Abstract
Active incentives or compensation measures plus conservation, sustainable management of forests, and enhancement of forest carbon stocks (denoted together as "REDD+") should be adopted in developing countries to reduce the greenhouse gas emissions caused by deforestation and forest degradation. Identification and analysis of the driving forces behind carbon stocks are crucial for the implementation of REDD+. In this study, the principal component model and the stepwise linear regression model were used to analyze the social and economic driving forces of stocks in three important types of forest change: deforestation, forestland transformation, and forest degradation in China's tropical rainforests of Xishuangbanna, based on the combination of satellite imagery and the normalized difference vegetation index. The findings show that there are different key driving forces that lead to carbon stock changes in the forest land conversion of Xishuangbanna. In particular, the agricultural development level is the main cause of emissions from deforestation, whereas poor performance of protection policies is the main cause of emissions from forest degradation. In contrast, the rural economic development interventions are significantly positive for emissions from forestland transformation. It is crucial to pay attention to distinguishing the driving forces behind carbon stock changes from forest degradation, deforestation, and transformation for optimizing REDD+ implementation and ensuring the effectiveness of REDD+.