Targeting area and comparing the effect of different land use/land cover (LULC) scenarios on greenhouse gases (GHGs) emission reduction (Case study: Hyrcanian forests in Iran)

Abstract

Background Because the greenhouse gases (GHGs) emissions are known to be strongly influenced by land use/land cover (LULC) change, reducing emissions from deforestation and degradation (REDD) mechanism has attracted much attention as a strategy for understanding how different LULC scenarios effect on the GHGs emissions. Transition to other LULC types is one of the major challenges of Iran's Hyrcanian forests in Golestan province. To consider how LULC change scenarios affect GHGs, REDD project was executed in a period of 30 years (2018–2048) at intervals of 5 years. In this regard, study area was divided into the project area and leakage belt based on the Multi Criteria Evaluation (MCE) derived forest suitability map. In the baseline scenario, it was assumed that the trend of past LULC changes will continue. Results By implementation of the project scenario, some degradation activities were controlled. Project scenario was executed with different project success rates (PSR) of 90, 80, 70, 60 and 50% to examine its efficiency rate in reducing GHGs emissions. According to the results, 38206.8 hectares of forests within the project area will be destroyed by 2047 under the baseline. The destroyed area will be reach 39784.4 hectares in the leakage belt. The highest rate of forest destruction in the project area will occur in the last 5 years (1352 hectares per year), so the highest CO2 and non-CO2 emissions equal to 662655.3 tons/year and 278.94 tCO2e/year will happen in the last 5 years (2042–2047). Based on the results, reducing the PSR affected the efficiency of the project scenario. The highest and lowest rates of emissions reduction were observed respectively with PSR of 90 and 50%. Conclusions That's very important for developing countries especially Iran that are facing many challenging forest conservation decisions. This study innovated in methodology by integrating the MCE into the REDD steps. The MCE as a spatial targeting method could be applied to increase the efficiency of the REDD project, as we illustrated for the case of Hyrcanian forests.