Modeling and Sensitivity Analysis of REDD Project to Reduce CO2 Emissions (Case Study: Hyrcanian Forests, Golestan Province)

Due to the increasing trend of Hyrcanian deforestation and Iran's significant proportion in CO2 emissions, the Reduction of Deforestation and Degradation (REDD) emission project was implemented in nine steps and two scenarios in Golestan province. In the baseline scenario it was assumed that the trend of past land use changes will continue. In this regard, land use changes were studied using Landsat images of 1984, 2008 and 2018. Transition potential modeling was performed by Multi-Layer Perceptron (MLP) neural network. Then future land use change was predicted under the baseline scenario over a period of 30 years (2018-2048) and CO2 emissions were estimated. 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 project scenario, some of deforestation activities were controlled. Due to the importance of the project success rate and leakage rate in CO2 emissions, the effectiveness of the project scenario was assessed by changing the leakage rate (10, 20, 30, 40 and 50%) and the project success rate (90, 80, 70, 60 and 50%). Based on the results in the best state (success and leakage rates of 90 and 10% respectively), the amount of CO2 emissions within the project area decreased from 573968.3 tons in 2018 year under the baseline scenario to 66265.5 tons in 2047 year. Evan in the worst state (success and leakage rate of 50% respectively), a decrease in emission was observed compared to the baseline scenario. If any management approach isn't implemented to conserve forests in Golestan province, the amount of CO2 emissions will increase to 662655.9 tons in the year 2047.