illustration of annual GHG emissions (Mt CO2 yr–1) from 2000 to 2016."/>
Figure 1.1 Annual GHG emissions (Mt CO2 yr–1) from 2000 to 2016.
Figure 1.2 Percentage of GHG emissions from the land‐based sector (2000–2016).
Table 1.2 Ten largest GHG emitters from land‐based sector from 2000 to 2016.
| Land cover changes | Total area (ha) | Percentage of emission (%) | |
|---|---|---|---|
| From | To | ||
| Secondary dryland forest | Dry Shrub land | 471,625.19 | 48.63 |
| Secondary dryland forest | Estate Cropland | 178,297.07 | 12.68 |
| Secondary dryland forest | Plantation forest | 142,460.67 | 10.04 |
| Primary dryland forest | Secondary dryland forest | 317,020.70 | 5.43 |
| Secondary dryland forest | Bare ground | 46,444.85 | 5.25 |
| Secondary dryland forest | Mixed dry agriculture | 31,092.77 | 3.31 |
| Secondary dryland forest | Mining areas | 28,919.68 | 3.27 |
| Secondary mangrove forest | Wet shrubland | 35,461.77 | 2.48 |
| Secondary mangrove forest | Fish pond/aquaculture | 23,129.13 | 1.85 |
| Secondary swamp forest | Wet shrubland | 17,834.51 | 1.66 |
1.3.2 Historical Baselines and Future Trajectories
In this study, we used linear regression to predict the future trajectories of GHG emissions, as shown in Figure 1.3. Furthermore, the annual GHG emissions from 2000–2010 and 2010–2016 were extrapolated using linear regression in order to predict future trajectories for 2030 under two type of scenario: without REDD+ commitment (white circle) and with REDD+ commitment (black circle).
Figure 1.3 The trend lines of annual GHG emissions for predicting future trajectories.
Figure 1.4 The percentage of REDD+ progress in East Kalimantan for 2030. Source: Based on East Kalimantan (2013).
Figure 1.4 shows the future trajectories of GHG emissions in the study area. It states that under REDD+ commitment, the GHG emissions will be reduced by 13.41% in 2020. Furthermore, the GHG emission for 2030 (70.11 Mt CO2) will be reduced by 18.89% from the historical baseline (86.44 Mt CO2) if the province applied the REDD+ strategy consistently. These results projected that the REDD+ program implemented in East Kalimantan could reduce GHG emissions from historical baselines for 2020 and 2030.
1.4 Discussion
This study estimated annual GHG emissions from 2000 to 2016 using the time‐series land cover maps to measure the REDD+ implementation progress of this province. Furthermore, the base year of 2010 was used to compare GHG emissions before and after the REDD+ program applied in East Kalimantan. The result revealed that GHG emission after 2010 was smaller than before 2010 (Figure 1.1), which proved the progress made by REDD+ in reducing GHG emissions. These efforts should be appreciated as a part of the accomplishment of the commitment to reducing emissions from deforestation and forest degradation. However, the progress (Figure 1.4) was still smaller than the target written in the local action plan of REDD+ in East Kalimantan (East Kalimantan 2013) which is expected to reduce GHG emissions by 22.38% from the historical baseline for 2020. Based on this result, East Kalimantan needs a further strategy for reaching the target of reducing GHG emissions more effectively.
This study also showed that deforestation and forest degradation significantly contributed to GHG emissions (Figure 1.2). Since 2010, the governor launched the East Kalimantan Green policy (East Kalimantan 2011b) and established Regional Council on Climate Change (East Kalimantan 2011c) for implementing green growth initiatives and low carbon development program (East Kalimantan 2013). Also, East Kalimantan has several options in the land use, land‐use change, and forestry (LULUCF) sector for reducing emissions at low cost (Harris et al. 2008). However, tropical deforestation and its effect on GHG emissions are inseparable from the political and economic parts in Indonesia (Brockhaus et al. 2012), even though land cover change consequences may only reflect the accumulation of ecological conditions (Carlson et al. 2013). For those reasons, the government of Indonesia, as well as East Kalimantan Province, should focus on issuing the proper policies to apply the strategy for restoring deforested and degraded forests in the context of REDD+.
Based on data shown in Table 1.2, deforestation of secondary dryland forest into the non‐forests contributed more than 70% of total GHG emissions, while primary dryland forest degraded into secondary dryland forest as well as secondary dryland forest degraded into plantation forest also contributed more than 15% of total GHG emissions. Thus, forest restoration strategy (Dumroese et al. 2015) is the critical aspect for reducing GHG emissions (van Noordwijk et al. 2014), because the main targets should be focused on reducing deforested and degraded forest landscapes (Bebber and Butt 2017)
