*2.4.1 Total peatland emissions*

*Land Use Change and Sustainability*

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

*Extent of peatland and the land use on peatland in Central Kalimantan.Data source: The Ministry of Forestry* 

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

to the household incomes of poor communities **Figure 1** [43, 44].

**2.3 Peatland management scenarios**

**Figure 1.**

*Republic of Indonesia. [57].*

**80**

GE scenario.

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

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 to forecast fire hotspots.3 Historical dry season data from the Nino3.4 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 analysis as can be seen in **Figure 2**.

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, was then used to calculate the total burnt area:

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

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]:

$$L\_{\hat{f}\hat{r}\hat{r}} = A \times \text{MB} \times \text{CF} \times G\_{\text{eff}}\hat{r}$$

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

<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.

#### **Figure 2.**

*Central Kalimantan hotspots and Nino3.4 SST correlation (for the period July–August–September–October) [57].*

available for combustion. The latter is estimated for the BAU scenario by multiplying the mean depth of burned peat with the bulk density (BD) as assumed in the studies by Mulyani et al. [32] and Ballhorn et al. [2]. From here, the average depth of burned peat in other scenarios was calculated by building a linear relationship between the assumed water table depth (WTD) and the burned depth. Furthermore, **Gef** denotes the CO2 emission factor calculated by multiplying the organic carbon content (Corg, % of weight) of 0.4986 [32] with the conversion factor from tC to tCO2e which is 3.67. This conversion factor was derived through dividing the atomic weight of carbon dioxide (i.e., 44) by the atomic weight of carbon (i.e., 12).

#### *2.4.2 Land subsidence and flooding*

Land subsidence was estimated to forecast the amount of flooded agricultural land to be subtracted from agricultural land, production, and profits in the Green GDP calculations. The KT-GEM Peatland Module calculates the subsidence rate using the equation from Hooijer et al. [21] which measured a relationship between water table depth and subsidence level, as follows:

$$Subsideance\ rate \ (cm\ per\ year) = 0.69 - 5.98 \times WTD$$

This formula was simulated for each land use category in all selected peatland management scenarios and adjusted the WTD accordingly. Based on the subsidence rate, the module then measured the risk of flooding in agricultural peatlands with an equation from that demonstrates the relationship between the accumulated agricultural subsidence and the proportion of flooded agricultural peatlands. The result was then multiplied with the existing agricultural land (in hectare) and the inverted Nino3.4 SST Index (where wet years are positive instead of the other way around) in order to obtain the extent of flooded agricultural land.

#### *2.4.3 Calculating costs and profits*

In estimating the total costs and profits, the KT-GEM included costs of rewetting and reforestation, costs from fires, and profits from palm oil plantations and jelutung.

**83**

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

Rewetting and reforestation costs in the green economy and jelutung scenarios applied mainly to production forests, secondary forests, and fallow lands and were gradually implemented between 2015 and 2025, after which only small rewetting costs for maintenance were calculated. In order to estimate rewetting and reforestation costs, data on peat forests rehabilitation costs by were used. The cost of fire damage was calculated by multiplying the extent of burnt areas with fire damage cost per unit, which were estimated at 172 USD per hectare of burned area by Tacconi [51]. Palm oil is the main crop on agricultural land, especially in the BAU + Palm scenario, and the profits of palm oil plantations were estimated based on calculations in the study by Suharno et al. [46]. This number was multiplied by the agricultural peatland (Ha) to obtain the total profits from oil palm production (IDR/Year). Jelutung profitability was calculated based on a cultivation period of 30 years [52] and the value reported in the ICRAF report, which was multiplied with the total area of jelutung (ha). The value used is the net profit per hectare per year, which contains all the annual costs. Hence, the capital (CAPEX) and operational (OPEX) costs associated with intervention are lumped together to minimize the complexity

The estimation of Green GDP was performed by adding the change in natural capital to real GDP. Real GDP of Central Kalimantan is calculated by adding the production value from several sectors, namely agriculture, fisheries, forestry, industry, services, labor, mining, and tourism [43, 44]. Natural capital change is calculated by adding the carbon loss value and the value of emissions and fires in peatlands. To do so, the study uses a fixed-rate carbon price (i.e., 5 USD per tCO2) through the entire study period and assumes a functioning carbon credit markets in

1.Policy interventions in the GE and Jelutung scenarios lead to lower cumulative

**Figure 3** shows the total cumulative peatland emissions in the four selected peatland management scenarios. Up to 2015, the year in which the interventions are expected to begin, total peatland emissions are the same in all the scenarios considered. The GE and Jelutung scenarios result in significantly lower cumulative peatland emissions in Central Kalimantan compared to the BAU and BAU + Palm scenarios in the simulation up to 2045, with the scenario BAU + Palm having the highest level of cumulative emissions. Given the large contribution of peat-related emissions in Central Kalimantan to Indonesia's total GHG emissions, the adoption of policies aimed at reducing peatland emissions will significantly help the country

High peatland emissions are correlated with higher costs associated with fires on peatlands, as reflected in **Figure 4**. Results of the BAU and BAU + Palm coincide and are highly fluctuating over time, signifying that there is a high variability in the probability that peat fires will take place on any given year. Ultimately, this trend illustrates that both the BAU and the BAU + Palm scenarios generate the highest costs related to annual fire damage as compared to the other two scenarios. The model forecasts that future fire damage costs in the BAU and the BAU + Palm scenarios could reach up to 700 billion IDR; whereas, in the GE and Jelutung scenarios,

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

*2.4.4 Calculating natural capital change and Green GDP*

order to incorporate the benefits from GHG emissions reduction.

of the model.

**3. Results and discussion**

peatland emissions

achieve its climate change mitigation goals [31].

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

Rewetting and reforestation costs in the green economy and jelutung scenarios applied mainly to production forests, secondary forests, and fallow lands and were gradually implemented between 2015 and 2025, after which only small rewetting costs for maintenance were calculated. In order to estimate rewetting and reforestation costs, data on peat forests rehabilitation costs by were used. The cost of fire damage was calculated by multiplying the extent of burnt areas with fire damage cost per unit, which were estimated at 172 USD per hectare of burned area by Tacconi [51].

Palm oil is the main crop on agricultural land, especially in the BAU + Palm scenario, and the profits of palm oil plantations were estimated based on calculations in the study by Suharno et al. [46]. This number was multiplied by the agricultural peatland (Ha) to obtain the total profits from oil palm production (IDR/Year). Jelutung profitability was calculated based on a cultivation period of 30 years [52] and the value reported in the ICRAF report, which was multiplied with the total area of jelutung (ha). The value used is the net profit per hectare per year, which contains all the annual costs. Hence, the capital (CAPEX) and operational (OPEX) costs associated with intervention are lumped together to minimize the complexity of the model.

## *2.4.4 Calculating natural capital change and Green GDP*

The estimation of Green GDP was performed by adding the change in natural capital to real GDP. Real GDP of Central Kalimantan is calculated by adding the production value from several sectors, namely agriculture, fisheries, forestry, industry, services, labor, mining, and tourism [43, 44]. Natural capital change is calculated by adding the carbon loss value and the value of emissions and fires in peatlands. To do so, the study uses a fixed-rate carbon price (i.e., 5 USD per tCO2) through the entire study period and assumes a functioning carbon credit markets in order to incorporate the benefits from GHG emissions reduction.
