**2.2 KT-GEM and system dynamics modeling of peatland scenarios**

Moving toward a greener economy involves the design and implementation of key interventions such as public expenditure, policy reforms, and regulation changes that aim to foster sustainable economic growth, employment generation, inclusive income opportunities, and environmental conservation. As a result, methodologies and models are needed in order to support policymakers in the assessment of cross-sectoral economic, social, and environmental impacts of green economy policies. In particular, methodological approaches and models should allow to quantitatively project and evaluate trends (for issue identification), identify entry points for interventions and set targets (for policy formulation), assess ex-ante the potential impact across sectors and the effectiveness in solving stated problems (or exploiting opportunities) of selected interventions (for policy assessment), as well as monitor and evaluate the impact of the interventions chosen against a baseline scenario (for policy monitoring and evaluation ex-post assessment/analysis).

Finding that most currently available national planning models are either too detailed or narrowly focused, this study proposes an approach that: (a) extends and advances the policy analysis carried out with other tools by accounting for the dynamic complexity embedded in the systems studied and (b) facilitates the investigation and understanding of the relations existing between natural capital, society, and the economy. The inclusion of cross-sectoral relations supports a wider analysis of the implication of alternative green economy policies, and the long-term perspective proposed allow for the identification of potential side effects and sustainability of different strategies.

The approach proposed uses the system dynamics (SD) methodology as its foundation, serving primarily as a knowledge integrator. System dynamics modeling is a form of computer simulation modeling designed to facilitate a comprehensive approach to development planning in the medium to long term [12, 30, 37]. A key characteristic of SD is that it allows to integrate the three spheres of sustainable development in its analytical process. SD operates by simulating historical data for a period of at least 1 decade and comparing simulation results with the available data. The purpose of such models is not to make precise predictions of the future; rather, they are a tool for exploring alternative policy scenarios in order to identify those policies which could improve conditions in the future and contribute

to the achievement of desired goals and objectives [36, 39]. System dynamics allows to represent explicitly stocks and flows of human, built and natural capital, and to create linkages among them through the use of feedbacks, delays, and non-linearity.

The green economy model (GEM) is well suited to: (1) generate projections of future developments, though acknowledging that long-term accurate projection cannot easily be produced, even when simulating a large number of endogenous key variables; (2) provide an integrated analysis and evaluation of policy choices; and (3) increase the understanding of the relations underlying the system analyzed. The following paragraphs briefly describe the principal aspects of the GEM application customized to Mauritius.

• *Boundaries*: Variables that are considered an essential part of relevant development mechanisms are endogenously calculated. For example, GDP and its main determinants, population and its main determinants, and the demand

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*Applying Systems Analysis to Evaluate Options for Sustainable Use of Peatlands...*

influenced by the issues analyzed, are exogenously represented.

to be addressed with more detailed sectoral models.

employment, and emissions).

and social structures.

and supply of natural resources are endogenously determined. Variables that have an important influence on the issues analyzed, but which are only weakly

• *Structure*: despite the variety of green economy opportunities considered, GEM is a relatively small model. Its complexity lies in the high number of crosssectoral linkages (dynamic complexity), but its vertical detail (within a sector, or detailed complexity) is far from overwhelming. This makes so that the model is fully tailored to a green economy analysis, being based on stakeholder inputs, and does not compete with the models already being used by the government and its partners. In fact, GEM is developed to fill a gap in the current modeling work in relation to the green economy, and to identify research needs

The main outputs of GEM, and of the green economy analysis carried out with it, include the investment required to implement the intervention desired, added benefits, and avoided costs. Among the benefits, indicators include sectoral value added (as driven by natural resources stocks and flows, e.g., sustainable agriculture yield and production), direct employment creation, and relative income generated, for example, additional employment in public transport or energy efficiency sectors. Avoided costs include savings from avoided consumption (e.g., water, through resource efficiency interventions), and potential avoided ecosystem restoration costs. These are compared with costs, and potential damages created by the business as usual case and by the policy implemented, to estimate the economy-wide annual cash flow, as well as the break-even point, and the return on investment (and, for instance, the return on

By generating systemic, broad, and cross-sectoral scenarios over time that address environmental, economic, and social issues in a single coherent framework, the GEM simulates the main short, medium, and long-term impacts of investing in a greener economy. The most important contribution of this model is its systemic structure that includes endogenous links within and across the economic, social, and environmental sectors through a variety of feedback loops. Most existing models focus on one or two sectors and make exogenous assumptions about other sectors that affect and are affected by the sector under consideration. Using endogenous formulations instead improves consistency over time and across sectors, because changes in the main drivers of the system analyzed are reflected throughout the model and analysis through feedback loops. While detailed sectoral analysis is very important, it is not adequate to demonstrate the whole set of relations and feedback loops that properly represent the functioning of the real world and that must be taken into account in making the necessary transitions to greener economic

The study uses different indicators that capture the value of natural capital in order to represent a green economy, which are green GDP and GDP of the poor. These indicators were developed in the I-GEM as an alternative to conventional GDP, which only captures a small portion of nature's contribution to people's livelihoods [43, 44]. The model mainly used Green GDP as an indicator of the Green Economy, which is an alternative measurement of GDP growth that accounts for

• *Time horizon*: GEM applications are built to analyze medium to long-term green economy scenarios. Also, simulations start in the past in order to allow validation against historical data. In the customization to Mauritius (M-GEM), the time horizon for simulation starts back in 1980 and extends up to 2030.

*DOI: http://dx.doi.org/10.5772/intechopen.85677*

*Applying Systems Analysis to Evaluate Options for Sustainable Use of Peatlands... DOI: http://dx.doi.org/10.5772/intechopen.85677*

and supply of natural resources are endogenously determined. Variables that have an important influence on the issues analyzed, but which are only weakly influenced by the issues analyzed, are exogenously represented.


The main outputs of GEM, and of the green economy analysis carried out with it, include the investment required to implement the intervention desired, added benefits, and avoided costs. Among the benefits, indicators include sectoral value added (as driven by natural resources stocks and flows, e.g., sustainable agriculture yield and production), direct employment creation, and relative income generated, for example, additional employment in public transport or energy efficiency sectors. Avoided costs include savings from avoided consumption (e.g., water, through resource efficiency interventions), and potential avoided ecosystem restoration costs. These are compared with costs, and potential damages created by the business as usual case and by the policy implemented, to estimate the economy-wide annual cash flow, as well as the break-even point, and the return on investment (and, for instance, the return on employment, and emissions).

By generating systemic, broad, and cross-sectoral scenarios over time that address environmental, economic, and social issues in a single coherent framework, the GEM simulates the main short, medium, and long-term impacts of investing in a greener economy. The most important contribution of this model is its systemic structure that includes endogenous links within and across the economic, social, and environmental sectors through a variety of feedback loops. Most existing models focus on one or two sectors and make exogenous assumptions about other sectors that affect and are affected by the sector under consideration. Using endogenous formulations instead improves consistency over time and across sectors, because changes in the main drivers of the system analyzed are reflected throughout the model and analysis through feedback loops. While detailed sectoral analysis is very important, it is not adequate to demonstrate the whole set of relations and feedback loops that properly represent the functioning of the real world and that must be taken into account in making the necessary transitions to greener economic and social structures.

The study uses different indicators that capture the value of natural capital in order to represent a green economy, which are green GDP and GDP of the poor. These indicators were developed in the I-GEM as an alternative to conventional GDP, which only captures a small portion of nature's contribution to people's livelihoods [43, 44]. The model mainly used Green GDP as an indicator of the Green Economy, which is an alternative measurement of GDP growth that accounts for

*Land Use Change and Sustainability*

industries and transportations [7, 45].

 [7]. Agriculture is the main economic sector contributing to local GDP, with the most important crops being, rice, oil palm, and rubber [8, 45]. Other important sectors include mining and tourism and to a limited extend other sectors such as

Moving toward a greener economy involves the design and implementation of key interventions such as public expenditure, policy reforms, and regulation changes that aim to foster sustainable economic growth, employment generation, inclusive income opportunities, and environmental conservation. As a result, methodologies and models are needed in order to support policymakers in the assessment of cross-sectoral economic, social, and environmental impacts of green economy policies. In particular, methodological approaches and models should allow to quantitatively project and evaluate trends (for issue identification), identify entry points for interventions and set targets (for policy formulation), assess ex-ante the potential impact across sectors and the effectiveness in solving stated problems (or exploiting opportunities) of selected interventions (for policy assessment), as well as monitor and evaluate the impact of the interventions chosen against a baseline scenario (for policy monitoring and evaluation ex-post assessment/analysis). Finding that most currently available national planning models are either too

**2.2 KT-GEM and system dynamics modeling of peatland scenarios**

detailed or narrowly focused, this study proposes an approach that:

effects and sustainability of different strategies.

improve conditions in the future and contribute

(a) extends and advances the policy analysis carried out with other tools by accounting for the dynamic complexity embedded in the systems studied and (b) facilitates the investigation and understanding of the relations existing between natural capital, society, and the economy. The inclusion of cross-sectoral relations supports a wider analysis of the implication of alternative green economy policies, and the long-term perspective proposed allow for the identification of potential side

The approach proposed uses the system dynamics (SD) methodology as its foundation, serving primarily as a knowledge integrator. System dynamics modeling is a form of computer simulation modeling designed to facilitate a comprehensive approach to development planning in the medium to long term [12, 30, 37]. A key characteristic of SD is that it allows to integrate the three spheres of sustainable development in its analytical process. SD operates by simulating historical data for a period of at least 1 decade and comparing simulation results with the available data. The purpose of such models is not to make precise predictions of the future; rather, they are a tool for exploring alternative policy scenarios in order to identify those policies which could

to the achievement of desired goals and objectives [36, 39]. System dynamics allows to represent explicitly stocks and flows of human, built and natural capital, and to create

The green economy model (GEM) is well suited to: (1) generate projections of future developments, though acknowledging that long-term accurate projection cannot easily be produced, even when simulating a large number of endogenous key variables; (2) provide an integrated analysis and evaluation of policy choices; and (3) increase the understanding of the relations underlying the system analyzed. The following paragraphs briefly describe the principal aspects of the GEM application

• *Boundaries*: Variables that are considered an essential part of relevant development mechanisms are endogenously calculated. For example, GDP and its main determinants, population and its main determinants, and the demand

linkages among them through the use of feedbacks, delays, and non-linearity.

km2

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customized to Mauritius.

#### **Figure 1.**

*Extent of peatland and the land use on peatland in Central Kalimantan.Data source: The Ministry of Forestry Republic of Indonesia. [57].*

natural capital depreciation and changes in the value of human capital. The GDP of the Poor indicator measures the contribution of nature and environmental services to the household incomes of poor communities **Figure 1** [43, 44].

#### **2.3 Peatland management scenarios**

Four peatland management scenarios in Central Kalimantan were chosen: a business as usual (BAU) scenario, a BAU and palm oil expansion (BAU + Palm) scenario, a green economy (GE) scenario, and a Jelutung scenario. The **BAU scenario** assumes the continuation of historical and present trends of peatland management, which includes land use changes, policies, and interventions currently implemented and enforced. The **BAU + Palm scenario** represents a likely future scenario of the rapid conversion of fallow lands into palm oil. It follows the assumptions of the BAU scenario with the additional assumption of gradually converting all fallow lands into palm oil starting from 2015 until the end of the study period in 2030. Under the **GE scenario**, the implementation of several management and conservation efforts are assumed, including the implementation of government regulation No. 71/2014 on the Protection and Management of Peatland Ecosystems; rehabilitating and rewetting the peatlands in order to keep the water table depth (WTD) below the peatland surface less than 20 cm; halting the conversion of peatlands; and gradually rewetting fallow lands and converting them to secondary peatland forests over the years. Other green economy transitions included are the implementation of sustainable agriculture, vessel removal, fish conservation, waste reuse, and energy and solar efficiency. The scenario assumes the implementation of Government Regulation 71/2014 from 2015 onward and the other policy changes from 2020 onward. Finally, the **Jelutung scenario** models the outcome of a policy that converts all palm oil plantations to Jelutung forest or agroforestry systems in order to provide an extreme case of using paludiculture to rehabilitate degraded peatlands from 2015 onward. The scenario further assumes the same policy changes as the GE scenario.

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*Applying Systems Analysis to Evaluate Options for Sustainable Use of Peatlands...*

To assess the impact of paludiculture development and other policies in a green economy scenario, the study looked at various indicators in the KT-GEM peatland module, namely total peatland emissions, subsidence and flooding impacts, and costs and profits, in order to calculate impacts on natural capital change, Green GDP and GDP of the Poor. The implications of the different policy scenarios were

Total peatland emissions were obtained by summing up the total biological emissions and emissions from fire. To estimate the biological emissions, a biological emission factor on different land types in Central Kalimantan was estimated. Land use and land use change in the KT-GEM Peatland Module was adapted from the classification of peatlands from Krisnawati et al. [25] and categorized into four land uses on peat: agricultural peatland, secondary peatland forest, production forest on peatland, and fallow peatland. The emission factor on the four different land uses was calculated by adapting the linear regression equations from Husnain et al. [23] and Hooijer et al. [22] and the water table depths for the land uses in each scenario

Fire emissions were calculated based on the amount of burnt areas, which were estimated by calculating fire hotspots. Because of the significant influence of the El Niño Southern Oscillation (ENSO) on fire activity in Indonesia [41], the KT-GEM integrated an ENSO indicator, namely Nino3.4 Sea Surface Temperature (SST)

Index from 2000 to 2014 and MODIS-derived hotspot data from 1998 to 2006 from Reynolds et al. [38] were used for the assessment. Data from the Nino3.4 Index was extrapolated to create a trend in the relationship between SST and hotspots until 2030. The historical and extrapolated data were then used to predict the amount of hotspots per dry season in Central Kalimantan by measuring the relationship between Nino3.4 index data and fire hotspots using an exponential regression

The exponential regression model was then adapted to each management scenario and set into formulas to forecast the amount of hotspots in each scenario.4 The formula developed by Tansey et al. [50], in their study in Central Kalimantan,

Finally, to calculate fire emissions, the KT-GEM Peatland Module adapted a method used by the Indonesia National Forest Reference Emissions Level or FREL [6]:

where **A** denotes the extent of burnt area (in hectares), **CF** is the combustion factor with a default factor that equals to 1.0, and **MB** denotes the mass of fuel

<sup>3</sup> A hotspot is a fire pixel in a satellite imagery that indicates fire in an area. Yet it does not specify the

<sup>4</sup> The study by Thoha et al. (2014) found that 63 percent of all hotspots in Central Kalimantan occur on

peatlands and total hotspots calculated were therefore multiplied by 0.63 to adjust the results.

Burnt area (hectare) = 2925 × Hotspots × 155.49.

*Lfire* = *A* × *MB* × *CF* × *Gef*

number, size or intensity of fires and burned areas. See further [40].

Historical dry season data from the Nino3.4 SST

came from data obtained from several publications [16, 21, 23].

*DOI: http://dx.doi.org/10.5772/intechopen.85677*

analyzed for the period 2015–2030.

*2.4.1 Total peatland emissions*

Index to forecast fire hotspots.3

analysis as can be seen in **Figure 2**.

was then used to calculate the total burnt area:

**2.4 KT-GEM key equations for peatland analysis**

*Applying Systems Analysis to Evaluate Options for Sustainable Use of Peatlands... DOI: http://dx.doi.org/10.5772/intechopen.85677*

## **2.4 KT-GEM key equations for peatland analysis**

To assess the impact of paludiculture development and other policies in a green economy scenario, the study looked at various indicators in the KT-GEM peatland module, namely total peatland emissions, subsidence and flooding impacts, and costs and profits, in order to calculate impacts on natural capital change, Green GDP and GDP of the Poor. The implications of the different policy scenarios were analyzed for the period 2015–2030.
