Climate Impacts on Sustainable Natural Resource Management. Группа авторов

of the changed area of the land cover, ha

      t₁= year of before the change

      t₂= year of after the change

      The sign (–/+) of the calculation result represents the C stock difference. The positive (+) stock difference represents the increase in C stocks known as negative emission (sequestration). The negative (–) stock changes represent the decreases in C stocks known as positive emission (emission). The remaining unchanged land cover class during the analysis period or among similar carbon stock would be estimated as no emission. For example, Table 1.1 showed similar carbon stock for paddy/rice field and port and harbor (5 tC yr^–1), transmigration areas and mixed dry agriculture (10 tC yr^–1), and bare ground, mining area, open swamp, open water, fish pond/aquaculture, and cloud/no data (0 tC yr^–1). The CO₂ emission based on the total C stock difference was estimated for each land cover class every year by multiplying the total C stock change by 44/12.

      1.2.4 Historical Baselines and Future Trajectories

      Annual GHG emissions from 2000 to 2016 were divided into two periods for developing the baseline and REDD+ progress. The period to estimate a historical baseline of GHG emission trend before the commitment of REDD+ was from 2000 to 2010. The period to estimate the REDD+ progress of GHG emission trend after the commitment was from 2010 to 2016. The selection of 2010 as the base year was based on the official submission of Indonesia's commitment to the UNFCCC in 2010 (Indonesia 2013).

      Both GHG emission baselines were then projected to estimate the future trajectories of GHG emissions in the target period of commitment. The target of Indonesia's commitment in 2030 (Indonesia, 2016) was considered to determine the final projection in this study. Some analytical tools can be used to predict the future trendlines in a possible downturn or upturn data by connecting many points on a graph. Understanding how to use the trendlines for predicting the trend in the future could help to reveal what might happen in the future. Both future trajectories of GHG emission were compared to measure the achievement of REDD+ progress in East Kalimantan for 2030.

1.3 Results

      1.3.1 Annual GHG Emissions

Figure 1.1 shows the annual GHG emissions in East Kalimantan between 2000 and 2016. The figure shows that a growing increase in GHG emissions occurred every year. Moreover, during the study period, the increase reached 31 Mt CO₂ with an increment rate of 2.1 Mt CO₂ yr^–1. Although the annual GHG emissions showed an increasing trend every year, the increment rate before REDD+ commitment (2000–2010) was larger (2.3 Mt CO₂ yr^–1) than the increment rate after the commitment (1.5 Mt CO₂yr^–1). So this result was able to illustrate the implication of the REDD+ strategies in East Kalimantan in reducing GHG emissions.

Figure 1.2 shows size of the contribution of land cover change to GHG emission in the study area. According to the figure, deforestation (i.e. the changes of forest cover to non‐forest cover) and forest degradation (the changes of dense forest to less dense forest) gave a significant contribution to GHG emission in East Kalimantan. The deforestation caused the annual GHG emission increase of 25 Mt CO₂ by 2016 with an increment rate of 1.7 Mt CO₂yr^–1 and it contributed about 80% of GHG emission in the study area, respectively. Forest degradation affected the annual GHG emission increase of 6 Mt CO₂ from 2000 to 2016, with an increment rate of 0.3 Mt CO₂yr^–1 and it contributed about 20% of GHG emission. Moreover, the changes within non‐forest cover only contributed to the annual GHG emission increase of 0.7 Mt CO₂ from 2000 to 2016, with an increment rate of 0.05 Mt CO₂yr^–1.

Table 1.2 shows the 10 largest emitters of GHG of land cover change over the 17 years. As shown in Table 1.2, most of the top 10 largest emitters were classified as the deforestation process. The changes of secondary dryland forest to dry shrubland, estate cropland, and plantation forest were the three largest GHG emitters in East Kalimantan (Table 1.2), represented by 48.63%, 12.68%, and 10.04% of the total GHG emissions from 2000 to 2016, respectively. Furthermore, the conversion of secondary mangrove forest into wet shrubland and fish pond/aquaculture contributed 2.48% and 1.85% of total GHG emissions, respectively. Meanwhile, the change of secondary swamp forest into wet shrubland caused 1.66% of total GHG emissions. Moreover, the largest forest degradation occurred in the study area, and was represented by the degradation of primary dryland forest into secondary dryland forest, which contributed 5.43% of total GHG emissions, respectively.

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	Скачать книгу В начало < 9 10 11 12 13 14 15 16 17 18 > В конец