mol number of species *i*.

$$P = \frac{\text{NRT}}{V - \text{Nb}} - \frac{N^2 a}{V^2 + \text{LUN}b + \text{W(Nb)^2}} \quad , \quad \text{if } = 2, \quad W = -1 \tag{1}$$

$$Nlb = \sum\_{i=1}^{nc} N\_i b\_i \tag{2}$$

$$N^2 a = \sum\_{i}^{nc} \sum\_{j}^{nc} N\_i N\_j \sqrt{a\_i} \sqrt{a\_j} \sqrt{\Phi\_i(T)} \sqrt{\Phi\_j(T)} \left(1 - K\_{ij}\right) \quad \text{(} K\_{ij} = K\_{ji}\text{)}\tag{3}$$

$$a\_i = 0.45724 \frac{R^2 T c\_i^2}{P c\_i} \quad b\_i = 0.07780 \frac{R T c\_i}{P c\_i} \tag{4}$$

$$\Phi\_i(T) = \left(1 + g(\alpha\_i)\left(1 - \sqrt{T \wedge T c\_i}\right)\right)^2 \tag{5}$$

$$g(o\_i) = 0.37464 + 1.54226o\_i - 0.26992o\_i^2 \tag{6}$$

If dimensionless terms in Eq. (7) are used in Eq. (1), the classic *Z* cubic form results in Eq. (8):

$$Z = \frac{PV}{NRT} \; , \; B = \frac{PNb}{NRT} \; \; \; \; A = \frac{PN^2a}{(NRT)^2} \tag{7}$$

$$
\Sigma^3 - (1 + B - \mathsf{U}B)\Sigma^2 + (A + \mathsf{W}B^2 - \mathsf{U}B - \mathsf{U}B^2)\Sigma - AB - \mathsf{W}B^2 - \mathsf{W}B^3 = 0\tag{8}
$$

## **3. The CO2 capture cycle**

compressibility factor (Z), molar enthalpy (H) and sound speed (C) below 1% for CO2 subcritical gas phases and between 5% and 20% for subcritical liquid phases. Near the CP, the errors in Z, H, C can be higher but the uncertainties of experimental values are also higher. In the SCF domain, the PR errors for Z, H, C fall between 1% and 15%. In all the aforementioned cases, errors are relative to Span-Wagner EOS for fluid CO2. The PR-EOS also seems adequate to describe the supercritical fluid (SCF) domain of CO2 and its rich mixtures near critical transitions. This means that PR-EOS can be used to address any property (density, enthalpy, entropy, exergy, etc) of liquid and vapor phases of CO2 and its mixtures with NG species under moderate errors, which are compatible with engineering applications (Li, 2008 and Li and Yan, 2009; Genesis, 2011). The PR-EOS is presented in Eq. (1), while its classical mixing rules follow

in Eqs. (2) and (3). PR-EOS component parameters are given in Eq. (4) to (6) where *Tci*

2

*NRT N a <sup>P</sup> U W V Nb V UVNb W Nb* <sup>=</sup> - = <sup>=</sup> - - + +

> *Nb* =∑ *i*=1 *nc Ni*

2 2

*i i*

( ) <sup>2</sup> ( ) ( )1 ,( )

0.45724 , 0.07780 *i i*

( ( ))

If dimensionless terms in Eq. (7) are used in Eq. (1), the classic *Z* cubic form results in Eq. (8):

<sup>2</sup> , , ( )

*R Tc RTc a b*

( ) 1 ( )1 / *i ii* F =+ - *T g T Tc* w

<sup>2</sup> ( ) 0.37464 1.54226 0.26992 *<sup>i</sup> i i <sup>g</sup>*

*PV PNb PN a ZB A*

*ij i j i j ij ij ji*

*i i*

*N a NN a a T T K K K* <sup>=</sup> åå FF - = (3)

*Pc Pc* = = (4)

2

2

*NRT NRT NRT* == = (7)

=+ - (6)

ww

(5)

pure CO2. In Eqs. (1) to (3), *V, T,P,R* and *Ni*

*nc nc*

*i j*

w

(bar), ideal gas constant (8.314.10-5 bar.m3

42 CO2 Sequestration and Valorization

and *nc* are, respectively, critical temperature, critical pressure, acentric factor for component *i*, and the number of components. *Kij* represents the binary interaction parameter (BIP) for species *i* and *j* which is symmetric, can be used as zero in some cases, and is not necessary for

represent volume (m3

2 2 , 2, 1 ( )

/mol.K) and the mol number of species *i*.

*bi* (2)

, *Pci* , *ω<sup>I</sup>*

(1)

), temperature (K), pressure

According to Oi (2010), CO2 removal from process streams at an industrial scale has occurred since about 1930, mainly from natural gas and from industrial gases at high pressures for ammonia and methanol production. Like any separation unit in any kind of process, the CO2 capture technology has to be judiciously chosen as it may undermine the profitability, controllability, safety, and simplicity of the plant. Nevertheless, even when properly selected, separations usually raise concerns like heat and mechanical energy consumption, increased utility use, carbon emission, chemicals demands, size, weight, footprint, construction re‐ straints, operational hazards, etc.

The CO2 capture cycle encompasses two or three main unit operations, which have to separate CO2 from the gas mixture and send it to an appropriate destination. First, there is the CO2 transfer across the gas phase into the medium that contains the binding material: a solvent or an adsorbent or a selective barrier. Second, there is (or not) the regeneration of the binding medium with concomitant CO2 release. Third, there is the compression and cooling of the captured CO2 because it has to be handled at high density or as a liquid.

Technologies for CO2 capture from gas streams include chemical absorption (e.g., aqueous ethanolamines and aqueous K2CO3), physical absorption (e.g., propylene carbonate, selexol and rectisol), physical adsorption, membrane permeators, membrane contactors, cryogenic distillation and hybrid technologies (e.g., membrane permeator followed by ethanolamine absorption). Among those, the most relevant technologies include membrane permeation and chemical absorption with aqueous ethanolamines, the later standing as the most mature CO2 capture technology from gas streams.

#### **3.1. Chemical absorption with aqueous alkanolamines**

With regards to the solvent, the advantages of the chemical absorption of acid gases – CO2 and H2S – by aqueous alkanolamines are well-known: the former are weak acids and the later are weak alkali, such that they reversibly bind at low temperature and high acid gas fugacity and subsequently untie at higher temperatures and low fugacity, leading to efficient acid gas stripping, which regenerates the solvent. The relevant variable in the liquid phase is the solvent loading (in mol of acid gas per mol of amine), which expresses the degree of conversion of amine in the solvent. Typically, the loading assumes values in the range 0 to 1.2 mol/mol amine.

AGWA (Acid Gas, Water and Amines) systems is a convenient denomination (de Medeiros et al., 2013a; de Medeiros et al., 2013b) of such reactive vapor-liquid equilibrium (RVLE) systems containing Acid Gas, Water and Amines. Amines are understood to be the common alkanol‐ amines like monoethanolamine (MEA, MW=61), diethanolamine (DEA, MW=105), methyldiethanolamine (MDEA, MW=119) and 1-amino-2-propanol (AMP, MW=75).

MEA is a benchmark co-solvent for CO2 capture, with: (i) satisfactory absorption capacity; (ii) fast kinetics; (iii) miscible with water in all proportions, and (iv) low cost. On the other hand, MEA is problematic in terms of solvent regeneration, because it exhibits: (i) the highest energy load per unit of stripped gas; (ii) corrosion and chemical/thermal degradation concerns; (iii) non-negligible evaporation losses due to its low boiling point; and (iv) high reactivity entailing low H2S/CO2 selectivity. As a secondary amine, DEA is less reactive than MEA but it has the following comparative advantages: (i) lower energy per unit of stripped gas; (ii) more resistant to degradation; (iii) less corrosive; and (iv) less volatile. MDEA exhibits the lowest reactivity with CO2 and the greatest resistance to degradation. When compared to DEA and MEA, MDEA presents the following advantages: (i) the highest equilibrium capacity of acid gas absorption (in the case of CO2, nearly two times the capacity of primary amine); (ii) lowest regeneration cost (does not form stable products with CO2); (iii) high H2S/CO2 selectivity thanks to its low reactivity with CO2; (iv) lowest enthalpies of reaction and lowest regeneration heat per unit of stripped gas; and (v) negligible losses due to very low vapor pressure. Alkanolamines with steric hindrance like AMP show reduced carbamate stability and the methyl adjacent to the amine group may affect absorption capacity and/or its rate. AMP exhibits absorption capacity and stripping heat similar to MDEA, but a faster reaction with CO2 during the capture step (Medeiros et al., 2013b).

AGWA chemical reactions are really three-reactant transformations in the liquid phase with 1:1 amine to acid gas mol ratio. Water is necessary at high mol ratio to amine (8:1 or 7:1), otherwise the reaction simply does not evolve sufficiently, leaving non-solvated amine unconverted (low loading). This is the reason why MEA concentration in the solvent is upper bounded at 30%w/w in water (or 11.2%mol MEA + 88.8%mol H2O), and 50%w/w in the case of MDEA (or 13.1%mol MDEA + 86.9%mol H2O). Figure 6 presents a schematic of the chemical absorption system for CO2/H2S capture. AGWA absorption reactions take place in the colder higher pressure absorption column, whereas in the hotter lower pressure stripper column, AGWA reactions are reverted, thus releasing free acid gas and regenerating amine at the bottom. The main consumption of heat occurs in the reboiler in the bottom of the stripper column, where water is vaporized breaking the liquid phase association of acid gas, water and amine. Consequently, typical heat consumptions of MEA strippers lay between 167kJ and 200kJ per mol of stripped CO2 (or between 3.8 GJ and 4.5 GJ/tonne of stripped CO2). These figures are impressive high values, equaling 4 to 5 times the molar heat of vaporization of water per mol of stripped CO2.

The literature presents several modeling approaches for absorption and stripping with AGWA systems. The most common approach involves cumulatively ionic species within ideal solution, ideal gas vapor phase, reversible chemical kinetics and rate-based interfacial mass transfer (de Medeiros et al., 2013b). This kind of approach is classical and is more adequate to low pressure and to dilute AGWA systems as in CO2 capture from combustion gases. For high capacity and high pressure AGWA with rich CO2 natural gas systems, high loadings and high heat effects may appear. For such AGWA systems de Medeiros et al. (2013a, 2013b) proposed a molecular Chemical Theory approach where molecular complex species are formed in the liquid phase via chemical equilibrium reactions having as reactants real AGWA species CO2,

**Figure 6.** CO2 capture by chemical absorption

MEA is a benchmark co-solvent for CO2 capture, with: (i) satisfactory absorption capacity; (ii) fast kinetics; (iii) miscible with water in all proportions, and (iv) low cost. On the other hand, MEA is problematic in terms of solvent regeneration, because it exhibits: (i) the highest energy load per unit of stripped gas; (ii) corrosion and chemical/thermal degradation concerns; (iii) non-negligible evaporation losses due to its low boiling point; and (iv) high reactivity entailing low H2S/CO2 selectivity. As a secondary amine, DEA is less reactive than MEA but it has the following comparative advantages: (i) lower energy per unit of stripped gas; (ii) more resistant to degradation; (iii) less corrosive; and (iv) less volatile. MDEA exhibits the lowest reactivity with CO2 and the greatest resistance to degradation. When compared to DEA and MEA, MDEA presents the following advantages: (i) the highest equilibrium capacity of acid gas absorption (in the case of CO2, nearly two times the capacity of primary amine); (ii) lowest regeneration cost (does not form stable products with CO2); (iii) high H2S/CO2 selectivity thanks to its low reactivity with CO2; (iv) lowest enthalpies of reaction and lowest regeneration heat per unit of stripped gas; and (v) negligible losses due to very low vapor pressure. Alkanolamines with steric hindrance like AMP show reduced carbamate stability and the methyl adjacent to the amine group may affect absorption capacity and/or its rate. AMP exhibits absorption capacity and stripping heat similar to MDEA, but a faster reaction with CO2 during the capture step

AGWA chemical reactions are really three-reactant transformations in the liquid phase with 1:1 amine to acid gas mol ratio. Water is necessary at high mol ratio to amine (8:1 or 7:1), otherwise the reaction simply does not evolve sufficiently, leaving non-solvated amine unconverted (low loading). This is the reason why MEA concentration in the solvent is upper bounded at 30%w/w in water (or 11.2%mol MEA + 88.8%mol H2O), and 50%w/w in the case of MDEA (or 13.1%mol MDEA + 86.9%mol H2O). Figure 6 presents a schematic of the chemical absorption system for CO2/H2S capture. AGWA absorption reactions take place in the colder higher pressure absorption column, whereas in the hotter lower pressure stripper column, AGWA reactions are reverted, thus releasing free acid gas and regenerating amine at the bottom. The main consumption of heat occurs in the reboiler in the bottom of the stripper column, where water is vaporized breaking the liquid phase association of acid gas, water and amine. Consequently, typical heat consumptions of MEA strippers lay between 167kJ and 200kJ per mol of stripped CO2 (or between 3.8 GJ and 4.5 GJ/tonne of stripped CO2). These figures are impressive high values, equaling 4 to 5 times the molar heat of vaporization of

The literature presents several modeling approaches for absorption and stripping with AGWA systems. The most common approach involves cumulatively ionic species within ideal solution, ideal gas vapor phase, reversible chemical kinetics and rate-based interfacial mass transfer (de Medeiros et al., 2013b). This kind of approach is classical and is more adequate to low pressure and to dilute AGWA systems as in CO2 capture from combustion gases. For high capacity and high pressure AGWA with rich CO2 natural gas systems, high loadings and high heat effects may appear. For such AGWA systems de Medeiros et al. (2013a, 2013b) proposed a molecular Chemical Theory approach where molecular complex species are formed in the liquid phase via chemical equilibrium reactions having as reactants real AGWA species CO2,

(Medeiros et al., 2013b).

44 CO2 Sequestration and Valorization

water per mol of stripped CO2.

H2S, H2O, MEA, DEA, MDEA and AMP. Each complex species is created by reacting three real species: an acid gas, water and an amine, as shown in Eq. (9). These complexes are reversibly created during the absorption step and destroyed during the stripping step. The main advantages of the molecular chemical theory of de Medeiros et al. (2013b) are:


The AGWA formalism of de Medeiros et al. (2013b) was calibrated with VLE AGWA data from Literature. These data configure a large database of AGWA-VLE with 1331 runs shown in Figure 7. This database includes several runs with alkanolamines (MEA, DEA, MDEA and AMP), water and two acid gases or solutes (CO2 and H2S) at pressures ranging from 0.1 bar to 30 bar and temperatures from 25o C to 140o C. The calibration parameters correspond to chemical equilibrium constants belonging to chemical reactions that convert acid gas, water and amine into molecular complex species in liquid phase as shown in Eqs. (9). These chemical reactions are chemical equilibrium (ChE) reactions, which evolve to the right when CO2 and H2S are absorbed by the solvent and to the left when CO2 and H2S are stripped from the solvent by the action of heat and low pressure. In other words, the set of chemical equations in Eq. (9) can reproduce both phenomena absorption and solvent regeneration. This is a physically sound approach that allows, among other things, to estimate heat effects that occur in these processes like the release of heat during gas absorption and the absorption of heat during stripping of CO2 and H2S.

$$\begin{aligned} \text{CO}\_2(\text{g}) + H\_2O(\text{g}) + \text{MEA(g)} &\xleftarrow{K\_1} \text{CO}\_2-H\_2O-\text{MEA(Liq)}\\ \text{CO}\_2(\text{g}) + H\_2O(\text{g}) + \text{MDEA}(\text{g}) &\xleftarrow{K\_1} \text{CO}\_2-H\_2O-\text{MDEA(Liq)}\\ \text{CO}\_2(\text{g}) + H\_2O(\text{g}) + \text{DEA(g)} &\xleftarrow{K\_1} \text{CO}\_2-H\_2O-\text{DEA(Liq)}\\ \text{CO}\_2(\text{g}) + H\_2O(\text{g}) + \text{AMP(g)} &\xleftarrow{K\_2} \text{CO}\_2-H\_2O-\text{AMP(Liq)}\\ \text{H}\_2\text{S}(\text{g}) + H\_2O(\text{g}) + \text{MEA(g)} &\xleftarrow{K\_3} \text{H}\_2\text{S}-H\_2O-\text{MEA(Liq)}\\ \text{H}\_2\text{S}(\text{g}) + H\_2O(\text{g}) + \text{MDEA(g)} &\xleftarrow{K\_3} \text{H}\_2\text{S}-H\_2O-\text{MDEA(Liq)}\\ \text{H}\_2\text{S}(\text{g}) + H\_2O(\text{g}) + \text{DEA(g)} &\xleftarrow{K\_3} \text{H}\_2\text{S}-H\_2O-\text{DEA(Liq)}\\ \text{H}\_2\text{S}(\text{g}) + H\_2O(\text{g}) + \text{AMP(g)} &\xleftarrow{K\_4} \text{H}\_2\text{S}-H\_2O-\text{AMP(Liq)} \end{aligned}$$

The parameter estimation of the AGWA model of de Medeiros et al. (2013b) involves estimat‐ ing ChE constants for the chemical reactions in Eq. (9) at selected temperatures. Thus, several experimental AGWA VLE points at a chosen temperature are extracted from the AGWA database in Figure 7 and are processed via a maximum likelihood algorithm to estimate the ChE constants. The estimation algorithm also assures that the set of nonlinear constraints of each selected experiment is satisfied. The set of constraints of a given experiment is shown in Table 1 with Eqs. (10) to (16) including: (i) Real species – CO2, H2S, H2O, MEA, DEA, MDEA, AMP – mass balance (RMB); (ii) mol fraction normalization for each phase (SXY); (iii) definition partial pressures of solutes (acid gases) (PPS); (iv) definition of loadings of solutes (LDG); (v) VLE of Real species (VLE); (vi) ChE of Complex formation in Eq. (9) (CHE). The nomenclature used in Table 1 is shown in Table 2, including the matrix of stoichiometric coefficients of Eq. (9) in Eq. (10).

**v.** All thermodynamics properties (of both vapor and liquid phases) in the formalism

**vi.** The formalism can be used with both acid gases CO2 and H2S and with blends of

The AGWA formalism of de Medeiros et al. (2013b) was calibrated with VLE AGWA data from Literature. These data configure a large database of AGWA-VLE with 1331 runs shown in Figure 7. This database includes several runs with alkanolamines (MEA, DEA, MDEA and AMP), water and two acid gases or solutes (CO2 and H2S) at pressures ranging from 0.1 bar to

chemical equilibrium constants belonging to chemical reactions that convert acid gas, water and amine into molecular complex species in liquid phase as shown in Eqs. (9). These chemical reactions are chemical equilibrium (ChE) reactions, which evolve to the right when CO2 and H2S are absorbed by the solvent and to the left when CO2 and H2S are stripped from the solvent by the action of heat and low pressure. In other words, the set of chemical equations in Eq. (9) can reproduce both phenomena absorption and solvent regeneration. This is a physically sound approach that allows, among other things, to estimate heat effects that occur in these processes like the release of heat during gas absorption and the absorption of heat during

promising in terms of gains relatively to individual amines.

C to 140o

6 7 8

The parameter estimation of the AGWA model of de Medeiros et al. (2013b) involves estimat‐ ing ChE constants for the chemical reactions in Eq. (9) at selected temperatures. Thus, several experimental AGWA VLE points at a chosen temperature are extracted from the AGWA database in Figure 7 and are processed via a maximum likelihood algorithm to estimate the ChE constants. The estimation algorithm also assures that the set of nonlinear constraints of each selected experiment is satisfied. The set of constraints of a given experiment is shown in Table 1 with Eqs. (10) to (16) including: (i) Real species – CO2, H2S, H2O, MEA, DEA, MDEA, AMP – mass balance (RMB); (ii) mol fraction normalization for each phase (SXY); (iii) definition

() () () ( ) () () () ( ) () () () ( )

*K K K*

*H S g H O g MDEA g H S H O MDEA Liq H S g H O g DEA g H S H O DEA Liq H S g H O g AMP g H S H O AMP Liq*

+ + ¬¾¾® - - + + ¬¾¾® - - + + ¬¾¾® - -

2


( )

(9)

*O MEA Liq*

() () () ( ) () () () ( ) () () () ( ) () () () ( )

*K K K K K*

*CO g H O g MEA g CO H O MEA Liq CO g H O g MDEA g CO H O MDEA Liq CO g H O g DEA g CO H O DEA Liq CO g H O g AMP g CO H O AMP Liq*

+ + ¬¾® - - + + ¬¾¾® - - + + ¬¾¾® - - + + ¬¾¾® - -

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

2 2 2

*H S g H O g MEA g H S H*

+ + ¬¾¾® -

2 2 2 2 2 2 2 2 2 2 2 2

() () ()

30 bar and temperatures from 25o

46 CO2 Sequestration and Valorization

stripping of CO2 and H2S.

of de Medeiros et al. (2013b) are calculated via cubic EOS (Soave-Redlich-Kwong - SRK and Peng-Robinson - PR), because all species considered are purely molecular.

alkanolamines, which are used for conjugating desirable qualities like good reactivity of MEA with lower costs of regeneration and corrosion of MDEA. Such blends are

C. The calibration parameters correspond to

**Figure 7.** AGWA Database used to estimate parameters in the Chemical Theory Model of de Medeiros et al. (2013b) [PPCO2, PPH2S – partial pressures (bar) of CO2 and H2S]

Figures 8 and 9 depict some results after calibration of the AGWA model of de Medeiros et al. (2013b) with the database of AGWA experiments in Figure 7. Both figures refer to parameter estimation using 320 experimental AGWA data points at 40o C. Figure 8 presents experimental versus predicted loadings (mol/mol amine) of solutes CO2 and H2S at 40o C, whereas Figure 9 presents experimental versus predicted partial pressures (bar) of solutes CO2 and H2S at 40o C. As can be observed, there is a satisfactory agreement between experimental values and predicted counterparts.

Predicted values are calculated solving the set of constraints of the AGWA model in Eqs. (I) to (VIII) with all thermodynamic properties (e.g. vapor and liquid fugacities) estimated with SRK-EOS. The estimated parameters are the eight ChE constants in Eq. (9) and (VIII) at the corresponding temperature of 40o C.

**Figure 8.** Experimental vs predicted Loadings of CO2 (blue) and H2S (red) after calibrating the AGWA model at 40oC with 320 data points (de Medeiros et al., 2013b) [LdgCO2, LdgH2S – Loadings of CO2 and H2S (mol/mol amine)]

**Figure 9.** Experimental vs predicted partial pressures of CO2 (blue) and H2S (red) after calibrating the AGWA model at 40oC with 320 data points (de Medeiros et al., 2013b) [PpCO2, PpH2S – Partial pressures of CO2 and H2S (bar)]


Predicted values are calculated solving the set of constraints of the AGWA model in Eqs.

the corresponding temperature of 40oC.

MEA, DEA, MDEA, AMP], [solutes (*ns=2*): CO2, H2S], [Complexes (*nr=8*): see Eq. (9)] **Symbol Definition Table 1.** Set of Constraints of AGWA VLE Experiments [real species (*n=7*): CO2, H2S, H2O, MEA, DEA, MDEA, AMP], [solutes (*ns=2*): CO2, H2S], [Complexes (*nr=8*): see Eq. (9)] *nr Reactions ChE* [CHE] <sup>ˆ</sup> ln ln ln ( ) 0 *<sup>T</sup> <sup>L</sup> X f KT <sup>C</sup>* VIII

*<sup>S</sup>* Vector of solute loadings (mol/mol of amine)

MEA, DEA, MDEA, AMP], [solutes (*ns=2*): CO2, H2S], [Complexes (*nr=8*): see Eq. (9)]

Table 1. Set of Constraints of AGWA VLE Experiments [real species (*n=7*): CO2, H2S, H2O,

Table 1. Set of Constraints of AGWA VLE Experiments [real species (*n=7*): CO2, H2S, H2O,


Table 2. **Table 2.** Nomenclature used in Table 1

Nomenclature used in Table 1

**Figure 8.** Experimental vs predicted Loadings of CO2 (blue) and H2S (red) after calibrating the AGWA model at 40oC with 320 data points (de Medeiros et al., 2013b) [LdgCO2, LdgH2S – Loadings of CO2 and H2S (mol/mol amine)]

48 CO2 Sequestration and Valorization

**Figure 9.** Experimental vs predicted partial pressures of CO2 (blue) and H2S (red) after calibrating the AGWA model at 40oC with 320 data points (de Medeiros et al., 2013b) [PpCO2, PpH2S – Partial pressures of CO2 and H2S (bar)]

$$
\underline{\Pi} = \begin{bmatrix} -1 & -1 & -1 & -1 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & -1 & -1 & -1 & -1 \\ -1 & -1 & -1 & -1 & -1 & -1 & -1 & -1 \\ -1 & 0 & 0 & 0 & -1 & 0 & 0 & 0 \\ 0 & -1 & 0 & 0 & 0 & -1 & 0 & 0 \\ 0 & 0 & -1 & 0 & 0 & 0 & -1 & 0 \\ 0 & 0 & 0 & -1 & 0 & 0 & 0 & -1 \end{bmatrix} \tag{10}
$$

The huge heat consumption of MEA strippers (167 kJ to 200kJ/mol of stripped CO2) forces investigation on more efficient alternatives of solvent regeneration. Wagener and Rochelle (2010) recognize as a "monumental task" reducing energy penalty of CO2 capture from coalfired power plants (approximately 30%). They presented an analysis of various stripper configurations, concluding that increasing complexity improves performance at the cost of higher capital and operational expenditures, i.e., an optimal scheme should exist. The alter‐ native configurations include simple stripping with vapor recompression, multi-pressure, double matrix, split product, internal exchange, and flashing feed. Wagener and Rochelle (2010) concluded that operating with multiple pressure levels reduces the energy requirement as "equivalent work" (including reboiler duty, pumping and heat exchangers) in 33.6 kJ/ molCO2 captured, with an optimal lean loading of 0.375 mol/mol amine. Moreover, they claim that the arrangement benefits from stripping at high pressure, whilst improves reversibility when returning to atmospheric conditions for the absorber. Wagner and Rochelle (2011) revisited several configurations with varying levels of complexity and reported that an interheated column and a simple stripper required 33.4 kJ/mol CO2 and 35.0 kJ/mol CO2 of equivalent work, respectively, at their optimum lean loadings.

#### **3.2. Membrane permeators**

The first membrane modules were developed as planar films. However, such arrangement has a low ratio of membrane transfer area per equipment volume. Presently, the majority of modules for CO2 gas separation are manufactured in hollow-fiber or spiral wound configu‐ rations. Figure 10 shows a schematic of a hollow-fiber membrane permeator module and the selective transport of molecules across the hollow-fiber membrane.

**Figure 10.** Hollow-fiber membrane permeator and the transfer through a hollow-fiber

The membrane acts selectively against gas diffusion from the high pressure side (the retentate) to the low pressure side (the permeate) creating separation. The membrane – either in hollowfiber or spiral wound configurations – is composed by two polymeric structures: a permse‐ lective dense (nonporous) skin over a thicker microporous substrate. Inside the permeator gas coming from the high pressure retentate stream solubilizes into the dense skin and diffuses to the low pressure side creating an almost adiabatic expansion with consequent fall of temper‐ ature via Joule-Thomson effect. Such fall of temperature may create problems to the proper functioning of the module due to eventual condensation of less volatile species that are retained and accumulate in the retentate. This condensation is undesirable and can damage the membrane plastic material.

The huge heat consumption of MEA strippers (167 kJ to 200kJ/mol of stripped CO2) forces investigation on more efficient alternatives of solvent regeneration. Wagener and Rochelle (2010) recognize as a "monumental task" reducing energy penalty of CO2 capture from coalfired power plants (approximately 30%). They presented an analysis of various stripper configurations, concluding that increasing complexity improves performance at the cost of higher capital and operational expenditures, i.e., an optimal scheme should exist. The alter‐ native configurations include simple stripping with vapor recompression, multi-pressure, double matrix, split product, internal exchange, and flashing feed. Wagener and Rochelle (2010) concluded that operating with multiple pressure levels reduces the energy requirement as "equivalent work" (including reboiler duty, pumping and heat exchangers) in 33.6 kJ/ molCO2 captured, with an optimal lean loading of 0.375 mol/mol amine. Moreover, they claim that the arrangement benefits from stripping at high pressure, whilst improves reversibility when returning to atmospheric conditions for the absorber. Wagner and Rochelle (2011) revisited several configurations with varying levels of complexity and reported that an interheated column and a simple stripper required 33.4 kJ/mol CO2 and 35.0 kJ/mol CO2 of

The first membrane modules were developed as planar films. However, such arrangement has a low ratio of membrane transfer area per equipment volume. Presently, the majority of modules for CO2 gas separation are manufactured in hollow-fiber or spiral wound configu‐ rations. Figure 10 shows a schematic of a hollow-fiber membrane permeator module and the

The membrane acts selectively against gas diffusion from the high pressure side (the retentate) to the low pressure side (the permeate) creating separation. The membrane – either in hollowfiber or spiral wound configurations – is composed by two polymeric structures: a permse‐ lective dense (nonporous) skin over a thicker microporous substrate. Inside the permeator gas

equivalent work, respectively, at their optimum lean loadings.

selective transport of molecules across the hollow-fiber membrane.

**Figure 10.** Hollow-fiber membrane permeator and the transfer through a hollow-fiber

**3.2. Membrane permeators**

50 CO2 Sequestration and Valorization

Ho and Wiley (2005) evaluated the economic performance of membrane separation for low pressure (flue gas applications) and high pressure (e.g., natural gas processing). The authors concluded that the highest share in capital cost was due to the compression phase (around 80%) while membrane and the respective shells exhibited 10% of the investment. For natural gas processing, however, membrane has the largest share of CAPEX (about 62%), as the compression is an existing stage of natural gas processing. Moreover, Ho and Wiley also report an alternative multi-stage configuration in order to obtain a CO2 rich permeate in a second membrane stage, which demands a recompression step corresponding to 30% of the total CAPEX.

In oil and natural gas deep-water offshore rigs, an issue in the processing of large volumes of NG with high CO2/CH4 ratio *vis-à-vis* climate concerns, involves large capacity CO2 separators, whose targets are exportation of saleable NG from the retentate product via long pipelines and feeding enhanced oil recovery (EOR) systems with hyperpressurized CO2 from the permeate product. In this scenario, robustness, angular indifference, modularity, and compactness also influence the selection of a separation technology. In this context, membrane permeation batteries are usually favored against the more traditional absorption columns with amine solvents (Medeiros et al., 2013a). On the other hand, large permeation batteries also have their own shortcomings, mostly related to the permselective dense (nonporous) skin over the microporous substrate. The permselective skin forces the existence of a high ΔP and exhibits low fluxes which means low capacity per unit area and high consumption of power for permeate recompression and/or permeate recycles. It also has CO2/CH4 limited selectivity and demands feed necessarily dew-point conditioned (to prevent gas condensation in the retentate provoked by the decrease of temperature associated with permeation) and continuous inspection looking for membrane bursts.

A model for hollow-fiber permeators can be built by writing mass, momentum and energy conservation principia for both permeate and retentate. Additionally, an appropriate model for trans-membrane flux transport is also necessary. Typically, trans-membrane flux models are written as products of a permeancy coefficient and a driving force term. Each species has a permeancy coefficient for a given membrane and given conditions of temperature, pressure and gas composition, but, in general, permeances are assumed independent of gas composition and pressure. The driving force term is usually expressed as a difference of component fugacities between the retentate and permeate sides. In this context and assuming a co-current compressible flow of both permeate and retentate, Nakao et al. (2009) proposed a stationary hollow-fiber membrane permeator rigorous model which can be used to predict CO2 separa‐ tion from CO2 rich NG. The model of Nakao et al. (2009) builds a spatial 1-dimensional axial description where species mass balances, energy and momentum balances are rigorously written for both permeate and retentate. All permeate and retentate thermodynamic properties (enthalpies, densities, fugacities, etc) are rigorously calculated via PR-EOS, Eq. (1). The model is too involved to be discussed here in detail, but the clarity and usefulness of it results are worth presenting in the context of CO2 capture. Figure 11 depicts a natural gas feed named GBS2 (from Basin of Santos, Brazil) with 2.79554 MMNm3 /d at 53bar and 38o C with, initially, 25.32%mol CO2 and 55.64%mol CH4. Figure 11 depicts the VLE locus of GBS2 and the heat capacity (*Cp* in kJ/mol.K) map versus *T X P* with vivid identification of the critical and supercritical neighborhoods due to the second-order transition across the critical state that is revealed by second order properties like *Cp*. In Figure 11 [A], the gas feed is located at 28o C above its dew boundary (blue). GBS2 passes through a battery of 40 horizontal modules (0.2m X 10m each) of cellulose-acetate hollow-fibers (0.5mm ID), with CO2 and CH4 permeances respectively of 1.27.10-8 and 4.4. 10-10 mol/s.m2 .Pa and 1854m2 of permeation area per module. Permeate pressure is 4bar and the external temperature is 27o C with a heat transfer coefficient of 5W/m2 .K. Resulting profiles in the axial flow direction (10m long) for one module are shown in Figure 12. Permeate and retentate initiate contact at axial position *X=0m* and cease contact at *X=10m*. The background along the axis of flow is painted in four colors for discrimination among the four quarters of a typical module. Figure 12 [A] depicts retentate profiles of %mol CO2 and CH4 showing a CO2 decrease from 25.62% to 9.3%mol and a CH4 increase from 55.64% to 65.5%mol. Figure 12 [B] shows that the final permeate recoveries of CO2 and CH4 are 72% and 11.5%. Figure 12 [C] depicts permeate and retentate temperature profiles revealing a drop of retentate temperature from 38o C to 29o C, insufficient for condensation (Figure 11 [A]).

#### **3.3. Gas-liquid contactors**

Gas−liquid contactors (GLC) constitute a new and versatile kind of membrane operation for CO2 removal from NG. A GLC unit admits a gas stream which is contacted with a liquid stream (the solvent stream) through a polymeric micro-porous membrane. The solvent phase is an aqueous solution of ethanolamines (e.g. MEA and/or MDEA) that can absorb CO2 as occurs in an absorption column. But the difference here is that the liquid and gas phases really do not mix as they do in direct contact devices like packing towers. Assuming that the GLC is manufactured with hollow-fiber membranes, the solvent phase flows in the hollow-fiber inner space while the gas phase flows in the outside shell space, but there are also configurations where the roles of liquid and gas phases are inverted. A module of hollow-fiber GLC with gas flowing in the shell side and with solvent flowing in the inner space of the hollow-fibers is sketched in Figure 13. As can be seen, the module resembles closely a shell and tube indirect contact heat exchanger.

GLC membrane operation can outperform common nonporous permeators in terms of capacity per unit area while sustaining high CO2/CH4 selectivity. High fluxes are possible in GLC because the membrane does not have a dense skin in order to be selective. The underlying reason is that selectivity is imposed by the solvent in the inner (permeate) side cutting the necessity of high ΔP across the membrane. As the reader can see, terms permeate and retentate are also used here despite some improperness, and refer, respectively, to the inner solvent flow and to the gas flow that was not transferred to the inner solvent.

written for both permeate and retentate. All permeate and retentate thermodynamic properties (enthalpies, densities, fugacities, etc) are rigorously calculated via PR-EOS, Eq. (1). The model is too involved to be discussed here in detail, but the clarity and usefulness of it results are worth presenting in the context of CO2 capture. Figure 11 depicts a natural gas feed named

25.32%mol CO2 and 55.64%mol CH4. Figure 11 depicts the VLE locus of GBS2 and the heat capacity (*Cp* in kJ/mol.K) map versus *T X P* with vivid identification of the critical and supercritical neighborhoods due to the second-order transition across the critical state that is revealed by second order properties like *Cp*. In Figure 11 [A], the gas feed is located at 28o

above its dew boundary (blue). GBS2 passes through a battery of 40 horizontal modules (0.2m X 10m each) of cellulose-acetate hollow-fibers (0.5mm ID), with CO2 and CH4 permeances

in Figure 12. Permeate and retentate initiate contact at axial position *X=0m* and cease contact at *X=10m*. The background along the axis of flow is painted in four colors for discrimination among the four quarters of a typical module. Figure 12 [A] depicts retentate profiles of %mol CO2 and CH4 showing a CO2 decrease from 25.62% to 9.3%mol and a CH4 increase from 55.64% to 65.5%mol. Figure 12 [B] shows that the final permeate recoveries of CO2 and CH4 are 72% and 11.5%. Figure 12 [C] depicts permeate and retentate temperature profiles revealing a drop

Gas−liquid contactors (GLC) constitute a new and versatile kind of membrane operation for CO2 removal from NG. A GLC unit admits a gas stream which is contacted with a liquid stream (the solvent stream) through a polymeric micro-porous membrane. The solvent phase is an aqueous solution of ethanolamines (e.g. MEA and/or MDEA) that can absorb CO2 as occurs in an absorption column. But the difference here is that the liquid and gas phases really do not mix as they do in direct contact devices like packing towers. Assuming that the GLC is manufactured with hollow-fiber membranes, the solvent phase flows in the hollow-fiber inner space while the gas phase flows in the outside shell space, but there are also configurations where the roles of liquid and gas phases are inverted. A module of hollow-fiber GLC with gas flowing in the shell side and with solvent flowing in the inner space of the hollow-fibers is sketched in Figure 13. As can be seen, the module resembles closely a shell and tube indirect

GLC membrane operation can outperform common nonporous permeators in terms of capacity per unit area while sustaining high CO2/CH4 selectivity. High fluxes are possible in GLC because the membrane does not have a dense skin in order to be selective. The underlying reason is that selectivity is imposed by the solvent in the inner (permeate) side cutting the necessity of high ΔP across the membrane. As the reader can see, terms permeate and retentate are also used here despite some improperness, and refer, respectively, to the inner solvent flow

and to the gas flow that was not transferred to the inner solvent.

.Pa and 1854m2

.K. Resulting profiles in the axial flow direction (10m long) for one module are shown

/d at 53bar and 38o

C, insufficient for condensation (Figure 11 [A]).

C with, initially,

of permeation area per module.

C with a heat transfer coefficient

C

GBS2 (from Basin of Santos, Brazil) with 2.79554 MMNm3

Permeate pressure is 4bar and the external temperature is 27o

C to 29o

respectively of 1.27.10-8 and 4.4. 10-10 mol/s.m2

of retentate temperature from 38o

**3.3. Gas-liquid contactors**

52 CO2 Sequestration and Valorization

contact heat exchanger.

of 5W/m2

**Figure 11.** GBS2 a CO2 rich natural gas (25.32%CO2,55.64%CH4,9.61%C2H6, 9.43%C3 +) for Hollow-Fiber Permeator: [A] VLE Locus *P*(bar) X *T*( oC); [B] *CP*(kJ/mol.K) vs *P*(bar) X *T*( oC)

**Figure 12.** Profiles in a hollow-fiber module in co-current flow with feed GBS2 in Fig. 3.6 (25.32% CO2 & 55.64% CH4 ): [A] CO2 & CH4 %mol in retentate; [B] CO2 & CH4 permeate % recoveries; [C] retentate and permeate temperatures.

**Figure 13.** Hollow-fiber gas-liquid contactor with solvent in the inner space of membrane

GLC can separate CO2 from NG offering the advantages of both membrane and absorption technologies, but leaving behind the respective shortcomings like the low flux coexisting with high difference of pressure between the membrane sides and the flooding concerns coexisting with dependence on gravity in the case of absorption columns. GLC combines polymeric membrane separation and chemical absorption, using a physically and chemically active solvent for selective CO2 removal. However, the new aspect is that there is a membrane standing as a physical barrier against the unnecessary mixing of gas and liquid phases. Other advantages of GLC are: (i) it allows independent manipulation of liquid and gas flows; (ii) offers larger area of gas−liquid interface; (iii) modularity ; (iv) it exhibits flexibility to increase/ decrease operational scales; (v) no dew-point conditioning of the gas feed is necessary; and (v) angular indifference allowing horizontal or vertical operational arrangements. Figure 14 sketches a typical process flowsheet for operation of a GLC unit capturing CO2 from a NG feed.

**Figure 14.** Typical process flowsheet for CO2 capture with gas-liquid contactor (GLC)

**Figure 12.** Profiles in a hollow-fiber module in co-current flow with feed GBS2 in Fig. 3.6 (25.32% CO2 & 55.64% CH4 ): [A] CO2 & CH4 %mol in retentate; [B] CO2 & CH4 permeate % recoveries; [C] retentate and permeate temperatures.

GLC can separate CO2 from NG offering the advantages of both membrane and absorption technologies, but leaving behind the respective shortcomings like the low flux coexisting with

**Figure 13.** Hollow-fiber gas-liquid contactor with solvent in the inner space of membrane

54 CO2 Sequestration and Valorization

Here the GLC operates t high NG pressure, but the rich solvent has to be regenerated in a low pressure stripper where CO2 is released by the action of heating and low pressure as occurs in the second regeneration column shown in Figure 6.

A model for stationary gas-liquid hollow-fiber contactor was proposed by de Medeiros et al. (2013a) for separating CO2 from CO2 rich natural gas. This model is based on the AGWA theory discussed in the sub-section 3.1 (de Medeiros et al., 2013b). The model assumes a hollow-fiber contactor with co-current compressible flows of both permeate and retentate, where the permeate corresponds to the inner space inside the hollow-fibers. Permeate and retentate are separated by the membrane. The permeate is supposed in two-phase flow because the transmembrane flux of methane will support the maintenance of a gas phase in the inner permeate flow. The permeate is, in fact, a two-phase reactive flow in continuous reactive vapor-liquid equilibrium (RVLE) because CO2 is in reactive vapor-liquid equilibrium with water and amine via a set of chemical equations similar to Eq. (9) (without the H2S chemical equations if the GLC is designed for CO2 capture only).

The model of de Medeiros et al. (2013a) is based on 1-dimensional axial geometry of the GLC. Species mass balances and momentum/energy balances are written for both RVLE permeate and gas retentate. All properties of gas and liquid phases are calculated with PR-EOS. The trans-membrane flux terms are written as products of permeancy coefficients and a driving force term. Each species has a permeancy coefficient for a given membrane and given tem‐ perature conditions. The driving force term is expressed as a difference of component fugac‐ ities between the retentate and RVLE permeate sides. The major difficulty encountered in the GLC model is to represent the RVLE two-phase flow in the inner membrane space. This is accomplished by solving the AGWA VLE model (de Medeiros et al., 2012b) described in Table 1, along the entire path of the permeate in the inner space of the hollow-fibers. As an example, consider the equilibrium map in Figure 15.

This map was calculated assuming that 1 mol of natural gas (with 16.7%mol CO2 +82.3%mol CH4) is contacted with 1.2 mol of liquid solvent containing 14.5%mol MEA +14.5%mol MDEA +71%mol H2O. Only the chemical reactions of CO2 absorption by MEA (Eq. 9a) and MDEA (Eq. 9b) are considered. Each point in Figure 15 represents the resulting equilibrium vapor phase mol fraction of CO2 (*YCO2)* versus *T*( o C) X *P* (bar) under reactive VLE. Clearly, the locations with low *YCO2* (blue) are dominated by absorption phenomena, while those with high *YCO2* (red) are dominated by stripping phenomena. This kind of inner RVLE solver is a key element in the construction of the GLC model of de Medeiros et al. (2013a).

## **3.4. The CO2 transportation cycle**

An efficient and reliable transportation system is required to displace enormous quantities of captured CO2 to their destination site at appropriate geological formations that are capable to accommodate hundreds of billions or trillions of Nm3 of CO2 under stable and secure condi‐ tions. Clearly a not especially large size thermoelectric coal plant can produce something like 1 million of metric tonnes (1Mt) of CO2 per year or about 500.106 Nm3 /y. Such huge capacities can only be attended by large pipeline systems operating with dense compressed fluid (liquid or dense supercritical CO2), because the other existing alternatives – road, railroad and barge transport – simply cannot cope with dense fluid pipelines in terms of unitary cost and capacity of transportation.

In other words, despite of the existence of many options for transporting compressed (gas or liquid) CO2 – including highway tankers, railway tankers, ships, and pipelines – it is evident that the impressive volumes that must be transported dictates that only pipelines working at high pressures, high densities and high capacities are suitable for this service. For instance, 2-3Mt/y of CO2 have to be transported to dispose of the entire production of a single 500MW coal-fired power plant. This corresponds to transporting 230-350t/h of CO2, just to service a single, medium-sized, client. Thus, only a network of large-scale pipelines could provide viable overland transport of such massive flow rates of CO2. Presently, about 50Mt/y of CO2 (equivalent to the output of 16 coal-fired power plants) are transported by 3100km of CO2 pipelines, mainly for EOR processes in the USA and Canada (de Medeiros et al., 2008). The best example is the 808 km long, 30" diameter, Cortez Pipeline that transports 13Mt/y of CO2 from highlands in Colorado to oilfields in Texas, USA.

separated by the membrane. The permeate is supposed in two-phase flow because the transmembrane flux of methane will support the maintenance of a gas phase in the inner permeate flow. The permeate is, in fact, a two-phase reactive flow in continuous reactive vapor-liquid equilibrium (RVLE) because CO2 is in reactive vapor-liquid equilibrium with water and amine via a set of chemical equations similar to Eq. (9) (without the H2S chemical equations if the

The model of de Medeiros et al. (2013a) is based on 1-dimensional axial geometry of the GLC. Species mass balances and momentum/energy balances are written for both RVLE permeate and gas retentate. All properties of gas and liquid phases are calculated with PR-EOS. The trans-membrane flux terms are written as products of permeancy coefficients and a driving force term. Each species has a permeancy coefficient for a given membrane and given tem‐ perature conditions. The driving force term is expressed as a difference of component fugac‐ ities between the retentate and RVLE permeate sides. The major difficulty encountered in the GLC model is to represent the RVLE two-phase flow in the inner membrane space. This is accomplished by solving the AGWA VLE model (de Medeiros et al., 2012b) described in Table 1, along the entire path of the permeate in the inner space of the hollow-fibers. As an example,

This map was calculated assuming that 1 mol of natural gas (with 16.7%mol CO2 +82.3%mol CH4) is contacted with 1.2 mol of liquid solvent containing 14.5%mol MEA +14.5%mol MDEA +71%mol H2O. Only the chemical reactions of CO2 absorption by MEA (Eq. 9a) and MDEA (Eq. 9b) are considered. Each point in Figure 15 represents the resulting equilibrium vapor

o

element in the construction of the GLC model of de Medeiros et al. (2013a).

locations with low *YCO2* (blue) are dominated by absorption phenomena, while those with high *YCO2* (red) are dominated by stripping phenomena. This kind of inner RVLE solver is a key

An efficient and reliable transportation system is required to displace enormous quantities of captured CO2 to their destination site at appropriate geological formations that are capable to

tions. Clearly a not especially large size thermoelectric coal plant can produce something like

can only be attended by large pipeline systems operating with dense compressed fluid (liquid or dense supercritical CO2), because the other existing alternatives – road, railroad and barge transport – simply cannot cope with dense fluid pipelines in terms of unitary cost and capacity

In other words, despite of the existence of many options for transporting compressed (gas or liquid) CO2 – including highway tankers, railway tankers, ships, and pipelines – it is evident that the impressive volumes that must be transported dictates that only pipelines working at high pressures, high densities and high capacities are suitable for this service. For instance, 2-3Mt/y of CO2 have to be transported to dispose of the entire production of a single 500MW coal-fired power plant. This corresponds to transporting 230-350t/h of CO2, just to service a

C) X *P* (bar) under reactive VLE. Clearly, the

of CO2 under stable and secure condi‐

/y. Such huge capacities

Nm3

GLC is designed for CO2 capture only).

56 CO2 Sequestration and Valorization

consider the equilibrium map in Figure 15.

phase mol fraction of CO2 (*YCO2)* versus *T*(

accommodate hundreds of billions or trillions of Nm3

1 million of metric tonnes (1Mt) of CO2 per year or about 500.106

**3.4. The CO2 transportation cycle**

of transportation.

**Figure 15.** Equilibrium vapor phase mol fraction of CO2 (*YCO2*) versus *T*( oC) X *P* (bar) under reactive VLE: 1mol of a 16.7%mol CO2 natural gas is contacted with 1.2 mol of a liquid solvent with 14.5%mol MEA, 14.5%mol MDEA and 71%mol water.

Based on historical capital and O&M data for a 480-km long CO2 pipeline without booster compressors, McCoy (2008) projected a fixed O&M coefficient of \$3,250/y/km for CO2 pipelines. Considering a horizontal pipeline without appreciable elevation changes and an annualized fixed cost of 15% of capital, McCoy estimated the total unitary cost of CO2 transportation as only \$1.16 per tonne of CO2 per 100km. Based on a Monte Carlo sensitivity analysis, McCoy (2008) determined a range of \$0.75 to \$3.56 per tonne of CO2 per 100km for this cost, recommending the median value of \$1.65 per tonne per 100km as a suitable estimate for investment decisions.

The design of CO2 pipelines depends on reliable compressible flow models for dense com‐ pressible fluid near critical conditions. This model should account for thermal compressibility effects inside the fluid, i.e., temperature increases (decreases) during downhill (uphill) flow due to gravity compression (expansion).

In the same way, external heat transfer and elevation effects must be allowed. The extremely high compressibility of CO2 near its critical state at 31o C (shown in Figures 4 and 5), leads to potential abrupt changes of velocity due to abrupt changes of density as the fluid compresses (decompresses) near the critical state. In other words, any candidate model for high capacity CO2 pipelines must be able to calculate thermodynamic properties of dense supercritical CO2 near its critical transition with accuracy. Such a CO2 pipeline model has been proposed by de Medeiros et al. (2008) by solving rigorous species mass balances and energy/momentum balances along the pipeline with all thermodynamic properties given by PR – EOS or SRK – EOS.

This model is demonstrated in the simulation of a sub-sea CO2 pipeline (Figures 16, 17 and 18) for transportation of 20 MMSm3 /d of CO2 from onshore plant to five EOR wellheads 2100m deep, 380km from the coast. The 20'' pipeline extends 380km from west to east and 320km from south to north with 700 km of length.

**Figure 16.** Hypothetical 20"X 700 km sub-sea pipeline for 20MMSm3/d of liquid CO2 (≈450kg/s) at 250 bar from on‐ shore plant to five offshore EOR wellheads (4X100kg/s + 1X50kg/s) 2100m deep, 380km from the coast.

As seen in Figure 17, a problematic factor was inserted between the continental shelf and the continental slope, namely, a big rift 1500m deep lies in the pipeline route. Rifts are not common in Santos Basin, Brazil, but they exist in the Norway arctic coast (Pettersen, 2011).

**Figure 17.** Elevation profile for the hypothetical sub-sea CO2 pipeline from onshore plant to five EOR offshore well‐ heads 2100m deep, 380km from the coast.

## **4. The CO2 utilization cycle**

The design of CO2 pipelines depends on reliable compressible flow models for dense com‐ pressible fluid near critical conditions. This model should account for thermal compressibility effects inside the fluid, i.e., temperature increases (decreases) during downhill (uphill) flow

In the same way, external heat transfer and elevation effects must be allowed. The extremely

potential abrupt changes of velocity due to abrupt changes of density as the fluid compresses (decompresses) near the critical state. In other words, any candidate model for high capacity CO2 pipelines must be able to calculate thermodynamic properties of dense supercritical CO2 near its critical transition with accuracy. Such a CO2 pipeline model has been proposed by de Medeiros et al. (2008) by solving rigorous species mass balances and energy/momentum balances along the pipeline with all thermodynamic properties given by PR – EOS or SRK –

This model is demonstrated in the simulation of a sub-sea CO2 pipeline (Figures 16, 17 and

deep, 380km from the coast. The 20'' pipeline extends 380km from west to east and 320km

**Figure 16.** Hypothetical 20"X 700 km sub-sea pipeline for 20MMSm3/d of liquid CO2 (≈450kg/s) at 250 bar from on‐

shore plant to five offshore EOR wellheads (4X100kg/s + 1X50kg/s) 2100m deep, 380km from the coast.

C (shown in Figures 4 and 5), leads to

/d of CO2 from onshore plant to five EOR wellheads 2100m

due to gravity compression (expansion).

58 CO2 Sequestration and Valorization

18) for transportation of 20 MMSm3

from south to north with 700 km of length.

EOS.

high compressibility of CO2 near its critical state at 31o

Although CGS has been regarded worldwide as a mitigation technology, it deals with CO2 as a waste with an energy and an economic penalty for its disposal (Armstrong, 2012). Rather than treating CO2 as a waste, carbon dioxide utilization (CDU) recognizes it as a raw material in chemical process to produce high added-value carbon containing products. It is also worth noting that the CDU is a complementary technology to CGS, not a competing technology, adding value to a process and thus it may help balance the costs of CGS.

While CO2 has broad industrial application as solvent (supercritical CO2, fire extinguishers) and in the food industry, it has disadvantages as a chemical raw material due to its low reactivity and few reactions are thermodynamically feasible. Furthermore, each potential use of CO2 as reactant has an energy requirement that needs to be determined and must not exceed the CO2 utilized and, although the utilization of CO2 has been subject of research since before

**Figure 18.** Calculated profiles for the hypothetical 20"X 700 km sub-sea pipeline with 20MMSm3/d (≈ 450kg/s): [A] pressure (bar); [B] temperature (oC); [C] mass flow rate (kg/s)

1970´s, there is much research still needed for CO2 activation. Moreover, the utilization of CO2 to cause an effective reduction in its emission into the atmosphere, must observe certain guidelines (Aresta, 2010): (i) the new process must reduce the overall CO2 emissions; (ii) it mustbe less energy - and material - intensive with respect to the on–stream processes that it aims to replace; (iii) the new process must reduce the overall CO2 emissions; (iv) it must employ safer and more eco-friendly working conditions; (v) it needs to be able to operate on a commercial scale and (vi) it must be economically viable.

According to Song (2006), the global market for CO2 is estimated to be \$3.2 billion/year in 2003. Carbon markets across the world were valued at 96 billion euros (\$122.28 billion) in 2011 (Reuters Agency, 2012). Utilization of CO2 by the chemical industry exists for more than one century as, for instance, the synthesis of urea (50 Mt/y) (Aresta, 1999) (Eq. 11), salicylic acid (Song, 2006) (Eq. 12) and inorganic carbonates (20Mt/y) (Aresta, 1999).

According to Song (2006), the worldwide production of urea in 2002 was about 110 million metric tonnes, which contains 51.8 million metric tonnes of nitrogen with an estimated value of US\$11.5 billion. This corresponds to about 81 million metric tons of CO2, and 22 million metric tonnes of carbon.

### **4.1. Thermodynamic and chemical considerations of CO2 conversion**

**Figure 18.** Calculated profiles for the hypothetical 20"X 700 km sub-sea pipeline with 20MMSm3/d (≈ 450kg/s): [A]

pressure (bar); [B] temperature (oC); [C] mass flow rate (kg/s)

60 CO2 Sequestration and Valorization

Chemical reactions are driven by the difference in Gibbs free energy between the products and reactants at certain conditions. The obstacle for utilizing CO2 as feedstock to industrial processes is its low energy level - CO2 is a highly stable molecule. Consequently, a substantial input of energy, effective reaction conditions, and often catalysts, are necessary for its chemical conversion. In other words, many reactions for CO2 conversion involve positive change in enthalpy, requiring an energy input. There are many large-scale chemical industrial processes that are operated based on endothermic reactions in the chemical industry (e.g., ammonia Haber process).

Song (2006) states properly that it is more energy-demanding if one were to use only CO2 as a single reactant, but it becomes easier in thermodynamically terms if CO2 is used as a co-reactant with another substance that has higher Gibbs free energy, such as CH4, graphite and H2. Song (2006) illustrates this trend by the change in the reaction heat for reactions with CO2 as the single reactant (Eq. 13) and with CO2 as a co-reactant (Eq. 14).

$$\text{CO}\_2 \xrightarrow{\longrightarrow \text{CO} + \text{V}\_2} \text{CO} + \text{V}\_2 \qquad \Delta H^0 = +2\text{93} \frac{k\text{J}}{mol\text{CO}\_2} \tag{13}$$

$$\text{CO}\_2 + \text{H}\_2 \xrightarrow{\text{-} \longrightarrow \text{CO}(g)} \text{CO(g)} + \text{H}\_2\text{O} \qquad \qquad \Delta H^0 = +51 \frac{kJ}{mol \text{CO}\_2} \tag{14}$$

Therefore, energy input is necessary to transform CO2 into chemicals. Four methods are possible: (i) reaction with high-energy molecules (e.g., ethylene oxide, H2, unsaturated compounds and organometallic compounds); (ii) reaction with low energy oxidized com‐ pounds (e.g., organic carbonates), (iii) shifting chemical equilibrium towards products (via removal of a reaction product) and (iv) supplying physical energy (e.g., light or electricity) (Sakakura et al., 2007). The appropriate selection of chemical reactions makes it possible to obtain a negative Gibbs energy change.

As the carbon of the carbonyl group has an electron deficiency, CO2 has great affinity for nucleophilic compounds and electron donors, i.e., as an anhydrous carboxylic acid, it promptly reacts with basic compounds. For instance, organometallic compounds, such as Grignard compounds, react promptly with CO2 even at low temperatures. Reactions with CO2 can be divided into two groups: (1) formation of a carboxylic group via a nucleophilic attack and (2) oxidative cycle addition yielding a ring of 5 members (Sakakura et al., 2007).

A relevant aspect to be considered is that utilization of CO2 as feedstock does not necessarily contribute to the mitigation of greenhouse effects, even though CO2 stands as a green reactant in many cases (Sakakura et al., 2007). Three points are hence relevant:


Among other utilization, CO2 is currently used as supercritical solvent, refrigerant fluid, beverage carbonation agent, inert medium (such as fire extinguisher), pressurizing agent, neutralizing agent, gas for greenhouses, "inerting" applications to inhibit unintended chemical reactions, welding (preventing atmospheric oxygen from reacting with molten metal), food processing (suppressing aerobic bacterial activity for preservation in processes like pneumatic conveying or food storage). In any of these applications for inerting, carbon dioxide serves as a cover against atmospheric oxygen and is thus implicitly released into the atmosphere. The carbonation of beverages accounts for around 1.0x106 t CO2/y. Nevertheless, they do not constitute CO2 sink as it is ultimately released to the atmosphere or remains in a closed loop (Ormerod et al., 1995). Hence, this study does not review such utilizations.

## **4.2. Supply chain considerations of CO2 conversion**

Song (2006) states properly that it is more energy-demanding if one were to use only CO2 as a single reactant, but it becomes easier in thermodynamically terms if CO2 is used as a co-reactant with another substance that has higher Gibbs free energy, such as CH4, graphite and H2. Song (2006) illustrates this trend by the change in the reaction heat for reactions with CO2 as the

0

CO CO + ½ O <sup>293</sup> *kJ <sup>H</sup>*

CO + H CO g + H O <sup>51</sup> *kJ <sup>H</sup>*

( ) <sup>0</sup>

Therefore, energy input is necessary to transform CO2 into chemicals. Four methods are possible: (i) reaction with high-energy molecules (e.g., ethylene oxide, H2, unsaturated compounds and organometallic compounds); (ii) reaction with low energy oxidized com‐ pounds (e.g., organic carbonates), (iii) shifting chemical equilibrium towards products (via removal of a reaction product) and (iv) supplying physical energy (e.g., light or electricity) (Sakakura et al., 2007). The appropriate selection of chemical reactions makes it possible to

As the carbon of the carbonyl group has an electron deficiency, CO2 has great affinity for nucleophilic compounds and electron donors, i.e., as an anhydrous carboxylic acid, it promptly reacts with basic compounds. For instance, organometallic compounds, such as Grignard compounds, react promptly with CO2 even at low temperatures. Reactions with CO2 can be divided into two groups: (1) formation of a carboxylic group via a nucleophilic attack and (2)

A relevant aspect to be considered is that utilization of CO2 as feedstock does not necessarily contribute to the mitigation of greenhouse effects, even though CO2 stands as a green reactant

**a.** The chemical (or biochemical) fixation of CO2 does not necessarily imply in reducing CO2 emissions as its transformation requires energy, both to drive reaction (high temper‐ atures and pressures) and separate products (separation occurs mainly at low pressures and, hence, recycling unreacted CO2 to the reactor will require recompression at the

**b.** The energy demand of the world is order of magnitude higher than the amount of CO2

**c.** In the critical phase of its life cycle, organic chemicals will emit CO2. Nevertheless, the relevance of CO2 as raw material stands for being a renewable feedstock, substituting the

oxidative cycle addition yielding a ring of 5 members (Sakakura et al., 2007).

in many cases (Sakakura et al., 2007). Three points are hence relevant:

2

*molCO*

2

*molCO* ¾¾® D =+ (13)

¾¾® D =+ (14)

single reactant (Eq. 13) and with CO2 as a co-reactant (Eq. 14).

2 2

2 2 2

obtain a negative Gibbs energy change.

62 CO2 Sequestration and Valorization

expense of high energy input);

conventional fossil based routes

fixed by chemical utilization of CO2, and

Bayer (2013) estimates that the chemical industry has over 40,000 final chemicals, produced from approximately 400 intermediate chemicals, derived from ~40 basic chemicals that, in turn, are based on 4 classic feedstocks: petroleum, natural gas, coal and biomass. The Company expands the set of feedstock with the inclusion of CO2.

For the near and middle term time-period, i.e*.*, next one or two decades, it is reasonable to assume that presently dominant technologies (from an economic standing point) will persist and, consequently, expanding economies´ demand of energy will be met by present technol‐ ogies. Consequently, GHG emission of chemical processes will expand. This same approach leads to a transition scenario to a low-carbon economy equally dominated by presently installed infrastructure.

Hence, the CO2 utilization cycle is likely to rely on commercially mature technologies or on technologies presently in large-scale pilot or demonstration plants as *Bridge Technologies*. Therefore, only peripheral technological advances are expected in these technologies such as process intensification, enhanced selectivity and activity of catalysts, and process optimization with increased mass and energy integration.

It is worth noting that 5 chemical commodities, ammonia, methanol, ethylene, propylene and BTX dominate energy consumption and GHG emissions in the chemical industry (IEA, 2013). In the conception of co-processing of fossil feedstock, with biomass and CO2, conversion routes to produce ammonia, methanol and olefins (e.g., ethylene and propylene) are considered (Figure 2 ). Although not included in Figure 2, catalytic fast pyrolysis of biomass can lead to the key aromatic compounds, Benzene, Toluene, and Xylene (BTX), with generation of paraxylene from the BTX and subsequent conversion to Purified Terephthalic acid (PTA) and PolyEthylene Terephthalate (PET).

Synthesis gas (SYNGAS), a mixture of hydrogen, carbon monoxide and CO2, is a versatile intermediate feedstock used in the production of a number of hydrocarbons such as methanol, ammonia, synthetic hydrocarbon liquids, and as a source of pure hydrogen and carbon monoxide. Applications of these products range from petrochemical feedstock to fuels.

According to Rostrup-Nielsen and Christiansen (2011), trends in the use of SYNGAS are dominated by the conversion of inexpensive remote natural gas into liquid fuels ("gas to liquids" or "GTL") and by a possible role in a future "hydrogen economy" mainly associated with the use of fuel cells. Some relevant synthesis to the chemical industry are:

*SYNGAS to Ammonia*: Ammonia serves as a building block in many pharmaceuticals, fertiliz‐ ers, ethanolamines, urea and cleaning products, as well as an anti-microbial agent in food processing. 50% of the world´s food production relies on ammonia fertilizers.

*SYNGAS to Methanol*: The main use for methanol is to produce other chemicals; about 40% is converted to formaldehyde, and further processed into plastics, plywood, paints, explosives and textiles. It is also used in anti-freeze, solvents, and fuels, and can serve as energy carrier.

*SYNGAS to Hydrogen:* Hydrogen generation is one of the largest energy-consuming steps in the production of the crucial chemical precursors of ammonia and methanol.

*SYNGAS to Synthetic Fuels:* Liquid hydrocarbons exhibit an excellent volumetric energy density and offer various opportunities for storing electric energy (Kaiser et al., 2013). Kaiser et al. (2013) point generation of SYNGAS by reverse water-gas shift (RWGS) at elevated tempera‐ tures as the first step, followed by Fischer-Tropsch (FT) synthesis. If CO is substituted by CO2, less synthetic fuels are formed, the water-gas shift is repressed, and methane selectivity increases.

## **4.3. CO2 to SYNGAS**

SYNGAS is a toxic, colorless and odorless mixture. Its efficient commercial production is gaining significant attention worldwide (Raju et al., 2009) as it is a versatile feedstock to produce a variety of fuels and chemicals.

Almost any carbon source ranging from natural gas and oil products to coal and biomass can be used in the SYNGAS production. The lowest cost routes for its production, however, is natural gas (Spath and Dayton, 2003), which is also the cleanest of all fossil fuels. Furthermore, steam methane reforming is a well-established process for the production of SYNGAS and hydrogen (Gangadharan et al., 2012). The H2/CO ratio varies over a wide range, depending on the primary feedstock and technology employed. Particular SYNGAS ratios are required depending on the chemical product desired, therefore creating flexibility for the chemical industry.

In the twofold context of avoiding emissions and standing as a renewable feedstock, carbon dioxide has been investigated as raw material in SYNGAS production. The new technologies involves CO2: (i) reforming processes using a hydrocarbon (methane, typically) as reducing agent; (ii) using CO2 as a co-reactant with hydrogen in the catalytic reverse water gas shift (RWGS); (iii) thermocatalytic (solar assisted) routes; (iv) electro- or photo-catalysis; (v) plasma processes, and (vi) bio-processes, e.g., by hybrid enzyme-nanoparticle systems, bioelectro‐ chemical reduction or using a biomass char and a catalyst such as Ni/Al2O3.

**CO2 Reforming of CH4 (Dry Reforming):** The dry (carbon dioxide) reforming of methane has been of interest for a long time, dating back to as early as the 1920s, and was first proposed by Fischer and Tropsch (1928), but it is only in recent years that interest in it has rapidly increased for both environmental and commercial reasons (Zhang et al., 2003). Its name derives from the fact that CO2 replaces steam in the conventional steam methane reforming process (Hartley & Tam, 2012). This reaction can be represented as

liquids" or "GTL") and by a possible role in a future "hydrogen economy" mainly associated

*SYNGAS to Ammonia*: Ammonia serves as a building block in many pharmaceuticals, fertiliz‐ ers, ethanolamines, urea and cleaning products, as well as an anti-microbial agent in food

*SYNGAS to Methanol*: The main use for methanol is to produce other chemicals; about 40% is converted to formaldehyde, and further processed into plastics, plywood, paints, explosives and textiles. It is also used in anti-freeze, solvents, and fuels, and can serve as energy carrier. *SYNGAS to Hydrogen:* Hydrogen generation is one of the largest energy-consuming steps in

*SYNGAS to Synthetic Fuels:* Liquid hydrocarbons exhibit an excellent volumetric energy density and offer various opportunities for storing electric energy (Kaiser et al., 2013). Kaiser et al. (2013) point generation of SYNGAS by reverse water-gas shift (RWGS) at elevated tempera‐ tures as the first step, followed by Fischer-Tropsch (FT) synthesis. If CO is substituted by CO2, less synthetic fuels are formed, the water-gas shift is repressed, and methane selectivity

SYNGAS is a toxic, colorless and odorless mixture. Its efficient commercial production is gaining significant attention worldwide (Raju et al., 2009) as it is a versatile feedstock to

Almost any carbon source ranging from natural gas and oil products to coal and biomass can be used in the SYNGAS production. The lowest cost routes for its production, however, is natural gas (Spath and Dayton, 2003), which is also the cleanest of all fossil fuels. Furthermore, steam methane reforming is a well-established process for the production of SYNGAS and hydrogen (Gangadharan et al., 2012). The H2/CO ratio varies over a wide range, depending on the primary feedstock and technology employed. Particular SYNGAS ratios are required depending on the chemical product desired, therefore creating flexibility for the chemical

In the twofold context of avoiding emissions and standing as a renewable feedstock, carbon dioxide has been investigated as raw material in SYNGAS production. The new technologies involves CO2: (i) reforming processes using a hydrocarbon (methane, typically) as reducing agent; (ii) using CO2 as a co-reactant with hydrogen in the catalytic reverse water gas shift (RWGS); (iii) thermocatalytic (solar assisted) routes; (iv) electro- or photo-catalysis; (v) plasma processes, and (vi) bio-processes, e.g., by hybrid enzyme-nanoparticle systems, bioelectro‐

**CO2 Reforming of CH4 (Dry Reforming):** The dry (carbon dioxide) reforming of methane has been of interest for a long time, dating back to as early as the 1920s, and was first proposed by Fischer and Tropsch (1928), but it is only in recent years that interest in it has rapidly increased for both environmental and commercial reasons (Zhang et al., 2003). Its name derives from the

chemical reduction or using a biomass char and a catalyst such as Ni/Al2O3.

with the use of fuel cells. Some relevant synthesis to the chemical industry are:

processing. 50% of the world´s food production relies on ammonia fertilizers.

the production of the crucial chemical precursors of ammonia and methanol.

increases.

industry.

**4.3. CO2 to SYNGAS**

64 CO2 Sequestration and Valorization

produce a variety of fuels and chemicals.

$$\text{CH}\_4 + \text{CO}\_2 \xrightarrow{\text{-} \longrightarrow 2\text{CO} + 2\text{H}\_2} \qquad \Lambda H = +247 \frac{\text{kJ}}{mol} \tag{15}$$

According to Hartley and Tam (2012), dry reforming utilizing CO2 produces synthesis gas with higher purity and lower H2/CO ratio than either partial oxidation or steam reforming. The produced SYNGAS has an H2/CO ratio of unity without further post-reformer reactions (Zhang et al., 2003). The interest in this reforming route in recent years (Treacy and Ross, 2004, Shi et al., 2013) is due to two main reasons: (i) it produces SYNGAS with a H2/CO molar ratio that is suitable for a variety of products including Fischer–Tropsch fuels and (ii) the reaction consumes two types of greenhouse gases, CO2 and CH4 (Zhang et al., 2003, Gangad‐ haran et al., 2012). Moreover, SYNGAS production stands as the most promising alternative of CO2 utilization as it presents flexibility of using installed infrastructure to the manufacture of important chemical commodities.

The biggest limitation to the dry reforming process appears to be the availability of a suitable catalyst. The high temperatures required to reach high conversions, due to the endothermic nature of the process, contribute to carbon deposition (both CO2 and CH4 give off carbon deposits), and a catalyst capable of operating at such severely deactivating conditions has not been well developed (Zhang et al., 2003, Shi et al., 2013). According to Shi et al (2013), from the viewpoint of GTL industry, developing a catalyst for CO2 reforming of CH4 is a challenge, because the catalyst must exhibit very high production rates to render the GTL methane reformer as small as possible. Nevertheless, progress in the development of suitable catalysts with higher activities and optimized lifetime stabilities have been reported (Bradford and Vannice, 1999, Souza and Schmal, 2003; Zhang et al., 2003, Ginsburg et al., 2005; Kahle et al., 2013; Shi et al., 2013; Zheng et al., 2013; Edwards, 1995; Wurzel et al., 2000; Nagaoka et al., 2001; Li et al., 2004). Nevertheless, there is still no process for the CO2 reforming currently considered to be commercially feasible. However, a variation of dry reforming has been used industrially (Hartley and Tam, 2012). The CALCOR process (Teuner, 1985, Kurz and Teuner, 1990, Teuner et al., 2001) involves dry reforming of methane, optimized to reduce the hydrogen content of the product gas. Furthermore, hydrogen separation by membrane permeators produces hydrogen gas that combusts a fuel (e.g., methane) producing pure carbon monoxide. The SPARG process (promotion by poisoning) is also a dry reforming reaction process (Gunardson, 1998; O'Connor and Ross, 1998; Rostrup-Nielsen, 2006). The active catalytic sites are blocked by poisoning the feed gas with hydrogen sulfide (H2S). The adsorption of sulfur at the catalytic sites is favored over carbon growth. The SPARG process is claimed to produce high CO content SYNGAS.

Also combined CO2 and steam reforming systems have been operational in the industry for a number of years (Gangadharan et al., 2012). By choosing the right proportions between CH4, water and CO2 (3/2/1), the combination of steam and dry reforming of methane can generate SYNGAS with a H2/CO ratio of 2, ideal, for example, for the synthesis of methanol (RostrupNielsen and Christiansen, 2011; Olah et al., 2009). This combination of steam and dry reforming was named bi-reforming.

Bi-reforming could be advantageous in the use of various natural gas sources even these containing substantial amounts of CO2. Some natural gas as well as biogas sources contain CO2 concentration up to 50−70%. Bi-reforming can also be used to recycle CO2 emissions from sources such as flue gases from fossil fuel (coal, petroleum, natural gas, etc.), burning power plants, exhaust of cement factories, among other industries (Olah et al., 2013). This reaction can be represented as

$$\text{Steam reforming:}\,\text{CH}\_4 + \text{H}\_2\text{O} \xrightarrow{\text{-}} \text{CO} + 3\text{H}\_2 \quad \Delta H = +206.3 \frac{\text{kJ}}{mol\text{CO}\_2} \tag{16}$$

$$\text{Dry reforming: } \text{CH}\_4 + \text{CO}\_2 \xrightarrow{\quad} 2\text{CO} + 2\text{H}\_2 \quad \Delta H = +247.3 \frac{\text{kJ}}{\text{mol}\text{CO}\_2} \tag{17}$$

$$\text{Bi-reforming: }\text{3CH}\_4 + 2\text{H}\_2\text{O} + \text{CO}\_2 \longrightarrow 4\text{CO} + 8\text{H}\_2\tag{18}$$

Bi-reforming is adaptable for reforming varied natural gas (containing hydrocarbon homo‐ logues) and CO2 sources, e.g., shale gas (Olah et al., 2013):

$$\text{CH}\_{\text{n}}\text{H}\_{\text{(2n+2)}} + \text{(3n-1)}\text{H}\_{2}\text{O} + \text{CO}\_{2} \longrightarrow \text{(3n+1)}\text{CO} + \text{(6n+2)}\text{H}\_{2}\tag{19}$$

Numerous authors (Ashcroft et al., 1991; O´Connor and Ross, 1998; Wang et al, 200;, Jarungth‐ ammachote, 2011) have studied a similar idea, which combines dry reforming with partial oxidation. The idea again being that the combination helps overcome the endothermic requirement of dry reforming with the exothermic nature of partial oxidation, resulting in lower total energy consumption. In addition, it allows altering the H2/CO ratio by controlling the extent to which each reaction takes place (Hartley and Tam, 2012). The combination of exothermic and endothermic reactions is called autothermal reaction (ATR). The ATR tech‐ nology requires addition of CO2 or CO2-rich gas, in order to adjust the SYNGAS composition to the desired H2/CO ratio.

$$\text{Dry reforming: } \text{CH}\_4 + \text{CO}\_2 \longrightarrow 2\text{CO} + 2\text{H}\_2 \quad \Delta H = +247.3 \frac{\text{kJ}}{\text{mol}} \tag{20}$$

$$\text{PartialOxidation of Methane:}\,\text{CH}\_4 + 0.5\text{O}\_2 \xrightarrow{\text{-}} \text{CO} + 2\text{H}\_2\text{ }\Delta H = -35.6 \frac{\text{kJ}}{mol} \tag{21}$$

The combined Dry Reforming and Partial Oxidation is hence:

Nielsen and Christiansen, 2011; Olah et al., 2009). This combination of steam and dry reforming

Bi-reforming could be advantageous in the use of various natural gas sources even these containing substantial amounts of CO2. Some natural gas as well as biogas sources contain CO2 concentration up to 50−70%. Bi-reforming can also be used to recycle CO2 emissions from sources such as flue gases from fossil fuel (coal, petroleum, natural gas, etc.), burning power plants, exhaust of cement factories, among other industries (Olah et al., 2013). This reaction

4 2 2

4 2 2

Bi-reforming is adaptable for reforming varied natural gas (containing hydrocarbon homo‐

Numerous authors (Ashcroft et al., 1991; O´Connor and Ross, 1998; Wang et al, 200;, Jarungth‐ ammachote, 2011) have studied a similar idea, which combines dry reforming with partial oxidation. The idea again being that the combination helps overcome the endothermic requirement of dry reforming with the exothermic nature of partial oxidation, resulting in lower total energy consumption. In addition, it allows altering the H2/CO ratio by controlling the extent to which each reaction takes place (Hartley and Tam, 2012). The combination of exothermic and endothermic reactions is called autothermal reaction (ATR). The ATR tech‐ nology requires addition of CO2 or CO2-rich gas, in order to adjust the SYNGAS composition

Dry reforming: CH + CO 2CO + 2H 247.3 4 2 <sup>2</sup>

4 2 <sup>2</sup> Partial Oxidation of Methane: CH + 0.5O CO + 2H 35.6 *kJ <sup>H</sup>*

Dry reforming: CH + CO 2CO + 2H 247.3 *kJ <sup>H</sup>*

logues) and CO2 sources, e.g., shale gas (Olah et al., 2013):

Steam reforming: CH + H O CO + 3H 206.3 *kJ <sup>H</sup>*

2

2

*molCO*

¾¾® =+D (16)

*molCO* ¾¾ <sup>D</sup> =+® (17)

*kJ <sup>H</sup>*

¾¾® =+D (20)

*mol*

¾¾® D =- (21)

*mol*

42 2 <sup>2</sup> Bi-reforming: 3CH + 2H O + CO 4CO + 8H ¾¾® (18)

( ) ( ) ( ) n 2n+( )2 <sup>2</sup> <sup>2</sup> <sup>2</sup> 3C H + 3n-1 H O + CO 3n+1 CO + 6n+2 H ¾¾® (19)

was named bi-reforming.

66 CO2 Sequestration and Valorization

can be represented as

to the desired H2/CO ratio.

$$\text{CH}\_4 + 0.5\text{CO}\_2 + 0.25\text{O}\_2 \xrightarrow{\text{-}} 1.5\text{CO} + 2\text{H}\_2\text{ }\Delta H = +211.7\frac{kJ}{mol} \tag{22}$$

Integrating steam reforming and partial oxidation with CO2 reforming could reduce or eliminate carbon formation on reforming catalyst, thus increasing catalyst life and process efficiency. Therefore, the tri-reforming, a synergetic combination of CO2 reforming, steam reforming, and partial oxidation of methane in a single reactor for effective production of industrially useful SYNGAS (Song, 2006) could solve two important problems encountered in individual processing. Incorporating oxygen in the reaction generates heat *in situ* that could increase energy efficiency; oxygen also reduces or eliminates carbon formation on the reform‐ ing catalyst. The tri-reforming can be achieved with natural gas and flue gases using the waste heat in power plants and the heat generated *in situ* from oxidation with the oxygen that is present in flue gas (Zhou et al., 2008; Zangouei et al., 2010; Moon et al., 2004).

The tri-reforming process is presented in Eqs. (23) to (26) (Song and Pan, 2004):

$$\text{Steam reforming: } \text{CH}\_4 + \text{H}\_2\text{O} \xrightarrow{\text{-}} \text{CO} + 3\text{H}\_2 \quad \Delta H = +206.3 \frac{\text{kJ}}{mol} \tag{23}$$

$$\text{Dry reforming: } \text{CH}\_4 + \text{CO}\_2 \longrightarrow 2\text{CO} + 2\text{H}\_2 \text{ } \Delta H = +247.3 \frac{\text{kJ}}{mol} \tag{24}$$

Partial Oxidation of Methane:

$$\text{CH}\_4 + 0.5\text{O}\_2 \xrightarrow{\text{-} \longrightarrow \text{CO}\_2 + 2\text{H}\_2} \text{Al} = -35.6 \frac{\text{kJ}}{mol} \tag{25}$$

$$\text{CH}\_4 + 0.5\text{O}\_2 \xrightarrow{-} \text{CO} + 2\text{H}\_2\text{O} \quad \Delta H = -880 \frac{kJ}{mol} \tag{26}$$

Song (2006) reports experimental and computational results to support that tri-reforming produces SYNGAS with desired H2/CO ratios (1.5–2.0) and eliminates carbon formation in the CO2 reforming of CH4. Song (2006) suggests that tri-reforming is especially suited to using CO2 in concentrated sources without prior CO2 separation, as in non-conventional (low-quality CO2-rich) natural gas, and has been demonstrated in pilot scale in Korea.

In general, produced SYNGAS from methane reforming is converted catalytically *in situ* via one of two main routes. The first is to use Fischer-Tropsch synthesis, a process that catalytically converts SYNGAS to hydrocarbons of varying molecular weights. The second is methanol synthesis. The latter has better atomic economy, since the oxygen atom in CO is included in the product and CO2 can be blended into SYNGAS as a reactant. However, production of methanol is very inefficient in this reaction: only 10-15% one pass conversion typically at 5.0-10.0 MPa and 523-573 K, due to the severe thermodynamic limitations of this exothermal reaction (CO+2H2→CH3OH) (Shi et al., 2013).

Finally, CO2 reforming of methane can also be used as a chemical energy storage alternative and an energy transmission system (Richardson and Paripatyadar, 1990, Levitan et al., 1991; Levy et al., 1993). According to Zhang et al. (2003), in this system, solar energy is used to drive the endothermic forward reaction, and the energy thus stored can be transported via pipelines such as SYNGAS and liberated at will by the reverse reaction at any location or time. The highly endothermic reaction could be an option to store solar energy in hot regions (Zhang et al., 2013).

**Reverse Water Gas-Shift (RWGS):** The reverse water gas shift (RWGS) reaction has been known from over two centuries and is a well-researched and understood process for SYNGAS ratio alteration (Hartley and Tam, 2012). In fact, both the water gas shift (WGS) and the RWGS reactions are mostly used in combination with reforming of hydrocarbons to adjust the H2/CO ratio, as shown in Eq. (27) (Song, 2006). Depending on the reaction conditions, the equilibrium for the WGS can be pushed in either the forward or the reverse direction. Efforts to explain the RWGS reaction mechanism are reported (Goguet et al., 2006, Meunier et al., 2007, Wang et al., 2013), and two main mechanisms have been proposed: the *redox* mechanism and the *associative formate* mechanism. The reversibility of the WGSR is important in the production of ammonia, methanol, and Fischer-Tropsch synthesis where the ratio of H2/CO is critical. Many industrial companies exploit the RWGS reaction as a source of the synthetically valuable CO from cheap CO2. In fact, catalytic RWGS reaction is the main route to produce SYNGAS from CO2.

$$\text{CH}\_2 + \text{CO}\_2 \longleftrightarrow \text{CO} + \text{H}\_2\text{O} \quad \Delta H = +51 \frac{\text{kJ}}{\text{mol}} \tag{27}$$

RWGS provides a source of hydrogen at the expense of carbon monoxide, which is important for the production of high purity hydrogen. This is a mildly endothermic reaction, as shown in Eq. (27).

Although high temperature reactions are effective for obtaining a high conversion, WGS reaction is an equilibrium-limited reaction that exhibits decreasing conversion with increasing temperature. In order to take advantage of both the thermodynamics and kinetics of the reaction, industrial scale WGS reaction is conducted in multiple adiabatic stages consisting of a high temperature shift (HTS) followed by a low temperature shift (LTS) with intersystem cooling (Byron, 2010). The initial HTS takes advantage of the high reaction rates, but is thermodynamically limited, which results in incomplete conversion of carbon monoxide and a 2-4% carbon monoxide exit composition. To shift the equilibrium towards hydrogen production, a subsequent low temperature shift reactor is employed to produce a carbon monoxide exit composition of less than 1% (Byron, 2010). A catalyst is required under these conditions because of the lower reaction rate at low temperatures. The RWGS reaction uses a variety of catalysts, including palladium, platinum on titania, copper, cobalt with manganese/ zinc oxide and rhodium with ceria (Tanaka et al., 2003, Saito and Murata, 2004, Meunier et al., 2007). Many research groups are looking at copper as a catalyst due to its effectiveness and its relatively low cost (Armstrong et al., 2013). However, there has been renewed interest in the WGSR at extreme temperatures, because of recent advances in high-temperature materials for hydrogen separation membranes (Bustamante et al., 2002).

## **4.4. CO2 to methanol**

synthesis. The latter has better atomic economy, since the oxygen atom in CO is included in the product and CO2 can be blended into SYNGAS as a reactant. However, production of methanol is very inefficient in this reaction: only 10-15% one pass conversion typically at 5.0-10.0 MPa and 523-573 K, due to the severe thermodynamic limitations of this exothermal

Finally, CO2 reforming of methane can also be used as a chemical energy storage alternative and an energy transmission system (Richardson and Paripatyadar, 1990, Levitan et al., 1991; Levy et al., 1993). According to Zhang et al. (2003), in this system, solar energy is used to drive the endothermic forward reaction, and the energy thus stored can be transported via pipelines such as SYNGAS and liberated at will by the reverse reaction at any location or time. The highly endothermic reaction could be an option to store solar energy in hot regions (Zhang et al.,

**Reverse Water Gas-Shift (RWGS):** The reverse water gas shift (RWGS) reaction has been known from over two centuries and is a well-researched and understood process for SYNGAS ratio alteration (Hartley and Tam, 2012). In fact, both the water gas shift (WGS) and the RWGS reactions are mostly used in combination with reforming of hydrocarbons to adjust the H2/CO ratio, as shown in Eq. (27) (Song, 2006). Depending on the reaction conditions, the equilibrium for the WGS can be pushed in either the forward or the reverse direction. Efforts to explain the RWGS reaction mechanism are reported (Goguet et al., 2006, Meunier et al., 2007, Wang et al., 2013), and two main mechanisms have been proposed: the *redox* mechanism and the *associative formate* mechanism. The reversibility of the WGSR is important in the production of ammonia, methanol, and Fischer-Tropsch synthesis where the ratio of H2/CO is critical. Many industrial companies exploit the RWGS reaction as a source of the synthetically valuable CO from cheap CO2. In fact, catalytic RWGS reaction is the main route to produce SYNGAS from CO2.

H + CO CO + H O 51 2 2 <sup>2</sup>

RWGS provides a source of hydrogen at the expense of carbon monoxide, which is important for the production of high purity hydrogen. This is a mildly endothermic reaction, as shown

Although high temperature reactions are effective for obtaining a high conversion, WGS reaction is an equilibrium-limited reaction that exhibits decreasing conversion with increasing temperature. In order to take advantage of both the thermodynamics and kinetics of the reaction, industrial scale WGS reaction is conducted in multiple adiabatic stages consisting of a high temperature shift (HTS) followed by a low temperature shift (LTS) with intersystem cooling (Byron, 2010). The initial HTS takes advantage of the high reaction rates, but is thermodynamically limited, which results in incomplete conversion of carbon monoxide and a 2-4% carbon monoxide exit composition. To shift the equilibrium towards hydrogen production, a subsequent low temperature shift reactor is employed to produce a carbon monoxide exit composition of less than 1% (Byron, 2010). A catalyst is required under these

*kJ <sup>H</sup>*

*mol* ¾® <sup>D</sup> =+¬ (27)

reaction (CO+2H2→CH3OH) (Shi et al., 2013).

68 CO2 Sequestration and Valorization

2013).

in Eq. (27).

Methanol is one of the most important commodity chemicals as it is used as a raw material in several intermediate chemicals and end uses. Methanol is produced industrially from SYN‐ GAS from natural gas or coal mainly containing CO, H2 and a small amount of CO2 in presence of a catalyst. Nevertheless, direct CO2 hydrogenation has also been reported. Other nonconventional routes are electro- or photoprocesses, as well as the use of enzymes. The impor‐ tance of methanol synthesis is demonstrated by widespread scientific publications of various reaction routes (Razali et al. 2012), and the development of several pilot plants to use waste carbon dioxide for methanol production.

Among the new technologies, in terms of potential for application, the CO2 catalytic hydro‐ genation to methanol appears to have the highest degree of commercialization. It may be already commercially interesting when cheap sources of renewable H2 are available, or to store excess electrical energy, as an alternative to actual systems. It is estimated that this reaction could reach the industrial stage in less than five years. This development would be pushed by experience in pilot or pre-commercial industrial plants, such as the Mitsui Chemicals Inc.'s plant (pilot in Japan capable of producing 100 t of methanol per year, and large unit expected in Singapore) and a plant by Carbon Recycling International (installed at the end of 2010) (Quadrelli et al., 2011). Mitsui´s pilot plant uses CO2 from an ethylene production plant of Osaka Works Petrochemical Complex (ADEME, 2010). It synthesizes methanol by CO2 hydrogenation and the simultaneous water gas shift reactions. The process claims 96% selectivity (Hartley and Tam, 2009). Carbon Recycling International is capable of producing 3000 t/y of methanol (ADEME, 2010). This unit has a capacity of about 10 t of methanol from 18 t of CO2 (Carbon Recycling International, 2009; Van-Dal and Bouallou, 2013), with CO2 from the Svartsengi geothermal plant and an aluminum production plant. Hydrogen is generated from the electrolysis of water using a renewable source of electricity.

**Methanol from SYNGAS:** Synthesis gas composed of the proper ratio of hydrogen, carbon monoxide and carbon dioxide is converted to methanol. Alternatives paths to methanol are via CO from RWGS reacting with hydrogen according to Eq. (28) and via CO2 being hydro‐ genated following Eq. (29).

$$\text{CH} + 2\,\text{H}\_2 \xrightarrow{\text{---}} \text{CH}\_3\text{OH} \qquad \Delta H = -90.6 \frac{\text{kJ}}{mol} \tag{28}$$

$$\text{CO}\_2 + 3\,\text{H}\_2 \xrightarrow{\text{-} \,\text{CH}\_3\text{OH} + \text{H}\_2\text{O}} \text{H} \quad \Delta H = -49.5 \frac{\text{kJ}}{mol} \tag{29}$$

From Eq. (28), production of methanol involves SYNGAS production as intermediate stage. Hence, two steps are required for the manufacture of methanol: reduction to SYNGAS and reaction to form methanol. There are process variations for implementing the sequence. Before being sent to the methanol production unit, the SYNGAS must thus be subjected to the WGS reaction to enhance its hydrogen content. Alternatively, H2 from other sources can be added. Recent efforts have been aimed at production of methanol in a one-step process without intermediate formation of SYNGAS. Homogeneous or heterogeneous catalysts are typically preferable. The conventional process occurs at relatively low pressures (5 to 10 MPa) and 210 to 350 °C employing a Cu/ZnO/Al2O3 catalyst.

**Catalytic Hydrogenation Conversion of Carbon Dioxide to Methanol:** The most direct and studied route to methanol from CO2 is the catalytic conversion of CO2 with hydrogen. Carbon dioxide hydrogenation to methanol is a relatively mature process. The main issue is the cost (and associated carbon footprint) of the H2 necessary for the reaction. Any available energy source (alternative energies such as solar, wind, geothermal, and atomic energy) can be used for the production of needed hydrogen and chemical conversion of CO2. The process can use lower operational pressures of 3 MPa at 240 °C. This direct CO2 hydrogenation exhibits low conversions resulting in high volumes of recycled gas. Literature indicates that methanol is synthesized following a 3:1 hydrogen to carbon dioxide stoichiometry using catalysts of copper oxide, zinc oxide, incorporating either titania, aluminum oxide, chromium oxide and alterna‐ tively lanthanum or gallinium (Lachowska and Skrzypek, 2004, Lee et al., 2004, Stoczynski et al., 2004).

An alternative approach to the use of solid catalysts and a gas phase process is to employ the so called low-temperature methanol synthesis (LTMS) (Dixneuf, 2011). LTMS is based on the catalytic hydrogenation of methanol to formic acid (HCOOH) with subsequent etherification to methanol formate (alternative to methyl formate from SYNGAS), followed by hydrogena‐ tion of formate to two methanol molecules using Pincer-type ruthenium(II) catalyst (Balara‐ man et al, 2011; Dixneuf, 2011; Huff and Sanford, 2011). A liquid-phase allows CO2 and H2 conversion to methanol of about 95% with very high selectivity in a single pass (Olah, 2009). Waugh (2012) has published a review on catalytic methanol synthesis which includes the use of carbon dioxide as a feedstock.

**Photoreduction of CO2 to methanol:** Photoelectrochemical reduction of carbon dioxide or photocatalysis generally uses semiconductors to promote reaction in the presence of sun light. The semiconductor is used as a catalyst to absorb solar energy and generate electrons and protons needed for the reduction of carbon dioxide. While hydrogenation of carbon dioxide requires high temperature and high pressure conditions, photocatalysis carries out under relatively mild conditions with advantageous energy input – sun light – a continuous and readily available source (Le, 2009).

Considerable research effort has been made on CO2 activation by visible light photocatalysts due to the natural abundance of sunlight. Nevertheless, the efficient photoreduction of CO2 with H2O remains one of the most challenging tasks of environmental catalysis.

CO + 3 H CH OH + H O 49.5 22 3 2

to 350 °C employing a Cu/ZnO/Al2O3 catalyst.

70 CO2 Sequestration and Valorization

al., 2004).

of carbon dioxide as a feedstock.

readily available source (Le, 2009).

From Eq. (28), production of methanol involves SYNGAS production as intermediate stage. Hence, two steps are required for the manufacture of methanol: reduction to SYNGAS and reaction to form methanol. There are process variations for implementing the sequence. Before being sent to the methanol production unit, the SYNGAS must thus be subjected to the WGS reaction to enhance its hydrogen content. Alternatively, H2 from other sources can be added. Recent efforts have been aimed at production of methanol in a one-step process without intermediate formation of SYNGAS. Homogeneous or heterogeneous catalysts are typically preferable. The conventional process occurs at relatively low pressures (5 to 10 MPa) and 210

**Catalytic Hydrogenation Conversion of Carbon Dioxide to Methanol:** The most direct and studied route to methanol from CO2 is the catalytic conversion of CO2 with hydrogen. Carbon dioxide hydrogenation to methanol is a relatively mature process. The main issue is the cost (and associated carbon footprint) of the H2 necessary for the reaction. Any available energy source (alternative energies such as solar, wind, geothermal, and atomic energy) can be used for the production of needed hydrogen and chemical conversion of CO2. The process can use lower operational pressures of 3 MPa at 240 °C. This direct CO2 hydrogenation exhibits low conversions resulting in high volumes of recycled gas. Literature indicates that methanol is synthesized following a 3:1 hydrogen to carbon dioxide stoichiometry using catalysts of copper oxide, zinc oxide, incorporating either titania, aluminum oxide, chromium oxide and alterna‐ tively lanthanum or gallinium (Lachowska and Skrzypek, 2004, Lee et al., 2004, Stoczynski et

An alternative approach to the use of solid catalysts and a gas phase process is to employ the so called low-temperature methanol synthesis (LTMS) (Dixneuf, 2011). LTMS is based on the catalytic hydrogenation of methanol to formic acid (HCOOH) with subsequent etherification to methanol formate (alternative to methyl formate from SYNGAS), followed by hydrogena‐ tion of formate to two methanol molecules using Pincer-type ruthenium(II) catalyst (Balara‐ man et al, 2011; Dixneuf, 2011; Huff and Sanford, 2011). A liquid-phase allows CO2 and H2 conversion to methanol of about 95% with very high selectivity in a single pass (Olah, 2009). Waugh (2012) has published a review on catalytic methanol synthesis which includes the use

**Photoreduction of CO2 to methanol:** Photoelectrochemical reduction of carbon dioxide or photocatalysis generally uses semiconductors to promote reaction in the presence of sun light. The semiconductor is used as a catalyst to absorb solar energy and generate electrons and protons needed for the reduction of carbon dioxide. While hydrogenation of carbon dioxide requires high temperature and high pressure conditions, photocatalysis carries out under relatively mild conditions with advantageous energy input – sun light – a continuous and

*kJ <sup>H</sup> mol* ¾® <sup>D</sup> <sup>=</sup> -¾ (29)

> CO2 can be reduced in water vapor or solvent by photocatalysts such as TiO2 and ZnS. Eq. (30) describes the overall reaction.

$$\text{CH}\_2 + 2\text{H}\_2\text{O} \xrightarrow{h\nu} \text{CH}\_3\text{OH} + 3\%\text{O}\_2\tag{30}$$

Due to the high energy requirements, this method is often combined with electrochemical methods via photoelectrocatalysis to drive the reaction (Hu et al., 2013). The catalysts tradi‐ tionally used are transition metal complexes, TiO2, ZnO, CdS, and functionalized metal surfaces (Yamashita et al., 1998; Kuwabata et al., 1994). A wide variety of CO2 photoreduction has been achieved on the surface of TiO2 under UV irradiation. The yield of photoproducts can be changed substantially under different experimental conditions such as UV wavelength, UV intensity, additives of reaction media and reactor configuration. Other variables, such as CO2 pressure, moisture and residence time are also important in photoreducing CO2 (Wu & Lin, 2005).

**Electrochemical Production of Methanol from CO2 and H2O:** The direct reduction of CO2 to CH3OH is known to occur at several types of electrocatalysts including oxidized Cu electrodes. The current stage of the technology is still very experimental. The majority of tests have been performed on a laboratory scale with a purpose of either kinetic analysis or proof-of-concept to examine product distribution for different material and condition combinations (Beck et al., 2010). An advantage of electrochemical CO2 reduction is that unlike many other hydrocarbon processes it can occur at ambient conditions.

The electrochemical reduction of carbon dioxide to methanol is thermodynamically possible, but there seems to be no well-established technique to achieve this reaction with high current efficiencies close to l00%. Nevertheless, methanol production has been reported with the use of ruthenium: gallium arsenide and RuO2-Ti02 mixed cathodes (Le, 2009). Cole and Bocarsly (2010) have reviewed electrochemical reduction processes, including electrochemical CO2 conversion to methanol. Few studies have investigated the feasibility of this technology, and none has been found to provide an in-depth analysis of its potential industrial implementation.

**Applications of Methanol:** Methanol has traditionally been used as feed for production of a range of chemicals including acetic acid, formaldehyde and MTBE (Olah, 2009). In recent years, methanol has also been used for other markets such as production of Di-methyl-ether (DME) and olefins by the so-called methanol to olefins process (MTO) or as blendstock for motor fuels. As a liquid fuel, methanol is of interest especially for use in fuel cells (Olah, 2009).

Methanol to Olefins (MTO) explores alternative pathways to produce small olefins, in particular ethylene and propylene. Conventional steam cracker feeds are either natural gas liquids (NGL) or heavy liquids (i.e., naphtha). Ethane cracking, however, is increasing its share as feedstock. A promising alternative route is dehydration of methanol (MTO). Methanol-toolefins (MTO) was first developed by ExxonMobil (1980s) as part of its methanol-to-gasoline (MTG) process. In the 1990s, UOP and Norsk Hydro built an MTO pilot plant in Norway. Since then, Lurgi has developed its own version of this process, methanol-to-propylene (MTP).

#### **4.5. CO2 to DMC**

Dimethyl carbonate (DMC) is a biodegradable and nontoxic chemical acceptable environmen‐ tally as a chemical destination of CO2. It is exempted from VOC classification and can be used as raw material for producing valuable chemicals, including aromatic polycarbonate, and qualifies as an octane booster component in gasoline and diesel. It is a safer and nontoxic substitute of well-established methylating-carbonylating hazardous chemicals like dimethyl sulfate and phosgene (Souza et al., 2013). Although DMC is presently produced on a relatively small scale, approximately 400 kt/y, its demand has grown strongly in recent times because of its green properties.

Currently, DMC is produced mainly by oxidative carbonylation of methanol (Aoussi et al., 2010). The direct methylation reaction is possible where, according to Ferreira et al. (2013), the most used catalyst is tin, employed as an oxide compound or as an organometallic complex, according to Eq. (31):

However, direct methylation presents low yields, inferior to 10%, due to the chemical inert‐ ness of CO2 and to the deactivation of catalysts induced by water formation in the reaction (Aouissi, 2010). For large-scale production of DMC from CO2, one route seems to be promis‐ ing: the indirect route (IR) for two-step conversion of CO2 with ethylene oxide (EO) to ethyl‐ ene carbonate (EC), which then reacts with excess methanol (MeOH) giving DMC and ethylene glycol (EG) as shown in Eqs. (32) and (33).

Souza et al. (2013) evaluated IR's performance from technical, economical and environmen‐ tal standpoints. Accordingly, the authors proposed a process flowsheet with two serial reac‐ tors' system followed by an integrated separation section, with extractive distillation using methyl-iso-butyl ketone (IR-MIBK) and ethylene glycol (IR-EG). For environmental per‐ formance assessment, Souza et al. (2013) defined Chemical Sequestration of CO2 (CSC) as the amount of CO2 consumed by chemical reaction minus the amount of CO2 emitted by heat, power and purges. Considering a production of 1.3x105 t/y of DMC produced, the au‐ thors reported CSC values for IR−MIBK and IR−EG of -15.9kt/y and -8.1kt/y, respectively. The negative values of CSC indicate that both alternatives are net emitters and illustrates that fixation of CO2 in chemical products does not necessarily imply into net CO2 reduction, and that the main aspect in CO2 utilization as feedstock is of substituting fossil carbon source for a renewable alternative. Furthermore, Souza et al. (2013) estimated net present values for IR−MIBK and IR−EG of \$71.5x106 and \$106.5x106 , respectively, and payback times of 5.5 and 4.5, respectively, concluding for economic feasibility of DMC production. It is worth noting that DMC production from CO2 is already in use at Asahi Chemical Industry.

#### **4.6. Biochemical conversion of CO2**

(31)

(32)

olefins (MTO) was first developed by ExxonMobil (1980s) as part of its methanol-to-gasoline (MTG) process. In the 1990s, UOP and Norsk Hydro built an MTO pilot plant in Norway. Since then, Lurgi has developed its own version of this process, methanol-to-propylene (MTP).

Dimethyl carbonate (DMC) is a biodegradable and nontoxic chemical acceptable environmen‐ tally as a chemical destination of CO2. It is exempted from VOC classification and can be used as raw material for producing valuable chemicals, including aromatic polycarbonate, and qualifies as an octane booster component in gasoline and diesel. It is a safer and nontoxic substitute of well-established methylating-carbonylating hazardous chemicals like dimethyl sulfate and phosgene (Souza et al., 2013). Although DMC is presently produced on a relatively small scale, approximately 400 kt/y, its demand has grown strongly in recent times because of

Currently, DMC is produced mainly by oxidative carbonylation of methanol (Aoussi et al., 2010). The direct methylation reaction is possible where, according to Ferreira et al. (2013), the most used catalyst is tin, employed as an oxide compound or as an organometallic complex,

However, direct methylation presents low yields, inferior to 10%, due to the chemical inert‐ ness of CO2 and to the deactivation of catalysts induced by water formation in the reaction (Aouissi, 2010). For large-scale production of DMC from CO2, one route seems to be promis‐ ing: the indirect route (IR) for two-step conversion of CO2 with ethylene oxide (EO) to ethyl‐ ene carbonate (EC), which then reacts with excess methanol (MeOH) giving DMC and

ethylene glycol (EG) as shown in Eqs. (32) and (33).

**4.5. CO2 to DMC**

72 CO2 Sequestration and Valorization

its green properties.

according to Eq. (31):

Biofixation of CO2 with microalgae is a promising route of utilization of CO2, as it exhibits fast growth (e.g., Picardo et al., 2013a) and produces numerous high-added-value bioproducts (Grima et al., 2003). Additionally, they do not contain lignin, a fact that renders microalgae better adapted to biochemical valorization. As advantages of CO2 bioconversion with relation to energy crops, microalgae grow in variable climates on non-arable land with non-potable water, releasing competition with food crops, and are able to use direct flue gases as their carbon source (Fernández et al., 2012). Alternatively, to inject directly flue gases into microal‐ gae cultures, adequate design and operation of the carbonation culture system unit are also necessary, otherwise almost all of the CO2 fed to the culture would be released into the atmosphere. In this aspect, photobioreactors are more appropriated arrangements. Further‐ more, photobioreactors provide cell requirements such as light, temperature, pH, and mixing (Fernández et al, 2012).

Chisti (2007) concluded that microalgae are the only alternative for the sustainable production of biodiesel. Accordingly, the fact that microalgae biomass is rich in lipids and are, hence, high energy density feedstock for fuels and chemicals is of relevance. Picardo et al (2013a) proposed a screening procedure for microalgae selection to meet production objectives such as SYNGAS for the production of synthetic fuels, since the biomass, or the residual biomass obtained after extraction of bioproducts, can be gasified to yield SYNGAS. An attractive alternative in this route is to employ CO2 as oxidation agent (Butterman and Castaldi, 2007). Butterman and Castaldi (2007) report that the injection of CO2 and H2O in gasification increases char reactivity that results in more efficient use of the feedstock with less residual to be post-processed.

According to Grima et al. (2003), production of microalgal biomass can be carried out in fully contained photobioreactors or in open ponds and channels. Biomass productivity depends on species, operational conditions and the choice of ponds (~20g/m2 .d) or photobioreactor (~50g/ m2 .d) geometry. Open-culture systems are almost always located outdoors and rely on natural light for illumination while closed photobioreactors may be located indoors or outdoors, although outdoor location is more common. Grima et al. (2003) list as biomass harvesting operations centrifugation, filtration or gravity sedimentation, which may be preceded by a flocculation step.

Microalgae contain lipids and fatty acids as membrane components, storage products, metabolites and sources of energy. Microalgae have been found to contain proportionally high levels of lipids (for some species this value can reach 50% oil by weight), with a convenient fatty acids profile and an unsaponifiable fraction allowing a biodiesel production with high oxidation stability (Grima et al., 2013). Lipid accumulation is promoted by stress, notably by nitrogen starvation (Picardo et al., 2013b).

Elemental analysis of carbon content of biomass points to ~50% (Picardo et al., 2013a), what leads to conclude that approximately 2t of CO2 can be converted into 1t of biomass, potentially amenable to 0.2t of lipids. Its massive extension to the energy sectors constitutes a vast potential for large-volume CO2 utilization. Fernandez et al. (2012) recognize that microalgae are not a storage strategy because the biomass produced cannot be stored for a long time. Its contribu‐ tion to reducing CO2 emissions is only possible if biofuels are produced to replace the fossil fuels use, and allowing the production of other commodities, or by-products from flue gases, which allows one to obtain revenues to mitigate the penalty of carbon capture. To illustrate the potential industrial application of microalgae, Figure 19 shows a schematic of microalgae bioconversion of CO2 and its downstream processing in a biorefinery arrangement producing long-chain fatty acids (PUFA´s, MUFA´s and PUFA´s), biodiesel, green diesel, gasoline, biogas, urea, N2 and propylene carbonate.

Monteiro et al. (2010) employed Pareto optimization of what was named an "industrial ecosystem" comprised of a biorefinery of microalgal biomass aiming at maximizing sustain‐ ability of the productive arrangement. The authors concluded that increasing the weight of environmental objectives against economic performance might make sectors of the proposed original superstructure of amenable processes unattractive. Therefore, the final structure of a biorefinery of microalgae depends on the priorities set for the productive complex.

### **4.7. Some pilot and commercial scale CO2 utilization processes**

**Polycarbonate:** Polycarbonate (PC) is a plastic with impact resistance and heat resistance, mainly produced (4t/y) by reacting CO and Cl2 to form phosgene as an intermediate material. The phosgene process has a number of disadvantages, including the risk of environmental

extraction of bioproducts, can be gasified to yield SYNGAS. An attractive alternative in this route is to employ CO2 as oxidation agent (Butterman and Castaldi, 2007). Butterman and Castaldi (2007) report that the injection of CO2 and H2O in gasification increases char reactivity that results in more efficient use of the feedstock with less residual to be post-processed.

According to Grima et al. (2003), production of microalgal biomass can be carried out in fully contained photobioreactors or in open ponds and channels. Biomass productivity depends on

.d) geometry. Open-culture systems are almost always located outdoors and rely on natural light for illumination while closed photobioreactors may be located indoors or outdoors, although outdoor location is more common. Grima et al. (2003) list as biomass harvesting operations centrifugation, filtration or gravity sedimentation, which may be preceded by a

Microalgae contain lipids and fatty acids as membrane components, storage products, metabolites and sources of energy. Microalgae have been found to contain proportionally high levels of lipids (for some species this value can reach 50% oil by weight), with a convenient fatty acids profile and an unsaponifiable fraction allowing a biodiesel production with high oxidation stability (Grima et al., 2013). Lipid accumulation is promoted by stress, notably by

Elemental analysis of carbon content of biomass points to ~50% (Picardo et al., 2013a), what leads to conclude that approximately 2t of CO2 can be converted into 1t of biomass, potentially amenable to 0.2t of lipids. Its massive extension to the energy sectors constitutes a vast potential for large-volume CO2 utilization. Fernandez et al. (2012) recognize that microalgae are not a storage strategy because the biomass produced cannot be stored for a long time. Its contribu‐ tion to reducing CO2 emissions is only possible if biofuels are produced to replace the fossil fuels use, and allowing the production of other commodities, or by-products from flue gases, which allows one to obtain revenues to mitigate the penalty of carbon capture. To illustrate the potential industrial application of microalgae, Figure 19 shows a schematic of microalgae bioconversion of CO2 and its downstream processing in a biorefinery arrangement producing long-chain fatty acids (PUFA´s, MUFA´s and PUFA´s), biodiesel, green diesel, gasoline, biogas,

Monteiro et al. (2010) employed Pareto optimization of what was named an "industrial ecosystem" comprised of a biorefinery of microalgal biomass aiming at maximizing sustain‐ ability of the productive arrangement. The authors concluded that increasing the weight of environmental objectives against economic performance might make sectors of the proposed original superstructure of amenable processes unattractive. Therefore, the final structure of a

**Polycarbonate:** Polycarbonate (PC) is a plastic with impact resistance and heat resistance, mainly produced (4t/y) by reacting CO and Cl2 to form phosgene as an intermediate material. The phosgene process has a number of disadvantages, including the risk of environmental

biorefinery of microalgae depends on the priorities set for the productive complex.

**4.7. Some pilot and commercial scale CO2 utilization processes**

.d) or photobioreactor (~50g/

species, operational conditions and the choice of ponds (~20g/m2

m2

flocculation step.

74 CO2 Sequestration and Valorization

nitrogen starvation (Picardo et al., 2013b).

urea, N2 and propylene carbonate.

**Figure 19.** Bioconversion of CO2 integrated into a biorefinery arrangement. Main external inputs are marked in red.

harm (Ushikubo, 2013). Asahi Kasei Corporation succeeded in commercializing the first nonphosgene polycarbonate production using ethylene oxide and CO2, a by-product of ethylene oxide synthesis. The Asahi Kasei Process has as co-products high-purity monoethylene glycol (MEG). The process employs reactive distillation in the monomer production and gravityutilized, non-agitation polymerization reactor in the melt polymerization. The monomer process consists of 3 production steps, ethylene carbonate (EC) from CO2 and EO, dimethyl carbonate (DMC) and MEG from EC and MeOH, and diphenyl carbonate (DPC) and MeOH from DMC and phenol (PhOH). All intermediates are recycled. The by-produced PhOH is recycled to the monomer process (Sinshuke et al., 2010). According to Ushikubo (2013), the new process reduces CO2 emissions by 0.173kg per one kg of polycarbonate. Five commercial plants using the Asahi Kasei process are operating in Taiwan (150,000 t/y), Korea (2 plants of 65,000 t/y), Russia (65,000 t/y) and Saudi Arabia (260,000 t/y) (Shinsuke et al., 2010).

**Monoethylene glycol (MEG):** MEG is used as an antifreeze and as a raw material for the production of polyester fibers and resins, mainly PET (Ushikubo, 2013). An expanding market share is foreseen in natural gas industry, where MEG is added into the pipeline or the gas conditioning process, either as hydrate inhibitors or for dehydration purposes to protect downstream pipelines. The pipeline can extend for thousands of kilometers and MEG is injected to inhibit hydrate formation, avoiding plugging (*e.g.,* Statoil´s Snøhvit field, Pettersen, 2011). Concerning the production process, a new technology was developed by Mitsubishi Chemical Corporation with 99% selectivity while the conventional (non-catalytic) process has selectivity around 89% (Kawabe, 2010). The conventional technology produces as co-product DEG and TEG (di- and triethylene glycols, whose demand is expanding at only 2-3% as opposed to MEG expansion (world demand amounts to 17t/y). Mitsubishi technology uses a two-step catalytic synthesis: production of ethylene carbonate (EC) as intermediate followed by EC hydrolysis under almost stoichiometric condition, while the conventional hydrolysis occurs at a higher H2O/ethylene oxide molar ratio, according to Eqs. (34) and (35).

$$\text{EO} + \text{CO}\_2 \longrightarrow \text{EC} \text{ (carbonation)}\tag{34}$$

$$\text{HO} + \text{H}\_2\text{O} \xrightarrow{\text{H}\_2\text{O}} \text{EG} + \text{CO}\_2 \text{(hydrolysis)}\tag{35}$$

The product purification is simpler (water removal and MEG purification distillation columns, while the conventional process has 4 distillation columns: water, MEG, DEG and TEG columns). Furthermore, CO2 remains in closed loop. Ushikubo (2013) reports that several commercial plants operating with the new (and greener) technology save resources and energy and reduces the amount of wastewater and CO2 production. It is worth noting that MEG is a co-product of the production of DMC in a process where Eq. (4.24) is replaced by Eq. (40).

**Polyurethane:**Bayer (2013) targets the production of polyurethane, via the utilization of CO2 as feedstock. In this route, CO2 is converted to polyols (HO-R-OH) which reacts with isocyanate to yield polyurethane. The conversion of CO2 starts with its reaction with an epoxide, (propy‐ lene oxide) of higher energy content, in a catalytic route. Polyol with 30%CO2 had 2.64 kg of equivalent CO2 emissions. A maximum theoretical value of 43% of CO2 can be incorporated in the polyol (Bayer, 2010). The development started in 1669, ended its laboratory scale in 2009, and is moving to industrial implementation of the named "Dream Production" with a pilot plant in Leverkusen to produce polyol, for testing purposes. In early 2013, the new method was successfully converted from the production of discrete quantities to continuous produc‐ tion, a key intermediate step for the industrial-scale production of CO2-based polyurethane, which Bayer is targeting for 2015.

## **4.8. Emerging CO2 utilization processes**

The fixation of CO2 into chemicals and polymers will not substantially contribute to a reduction in antropogenic GHG emissions given the current energy demand. Nevertheless, using CO2 as a feedstock meets the requirements of sustainable development. An insight into advanced process concepts focus on chemical sequestration of CO2 creating manufactured products from captured CO2 with large potential markets. Integration of capture technologies into energy production schemes or oil and gas refining installations is the idea behind cPSE approach, namely, to abate chemical emissions while producing industrial products.

**Formic acid:** According to Armstrong et al. (2013), the amount of energy required to utilize carbon dioxide as a feedstock largely depends on the oxidation state of the intended products. The next-highest oxidation state molecules from CO2 are formic acid (HCOOH) and carbon monoxide (CO). So, carbon dioxide utilization to manufacture formic and other carboxylic acids is a relatively low-energy transformation. Formic acid has numerous applications, including food technology, agriculture, and the leather and rubber industries. Moreover, it has recently been considered as a promising candidate material for hydrogen storage and it is an important chemical with numerous applications. Moreover, formic acid has limited uses for further conversion, except reduction to methanol. The industrial methods used for its pro‐ duction employ CO as a raw material. Maihom et al. (2013) concluded that a first step occurs where CO2 is hydrogenated to a formate intermediate. In the second step, the formate is further hydrogenated into formic acid. The hydrogenation of CO2 would complete the chemical loop for hydrogen storage using CO2. The complementary step is the catalyzed decomposition of formic acid to pure H2 and reusable CO2.

selectivity around 89% (Kawabe, 2010). The conventional technology produces as co-product DEG and TEG (di- and triethylene glycols, whose demand is expanding at only 2-3% as opposed to MEG expansion (world demand amounts to 17t/y). Mitsubishi technology uses a two-step catalytic synthesis: production of ethylene carbonate (EC) as intermediate followed by EC hydrolysis under almost stoichiometric condition, while the conventional hydrolysis

The product purification is simpler (water removal and MEG purification distillation columns, while the conventional process has 4 distillation columns: water, MEG, DEG and TEG columns). Furthermore, CO2 remains in closed loop. Ushikubo (2013) reports that several commercial plants operating with the new (and greener) technology save resources and energy and reduces the amount of wastewater and CO2 production. It is worth noting that MEG is a co-product of the production of DMC in a process where Eq. (4.24) is replaced by Eq. (40).

**Polyurethane:**Bayer (2013) targets the production of polyurethane, via the utilization of CO2 as feedstock. In this route, CO2 is converted to polyols (HO-R-OH) which reacts with isocyanate to yield polyurethane. The conversion of CO2 starts with its reaction with an epoxide, (propy‐ lene oxide) of higher energy content, in a catalytic route. Polyol with 30%CO2 had 2.64 kg of equivalent CO2 emissions. A maximum theoretical value of 43% of CO2 can be incorporated in the polyol (Bayer, 2010). The development started in 1669, ended its laboratory scale in 2009, and is moving to industrial implementation of the named "Dream Production" with a pilot plant in Leverkusen to produce polyol, for testing purposes. In early 2013, the new method was successfully converted from the production of discrete quantities to continuous produc‐ tion, a key intermediate step for the industrial-scale production of CO2-based polyurethane,

The fixation of CO2 into chemicals and polymers will not substantially contribute to a reduction in antropogenic GHG emissions given the current energy demand. Nevertheless, using CO2 as a feedstock meets the requirements of sustainable development. An insight into advanced process concepts focus on chemical sequestration of CO2 creating manufactured products from captured CO2 with large potential markets. Integration of capture technologies into energy production schemes or oil and gas refining installations is the idea behind cPSE approach,

**Formic acid:** According to Armstrong et al. (2013), the amount of energy required to utilize carbon dioxide as a feedstock largely depends on the oxidation state of the intended products. The next-highest oxidation state molecules from CO2 are formic acid (HCOOH) and carbon

namely, to abate chemical emissions while producing industrial products.

which Bayer is targeting for 2015.

76 CO2 Sequestration and Valorization

**4.8. Emerging CO2 utilization processes**

( ) <sup>2</sup> EO + CO EC carbonation ¾¾® (34)

( ) 2 2 EO + H O EG + CO hydrolysis ¾¾® (35)

occurs at a higher H2O/ethylene oxide molar ratio, according to Eqs. (34) and (35).

**Carbon dioxide-based copolymers:** The synthesis of organic carbonates has been one of the most widely studied areas of CDU. Typically, CO2 is inserted into a molecule without the loss of any atoms in either the co-reactant or the gas itself (Armstrong, 2013). Carbonates are formed by the insertion of a CO2 molecule into a guest co-reactant, typically an epoxide. Poly(propy‐ lene carbonate) (PPC), an alternating copolymer of CO2 and propylene oxide, is one of the emerging low-cost biodegradable plastics. The fast development in catalyst design and performance improvement for PPC has created new chances for the chemical industry. In particular, high molecular weight PPC from rare earth ternary catalyst is becoming an economically viable biodegradable plastic with tens of thousands of tons produced per year, providing a new solution to overcoming the problem of high cost in biodegradable plastics (Qin and Wang, 2010). According to Qin and Wang (2010), with the continuous improvement in catalyst systems, commercialization of CO2 copolymer is possible. The authors report industrial activities by Empower Materials producing polypropylene carbonate (QPAC®40), polyethylene carbonate (QPAC®25), polybutylene carbonate (QPAC®60), and polycyclohex‐ ene oxide(QPAC®130) on a pilot scale.

**Electrochemical Reduction of CO2.**Delacourt (2010) studied the electrochemical conversion of CO2 into SYNGAS. The driver of the proposed route is that renewable energies (e.g., solar and wind) are only alternatives to fossil fuel as they are not available on demand, thus requiring storage. Delacourt (2010) lists as a storage opportunity the conversion to liquid fuels (e.g., methanol), in which SYNGAS is the required intermediate, by converting solar energy into electricity through photovoltaic arrays, and then by using this electricity to produce fuels by electrolysis. Evolved H2 reacts with CO2 in a water-gas-shift reactor to make CO (and H2O). The resulting SYNGAS is converted to methanol. Delacourt (2010) decided for a low-temper‐ ature technology (room temperature) although reported that high-temperature electrolysis (800 to 900o C) could be an attractive alternative. Because of the relatively low solubility of CO2 in water under ambient conditions, gas-diffusion electrodes were applied to operate at higher current densities, and ion-exchange membrane was used as the electrolytic medium to limit gas crossover resulting in a decrease of the current efficiency of the electrochemical cell. Catalysts capable of reduction of CO2 to CO at low overpotentials were selected.

**Light-Driven Technologies.** The rubisco enzyme is probably the most abundant enzyme of the biosphere. The fixation of CO2 and its transfer to organic substrates in the Calvin cycle leads by way of starch to an annual production of 1011 t of biomass (Walther et al., 1999). With the development of catalysts able to reproduce the key steps of photosynthesis, water and sunlight would ultimately be the only needed sources for clean energy production. Light driven technologies under development include (a) photoelectrochemical cells where CO2 present in a moistened gas stream is converted into organic molecules based on the photoox‐ idation of water into oxygen gas O2, protons H+, and electrons. The conversion of CO2 occurs at the photocathode and involves the generated protons, electrons and the "fuel" CO2 (Kayaert et al., 2013); (b) direct water oxidation - photocatalytic water splitting - to produce H2 and O2 over a metal-oxide-based photocatalyst using solar energy (Maeda and Domen, 2013); (c) hydrogen-producing systems consisting of a hydrogen-evolving catalyst linked to a photo‐ sensitizer (Badura et al., 2012). Although promising alternatives, biomimetic CO2 conversions are still in its early stage of technological development.

**CO2 Mineralization for Environmental Remediation:** Lim et al. (2013) reviewed the applica‐ tion of carbonation to solidify or stabilize solid combustion residues from municipal solid wastes, paper mill wastes, etc. and contaminated soils, and to manufacture precipitated calcium carbonate. For instance, the red mud - a highly alkaline waste of Bayer's process - can be treated by absorption of CO2. Machado (2012) analyzed the process of red mud carbonation with the exhausted gases from the alumina production calcinators, by developing a dynamic model representative of the mass and energy balances involved in the process, and chemical reactions occurring in the mud under carbonation. Machado (2012) was able to predict the species behavior, as well as the decrease in mud pH and the rebound phenomenon observed when the CO2 concentration is reduced. The transient profile of the main process responses indicated a substantial reduction of CO2 concentration in the output gas, in consequence of tons of CO2 captured, and a significant reduction in mud pH. Concerning other environmental applications, Lim et al. (2013) report that carbonated products can be utilized as aggregates in the concrete industry and as alkaline fillers in the paper (or recycled paper) industry. Mineral carbonation consist in reacting CO2 and Ca or Mg-bound compounds such as wollastonite (CaSiO3), olivine (Mg2SiO4), and serpentine (Mg3Si2O5(OH)4). As a result, CO2 is stably stored in final products such as CaCO3 and MgCO3. Last, the accelerated carbonation of solid wastes containing alkaline minerals such as Ca and Mg before their landfill treatment is effective for decreasing the mobility of heavy metals by adjusting pH to below 9.5 at which their solubility is lowest.

#### **4.9. Non-conversion utilization of CO2**

CO2 utilization that does not involve its chemical conversion is an alternative destination of captured emissions. Among such alternatives the injection of supercritical CO2 into depleted oil wells to enhance the further recovery of oil is well established. Indeed, this is presently the only commercially viable technology adding value to large volumes to CO2 in the order of magnitude of emissions from fossil fuel based energy generation. It has been estimated that CO2 injection can enhance oil recovery from a depleting well by about 10 to 20 % of the original oil in place. Similarly, CO2 can be used to recover methane from unmined coal seams. It has been estimated that, in the U.S. alone, 89 billion barrels of oil could technically be recovered using CO2, leading to a storage of 16 Gt of CO2 in the depleted oil reservoirs (DNV, 2011).

The use of supercritical CO2 as a solvent in processing chemicals (e.g., flavor extraction) is also well established. New uses of supercritical CO2 in chemical processing are emerging, and have the added benefit of reducing water usage. Supercritical CO2 is also being explored as a heat transfer fluid for some geothermal applications. These non-conversion methods of utilization constitute a significant fraction of the total CO2 emissions (DNV, 2011).

**Enhanced Oil Recovery (CO2-EOR)***:* Through CO2-EOR, oil producers inject CO2 into wells to help sustain production in otherwise declining oil fields. The main goal of this technology is to draw more oil to the surface. In 2012, CO2-EOR accounted for 6% of current U.S. domestic oil production. The limited CO2 source is the main barrier to reaching higher levels of CO2- EOR production due to insufficient supplies of affordable CO2. With the discovery of offshore gas fields with high CO2 contents in Brazil, there is a great opportunity to implement CO2-EOR at those fields.

Furthermore, the offshore removal of acid gases poses a choice of onshore processing against offshore processing. Factors like safety and operability may favor onshore processing in comparison with offshore processing. The proper on land disposal of the CO2 removed from natural gas requires the construction of CO2 pipelines to transport CO2 to offshore EOR applications. Another aspect is the high cost of ship hulls as shifting CO2 removal to onshore facilities releases the weight shipped, which could overload the cost of building the required CO2 pipelines.

In the option of onshore processing, CO2 rich natural gas would be available as feedstock to SYNGAS production from CO2 reforming, besides CO2 separation and transport back to oil fields for CO2-EOR. The current estimated cost gap for CGS from power, steel and cement plants is several times larger than the current CO2 market price, and downward pressure on this market price is likely to increase. Investments in CO2 reuse technologies need to be assessed as a screening procedure among potential alternatives.

## **5. Concluding remarks**

leads by way of starch to an annual production of 1011 t of biomass (Walther et al., 1999). With the development of catalysts able to reproduce the key steps of photosynthesis, water and sunlight would ultimately be the only needed sources for clean energy production. Light driven technologies under development include (a) photoelectrochemical cells where CO2 present in a moistened gas stream is converted into organic molecules based on the photoox‐ idation of water into oxygen gas O2, protons H+, and electrons. The conversion of CO2 occurs at the photocathode and involves the generated protons, electrons and the "fuel" CO2 (Kayaert et al., 2013); (b) direct water oxidation - photocatalytic water splitting - to produce H2 and O2 over a metal-oxide-based photocatalyst using solar energy (Maeda and Domen, 2013); (c) hydrogen-producing systems consisting of a hydrogen-evolving catalyst linked to a photo‐ sensitizer (Badura et al., 2012). Although promising alternatives, biomimetic CO2 conversions

**CO2 Mineralization for Environmental Remediation:** Lim et al. (2013) reviewed the applica‐ tion of carbonation to solidify or stabilize solid combustion residues from municipal solid wastes, paper mill wastes, etc. and contaminated soils, and to manufacture precipitated calcium carbonate. For instance, the red mud - a highly alkaline waste of Bayer's process - can be treated by absorption of CO2. Machado (2012) analyzed the process of red mud carbonation with the exhausted gases from the alumina production calcinators, by developing a dynamic model representative of the mass and energy balances involved in the process, and chemical reactions occurring in the mud under carbonation. Machado (2012) was able to predict the species behavior, as well as the decrease in mud pH and the rebound phenomenon observed when the CO2 concentration is reduced. The transient profile of the main process responses indicated a substantial reduction of CO2 concentration in the output gas, in consequence of tons of CO2 captured, and a significant reduction in mud pH. Concerning other environmental applications, Lim et al. (2013) report that carbonated products can be utilized as aggregates in the concrete industry and as alkaline fillers in the paper (or recycled paper) industry. Mineral carbonation consist in reacting CO2 and Ca or Mg-bound compounds such as wollastonite (CaSiO3), olivine (Mg2SiO4), and serpentine (Mg3Si2O5(OH)4). As a result, CO2 is stably stored in final products such as CaCO3 and MgCO3. Last, the accelerated carbonation of solid wastes containing alkaline minerals such as Ca and Mg before their landfill treatment is effective for decreasing the mobility of heavy metals by adjusting pH to below 9.5 at which their solubility

CO2 utilization that does not involve its chemical conversion is an alternative destination of captured emissions. Among such alternatives the injection of supercritical CO2 into depleted oil wells to enhance the further recovery of oil is well established. Indeed, this is presently the only commercially viable technology adding value to large volumes to CO2 in the order of magnitude of emissions from fossil fuel based energy generation. It has been estimated that CO2 injection can enhance oil recovery from a depleting well by about 10 to 20 % of the original oil in place. Similarly, CO2 can be used to recover methane from unmined coal seams. It has been estimated that, in the U.S. alone, 89 billion barrels of oil could technically be recovered using CO2, leading to a storage of 16 Gt of CO2 in the depleted oil reservoirs (DNV, 2011).

are still in its early stage of technological development.

78 CO2 Sequestration and Valorization

is lowest.

**4.9. Non-conversion utilization of CO2**

Technologies for utilization of CO2 amenable to commercial scales are presently a very small fraction of anthropogenic CO2 emissions, and very endothermic due to the inertness of CO2, what reduces their abatement potential. Furthermore, chemical and biochemical conversion of CO2 presents a sequestration potential that is orders of magnitude lower than the CO2 emissions associated to energy generation from fossil fuels.

Geographical synergies of CO2 supply (power plant emissions or natural gas processing) should guide in the medium term feasible utilization alternatives. The main synergy is identified in offshore gas processing and EOR, which, due to the economic benefit, process scale and maturity, stands as the most relevant utilization route in the short to medium term. Furthermore, most of the emerging alternatives reviewed are at their early stage of techno‐ logical development.

However, CO2 stands as a promising renewable feedstock to the chemical industry, which has been limited to oil, natural gas, coal and, recently, biomass. Such as posed, SYNGAS based conversions to the downstream supply chain is a route for flexibility of raw materials. Gasification of a variety of feedstock can lead to SYNGAS. Furthermore, expanding nonconventional gas supply enforces natural gas reforming in the upstream of the chemical supply chain. CO2 captured from emissions and natural gas processing may drop into the supply chain via Dry Reform. As SYNGAS derived products, hydrogen, methanol and synthetic fuels (e.g., olefins, naphta, diesel, lubricants and kerosene) from Fischer-Tropsch process are likely to dominate the scenario.

Additionally, methanol (MeOH) is expected to grow in relevance either as hydrogen carrier and as intermediate product such as feedstock to MeO (Methanol to Olefins) process, as well as trans-esterification agent in biodiesel and dimethyl carbonate (DMC) production processes. Nowadays, the interest in DMC has grown significantly because it is considered to be a safe and nontoxic substitute for well-established methylating and carbonylating agents (e.g., phosgene), and has potential as an oxygen-containing fuel additive. There are several techno‐ logical routes to produce DMC, however, the one route considered promising for large-scale commercialization is the trans-esterification of ethylene carbonate (EC) with methanol. In this indirect route, EC is obtained by a previous reaction of CO2 with ethylene oxide. The route yields DMC and ethylene glycol (EG) as co-products in equimolar ratio.

The use of CO2 as a carbon source in the synthesis of chemicals, in contrast to disposal, reduces dependence on fossil fuels, generates profit and is in line with a sustainable chemical industry. However, the actual use of CO2 corresponds to about 0.4% of the potential CO2 suitable to be converted to chemicals (Navarro et al., 2013).

Finally, large-scale utilization of CO2 require energy efficient CO2 capture technologies and an expansion of CO2 transportation infrastructure.

## **Acknowledgements**

O. Araujo and J.L. Medeiros kindly acknowledge CNPq for scholarships and financial grants; and CAPES for grant no. 113/2008.

## **Author details**

Ofélia de Queiroz F. Araújo1 , José Luiz de Medeiros1 and Rita Maria B. Alves2,3

1 Federal University of Rio de Janeiro, Brasil

2 BRASKEM S.A., Brasil

3 University of São Paulo, Brasil

### **References**

conversions to the downstream supply chain is a route for flexibility of raw materials. Gasification of a variety of feedstock can lead to SYNGAS. Furthermore, expanding nonconventional gas supply enforces natural gas reforming in the upstream of the chemical supply chain. CO2 captured from emissions and natural gas processing may drop into the supply chain via Dry Reform. As SYNGAS derived products, hydrogen, methanol and synthetic fuels (e.g., olefins, naphta, diesel, lubricants and kerosene) from Fischer-Tropsch process are likely to

Additionally, methanol (MeOH) is expected to grow in relevance either as hydrogen carrier and as intermediate product such as feedstock to MeO (Methanol to Olefins) process, as well as trans-esterification agent in biodiesel and dimethyl carbonate (DMC) production processes. Nowadays, the interest in DMC has grown significantly because it is considered to be a safe and nontoxic substitute for well-established methylating and carbonylating agents (e.g., phosgene), and has potential as an oxygen-containing fuel additive. There are several techno‐ logical routes to produce DMC, however, the one route considered promising for large-scale commercialization is the trans-esterification of ethylene carbonate (EC) with methanol. In this indirect route, EC is obtained by a previous reaction of CO2 with ethylene oxide. The route

The use of CO2 as a carbon source in the synthesis of chemicals, in contrast to disposal, reduces dependence on fossil fuels, generates profit and is in line with a sustainable chemical industry. However, the actual use of CO2 corresponds to about 0.4% of the potential CO2 suitable to be

Finally, large-scale utilization of CO2 require energy efficient CO2 capture technologies and an

O. Araujo and J.L. Medeiros kindly acknowledge CNPq for scholarships and financial grants;

and Rita Maria B. Alves2,3

, José Luiz de Medeiros1

yields DMC and ethylene glycol (EG) as co-products in equimolar ratio.

converted to chemicals (Navarro et al., 2013).

expansion of CO2 transportation infrastructure.

**Acknowledgements**

**Author details**

Ofélia de Queiroz F. Araújo1

2 BRASKEM S.A., Brasil

3 University of São Paulo, Brasil

1 Federal University of Rio de Janeiro, Brasil

and CAPES for grant no. 113/2008.

dominate the scenario.

80 CO2 Sequestration and Valorization


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Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57386

## **1. Introduction**

The ocean is the largest sink of atmospheric CO2 (about 7 petagrams (Pg) per year) (1 Pg = 1 gigaton = 1015 g). Dissolved CO2 (passively entering the ocean via diffusion from the atmos‐ phere) has already acidified the surface ocean, the most productive region of the ocean. Ocean carbon sequestration (OCS) is a method to distribute CO2 more evenly throughout ocean depth and minimize surface ocean impacts. There are two major methods of OCS – direct injection and ocean fertilization (promoting photosynthetic fixation of CO2 by ocean organisms). This chapter focuses only on the direct injection as a method of OCS. This chapter will first describe the physical mechanism by which CO2 can be stored in the ocean water column at depth. It will summarize past ocean direct injection studies, and outline the effects of increased dissolved CO2 and locally increased CO2 partial pressure on marine organisms. It will also include a discussion of the engineering challenges of delivering CO2 to the water column, including the selection of injection sites to minimize CO2 outgassing to the atmosphere as well as minimizing marine life impacts. Finally, this chapter will address the legal, policy and public outreach issues that have ultimately precluded implementation of OCS using direct injection.

#### **1.1. Motivation of Ocean Carbon Sequestration (OCS)**

The ocean is presently the largest sink of atmospheric CO2 (about 7 Pg per year) [1]. The Earth's oceans cover over 70% of the Earth's surface, and have an average depth of 3,800 m. However, dissolved CO2 is already causing surface ocean acidification (most productive region of ocean) as it equilibrates with the atmospheric CO2[2]. By 1994, the total atmospheric release of anthropogenic (i.e., man-made) carbon was about 244 Pg of carbon (PgC) from fossil fuel combustion, and about 140 PgC from land use change (e.g., deforestation) [3]. The oceans have absorbed about one-third of anthropogenic CO2 (the atmosphere retained about 43%, while the oceans absorbed about 30%), leading to a decrease of surface-ocean total pH by about 0.1 units from about 8.2 to 8.1. If CO2 emissions continue unabated the subsurface ocean total could

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

decline by 0.7 units by 2300. To place in a geological perspective, the surface ocean pH (on a total scale) has not been below 8.1 during the past 2.1 million years. The total addition of carbon into the atmosphere is expected to be about 5000 PgC – the estimated fossil fuel reserves excluding hydrates -- in the next 500 years. This is a higher rate of carbon addition than ever experienced by the earth over a short geological time scale [3].

At the same time the ocean pH in the deep ocean has been decreasing at a relatively slower rate compared with the surface ocean [1, 4]. Rising concentrations of greenhouse gases in the atmosphere are implicated in adverse climate changes and two-thirds of the change is attributed to CO2 [1]. Ocean carbon sequestration was conceived as a method to distribute CO2 more evenly throughout the ocean column, especially into deep ocean waters, and minimize surface ocean impacts while the ocean CO2 levels equilibrate with the atmosphere.

There are two major methods of OCS – direct injection and ocean fertilization (promoting photosynthetic fixation of CO2 by ocean organisms). This chapter focuses only on the direct injection of CO2.

## **2. Physical description of direct injection**

## **2.1. Physical properties of ocean/CO2 system**

The conditions under which CO2 can exist in a gas, liquid, solid or hydrate, and aqueous phases are depicted in the phase diagram (see Figure 1) [5] At typical ocean temperatures and pressures, CO2 exists as a gas above 500 m depth, and a liquid below this depth. Between 500 and 2700 m depth, liquid CO2 is less dense than seawater and would float, while below 2700 m CO2 is denser than seawater and would sink. A solid CO2 hydrate phase is thermodynam‐ ically stable in the ocean at low temperatures; CO2 hydrates are discussed in Section 2.3.

## *2.1.1. Solubility of CO2 in the ocean*

CO2 dissolves in ambient seawater that is not saturated with CO2. Once dissolved, aqueous CO2 exists in various charged forms in water according to these main reactions, known as the carbonate system [6]:

$$\rm{CO\_2(aq)} + \rm{H\_2O} = \rm{H\_2CO\_3(aq)}\tag{1}$$

$$\rm H\_2CO\_3 \text{(aq)} = H^+ + HCO\_3^- \tag{2}$$

$$\text{HCO}\_3^\cdot = \text{H}^+ + \text{CO}\_3^{2\cdot} \tag{3}$$

The total dissolved inorganic carbon (CT) is defined as:

Ocean Carbon Sequestration by Direct Injection http://dx.doi.org/10.5772/57386 91

$$\mathbf{C\_{T}} = \left[\mathbf{H\_{2}CO\_{3}(aq)}\right] + \left[\mathbf{HCO\_{3}^{\cdot}}\right] + \left[\mathbf{CO\_{3}^{\cdot 2^{\cdot}}}\right] \tag{4}$$

**Figure 1.** Phase Diagram for CO2 in the ocean [5]. Gray area: region of stability for CO2 hydrates; dashed line – gas/ liquid phase transition for pure CO2; red line – depth - temperature profile of the Pacific Ocean at 152°W, 40°N; green line – CO2 density; blue line – seawater density (35.0 PSU).

The result of this is that increasing dissolved CO2 will shift the equilibria (1) and (2) above to the right, and lower the local pH of the ambient seawater. [7]. At a typical surface seawater water pH of 8.2, the equilibrium (3) is shifted to the left with the addition of CO2. The net result of an increase in dissolved CO2 is the decrease of pH, an increase in HCO3 - and a decrease of CO3 2- (Figure 2).

In turn, the dissolved CO2 causes an increase of the density of the seawater via the solute density effect [9] that has implications for the physical design of a direct injection of CO2 into the ocean.

#### *2.1.2. CO2 partial pressure (pCO2)*

decline by 0.7 units by 2300. To place in a geological perspective, the surface ocean pH (on a total scale) has not been below 8.1 during the past 2.1 million years. The total addition of carbon into the atmosphere is expected to be about 5000 PgC – the estimated fossil fuel reserves excluding hydrates -- in the next 500 years. This is a higher rate of carbon addition than ever

At the same time the ocean pH in the deep ocean has been decreasing at a relatively slower rate compared with the surface ocean [1, 4]. Rising concentrations of greenhouse gases in the atmosphere are implicated in adverse climate changes and two-thirds of the change is attributed to CO2 [1]. Ocean carbon sequestration was conceived as a method to distribute CO2 more evenly throughout the ocean column, especially into deep ocean waters, and minimize surface ocean impacts while the ocean CO2 levels equilibrate with the atmosphere. There are two major methods of OCS – direct injection and ocean fertilization (promoting photosynthetic fixation of CO2 by ocean organisms). This chapter focuses only on the direct

The conditions under which CO2 can exist in a gas, liquid, solid or hydrate, and aqueous phases are depicted in the phase diagram (see Figure 1) [5] At typical ocean temperatures and pressures, CO2 exists as a gas above 500 m depth, and a liquid below this depth. Between 500 and 2700 m depth, liquid CO2 is less dense than seawater and would float, while below 2700 m CO2 is denser than seawater and would sink. A solid CO2 hydrate phase is thermodynam‐ ically stable in the ocean at low temperatures; CO2 hydrates are discussed in Section 2.3.

CO2 dissolves in ambient seawater that is not saturated with CO2. Once dissolved, aqueous CO2 exists in various charged forms in water according to these main reactions, known as the

CO aq + H O = H CO aq 2 2 23 ( ) ( ) (1)

( ) + - H CO aq = H + HCO 2 3 <sup>3</sup> (2)


experienced by the earth over a short geological time scale [3].

**2. Physical description of direct injection**

The total dissolved inorganic carbon (CT) is defined as:

**2.1. Physical properties of ocean/CO2 system**

*2.1.1. Solubility of CO2 in the ocean*

carbonate system [6]:

injection of CO2.

90 CO2 Sequestration and Valorization

The partial pressure of CO2 of a sample of water, denoted by pCO2, is the pressure of gaseous CO2 which, if allowed to equilibrate with water, will result in the same amount of dissolved CO2 as observed in the sample. It is related to the solubility of CO2, Cs, and the concentration of H2CO3(aq) by the following:

$$pCO\_2 = \frac{[H\_2CO\_3(aq)]}{C\_s(T, S)}\tag{5}$$

where Cs(T,S) (usually expressed in μatm) is dependent on the local temperature T and salinity [10] provide empirical relations to obtain Cs.

**Figure 2.** Bjerrum (pHT (total scale pH) – relative speciation) plot showing the relative contributions of CO2, HCO3- and CO3 2- to the dissolved inorganic carbon as a function of pH, at 15 deg C and a salinity of 35 PSU. The dashed vertical lines indicate the average open ocean surface pHT during the Last Glacial Maximum (LGM), 1766, 2007 and 2100 (pro‐ jected) [8].

As the atmospheric CO2 concentration increases, pCO2 levels increase in the surface ocean as it equilibrates with the atmosphere. The ratio of the relative change in pCO2 to the relative change in CT is known as the Revelle factor, and is inversely proportional to [CO3 2-]. The inverse of the Revelle factor is also often termed as the buffering capacity of the ocean. According to this relation, a doubling in atmospheric CO2 would only increase the total dissolved CO2 by about 10%. [11]

While sea-air equilibria for most gases like oxygen occur over a time scale of days, it can take ~8 months for CO2 to reach equilibrium at the surface, because the dissolved CO2 in the carbonate system does not remain a dissolved gas but instead causes an increase in HCO3 - . [4].

#### *2.1.3. Calcium carbonate saturation state of seawater*

2 3 ( )

*H CO aq pCO C TS* (5)

[ ] (,) <sup>=</sup> *s*

where Cs(T,S) (usually expressed in μatm) is dependent on the local temperature T and salinity

**Figure 2.** Bjerrum (pHT (total scale pH) – relative speciation) plot showing the relative contributions of CO2, HCO3- and

As the atmospheric CO2 concentration increases, pCO2 levels increase in the surface ocean as it equilibrates with the atmosphere. The ratio of the relative change in pCO2 to the relative

of the Revelle factor is also often termed as the buffering capacity of the ocean. According to this relation, a doubling in atmospheric CO2 would only increase the total dissolved CO2 by

While sea-air equilibria for most gases like oxygen occur over a time scale of days, it can take ~8 months for CO2 to reach equilibrium at the surface, because the dissolved CO2 in the carbonate system does not remain a dissolved gas but instead causes an increase in HCO3

2-]. The inverse


change in CT is known as the Revelle factor, and is inversely proportional to [CO3

2- to the dissolved inorganic carbon as a function of pH, at 15 deg C and a salinity of 35 PSU. The dashed vertical lines indicate the average open ocean surface pHT during the Last Glacial Maximum (LGM), 1766, 2007 and 2100 (pro‐

2

[10] provide empirical relations to obtain Cs.

92 CO2 Sequestration and Valorization

CO3

jected) [8].

about 10%. [11]

The CaCO3 saturation state of seawater is defined as Ω:

$$
\Omega = \frac{\left[Ca^{2+}\right]\left[CO\_3^{2-}\right]}{K\_{sp}}\tag{6}
$$

where [Ca2+] and [CO3 2-] are the seawater concentrations of Ca2+ and CO3 2-, and Ksp is the solubility product of either calcite or aragonite (the two major forms of CaCO3). If Ω for aragonite (Ωa) for instance is greater than 1, then aragonite is supersaturated and solid aragonite would begin to precipitate; if Ωa drops to below 1 then aragonite is undersaturated with respect to the ambient ocean and solid aragonite would begin to dissolve. Because Ksp increases with pressure, for both aragonite and calcite there is a transition of the saturation state from Ω > 1 to Ω < 1 sediments with depth [3]. The depth at which Ω = 1 for a mineral is known as its saturation horizon.

#### **2.2. Methods of direct injection**

CO2 sequestration first involves capture from their sources, of which one major type of the coal-fired power plant. The CO2 emissions are relatively pure from coal-fired power plants and could be isolated and injected into the ocean. A typical 500 MW power plant produces about 130 kg/s of CO2. [12]. After CO2 capture, the CO2 would be transported to the ocean via a pipe or ship to the ocean for direct injection. Technologies for CO2 direct injection include: Liquid CO2 droplets [13]; CO2 laden seawater [9,14,15]; Solid CO2 (dry ice) [16,17]; and CO2 lake formation (See Figure 3)

**Figure 3.** Ocean storage strategies (From Goddard, in [1]).

## **2.3. CO2 hydrates**

As shown in Figure 1, at lower temperatures (below about 5 - 10 degrees C) and at high pressure (corresponding to an ocean depth of about 400 m or greater) solid CO2 hydrates are thermo‐ dynamically stable. CO2 hydrates consist of molecules of CO2 inside a cage-like structure of hydrogen-bonded water molecules [18]. They are of interest as a possible vehicle for deeper ocean carbon sequestration, because they are denser than seawater, and will sink unaided while dissolving to promote dispersion in the ocean. Pure hydrate particles are difficult to produce, but the Oak Ridge National Laboratory (ORNL) has designed a continuous CO2 seawater co-flowing injector to create cylindrical composite particles comprised of CO2 hydrate (negatively buoyant), liquid CO2 (slightly positively buoyant at 1000-1500 m depths) and seawater [19].

Although CO2 hydrates are thermodynamically stable, they will dissolve in ambient seawater upon release, because CO2 is under-saturated in the ambient water. Field and laboratory observations confirmed that both pure hydrates and partially reacted cylindrical composite particles dissolved in the ambient seawater [20,21,23].

## **2.4. CO2 droplet and hydrate studies**

Numerical efforts to simulate the behaviour of CO2 droplet plumes have included solving the full three dimensional Navier-Stokes equations in quiescent ambient sea conditions [7,22,24]. Bubble plume models calibrated using laboratory observations have also been applied to CO2 droplet releases [25-27].

Field tests were conducted using CO2 hydrate composite injectors [23,28]. The latest survey, with a hydrate reactor located at an ocean depth of ~1500 m, produced curved negatively buoyant cylindrical particles with diameters ~2.2 cm and lengths up to ~1 m. Applying a drag coefficient model to observed initial settling velocities and dissolution rates during the most recent survey [29,30], the hydrate conversion efficiency (percentage of liquid CO2 converted to hydrate) in the field was ~ 15-20% resulting in particles with specific gravity 1-2% greater than seawater, which lead them to sink to a depth below discharge of roughly 100 m. Greater sinking could be achieved using larger particles. Discharging particles with a range of sizes and densities (reflecting different conversion rates) would cause differential settling resulting in spreading in the down-current and vertical directions. Furthermore, towing the source from a moving ship would contribute additional dispersion [29].

An alternative approach to enhancing mixing and vertical descent is to release a continuous stream of particles, forming a dense plume which would sink both due to the density of the particles as well as the increased density of seawater containing dissolved CO2. An integral double plume model [25,29,31] was used to simulate the behavior of continuous streams of composite particles released to a quiescent ocean, with typical ambient stratification, at CO2 loadings of 0.01 to 1000 kg/s. Results showed that, for a CO2 release of 100 kg/s (roughly the emission from a 500 MW coal-fired power plant), a plume composed of 2.2 cm diameter composite particles with 16% reaction efficiency would sink about 1000 m, approximately 10 times the individual particle sinking depth. A plume composed of similar particles, but with a diameter of 5 cm, would sink about 2000 m (~5 times the individual particle depth), while plumes composed of larger particles, or particles exhibiting higher reaction efficiency, would reach the seafloor (as would the individual particles).

Two ambient effects reduce the performance of a plume: stratification and ocean currents. Plume sinking is hampered by strong ambient stratification which causes trapping of entrained seawater at intermediate depths below release. Density stratification weakens at depths below 1500 m [32], so from the perspective of reduction of plume trapping, regions of the ocean deeper than 1500 m are potentially favourable for depositing CO2 [33].

## **3. Environmental impacts/challenges**

**2.3. CO2 hydrates**

94 CO2 Sequestration and Valorization

seawater [19].

As shown in Figure 1, at lower temperatures (below about 5 - 10 degrees C) and at high pressure (corresponding to an ocean depth of about 400 m or greater) solid CO2 hydrates are thermo‐ dynamically stable. CO2 hydrates consist of molecules of CO2 inside a cage-like structure of hydrogen-bonded water molecules [18]. They are of interest as a possible vehicle for deeper ocean carbon sequestration, because they are denser than seawater, and will sink unaided while dissolving to promote dispersion in the ocean. Pure hydrate particles are difficult to produce, but the Oak Ridge National Laboratory (ORNL) has designed a continuous CO2 seawater co-flowing injector to create cylindrical composite particles comprised of CO2 hydrate (negatively buoyant), liquid CO2 (slightly positively buoyant at 1000-1500 m depths) and

Although CO2 hydrates are thermodynamically stable, they will dissolve in ambient seawater upon release, because CO2 is under-saturated in the ambient water. Field and laboratory observations confirmed that both pure hydrates and partially reacted cylindrical composite

Numerical efforts to simulate the behaviour of CO2 droplet plumes have included solving the full three dimensional Navier-Stokes equations in quiescent ambient sea conditions [7,22,24]. Bubble plume models calibrated using laboratory observations have also been applied to

Field tests were conducted using CO2 hydrate composite injectors [23,28]. The latest survey, with a hydrate reactor located at an ocean depth of ~1500 m, produced curved negatively buoyant cylindrical particles with diameters ~2.2 cm and lengths up to ~1 m. Applying a drag coefficient model to observed initial settling velocities and dissolution rates during the most recent survey [29,30], the hydrate conversion efficiency (percentage of liquid CO2 converted to hydrate) in the field was ~ 15-20% resulting in particles with specific gravity 1-2% greater than seawater, which lead them to sink to a depth below discharge of roughly 100 m. Greater sinking could be achieved using larger particles. Discharging particles with a range of sizes and densities (reflecting different conversion rates) would cause differential settling resulting in spreading in the down-current and vertical directions. Furthermore, towing the source from

An alternative approach to enhancing mixing and vertical descent is to release a continuous stream of particles, forming a dense plume which would sink both due to the density of the particles as well as the increased density of seawater containing dissolved CO2. An integral double plume model [25,29,31] was used to simulate the behavior of continuous streams of composite particles released to a quiescent ocean, with typical ambient stratification, at CO2 loadings of 0.01 to 1000 kg/s. Results showed that, for a CO2 release of 100 kg/s (roughly the emission from a 500 MW coal-fired power plant), a plume composed of 2.2 cm diameter composite particles with 16% reaction efficiency would sink about 1000 m, approximately 10 times the individual particle sinking depth. A plume composed of similar particles, but with

particles dissolved in the ambient seawater [20,21,23].

a moving ship would contribute additional dispersion [29].

**2.4. CO2 droplet and hydrate studies**

CO2 droplet releases [25-27].

Some of the concepts relevant to the impacts of OCS by direct injection (e.g. ocean acidification) are presented in this section. The reader is directed to [1,11] for a more detailed and compre‐ hensive summary of the causes and effects of ocean acidification.

## **3.1. Long term stability of dissolved CO2 in the ocean**

Investigations and estimation of the long term stability is described in greater detail in [1]. Numerical ocean models indicate that placing CO2 in the deep ocean would isolate most of the CO2 from the atmosphere for several centuries, but over longer times the ocean and atmosphere would equilibrate.

Relative to direct atmospheric release, direct injection of CO2 into the ocean could reduce the rise and peak of atmospheric CO2 levels over the next several centuries. After several centuries, the CO2 released in the ocean would be transported back to the ocean surface and interact with the atmosphere again. However, in the new equilibrium, most (66% to 85%) of the injected CO2 would still remain in the ocean despite contacting the atmosphere [1].

Generally, carbon injected in the deep ocean would equilibrate with the atmosphere over a time scale of 300 to 1000 years, based on radiocarbon and other tracer dating to estimate the age of the deep seawater. The estimated age of the North Pacific deep water is 700 – 1000 years, while the North Atlantic deep water is estimated to be only about 300 years old. A large number of numerical three dimensional ocean general circulation models were used to study CO2 retention. The models generally predict a higher retention time with a deeper injection depth (isolation of CO2 from the atmosphere is nearly complete for 100 years with an injection depth of 3000 m). Consistent with the radioactive tracer dating, many of the models suggest that the Pacific Ocean would retain a larger fraction than the Atlantic Ocean. However, the models vary greatly in their predictions on the actual time taken for CO2 injected at a particular site to once again make contact with the atmosphere [1, 34].

Additionally, other geochemical factors may affect these predictions. For example, a higher ocean temperature, as well as a higher dissolved inorganic carbon concentration may lead to a lower efficiency for the ocean to absorb additional CO2. (See [11]).

## **3.2. Potential pH and carbonate system changes from added CO2**

As described in Section 2.1, ocean acidification has been occurring since the Industrial Revolution. This section describes the effect of continued ocean acidification on the ocean's carbon cycle and marine ecosystems. Between 1991 and 2006, North Pacific ocean pHT showed a decrease of 0.06 units over the upper 500 m of ocean. In the Iceland Sea, the trend of pHT decrease between 1985 and 2008 in the surface ocean was 0.0024 units per year, with a corresponding decrease in Ωa of 0.0117 units per year. The decline in pHT below 1,500 m in the Iceland Sea was one-quarter of that on the surface, with a corresponding decrease in Ω<sup>a</sup> at 0.0009 units per year [4].

Another consequence of the increased dissolved CO2 in the ocean, as described in Section 2.1.1, is the increase of HCO3 - and a decrease of CO3 2- in the ocean. The decreased CO3 2-in turn leads to the decrease of the local value of Ω in the ocean. As there is a transition from saturation to undersaturation from Ω = 1, this means that the saturation horizons for both aragonite and calcite would both become less deep with time [3]. The decrease in Ω<sup>a</sup> caused the aragonite saturation horizon (ASH), the interface between supersaturated waters above and undersa‐ turated waters below, to rise (shoal) at a rate of 4 m per year. The decrease in Ω, and therefore the shoaling rate for the ASH, is predicted to be more pronounced near the poles, and more severe in the Arctic Ocean than the Southern Ocean, partly because the polar oceans have lower initial concentrations of CO3 2- [4].

It was proposed [3] that the addition of CO2 followed by global increase in surface temperature can be compared to that which occurred during the Paleocene-Eocene Thermal Maximum (PETM, ~55 million years ago). During PETM, about 3000 PgC was added to the over an estimated 6000 years. However, the current estimate for expected total anthropogenic carbon addition is a larger rate of carbon input over a shorter period of time, about 5000 PgC over about ~500 years. The next highest global carbon addition was experienced by the earth during the Paleocene-Eocene Thermal Maximum, (~55 million years ago) where about 3000 PgC was added over ~6000 years. During the PETM, the effects of ocean acidification on surface calcifying organisms was limited, but the conditions of the PETM were not identical to the predicted future scenario, notably in that the carbon input rate was still much slower than the modern anthropogenic carbon addition. Nevertheless, studies of the PETM may inform future predictions of the behavior of ocean marine life with a large increase of atmospheric CO2. [3,4].

#### **3.3. Effect of pCO2 increase on organisms**

Effects of elevated CO2 levels and acidified seawater on marine organisms are explained in in more detail in [1,11,35,36].

At acute levels CO2 has a narcotic effect on animals and causes respiratory distress and death. The work of [37 – 41] that model the lowered pH on passive marine organisms such as zooplankton that spend varying times in and out of a CO2 plume, and found that minimizing the local dissolved CO2 and pH drops will reduce the mortality rate.

Non-lethal effects have also been observed due to hypercapnia (elevated CO2 exposure) [42-44]. Tamburri et al. [42] have observed the narcotic effects of increased CO2 levels on mobile deep sea animals in the field; they also observe that while many tend to avoid CO2 plumes, some may risk the narcotic effects to obtain food. They note [42] that increased partial pressure of carbon dioxide will also have a detrimental effect on marine organisms, such as causing slow respiratory distress and inducing a narcotic effect on fish. Passive marine animals may experience depressed ion exchange capability and metabolism when exposed to lower, chronic levels CO2. Some studies also show slowed growth in mussels and corals, as well as develop‐ mental effects on some marine larvae and eggs (brittle stars and bivalves) [35].

**3.2. Potential pH and carbonate system changes from added CO2**


2- [4].

0.0009 units per year [4].

96 CO2 Sequestration and Valorization

is the increase of HCO3

initial concentrations of CO3

**3.3. Effect of pCO2 increase on organisms**

more detail in [1,11,35,36].

As described in Section 2.1, ocean acidification has been occurring since the Industrial Revolution. This section describes the effect of continued ocean acidification on the ocean's carbon cycle and marine ecosystems. Between 1991 and 2006, North Pacific ocean pHT showed a decrease of 0.06 units over the upper 500 m of ocean. In the Iceland Sea, the trend of pHT decrease between 1985 and 2008 in the surface ocean was 0.0024 units per year, with a corresponding decrease in Ωa of 0.0117 units per year. The decline in pHT below 1,500 m in the Iceland Sea was one-quarter of that on the surface, with a corresponding decrease in Ω<sup>a</sup> at

Another consequence of the increased dissolved CO2 in the ocean, as described in Section 2.1.1,

to the decrease of the local value of Ω in the ocean. As there is a transition from saturation to undersaturation from Ω = 1, this means that the saturation horizons for both aragonite and calcite would both become less deep with time [3]. The decrease in Ω<sup>a</sup> caused the aragonite saturation horizon (ASH), the interface between supersaturated waters above and undersa‐ turated waters below, to rise (shoal) at a rate of 4 m per year. The decrease in Ω, and therefore the shoaling rate for the ASH, is predicted to be more pronounced near the poles, and more severe in the Arctic Ocean than the Southern Ocean, partly because the polar oceans have lower

It was proposed [3] that the addition of CO2 followed by global increase in surface temperature can be compared to that which occurred during the Paleocene-Eocene Thermal Maximum (PETM, ~55 million years ago). During PETM, about 3000 PgC was added to the over an estimated 6000 years. However, the current estimate for expected total anthropogenic carbon addition is a larger rate of carbon input over a shorter period of time, about 5000 PgC over about ~500 years. The next highest global carbon addition was experienced by the earth during the Paleocene-Eocene Thermal Maximum, (~55 million years ago) where about 3000 PgC was added over ~6000 years. During the PETM, the effects of ocean acidification on surface calcifying organisms was limited, but the conditions of the PETM were not identical to the predicted future scenario, notably in that the carbon input rate was still much slower than the modern anthropogenic carbon addition. Nevertheless, studies of the PETM may inform future predictions of the behavior of ocean marine life with a large increase of atmospheric CO2. [3,4].

Effects of elevated CO2 levels and acidified seawater on marine organisms are explained in in

At acute levels CO2 has a narcotic effect on animals and causes respiratory distress and death. The work of [37 – 41] that model the lowered pH on passive marine organisms such as zooplankton that spend varying times in and out of a CO2 plume, and found that minimizing

Non-lethal effects have also been observed due to hypercapnia (elevated CO2 exposure) [42-44]. Tamburri et al. [42] have observed the narcotic effects of increased CO2 levels on mobile

the local dissolved CO2 and pH drops will reduce the mortality rate.

2- in the ocean. The decreased CO3

2-in turn leads

The primary effect of acidified seawater exposure by organisms is acidosis, the decrease of pH in body fluids. Intracellular and extracellular processes have been shown to be disrupted when seawater pH drops to a range of about 6.0 – 7.8. Many marine animals counter acidosis by increasing bicarbonate ion production (e.g. in the gills) [35]. Barry et al. report that organisms that have weaker control of their internal fluid chemistry, and that rely on passive molecular diffusion for gas exchange such as sponges, echinoderms, may have greater sensitivity to ocean acidification [45].

Some organisms may adapt to hypercapnia (elevated CO2) better than others [46]. For example, tropical fishes, as they live closer to the edge of oxygen limitation than temperate fishes, may make them more sensitive to the combined effects of ocean temperature and ocean acidification than their temperate counterparts. For example, studies on acutely exposed tropical cardinal fishes to 1 week of pCO2 of 1000 μatm resulted in decreases of aerobic scope and critical swimming speeds by about 40 – 50%, but a similar study conducted for Atlantic cod after 12 months of exposure to both 3000 and 6000 μatm did not result in any significant change in swimming capacity.

High CO2 levels (up to a pCO2 of 16,000 ppm [47] have also been observed in ocean bottom waters and marine sediments where there are high rates organic matter oxidation and low rates of mixing with the overlying seawater. Under these conditions, high CO2 concentrations are often accompanied by low O2 concentrations. Near the surface at night, respiratory fluxes in some relatively confined rock pools of the intertidal zone can produce high CO2 levels. [1]. Portner et al. [46] report that high pCO2 is found in oxygen minimum layers. They report that elevated pCO2 is linked to acid-base regulation and respiration in fish. However, they also report that coastal and mid-water animals (both pelagic and benthic) regularly experience a large range of pCO2 values (500 to 9400 μatm) in estuaries [46]. "These patterns suggest that in some environments, organisms have evolved to tolerate relatively wide pH oscillations and/ or low pH values." [1]

Organisms such as the Humboldt squid, although thought not to be able to adapt physiolog‐ ically to future changes to the oceans oxygen balance, have been observed to thrive in oxygen minimum layers which tend to have low pH and are undersaturated with respect to calcium carbonates [46].

Deep sea ecosystems depend on sinking particles of organic carbon, made by photosynthesis near the ocean, settling down through the water. Most species living in the deep sea display very low metabolic rates [48, 49], especially in oxygen minimum layers [51]. Organisms living in the deep seawaters have adapted to the energy limited environment by conserving energy stores and minimizing energy turnover. Turley et al. also suggest the depletion of oxygen as a contributing factor to the increased prevalence of harmful algal blooms, though the link between anthropogenic CO2 and algal blooms remains controversial [36].

Finally, as many marine organisms synthesize and depend on calcium carbonate structures (e.g. shells), the implication of a lowered CO3 2 - and Ω in the ocean is the potential for reduction of their habitats.

As a guide, [1] uses a pH drop of 0.1 units as the threshold pH drop for insignificant marine life impact; it is also within the observed natural variability in the ocean. The US Environmental Protection Agency proposed that the threshold for open waters at depths greater than the euphotic zone, the pH value should not drop more than 0.2 pH units outside the range of natural variation [11]. [39] shows that some theoretically modeled scenarios of carbon dioxide releases (for example, releasing sinking CO2 hydrates from a fixed or moving source at 1,500 m, injecting 10 to 1000 kg/s) would result in local pH drops within this guideline threshold in the vicinity of the release point. Others (e.g. Rockstrom et al.) have introduced the concept of planetary boundaries, and for CO2 they have proposed a threshold carbonate ion concentra‐ tion. As a first estimate, they proposed that the oceanic aragonite saturation state Ω<sup>a</sup> be maintained at 80% or higher of the average global pre-industrial surface seawater level of 3.44 [50]. As with [1], these planetary boundaries are guides for a sustainable global environment, and (with the exception of the US Environmental Protection Agency for pH) have not been implemented as a regulatory threshold.

#### *3.3.1. Comparison with naturally occurring ocean CO2 vents*

In the ocean, hydrothermal vents are submarine volcanic structures that act as natural sources of CO2 in the ocean. These have been observed as potential natural analogues of OCS direct injection points. Field observations of hydrothermal vents have shown large fluctuations of pCO2 (up to 80,000 ppm), over 100 times that observed in typical deep seawater). Over time, the vents have sustained organisms that are specially adapted to living in elevated pCO2 conditions [52].

Observations near hydrothermal vents have shown that ocean acidification reduced biodiver‐ sity below a mean pHT of 7.8 [53]. While Echinoderms are notably absent from habitats with naturally high CO2 levels such as hydrothermal vents and shallow CO2 vents off the coast of Italy [53], sponges appeared to tolerate these same sites.

As observed in [45], "[h]owever, while commonly the literature contains results of short term studies of organism physiology and survival, they may not be indicative of eventual long term consequences of ocean acidification."

## **4. Engineering feasibility/challenges**

#### **4.1. Site selection for injection**

stores and minimizing energy turnover. Turley et al. also suggest the depletion of oxygen as a contributing factor to the increased prevalence of harmful algal blooms, though the link

Finally, as many marine organisms synthesize and depend on calcium carbonate structures

As a guide, [1] uses a pH drop of 0.1 units as the threshold pH drop for insignificant marine life impact; it is also within the observed natural variability in the ocean. The US Environmental Protection Agency proposed that the threshold for open waters at depths greater than the euphotic zone, the pH value should not drop more than 0.2 pH units outside the range of natural variation [11]. [39] shows that some theoretically modeled scenarios of carbon dioxide releases (for example, releasing sinking CO2 hydrates from a fixed or moving source at 1,500 m, injecting 10 to 1000 kg/s) would result in local pH drops within this guideline threshold in the vicinity of the release point. Others (e.g. Rockstrom et al.) have introduced the concept of planetary boundaries, and for CO2 they have proposed a threshold carbonate ion concentra‐ tion. As a first estimate, they proposed that the oceanic aragonite saturation state Ω<sup>a</sup> be maintained at 80% or higher of the average global pre-industrial surface seawater level of 3.44 [50]. As with [1], these planetary boundaries are guides for a sustainable global environment, and (with the exception of the US Environmental Protection Agency for pH) have not been

In the ocean, hydrothermal vents are submarine volcanic structures that act as natural sources of CO2 in the ocean. These have been observed as potential natural analogues of OCS direct injection points. Field observations of hydrothermal vents have shown large fluctuations of pCO2 (up to 80,000 ppm), over 100 times that observed in typical deep seawater). Over time, the vents have sustained organisms that are specially adapted to living in elevated pCO2

Observations near hydrothermal vents have shown that ocean acidification reduced biodiver‐ sity below a mean pHT of 7.8 [53]. While Echinoderms are notably absent from habitats with naturally high CO2 levels such as hydrothermal vents and shallow CO2 vents off the coast of

As observed in [45], "[h]owever, while commonly the literature contains results of short term studies of organism physiology and survival, they may not be indicative of eventual long term


2

between anthropogenic CO2 and algal blooms remains controversial [36].

(e.g. shells), the implication of a lowered CO3

implemented as a regulatory threshold.

*3.3.1. Comparison with naturally occurring ocean CO2 vents*

Italy [53], sponges appeared to tolerate these same sites.

consequences of ocean acidification."

of their habitats.

98 CO2 Sequestration and Valorization

conditions [52].

As described in [1,54], to date there are no publications dedicated to site selection for direct ocean injection. Although numerical models have predicted CO2 retention time as a function of the injection location, they have not consistently agreed on any individual location for direct injection. The only agreement appeared to be that a larger depth of injection would result in a longer isolation of CO2 from the atmosphere [34]. In contrast, [55] presented a study of site selection for deep sea geological storage, highlighting the potential of storage in basalt aquifers along particular seismic and aseismic oceanic ridges. This section therefore discusses factors that should be considered site selection criteria based on to be consid‐ ered when selecting a site for OCS. Environmental goals of site selection include reduc‐ ing the likelihood of outgassing, and minimizing acute impacts to ocean organisms, as described in Section 3. Additional considerations include the costs of OCS, applicable international policies (such as regulations regarding disposal and cross border transport) – these factors are presented in Sections 4.2 and 4.3.

#### **4.2. Cost of OCS**

Costs were estimated for ship transport of liquid CO2 to an injection platform, with CO2 injection from a vertical pipe, or a ship trailing an injection pipe, to water at 3000m [1]. The cost estimate of ocean storage is the sum of three major components: tank storage of CO2 onshore awaiting shipping; the shipping of CO2; and direct injection of CO2 into the ocean (either via an ocean platform, a moving ship, or a pipeline). The estimated sum of the three components (including an assumption of 3% CO2 emissions from boil off and fuel consump‐ tion) is 11.9 and 13.2 US\$/ton CO2 net stored from shipping to 100 km and 500 km offshore, respectively [56]. Cost estimates presented do not include transport of CO2 onshore.

The cost for transporting CO2 from a power plant located at the shore through a pipeline running on the sea floor to an injection nozzle was also estimated in [56]. CO2 captured from a pulverized coal fired power plant with a net generation capacity of 600 MWe is transported either 100 or 500 km by a CO2 pipeline for injection at a depth of 3000 m at a cost of 6.2 US \$/ton CO2 net stored (100 km case) to 31.1 US\$/ton CO2 net stored (500 km case). Other technical challenges that may not be accounted for include: residual chemicals, metals, minerals and oils that may be released during drilling activities; and the fact that liquefied CO2 is highly corrosive, requiring that piping for CO2 delivery would require anti-corrosion coatings, which themselves may pose contamination issues [35].

There are no published cost estimates specific to the production of a CO2 lake on the sea floor; however, given the dominance of pipeline costs, it is reasonable to assume it to be similar to deep water injection. [1,56].

## **5. Policy issues/challenges**

Since offshore OCS is likely to take place in international waters, several international environmental agreements may apply, mainly those that aim to minimize potential risk s to the marine environment. The main international treaties are the Law of the Sea, the London Convention, London Protocol, and the OSPAR Convention. A succinct background of these treaties is taken directly from [57]:

"International marine environment protection was established in 1972 with the London Convention to regulate the dumping of wastes and other matter at sea. In 1982, this field was extended through the adoption of the United Nations Convention on the Law of the Seas (UNCLOS). Being an overarching construction, UNCLOS does not contain detailed operative provisions on most maritime issues; rather, it provides a framework for all areas, including marine protection, and allows other, more targeted treaties to fill in the gaps…With regard to marine pollution, global standards are set by the Convention on the Prevention of Marine Pollution by Dumping of Wastes and other Matter, signed in London in 1972 (London Convention). Beneath the London Convention exist several regional agreements that cover specific areas of the ocean [Also listed in [57]]. The most widely known of these is OSPAR, the Convention for the Protection of the Marine Environment of the North-East Atlantic. OSPAR is also notable as its regulations on marine pollution are markedly stricter than those of the London Convention, and its decisions are legally as opposed to politically binding on its Contracting Parties."

#### **5.1. 1996 London protocol**

UN Convention on Climate Change encouraged the use of the oceans as a reservoir for CO2, but the UNCLOS (in force since 1994) did not give clear guidance on OCS [1]. With respect to CO2 storage, the original London Convention (with 80 contracting parties, and in force since 1975) only applied to storage by aircraft and vessels and platforms in the water column. As a result, the London Convention did not apply to storage of CO2 in the seabed or the water column itself [57].

In November 1996, the London Protocol was established that prohibited the disposal of "industrial waste" into international waters. The list of prohibited substances that were categorized as "industrial waste" were contained in Annex I of the London Protocol. However, in 1996 the London Protocol did not give an opinion whether CO2 was categorized as a "waste material generated by manufacturing or processing operations" [1]. The London Protocol entered into force March 2006 [57].

#### **5.2. OSPAR convention**

In 1992, the OSPAR Commission for the Protection of the Marine Environmental of the North-East Atlantic, was formed which unified the 1972 Oslo and 1974 Paris Conventions. It brought together the governments of Belgium, Denmark, Finland, France, Germany, Iceland, Ireland, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and the United Kingdom, together with the European Community (EC). It is considered the most compre‐ hensive and strict legal framework governing the marine environment. As mentioned above, the contracting parties are legally bound to OSPAR's decisions [57,58].

## **5.3. 2007 OSPAR amendments for CO2 storage and implications to OCS**

**5. Policy issues/challenges**

100 CO2 Sequestration and Valorization

treaties is taken directly from [57]:

Contracting Parties."

column itself [57].

**5.1. 1996 London protocol**

entered into force March 2006 [57].

**5.2. OSPAR convention**

Since offshore OCS is likely to take place in international waters, several international environmental agreements may apply, mainly those that aim to minimize potential risk s to the marine environment. The main international treaties are the Law of the Sea, the London Convention, London Protocol, and the OSPAR Convention. A succinct background of these

"International marine environment protection was established in 1972 with the London Convention to regulate the dumping of wastes and other matter at sea. In 1982, this field was extended through the adoption of the United Nations Convention on the Law of the Seas (UNCLOS). Being an overarching construction, UNCLOS does not contain detailed operative provisions on most maritime issues; rather, it provides a framework for all areas, including marine protection, and allows other, more targeted treaties to fill in the gaps…With regard to marine pollution, global standards are set by the Convention on the Prevention of Marine Pollution by Dumping of Wastes and other Matter, signed in London in 1972 (London Convention). Beneath the London Convention exist several regional agreements that cover specific areas of the ocean [Also listed in [57]]. The most widely known of these is OSPAR, the Convention for the Protection of the Marine Environment of the North-East Atlantic. OSPAR is also notable as its regulations on marine pollution are markedly stricter than those of the London Convention, and its decisions are legally as opposed to politically binding on its

UN Convention on Climate Change encouraged the use of the oceans as a reservoir for CO2, but the UNCLOS (in force since 1994) did not give clear guidance on OCS [1]. With respect to CO2 storage, the original London Convention (with 80 contracting parties, and in force since 1975) only applied to storage by aircraft and vessels and platforms in the water column. As a result, the London Convention did not apply to storage of CO2 in the seabed or the water

In November 1996, the London Protocol was established that prohibited the disposal of "industrial waste" into international waters. The list of prohibited substances that were categorized as "industrial waste" were contained in Annex I of the London Protocol. However, in 1996 the London Protocol did not give an opinion whether CO2 was categorized as a "waste material generated by manufacturing or processing operations" [1]. The London Protocol

In 1992, the OSPAR Commission for the Protection of the Marine Environmental of the North-East Atlantic, was formed which unified the 1972 Oslo and 1974 Paris Conventions. It brought together the governments of Belgium, Denmark, Finland, France, Germany, Iceland, Ireland, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and the United Kingdom, together with the European Community (EC). It is considered the most compre‐

In June 2007, the OSPAR issued two amendments to the OSPAR Convention: the first, a decision to prohibit the storage of CO2 streams in the water column or on the sea bed in the Northeast Atlantic; and the second, a decision to allow the storage of CO2 in subsea sediments. [59,60]

In the first amendment, OSPAR stated that CO2 storage in the water column or on the sea bed "is not a sustainable storage option, is likely to result in harm to living resources and marine ecosystems and is thus neither a viable solution with regard to mitigating climate change nor compatible with the aims of the [OSPAR] Convention." However, in the first amendment, OSPAR indicated that ocean storage of CO2 in the water column or on the seabed is neverthe‐ less still under consideration in international forums. [59]

The OSPAR amendments provided a framework for its contracting national governments to develop permitting programs for CO2 storage. For example, a list of the minimum items required in an offshore CO2 storage permit included: a description of the project, including injection rates; types, amounts and sources of CO2; the location of the facility; characteristics of the geological formation; methods of transport; and a risk management plan, with moni‐ toring and verification measures, mitigation steps and a site closure plan [60].

In July 2011 the contracting parties of the OSPAR Convention ratified the 2007 Amendments to allow for CO2 storage in subsea geological formations [58].

#### **5.4. 2007 Amendment to the London Protocol**

In 2007, an amendment to the London Protocol (Annex 1) allowed for storage of CO2, if the disposal is into a sub-seabed geological formation, if CO2 streams are "overwhelmingly" carbon dioxide, and as long as no wastes are added. This amendment provided that CO2 streams may only be considered if [60,61]:


The amendments to Annex 1 entered into force on 10 February 2007. In contrast to the OSPAR Convention Amendment that only covered the Northeast Atlantic, the 2007 London Protocol Amendment specifically prohibited direct injection of CO2 for OCS for all London Protocol contracting parties.

## **5.5. 2009 London Protocol amendment for transboundary transport of CO2**

Article 6 of the London Protocol (on the export of wastes or other material) was largely interpreted by the contracting parties as prohibiting the export of CO2 from a contracting party for injection into sub-seabed geological formations. In 2009 Article 6 was amended to allow for cross-border transportation of CO2. [61]

As of 2011, there were 40 contracting parties to the London Protocol. Of these parties, 27 must also accept the 2009 amendment to Article 6 for it to enter into force. However, not all of the parties have been interested in offshore CO2 storage or cross-border movement of CO2, and have placed the ratification of Article 6 as a low priority. Cross-government cooperation will probably be required for ratification to occur. In some countries, the ratification may also be pending other laws and regulations that need to be changed for carbon storage and seques‐ tration in general [61].

Therefore, although geological carbon sequestration in the ocean has been approved in principle the OSPAR Convention and even the London Protocol, the Article 6 amendment may continue to pose a policy barrier to OCS deployment in the foreseeable future.

#### **5.6. Public outreach: Lessons from Hawaii**

It is noteworthy that no field studies demonstrating OCS at a significant scale have been conducted so far prior to its prohibition through the 2007 London Protocol and OSPAR Convention amendments. The largest scale attempt at demonstrating OCS was the Hawaii CO2 direct injection experiment This section outlines the failure of the Hawaii experiment mainly fuelled by a lack of early public outreach, and outlines some lessons learnt from the Hawaii project. [62,63]

In 1997 the US Department of Energy, the New Energy and Industrial Technology Develop‐ ment Organization of Japan (NEDO) and the Norwegian Research Council (NRC) signed an agreement to conduct experiments to evaluate the behaviour of liquid CO2 releases in to the ocean. While the project was announced in Kyoto in 1997, with a high profile to demonstrate the sponsors' commitment to CO2 mitigation, few resources were subsequently invested in public outreach.

The project scientists and sponsors selected an area off the coast of the Big Island of Hawaii to conduct the pilot CO2 study, based on technical feasibility and existing research infrastructure. However, they did not gauge the public perception prior to site selection. The local population only learnt of the injection project planned in their waters when it was first published in a newspaper article. In an area where the ocean is viewed as a major natural resource, the public perceived of the "dumping" of CO2 as a violation, and strongly opposed its continuation.

Eventually the pilot injection project was abandoned in Hawaii. In order to salvage the project, scientists attempted to instead conduct an injection study in Norway. However, here the actions of Greenpeace stopped any further testing, thus precluding completely any chance of field scale testing of direct-injection OCS.

Although the introduction of OCS was initially high profile the sponsors' commitment to CO2 mitigation, few resources were subsequently invested in public outreach. Moreover, the sponsors largely did not include the public in their decision to site the pilot injection experi‐ ment in Hawaii, nor did they factor public perception of potentially conducting a CO2 injection experiment in an area where the ocean is viewed as a major environmental resource.

Reiner (2008) cited the US National Institute of Standards and Technology (NIST) 2002 workshop for "Best Practices for Communication of Science and Technology to the Public" as a resource that offered key recommendations for public outreach, including:


**5.5. 2009 London Protocol amendment for transboundary transport of CO2**

for cross-border transportation of CO2. [61]

**5.6. Public outreach: Lessons from Hawaii**

field scale testing of direct-injection OCS.

tration in general [61].

102 CO2 Sequestration and Valorization

Hawaii project. [62,63]

public outreach.

Article 6 of the London Protocol (on the export of wastes or other material) was largely interpreted by the contracting parties as prohibiting the export of CO2 from a contracting party for injection into sub-seabed geological formations. In 2009 Article 6 was amended to allow

As of 2011, there were 40 contracting parties to the London Protocol. Of these parties, 27 must also accept the 2009 amendment to Article 6 for it to enter into force. However, not all of the parties have been interested in offshore CO2 storage or cross-border movement of CO2, and have placed the ratification of Article 6 as a low priority. Cross-government cooperation will probably be required for ratification to occur. In some countries, the ratification may also be pending other laws and regulations that need to be changed for carbon storage and seques‐

Therefore, although geological carbon sequestration in the ocean has been approved in principle the OSPAR Convention and even the London Protocol, the Article 6 amendment may

It is noteworthy that no field studies demonstrating OCS at a significant scale have been conducted so far prior to its prohibition through the 2007 London Protocol and OSPAR Convention amendments. The largest scale attempt at demonstrating OCS was the Hawaii CO2 direct injection experiment This section outlines the failure of the Hawaii experiment mainly fuelled by a lack of early public outreach, and outlines some lessons learnt from the

In 1997 the US Department of Energy, the New Energy and Industrial Technology Develop‐ ment Organization of Japan (NEDO) and the Norwegian Research Council (NRC) signed an agreement to conduct experiments to evaluate the behaviour of liquid CO2 releases in to the ocean. While the project was announced in Kyoto in 1997, with a high profile to demonstrate the sponsors' commitment to CO2 mitigation, few resources were subsequently invested in

The project scientists and sponsors selected an area off the coast of the Big Island of Hawaii to conduct the pilot CO2 study, based on technical feasibility and existing research infrastructure. However, they did not gauge the public perception prior to site selection. The local population only learnt of the injection project planned in their waters when it was first published in a newspaper article. In an area where the ocean is viewed as a major natural resource, the public perceived of the "dumping" of CO2 as a violation, and strongly opposed its continuation.

Eventually the pilot injection project was abandoned in Hawaii. In order to salvage the project, scientists attempted to instead conduct an injection study in Norway. However, here the actions of Greenpeace stopped any further testing, thus precluding completely any chance of

continue to pose a policy barrier to OCS deployment in the foreseeable future.


In addition, Reiner suggested that early outreach to the public via the internet during devel‐ opmental stages of a project is important before the project becomes newsworthy and receives attention from mainstream media outlets. [63].

The lack of outreach is reflected in the low level of understanding that has remained among the public, as well as relatively low public acceptance of carbon capture and sequestration (both geologic and ocean). Reiner summarized the European Commission's survey of the public from 25 countries of the European Union (the Eurobarometer) that showed that, in 2007 (at the same time as the OSPAR convention and London Protocol amendments), only 21% of those surveyed have heard of carbon capture and storage (geologic or ocean), compared with 53% for hydrogen energy and cars, 41% for fuel cells, and 44% for geothermal energy [63]. In the US in 2004, only 2.5% of 1200 respondents in a web-based survey had previously heard of carbon sequestration. In 2007, Palmgren et al. surveyed 126 community respondents, who ranked OCS less favourable than geological carbon sequestration. Both carbon sequestration options were less favourable to the respondents than nuclear power [64].

## **6. Conclusions**

Whether CO2 is introduced intentionally, or passively diffusing from the atmosphere to the ocean, the ocean is and will remain the largest sink of anthropogenic CO2. In addition to climate change implications of elevated atmospheric CO2, a further impact is the acidification of the ocean. Effects of increased acidity and pCO2 in organisms include respiratory distress (but some deep sea organisms take advantage of the CO2/O2 balance). There is also a risk of a reduced habitat as calcium carbonate stability zones decrease. However, further study is required to determine the variability of responses among marine species.

This chapter presented several methods by which direct injection of CO2 into the ocean could be introduced. Some injection technologies were developed that would theoretically, com‐ bined with proper siting of injection points, cause a relatively minor impact to marine ecosystems. Some pilot scale field studies began that would have provided more information about environmental impacts, but they were nixed due to public opposition stemming from a lack of extensive and continuous public outreach from the onset. Since 2007, international policies began to prohibit direct discharge of CO2 into the ocean, while favouring deep sea geological sequestration. CO2 leaks (e.g. in the form of droplets [65]) from geological structures to the ocean water column are however still possible [35], so continued research and studies about the mechanisms of CO2 leakage and the effects of increased dissolved carbon in the ocean continues to be an important topic of study for carbon sequestration.

## **Acknowledgements**

This chapter contains work funded by Ocean Carbon Sequestration Program, Biological and Environmental Research (BER), U.S. Dept. of Energy (grant number DE-FG02-01ER63078), the National Energy Technology Laboratory, U.S. Dept. of Energy (grant number DE-FG26-98FT40334) and the Martin Family Fellows for Sustainability.

## **Author details**

Aaron Chow

Berkeley Research Group, Waltham, Massachusetts, USA

## **References**

[1] Intergovernmental Panel on Climate Change (IPCC) (B. Metz B., Davidson O. eds. Carbon Dioxide Capture and Storage: A Special Report of IPCC Working Group III, Cambridge University Press, Cambridge UK; 2005

[2] Caldeira, K., Wickett, ME. Anthropogenic carbon and ocean pH. Nature 2003;425, 365-365.

**6. Conclusions**

104 CO2 Sequestration and Valorization

**Acknowledgements**

**Author details**

Aaron Chow

**References**

Whether CO2 is introduced intentionally, or passively diffusing from the atmosphere to the ocean, the ocean is and will remain the largest sink of anthropogenic CO2. In addition to climate change implications of elevated atmospheric CO2, a further impact is the acidification of the ocean. Effects of increased acidity and pCO2 in organisms include respiratory distress (but some deep sea organisms take advantage of the CO2/O2 balance). There is also a risk of a reduced habitat as calcium carbonate stability zones decrease. However, further study is

This chapter presented several methods by which direct injection of CO2 into the ocean could be introduced. Some injection technologies were developed that would theoretically, com‐ bined with proper siting of injection points, cause a relatively minor impact to marine ecosystems. Some pilot scale field studies began that would have provided more information about environmental impacts, but they were nixed due to public opposition stemming from a lack of extensive and continuous public outreach from the onset. Since 2007, international policies began to prohibit direct discharge of CO2 into the ocean, while favouring deep sea geological sequestration. CO2 leaks (e.g. in the form of droplets [65]) from geological structures to the ocean water column are however still possible [35], so continued research and studies about the mechanisms of CO2 leakage and the effects of increased dissolved carbon in the ocean

This chapter contains work funded by Ocean Carbon Sequestration Program, Biological and Environmental Research (BER), U.S. Dept. of Energy (grant number DE-FG02-01ER63078), the National Energy Technology Laboratory, U.S. Dept. of Energy (grant number DE-

[1] Intergovernmental Panel on Climate Change (IPCC) (B. Metz B., Davidson O. eds. Carbon Dioxide Capture and Storage: A Special Report of IPCC Working Group III,

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## **Estimation of Regional Carbon Storage Potential in Mangrove Soils on Carmen Island, Campeche, Mexico**

Julia Griselda Cerón-Bretón, Rosa María Cerón-Bretón, Jesús Jaime Guerra-Santos and Atl Victor Córdova-Quiroz

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57055

**1. Introduction**

Warming because of the increasing concentration of greenhouse gases is a global concern. Of all greenhouse gases, CO2 has the greatest contribution to global warming. CO2 can be stored in forests through biogeochemical processes governing the exchange between the atmosphere and forestry systems. Although forests are carbon sources, they can also mitigate global warming through carbon sequestration in different plant ecosystems, known as sinks, where carbon is accumulated by absorbing atmospheric CO2. Carbon is assimilated and stored, both in live biomass (stems, branches, leaves and roots) and dead biomass (litter, wood waste, organic matter from soils and forestry products), and oxygen is released to the atmosphere during this process. Therefore, forests play an important role in the global carbon cycle [1].

IPCC has pointed out the importance of carbon sequestration by vegetation as a low-cost choice to reduce the atmospheric CO2 content. Tropical forests store almost 50% more carbon than forests located outside the tropics [2]. Among the world's most productive wetlands are the mangroves, which fix and store large amounts of carbon in soils with unique characteristics of salinity, under anoxic and acidic conditions, with frequent flooding. The fact that these systems are productive under extreme conditions has been a point of interest to the scientific community. Therefore, correct management of tropical forests and wetlands constitutes an opportunity to store carbon. In the specific case of mangrove soils, where decomposition rates are low and their ability to store carbon is high, mangrove forests are an attractive alternative for carbon sequestration. Organic matter decomposition constitutes the main flux of carbon

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

and nutrients in most terrestrial ecosystems. In the case of tropical forests, this is regulated by several factors: vegetation species, morphology, C:N ratio, climate, flooding frequency, moisture, salinity, temperature, and so on [3-4].

Mangrove forests have a special adaptation capacity since they can tolerate oxygen deficit, high levels of salinity and different flooding patterns. Mangroves like many other plants have the ability to change the physical and chemical properties of soils in which they occur [5]. The fast-growing young trees absorb about 30% more carbon than mature wood, but an old-growth forest generally stores more carbon in the soil, groundwater and surface vegetation than a plantation of trees with the same size. Latitude, climate, species diversity and other biological factors also affect carbon fluxes in forests [3]. It has been reported that degradation of fresh organic matter is slower in anaerobic environments (such as mangroves), which additionally involves oxidizing agents such as nitrate and microorganisms like bacteria and fungi [6], which only operate when the tannin concentrations are low, because they inhibit growth [7-8]. The presence and abundance of macro invertebrates such as amphipods (crabs) and isopods (cochineal) also accelerates the breakdown of tissues by direct consumption of leaves [6].

Carbon storage in estuarine wetlands is an efficient process with minimal release of greenhouse gases [9-11]. Although these wetlands cover only about 5% of the earth's surface, they contain around 40% of global soil organic carbon. Mangrove forests cover vast extensions along the coastal zone in tropical regions. Mexico is the fifth country in the world in terms of the size of mangrove cover (655,667 ha). However, this surface is being lost at a yearly rate between 10,000 and 40,000 ha, mainly due to aquaculture, agriculture, deforestation and change in land use. Mangroves in Mexico are distributed within coastal lagoons and deltaic systems of the Gulf of Mexico (Veracruz, Tabasco and Campeche States) and the Pacific Ocean (Baja California, Sonora, Nayarit, Oaxaca and Chiapas) [12-14], with some coastal lagoons having ephemeral mouths which open during the rainy season or by action of the fishermen [5]. In the state of Campeche, mangrove cover accounts for 29.98% of the total mangrove cover in the country, with an extension of 196,552 ha [15]. Along the shoreline of Carmen Island, there are four main mangrove species: *Rhizophora mangle*, *Laguncularia racemosa*, *Avicennia germinans* and *Conocar‐ pus erectus*. It is common to find them in associations, as a result of a succession process depending on the tidal levels that flood them but establishing clear control of one prevailing species or associations of two or three species.

Estimation of the dynamic of net fluxes of carbon between forests and the atmosphere is currently an open question in the main discussion forums about climate change. Sequestration and emission processes comprise a system with four main carbon sinks: vegetation, decom‐ position of organic matter, soils and forestry products. Since these sinks are interrelated, it is necessary to undertake a comprehensive systemic analysis. However, this is particularly difficult for tropical forests, where data about carbon content in vegetation and soils are scarce or unavailable. In spite of the importance of mangrove forests as carbon sinks, most of the research on carbon storage is focused on terrestrial ecosystems and little attention has been given to this type of ecosystem. Although in Mexico there are 113 Ramsar sites, the location of specific carbon sinks and their potential to sequester atmospheric carbon remains poorly defined, and there are uncertainties concerning carbon stock in wetlands at the local and regional scale. In order to complete carbon storage inventories and to reduce these uncertain‐ ties, it is necessary to obtain more local and regional information to support environmental policies to promote conservation and restoration projects as mitigation strategies. Therefore, this study focused on estimating regional carbon storage potential in mangrove soils in six sites uniformly distributed along Carmen Island in the region of Terminos Lagoon, in Cam‐ peche, Mexico, considering three climatic periods (cold fronts, called "north season", dry season and rainy season) and at two different soil depths (0-30 and 30-60 cm) during 2009 and 2010.

## **2. Sampling procedure**

and nutrients in most terrestrial ecosystems. In the case of tropical forests, this is regulated by several factors: vegetation species, morphology, C:N ratio, climate, flooding frequency,

Mangrove forests have a special adaptation capacity since they can tolerate oxygen deficit, high levels of salinity and different flooding patterns. Mangroves like many other plants have the ability to change the physical and chemical properties of soils in which they occur [5]. The fast-growing young trees absorb about 30% more carbon than mature wood, but an old-growth forest generally stores more carbon in the soil, groundwater and surface vegetation than a plantation of trees with the same size. Latitude, climate, species diversity and other biological factors also affect carbon fluxes in forests [3]. It has been reported that degradation of fresh organic matter is slower in anaerobic environments (such as mangroves), which additionally involves oxidizing agents such as nitrate and microorganisms like bacteria and fungi [6], which only operate when the tannin concentrations are low, because they inhibit growth [7-8]. The presence and abundance of macro invertebrates such as amphipods (crabs) and isopods (cochineal) also accelerates the breakdown of tissues by direct consumption of leaves [6].

Carbon storage in estuarine wetlands is an efficient process with minimal release of greenhouse gases [9-11]. Although these wetlands cover only about 5% of the earth's surface, they contain around 40% of global soil organic carbon. Mangrove forests cover vast extensions along the coastal zone in tropical regions. Mexico is the fifth country in the world in terms of the size of mangrove cover (655,667 ha). However, this surface is being lost at a yearly rate between 10,000 and 40,000 ha, mainly due to aquaculture, agriculture, deforestation and change in land use. Mangroves in Mexico are distributed within coastal lagoons and deltaic systems of the Gulf of Mexico (Veracruz, Tabasco and Campeche States) and the Pacific Ocean (Baja California, Sonora, Nayarit, Oaxaca and Chiapas) [12-14], with some coastal lagoons having ephemeral mouths which open during the rainy season or by action of the fishermen [5]. In the state of Campeche, mangrove cover accounts for 29.98% of the total mangrove cover in the country, with an extension of 196,552 ha [15]. Along the shoreline of Carmen Island, there are four main mangrove species: *Rhizophora mangle*, *Laguncularia racemosa*, *Avicennia germinans* and *Conocar‐ pus erectus*. It is common to find them in associations, as a result of a succession process depending on the tidal levels that flood them but establishing clear control of one prevailing

Estimation of the dynamic of net fluxes of carbon between forests and the atmosphere is currently an open question in the main discussion forums about climate change. Sequestration and emission processes comprise a system with four main carbon sinks: vegetation, decom‐ position of organic matter, soils and forestry products. Since these sinks are interrelated, it is necessary to undertake a comprehensive systemic analysis. However, this is particularly difficult for tropical forests, where data about carbon content in vegetation and soils are scarce or unavailable. In spite of the importance of mangrove forests as carbon sinks, most of the research on carbon storage is focused on terrestrial ecosystems and little attention has been given to this type of ecosystem. Although in Mexico there are 113 Ramsar sites, the location of specific carbon sinks and their potential to sequester atmospheric carbon remains poorly defined, and there are uncertainties concerning carbon stock in wetlands at the local and

moisture, salinity, temperature, and so on [3-4].

112 CO2 Sequestration and Valorization

species or associations of two or three species.

#### **2.1. Study area description**

The study area is located within the Atasta peninsula in southeastern Mexico in Campeche State, within the natural protected area called "Laguna de Terminos". This area has a warmhumid climate with rains occurring mainly in summer [Am(f)], according to the Köppen classification modified by García [16]. The geomorphology is characterized by marshes and wetlands, with altitudes between 0 to 20 masl, and the annual mean temperature is between 21 to 24.7°C.

Soils in this region are clayey with high fertility and high organic matter content, associated with dominant vegetation of red mangrove (*Rhizophora mangle*), black mangrove (*Avicennia germinans*), white mangrove (*Laguncularia racemosa*) and buttonwood mangrove (*Conocarpus erectus*) [17]. Climate conditions in this zone show three well-defined periods: dry season (from March to May), rainy season (from June to October) and "north season" (from November to February).

Sampling campaigns were carried out between February 2009 and October 2010 in the three climatic periods. Based on visual inspections, transects were established in a representative area of mangrove forest in each sampling site. They were selected to assure representative regional samples, taking into account the type of vegetation, easy access and the hydrology. The locations of the selected areas for this study are shown in Figure 1:

1.-Puerto Rico (P), located at 18° 36' 55" N and 91° 56' 35" W. Altitude: 11 masl. A great portion of this area has been deforested to establish farms. The remaining mangrove areas present in this zone correspond to the basin [18]. Three sampling zones were selected (P1, P2, and P3), which were located inland, with minimum ground slope and slow water renewal. Accumu‐ lated floodwaters in depressions in these sites correspond to cycling of organic matter and nutrients in a closed ecosystem [19]. Individuals of *Avicennia germinas*, *Rhizophora mangle* and *Laguncularia racemosa* were registered in this site, with dominance of *Avicennia germinans.*

2.-Xicalango (XC), located at 18° 37' 02" N and 91° 58' 20" W, with dominance of *Avicennia germinans* and *Conocarpus erectus*. Three sampling zones were selected (XC1, XC2 and XC3). The first one was completely flooded, the second one was dry and the last one was partially flooded.

3.-Nuevo Campechito (NC), located at 18° 38' 28" N and 91° 57' 29" W, with an association of *Conocarpus erectus*, *Laguncularia racemosa* and *Rhizophora mangle*. Three sampling zones were selected (NC1, NC2 and NC3). During the three climatic periods, the first one was flooded all the time, the second one was partiallyflooded and the last one was dry.

4.-Estero Pargo (EP), a 5.3 km long tidal channel on the lagoon side of Carmen Island, located at 18° 39' 02.8" N and 91° 45' 46.9" W, with an association of *Rhizophora mangle-Laguncularia racemosa* and *Conocarpus erectus.* This site only was flooded by rainfall during rainy and "north" seasons. Three sampling zones were selected (EP1, EP2 and EP3), which were moderately flooded during the dry and rainy seasons.

5.-Bahamitas (BH), located at the border of Términos Lagoon. This site is within the natural protected area named "Términos Lagoon", a Ramsar site since 2004. This site is located at 18° 41′ 57.1′′ N and 91° 41′ 50.7′′ W, with an association of *Rhizophora mangle*, *Laguncularia racemosa* and *Avicennia germinans*. Again, three sampling zones were selected (BH1, BH2 and BH3). This site was always flooded (received regular tidal inundation and was covered with fresh water during the "north" and rainy seasons).

6.-Puerto Rico 2 (R), located at 18° 36' 56.1 "N and 91 ° 54' 33.6" W. The sampling zones selected were R1, R2 and R3. This site shows a great diversity of tropical vegetation, including *Rhizophora mangle*, *Avicennia germinans*, *Laguncularia racemosa*, and *Conocarpus erectus*.

#### **2.2. Sampling methodology**

The field study was carried out at six sites with three sampling zones for each one, between February 2009 and October 2010. In all sampling sites, the mangrove forest is subject to anthropogenic exploitation and can be considered as perturbed. We performed sampling campaigns to assess seasonal changes in carbon storage, considering three climatic periods: "north" season, dry season and the beginning of the rainy season, with six sampling cam‐ paigns. Not all sampling sites had comparable tidal influences during the study period. Some of them were always flooded (received regular tidal inundation and were covered with fresh water during "north" and rainy seasons), whereas other sites only were flooded because of the occurrence of rainfall during rainy and "north" seasons. Three sampling plots of 4 m x 12 m in each site were selected considering free access to the zone, risks, mangrove distribution by species and disturbances (Figure 1, Table 1). Based on visual inspections of the study area, transects were established in a representative area of the mangrove forest, locating three sampling points of 1 m2 approximately for each of three sampling zones for all sites.

Duplicate soil samples were collected from the ground to 30 and 60 cm depth using a 193.3 cm3 soil sampler in an area of 48 m2 [20]. The corer was carefully inserted into the soil and pushed down to 0.3 and 0.6 m. Because sampled soils are typically moist, the corer has a oneway check valve that creates a vacuum inside the liner as it is pushed into the soil, and when the device is pulled out it creates a suction force that retains the sample in the tube. This sampling method used was as proposed by Grossman and Reinsch [21] and Bernal and Mitsch [22] for sampling wet or inundated soils.

A total of 326 soil samples with replicates were taken. After extraction, each core was labeled and, sealed using tight-fitting end caps, and sent for laboratory analysis.

**Figure 1.** Location of the sampling sites.

3.-Nuevo Campechito (NC), located at 18° 38' 28" N and 91° 57' 29" W, with an association of *Conocarpus erectus*, *Laguncularia racemosa* and *Rhizophora mangle*. Three sampling zones were selected (NC1, NC2 and NC3). During the three climatic periods, the first one was flooded all

4.-Estero Pargo (EP), a 5.3 km long tidal channel on the lagoon side of Carmen Island, located at 18° 39' 02.8" N and 91° 45' 46.9" W, with an association of *Rhizophora mangle-Laguncularia racemosa* and *Conocarpus erectus.* This site only was flooded by rainfall during rainy and "north" seasons. Three sampling zones were selected (EP1, EP2 and EP3), which were moderately

5.-Bahamitas (BH), located at the border of Términos Lagoon. This site is within the natural protected area named "Términos Lagoon", a Ramsar site since 2004. This site is located at 18° 41′ 57.1′′ N and 91° 41′ 50.7′′ W, with an association of *Rhizophora mangle*, *Laguncularia racemosa* and *Avicennia germinans*. Again, three sampling zones were selected (BH1, BH2 and BH3). This site was always flooded (received regular tidal inundation and was covered with

6.-Puerto Rico 2 (R), located at 18° 36' 56.1 "N and 91 ° 54' 33.6" W. The sampling zones selected were R1, R2 and R3. This site shows a great diversity of tropical vegetation, including

The field study was carried out at six sites with three sampling zones for each one, between February 2009 and October 2010. In all sampling sites, the mangrove forest is subject to anthropogenic exploitation and can be considered as perturbed. We performed sampling campaigns to assess seasonal changes in carbon storage, considering three climatic periods: "north" season, dry season and the beginning of the rainy season, with six sampling cam‐ paigns. Not all sampling sites had comparable tidal influences during the study period. Some of them were always flooded (received regular tidal inundation and were covered with fresh water during "north" and rainy seasons), whereas other sites only were flooded because of the occurrence of rainfall during rainy and "north" seasons. Three sampling plots of 4 m x 12 m in each site were selected considering free access to the zone, risks, mangrove distribution by species and disturbances (Figure 1, Table 1). Based on visual inspections of the study area, transects were established in a representative area of the mangrove forest, locating three

approximately for each of three sampling zones for all sites.

[20]. The corer was carefully inserted into the soil and

Duplicate soil samples were collected from the ground to 30 and 60 cm depth using a 193.3

pushed down to 0.3 and 0.6 m. Because sampled soils are typically moist, the corer has a oneway check valve that creates a vacuum inside the liner as it is pushed into the soil, and when the device is pulled out it creates a suction force that retains the sample in the tube. This sampling method used was as proposed by Grossman and Reinsch [21] and Bernal and Mitsch

*Rhizophora mangle*, *Avicennia germinans*, *Laguncularia racemosa*, and *Conocarpus erectus*.

the time, the second one was partiallyflooded and the last one was dry.

flooded during the dry and rainy seasons.

114 CO2 Sequestration and Valorization

**2.2. Sampling methodology**

sampling points of 1 m2

soil sampler in an area of 48 m2

[22] for sampling wet or inundated soils.

cm3

fresh water during the "north" and rainy seasons).

## **3. Analytical procedure**

Free water was drained away and all biomass and solid materials (shells, roots, leaves, and so on) were removed. Then the samples were ground, dried at room temperature and sieved to pass through a 2 mm mesh. Organic carbon (OC) was quantified by using the ignition method and organic matter (OM) was determined by warming samples to 550 °C during 4 h[22] and the content of organic carbon was estimated by multiplying by a factor of 0.4 [23]. Total nitrogen (NT) was determined according to the semimicro-Kjeldahl method [24]. By this method, 0.5 g of soil sifted through 0.250 mm is weighed and then digested with a mixture of catalyst and sulfuric acid (H2SO4), after which it is distillated with NaOH and titrated with H3BO3 using a Shiro-Tashiro mixture as indicator [24].

To determine bulk density (Da), we used the test tube technique [25], by which a dry sample is passed through a 2 mm sieve, then a 50 ml plastic test tube is weighed and then 20 to 50 g of sifted soil is added. After this, the sample is placed on a firm surface and then it is hit 30 times per second with a rubber mallet in a vertical trajectory from 0.20 to 0.30 m. Finally, the volume and sample weight are registered [25]. Electrical conductivity (CE) was measured with a CL35 conductivity meter by using a 1:5 soil/water solution [26]. Soil pH was measured with a Thermo Orion model 290A pH meter by using a 1:2 soil/water solution [27]. Texture determination was carried out by the Bouyoucos method using a 5% sodichexametaphosphate solution as dispersant [28].

To estimate the carbon storage rate (CA), the following equation was used:

### CA= CO% Da Pr,

Where CA= carbon storage rate, CO% = organic carbon content, Da= density and Pr= soil depth [29].

Descriptive, comparative and relational statistical analyses were performed for carbon storage rate, sampling site, sampling depth and climatic period.

## **4. Results**

#### **4.1. Relative humidity**

Figures 2a-2c present the relative humidity (RH) values at 30 and 60 cm depth for all the sampling zones for north, dry and rainy seasons, respectively. Puerto Rico 1, Estero Pargo and Bahamitas showed the highest relative humidity whereas Nuevo Campechito and Xicalango showed the lowest values. Most of the sampling zones showed higher RH at 30 cm depth. Seasonal variation for all the sampling zones showed higher values of RH for the rainy season and the lowest values for the north season.

#### **4.2. Soil texture**

Bahamitas (BH) and Estero Pargo (EP) showed sandy texture, whereas the texture was sandy clay loam for Nuevo Campechito (NC), and Xicalango (XC). Puerto Rico 1 (P) and Puerto Rico 2 (R) showed sandy clay texture.

#### **4.3. pH and electrical conductivity**

The mean values for pH and electrical conductivity for all sampling sites during the three climatic periods are presented in Figures 3a-3c and Figures 4a-4c. pH ranged from 6.7 to 7.5 for Bahamitas, and from 6.89 to 7.12 for Estero-Pargo. This suggests that neutral soils are dominant in this study area. Significant differences were not found among the three climatic periods and sampling depths in BH. Soils in this site can be considered moderately to strongly saline. Electrical conductivity values were higher during the dry season for BH1 and during the rainy season for BH2 and BH3, where *Rhizophora mangle* and *Avicennia germinans* are the dominant species. The highest values for electrical conductivity in EP was found at 30 cm

Estimation of Regional Carbon Storage Potential in Mangrove Soils on Carmen Island, Campeche, Mexico http://dx.doi.org/10.5772/57055 117

of sifted soil is added. After this, the sample is placed on a firm surface and then it is hit 30 times per second with a rubber mallet in a vertical trajectory from 0.20 to 0.30 m. Finally, the volume and sample weight are registered [25]. Electrical conductivity (CE) was measured with a CL35 conductivity meter by using a 1:5 soil/water solution [26]. Soil pH was measured with a Thermo Orion model 290A pH meter by using a 1:2 soil/water solution [27]. Texture determination was carried out by the Bouyoucos method using a 5% sodichexametaphosphate

Where CA= carbon storage rate, CO% = organic carbon content, Da= density and Pr= soil depth

Descriptive, comparative and relational statistical analyses were performed for carbon storage

Figures 2a-2c present the relative humidity (RH) values at 30 and 60 cm depth for all the sampling zones for north, dry and rainy seasons, respectively. Puerto Rico 1, Estero Pargo and Bahamitas showed the highest relative humidity whereas Nuevo Campechito and Xicalango showed the lowest values. Most of the sampling zones showed higher RH at 30 cm depth. Seasonal variation for all the sampling zones showed higher values of RH for the rainy season

Bahamitas (BH) and Estero Pargo (EP) showed sandy texture, whereas the texture was sandy clay loam for Nuevo Campechito (NC), and Xicalango (XC). Puerto Rico 1 (P) and Puerto Rico

The mean values for pH and electrical conductivity for all sampling sites during the three climatic periods are presented in Figures 3a-3c and Figures 4a-4c. pH ranged from 6.7 to 7.5 for Bahamitas, and from 6.89 to 7.12 for Estero-Pargo. This suggests that neutral soils are dominant in this study area. Significant differences were not found among the three climatic periods and sampling depths in BH. Soils in this site can be considered moderately to strongly saline. Electrical conductivity values were higher during the dry season for BH1 and during the rainy season for BH2 and BH3, where *Rhizophora mangle* and *Avicennia germinans* are the dominant species. The highest values for electrical conductivity in EP was found at 30 cm

To estimate the carbon storage rate (CA), the following equation was used:

rate, sampling site, sampling depth and climatic period.

solution as dispersant [28].

116 CO2 Sequestration and Valorization

CA= CO% Da Pr,

[29].

**4. Results**

**4.2. Soil texture**

**4.1. Relative humidity**

and the lowest values for the north season.

2 (R) showed sandy clay texture.

**4.3. pH and electrical conductivity**

**Figure 2.** Relative humidity at 30 and 60 cm depth for all the sampling zones during north season (a), dry season (b) and rainy season (c).

depth, being higher during the dry season in EP1 and EP3, where *Conocarpus erectus* and *Rhizophora mangle* are the dominant species.

The pH values in Xicalango ranged from 7.96 to 8.35, whereas in Nuevo Campechito, the pH ranged from 6.55 to 8.46, suggesting that the soils are slightly alkaline in these sites and indicating the presence of soluble salts. Soils in these sites were acid, with the lowest pH values (6.5) and high values of electrical conductivity. No significant differences were found for electrical conductivity in NC and XC at 30 and 60 cm depth (ANOVA, P< 0.05). The highest values were found during the dry season, being highest for NC at 60 cm and highest for in XC at 30 cm depth.

Electrical conductivity in Puerto Rico did not show a clear pattern of variation regarding sampling depth. Marine aerosols due to its proximity to the coast probably influence its high salinity. Likewise, the low permeability in Puerto Rico soil promotes water accumulation, increasing sodium concentrations and contributing to low micro biota activity in these soils. Moreover, the hydrological characteristics of this mangrove forest are similar to those typical from a basin, characterized by heavy flooding, little or no tidal contact and high salinity. Soil

**Figure 3.** pH at 30 and 60 cm depth for all the sampling zones during north season (a), dry season (b) and rainy sea‐ son (c).

pH variations in Puerto Rico were significant at the different depths and climatic periods (Tukey test, P<0.05). Soils in these sampling zones were more acidic at 30 cm depth, being more alkaline during the north season.

#### **4.4. Organic carbon and organic matter content**

The organic carbon concentrations for the three sampling zones in Bahamitas and Estero Pargo during the north, dry and rainy seasons are reported in Figures 5 a and 5b. The organic carbon (OC) content ranged from 4.76 to 15.73% for Bahamitas, and from 2.81 to 19.7% for Estero-Pargo. The mean carbon storage levels were 23.16 and 23.08 Kg C m-2 for Bahamitas and Estero-Pargo, respectively. Figures 6 a and 6b show the organic matter content (%) for the three sampling zones in Bahamitas and Estero Pargo during the north, dry and rainy seasons. In these sites, the long periods of tidal flooding (sites were flooded throughout the sampling

Estimation of Regional Carbon Storage Potential in Mangrove Soils on Carmen Island, Campeche, Mexico http://dx.doi.org/10.5772/57055 119

**Figure 4.** Electrical conductivity at 30 and 60 cm depth for all the sampling zones during north season (a), dry season (b) and rainy season (c).

pH variations in Puerto Rico were significant at the different depths and climatic periods (Tukey test, P<0.05). Soils in these sampling zones were more acidic at 30 cm depth, being more

**Figure 3.** pH at 30 and 60 cm depth for all the sampling zones during north season (a), dry season (b) and rainy sea‐

The organic carbon concentrations for the three sampling zones in Bahamitas and Estero Pargo during the north, dry and rainy seasons are reported in Figures 5 a and 5b. The organic carbon (OC) content ranged from 4.76 to 15.73% for Bahamitas, and from 2.81 to 19.7% for Estero-Pargo. The mean carbon storage levels were 23.16 and 23.08 Kg C m-2 for Bahamitas and Estero-Pargo, respectively. Figures 6 a and 6b show the organic matter content (%) for the three sampling zones in Bahamitas and Estero Pargo during the north, dry and rainy seasons. In these sites, the long periods of tidal flooding (sites were flooded throughout the sampling

alkaline during the north season.

118 CO2 Sequestration and Valorization

son (c).

**4.4. Organic carbon and organic matter content**

period) maintained anoxic conditions (below 10 cm of depth) and high organic matter and organic carbon contents. This explains why the highest values were found in the surface layer. This pattern is a general phenomenon observed in forests. The accumulation of organic matter is enhanced in sites with abundant rainfall or with deficient drainage. The highest values for organic matter, organic carbon and carbon storage were found during the north (when heavy rains occurred) and dry seasons. During the rainy season in these sites, accumulation of organic matter increases, but decomposition is slow and the accumulation remains until the north and dry seasons, resulting in values slightly lower during the rainy season in comparison with subsequent seasons. During the dry season, salts and dissolved organic carbon concentrate with increasing evaporation, whereas during the rainy season, pore waters were diluted with rain and runoff waters, resulting in lower organic carbon concentration.

**Figure 5.** Organic carbon content at 30 and 60 cm depth for all the sampling zones during the three climatic periods for a) Bahamitas and b) Estero Pargo.

**Figure 6.** Organic matter content (%) at 30 and 60 cm depth for all the sampling zones during the three climatic peri‐ ods for a) Bahamitas and b) Estero Pargo.

The organic matter content for the three sampling zones in Nuevo Campechito and Xicalango during the north, dry and rainy seasons are shown in Figures 7a and 7b. The organic matter and organic carbon concentrations were higher at 30 cm depth. The same was observed for the organic matter content (%) for the three sampling zones in Nuevo Campechito and Xicalango during the north, dry and rainy seasons (Figures 8a and 8b). Organic matter ranged from 5.2 to 5.6% for Nuevo Campechito, and from 3.4 to 3.77% for Xicalango. The mean organic carbon contents were 72.78 Kg C m-2 for Nuevo Campechito and 79.29 Kg C m-2 for Xicalango. These high organic carbon contents were associated with the high productivity of the mangrove species that prevail in these sites, where litter accumulation causes high organic matter levels in the soils. In addition, the flooded conditions that prevailed during the sampling period maintained anoxic conditions that enhanced the carbon storage. Carbon storage was higher in live biomass in comparison with the soils. The forestry inventory revealed that most of the trees in these sites were younger than 7 years. Since it is well known that young trees store 30% more carbon than mature trees, this suggests that young individuals in growing stage in these sites store more carbon in live biomass than in soils. The organic matter and organic carbon contents were slightly higher in Nuevo Campechito than in Xicalango. Individuals of *Rhizophora mangle* with higher diameter and height were found in Nuevo Campechito in comparison with Xicalango. This species contributes a great quantity of organic matter due to litter falling on flooded soils, resulting in a low decomposition rate that increased the organic matter and carbon concentrations. On the other hand, in Xicalango there were zones where *Conocarpus erectus* was the prevailing species of mangrove, where litter was deposited on sandy soils with low moisture, resulting in a slower accumulation process.

**Figure 5.** Organic carbon content at 30 and 60 cm depth for all the sampling zones during the three climatic periods

a) Bahamitas

0 5 10 15 20 25 30

Rainy season Dry season North season

Organic Matter (%)

Rainy season Dry season North season

b) Estero Pargo Bahamitas: BH1, BH2, BH3 Estero Pargo: EP1, EP2, EP3

**Figure 6.** Organic matter content (%) at 30 and 60 cm depth for all the sampling zones during the three climatic peri‐

0 10 20 30 40

Organic Matter (%)

for a) Bahamitas and b) Estero Pargo.

120 CO2 Sequestration and Valorization

BH1 (0-30 cm) BH1(0-60 cm) BH2 (0-30 cm) BH2 (0-60 cm) BH3 (0-30 cm) BH3 (0-60 cm)

EP1 (0-30 cm) EP1 (0-60 cm) EP2 (0-30 cm) EP2 (0-60 cm) EP3 (0-30 cm) EP3 (0-60 cm)

ods for a) Bahamitas and b) Estero Pargo.

**Figure 7.** Organic carbon content at 30 and 60 cm depth for all the sampling zones during the three climatic periods for a) Nuevo Campechito and b) Xicalango.

Figures 9a and 9b show the result of the organic carbon content of the three sampling areas in Puerto Rico 1 and 2 in Puerto Rico for the north, dry and rainy seasons. In Puerto Rico, organic carbon ranged from 4.8 to 23.16% at 30 cm depth and from 2.99 to 18.61% at 60 cm depth for P1 and R1 sampling zones. Higher organic carbon concentrations were found in P3 and R1, reaching almost 14% of the content for the three climatic periods. P1, P2, R2 and R3 together accounted for almost 10% of the organic carbon content. The OC content diminished as the sampling depth decreased for all the sampling zones of this site. No significant differences were found for OC in the three climatic periods. These sites showed an organic carbon content between 7 and 11%, 0.2-0.35% of nitrogen, a C:N ratio from 41-47, CE and a carbon storage rate from 160 to 220 Kg C m-2. The carbon storage rate decreased slightly with depth. Long periods of flooding maintained anoxic conditions. This could explain the finding of highest organic carbon and organic matter concentrations at 30 cm depth in this site.

**Figure 8.** Organic matter content (%) at 30 and 60 cm depth for all the sampling zones during the three climatic peri‐ ods for a) Nuevo Campechito and b) Xicalango.

The carbon storage in Bahamitas showed significant differences between the different climatic periods, with the highest values being found during the north season.The highest values found were for BH1 at 30 cm depth, for the three climatic periods, and for BH2 and BH3, during the dry season. However, for BH2 and BH3, the highest values found were at 60 cm depth during the north and rainy seasons. Carbon storage in Estero Pargo showed the highest values for EP2 and EP3, for the three climatic periods, where *Laguncularia racemosa* and *Rhizophora mangle* are the dominant species. the The lowest values for carbon storage for all three climatic periods were found in EP1, where the dominant species is *Conocarpus erectus*.

Estimation of Regional Carbon Storage Potential in Mangrove Soils on Carmen Island, Campeche, Mexico http://dx.doi.org/10.5772/57055 123

Figures 9a and 9b show the result of the organic carbon content of the three sampling areas in Puerto Rico 1 and 2 in Puerto Rico for the north, dry and rainy seasons. In Puerto Rico, organic carbon ranged from 4.8 to 23.16% at 30 cm depth and from 2.99 to 18.61% at 60 cm depth for P1 and R1 sampling zones. Higher organic carbon concentrations were found in P3 and R1, reaching almost 14% of the content for the three climatic periods. P1, P2, R2 and R3 together accounted for almost 10% of the organic carbon content. The OC content diminished as the sampling depth decreased for all the sampling zones of this site. No significant differences were found for OC in the three climatic periods. These sites showed an organic carbon content between 7 and 11%, 0.2-0.35% of nitrogen, a C:N ratio from 41-47, CE and a carbon storage rate from 160 to 220 Kg C m-2. The carbon storage rate decreased slightly with depth. Long periods of flooding maintained anoxic conditions. This could explain the finding of highest organic

a) Nuevo Campechito

0 2 4 6 8 10

Rainy season Dry season North season

b) Xicalango Nuevo Campechito: NC1, NC2, NC3 Xicalango: XC1, XC2, XC3

**Figure 8.** Organic matter content (%) at 30 and 60 cm depth for all the sampling zones during the three climatic peri‐

The carbon storage in Bahamitas showed significant differences between the different climatic periods, with the highest values being found during the north season.The highest values found were for BH1 at 30 cm depth, for the three climatic periods, and for BH2 and BH3, during the dry season. However, for BH2 and BH3, the highest values found were at 60 cm depth during the north and rainy seasons. Carbon storage in Estero Pargo showed the highest values for EP2 and EP3, for the three climatic periods, where *Laguncularia racemosa* and *Rhizophora mangle* are the dominant species. the The lowest values for carbon storage for all three climatic

periods were found in EP1, where the dominant species is *Conocarpus erectus*.

Organic Matter (%)

3.1 3.2 3.3 3.4 3.5 3.6 3.7

carbon and organic matter concentrations at 30 cm depth in this site.

Rainy season Dry season North season

Organic Matter (%)

NC1 (0-30 cm) NC1(0-60 cm) NC2 (0-30 cm) NC2 (0-60 cm) NC3 (0-30 cm) NC3 (0-60 cm)

122 CO2 Sequestration and Valorization

XC1 (0-30 cm) XC1 (0-60 cm) XC2(0-30 cm) XC2 (0-60 cm) XC3 (0-30 cm) XC3 (0-60 cm)

ods for a) Nuevo Campechito and b) Xicalango.

**Figure 9.** Organic carbon content at 30 and 60 cm depth for all the sampling zones during the three climatic periods for a) Puerto Rico 1 and b) Puerto Rico 2.

**Figure 10.** Organic matter content (%) at 30 and 60 cm depth for all the sampling zones during the three climatic periods for a) Puerto Rico 1 and b) Puerto Rico 2.


**Table 1.** Comparison of carbon storage rate found in this work with those obtained in other sites.

*Conocarpus erectus* and *Laguncularia racemosa* are the dominant species in NC3 and NC1. Carbon storage in soils of NC was lower in NC1 and NC2, which have associations of *Rhizophora mangle, Conocarpus erectus* and *Laguncularia racemosa*. In contrast, XC2 and XC3 did not show significant differences between each other. Carbon storage in soils of NC3 and XC1 were similar to those reported in forests with associations of *Rhizophora mangle-Avicennia germinans* (143.3 y 122.2 t C ha-1) in French Guiana [29]. XC showed the highest values of carbon storage, ranging from 74.10±4.16 to 119.50±20.40 t C ha-1, with significant differences (p<0.05) with NC. This behavior is due to the high productivity of species associated with this type of soil, resulting from defoliation and incorporation of high organic matter content in these soils.

There were significant differences for carbon storage in Puerto Rico 1 and Puerto Rico 2, showing the highest values in P3 and R2, with 325 and 320.50 t ha-1, respectively, followed by R3 with 141.56 t ha-1 and finally P2 with 101.78 t ha-1. In general, carbon storage in all sampling zones in the six studied sites showed significant differences regarding sampling depth.

In mangrove forests, the stand age is a determinant factor that influences the amount of organic carbon in the soil regardless of season. Comparing our results with carbon storage data obtained in other sites (Table 1), we can suggests that sandy and neutral soils as in our study area and associations of red and white mangrove have good potential of carbon sequestration, considering that our mangrove individuals were from young to mature in reproductive age. We can expect this potential to increase in the coming years.

### **5. Conclusions**

**Site/Land Use carbon storage**

Southern California, USA/coastal lagoon and wetland complex. 0.033 [30] Okinawa Island, Japan/pioneer mangrove stand. 5.73 [31] Tabasco, Mexico/ Red and white mangrove stand. 47.2-82.2 [9]

Sydney, Australia/coastal wetlands. 13.9 [33] Tropical and subtropical China/marsh vegetation 40.0 [34] Brazil/Oxisol. 12.0-24.0 [35] Brazil/agricultural soils. 2.0-10.0 [36]

**Table 1.** Comparison of carbon storage rate found in this work with those obtained in other sites.

*Conocarpus erectus* and *Laguncularia racemosa* are the dominant species in NC3 and NC1. Carbon storage in soils of NC was lower in NC1 and NC2, which have associations of *Rhizophora mangle, Conocarpus erectus* and *Laguncularia racemosa*. In contrast, XC2 and XC3 did not show significant differences between each other. Carbon storage in soils of NC3 and XC1 were similar to those reported in forests with associations of *Rhizophora mangle-Avicennia germinans* (143.3 y 122.2 t C ha-1) in French Guiana [29]. XC showed the highest values of carbon storage, ranging from 74.10±4.16 to 119.50±20.40 t C ha-1, with significant differences (p<0.05) with NC. This behavior

Gahanna woods, Ohio, USA/isolated and forested wetland. 3.03

Old Woman Creek, Ohio, USA/riverine flow-through wetland. 2.77 La Selva, Costa Rica/isolated and forested wetland. 0.43 Earth University, Costa Rica/slow-flowing slough wetland. 1.67 Palo Verde, Costa Rica/riverine flow-through wetland. 1.36

Southeast Australia/disturbed wetland soils. 6.61

Southeast Australia/undisturbed wetland soils. 11.19

Bahamitas, Campeche, Mexico/neutral and sandy soils, perturbed

Estero Pargo, Campeche, Mexico/neutral and sandy soils, perturbed

Nuevo Campechito, Campeche, Mexico/sandy clayloam, associations of *Rhizophora mangle-Lagunculariaracemosa-*

Xicalango, Campeche, Mexico/sandy clay loam, associations of *Avicenniagerminans-Lagunculariaracemosa-Conocarpuserectus*

Puerto Rico, Campeche, Mexico/sandy clayloam, associations of *Avicenniagerminans-Rhizophora mangle-Lagunculariaracemosa.*

mangrove forest.

*Conocarpuserectus*.

mangrove forest. Estero-Pargo.

124 CO2 Sequestration and Valorization

**(kg C m-2).**

23.16

23.08

70.00

55.00

190

**Reference**

[22]

[32]

This study

Climatic season and soil type (vegetative community present and hydrogeomorfology) were the most important variables in this study. Concentrations of carbon in tropical soils tend to decrease with depth. This behavior indicates that a small fraction of carbon that is being introduced into the soil remains there, being typical of tropical forests where organic matter and nutrients do not accumulate because they are quickly used by biotic systems. It can be concluded that the accumulation of organic matter and carbon storage are determined by the rate of decay rather than the production rate of organic matter. The combination of anaerobic conditions on site and productivity of the system tends to cause the soils that remain flooded most of the time to be highly organic. High rates of carbon sequestration in this study indicate that conservation efforts to protect wetlands would have high benefits for the mitigation of global warming through the regulation of atmospheric carbon concentrations in this area.

## **Author details**

Julia Griselda Cerón-Bretón1\*, Rosa María Cerón-Bretón1 , Jesús Jaime Guerra-Santos2 and Atl Victor Córdova-Quiroz1

\*Address all correspondence to: jceron@pampano.unacar.mx

1 Universidad Autónoma del Carmen (UNACAR), Chemistry School, Col. Benito Juárez, Ciudad del Carmen, Campeche, Mexico

2 Centro de Investigación de Ciencias Ambientales (Environmental Sciences Research Cen‐ ter), Col. Benito Juárez, Ciudad del Carmen, Campeche, Mexico

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