Climate Impacts on Sustainable Natural Resource Management. Группа авторов

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) as well as improving carbon stock on the forests (Edwards et al. 2010). Tropical forests are being cleared at an alarming rate (Gaveau et al. 2016) so that the selection of location targets is required to facilitate the appropriate strategies for restoring (Corbin and Holl, 2012) and managing forests (Stanturf et al. 2014). Moreover, based on carbon stock in each land cover class (Table 1.1), for example, forest restoration which is concentrated in dry shrubland with carbon stock of 15 tC ha^–1 and is targeted for improvements to secondary dryland forest with carbon stock of 169.7 tC ha^–1 will contribute to reducing GHG emission by 567.23 tC ha^–1. If dry shrubland can be restored to secondary dryland forest of 471,625.19 ha (Table 1.2), forest restoration will contribute to reducing GHG emission by 267.52 MtC ha^–1. This calculation illustrated that the appropriate decision of forest restoration significantly affect the GHG emissions reduction target because the major challenge is how to choose the optimal objectives (Bolliger and Kienast 2010) among many land purposes (de Groot et al. 2010).

Regardless of such resolutions, the strategies for avoiding deforestation and forest degradation are relatively cheaper than restoring degraded forests. Indonesia's moratorium on the issuing new licenses in primary forests and peatlands (Indonesia 2011a) is the leading strategy for protecting the high conservation values of forests (HCVF). The conservation of biological diversity has become the highest priority for managing forests using an ecologically sustainable approach (Lindenmayer et al. 2000). The SFM system is also one of the targets for avoiding deforestation and its effect on GHG emissions (Clark and Kozar 2011). For example, protecting primary dryland forest from degradation into secondary dryland forest using forest moratorium or forest management will contribute to preventing GHG emission by 94.23 tC ha^–1 (i.e. the calculation based on the refereed carbon stock in Table 1.1).

      Moreover, the socio‐economic development program issued by the government should also consider climate change mitigation targets. Based on carbon stock in each land cover class (Table 1.1), for example, expansion of estate cropland (carbon stock of 63 tC ha–1) converted from secondary dryland forest (carbon stock of 169.7 tC ha–1) contributed to GHG emissions by 391.23 tC ha^–1. However, estate cropland expansion from dry shrubland will contribute to reducing GHG emission by 176 tC ha^–1. The conversion of natural forest to oil palm plantation (estate cropland) will have negative impacts on forest species (Casson et al. 2015; Tsujino et al. 2016; Ghazoul and Chazdon 2017), while new plantations developed on degraded lands can make modest contributions to GHG emissions reductions (Verchot et al. 2010). A simple decision‐making tool can be used by policymakers to protect forests (Austin et al. 2012), improve the carbon benefits (Harris et al. 2008), and develope socio‐economic values (Arima et al. 2014) at the same time.

1.5