Scenario prediction and decoupling analysis of carbon emission in Jiangsu Province, China

Abstract

As a major energy consumption province in China, Jiangsu Province is a key area for carbon emission reduction in China. Grasping the future trends of carbon emission in Jiangsu Province will help to find effective ways to reduce carbon emission. This paper proposes the STIRPAT-IGWO-SVR model, including screening the carbon emission influencing factors based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, optimizing the parameters of the support vector regression (SVR) model using the gray wolf optimization (GWO) algorithm improved by the differential evolution (DE) algorithm, and sets five scenarios to predict and compare the carbon emissions of Jiangsu Province under different scenarios. In addition, the Tapio decoupling model is used to analyze the decoupling relationship of carbon emission and economic growth in each scenario. The results show that the STIRPAT-IGWO-SVR model proposed in this paper shows good performance compared with other models. For Jiangsu Province, improving the energy structure has the strongest inhibitory effect on carbon emission, stronger than reducing energy intensity, and far stronger than optimizing the industrial structure. Compared with a single plan, even if the measures are slightly weakened, the implementation of combined planning measures can more effectively control carbon emission.