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 Conclusions
Estimated GHG emissions in East Kalimantan Province over a period of 17 years and their future projection under two different scenarios were calculated. Annual time‐series of land cover maps (2000–2016) and carbon stock reference were used to calculate GHG emissions. The results showed that deforestation was the major contributor to GHG emissions in the study area. Also, REDD+ commitment from the local government proved to be a good step forward in reducing GHG emissions in the area. However, the projected reduction in GHG emissions under the REDD+ scenario was smaller than the target written in the local action plan of REDD+. Thus, the provincial government needs to accelerate the strategies for reducing GHG emissions by restoring degraded forest landscapes, avoiding further deforestation and forest degradation, and making appropriate decisions for the socio‐economic development program. Furthermore, the spatial information of the largest GHG emitters in East Kalimantan, which were caused by forest cover changes, could assist the provincial government in locating planned forest restoration action on the same forest landscapes where deforestation and forest degradation occurred. Some illustrations of GHG emission targets resulting from various decisions were also demonstrated in this study.
Acknowledgement
We would like to thank to the reviewers for improving this manuscript.
Author Contribution
Kiswanto, Martiwi Diah Setiawati and Satoshi Tsuyuki conceptualized the research. Kiswanto as the first author performed the data analysis and wrote the draft paper while Martiwi Diah Setiawati performed a literature review and edited the manuscript. Kiswanto and Martiwi Diah Setiawati contributed equally.
List of Appendix
1.A Sample of transition matrix for calculating the total area of land cover changes in each period.
| t1 | Total (ha) | ||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PMF | PSF | SDF | SMF | SSF | PF | ECL | PDA | MDA | DSL | WSL | PRF | SA | TA | PH | OS | FPA | BG | MA | OW | ||||
| t2 | 249.62 | — | — | 1.19 | — | — | — | — | — | — | 0.00 | — | — | — | — | — | — | — | — | — | 250.80 | 250.80 | |
| PMF | — | 5.66 | — | — | 0.01 | — | — | — | — | — | — | 0.03 | — | — | — | — | — | 0.01 | — | — | 5.71 | 5.71 | |
