Regional differences and driving factors analysis of carbon emissions from power sector in China

Abstract

Carbon emissions from the power sector (CEPS11 CEPS: Carbon emissions from the power sector.) contribute more than 40% of carbon emissions to both the world and China, which is the key link of carbon emission reduction. In the context of China’s “carbon peaking and carbon neutrality” goals, clarifying the regional differences and driving factors of CEPS is helpful to formulate carbon emission reduction policies. Based on the CEPS data of 30 provinces in China from 2004 to 2019, this study uses data statistics and spatial autocorrelation analysis to test the temporal and spatial differences of CEPS among provinces. Ten indicators are selected to construct a geographically and temporally weighted regression (GTWR) model for the regression analysis of driving factors of CEPS in the provinces. During 2004–2019, the CEPS in China’s 30 provinces show obvious differences. The CEPS in Shandong, Inner Mongolia and Shanxi maintain a high rise, while that in Sichuan and Yunnan shows a downward trend. The global Moran’s I of provincial CEPS in China shows significant positive spatial autocorrelation, high-high clustering area is concentrated in North and East China, and low-low clustering area is distributed in Southwest China. The effects of various driving factors on provincial CEPS show obvious temporal and spatial heterogeneity. The power structure (PS), the thermal power energy consumption rate (TE) and the per capita electricity consumption (PC) have positive effects on CEPS in most provinces, while the contribution of the power industry (IC) has a negative effect. The effects of other indicators on different provinces fluctuate in different periods. In order to realize the low-carbon sustainable development of power sector, relevant measures such as improving the efficiency of thermal power production, optimizing power structure by region, and support the major power producing provinces are proposed.

Keywords

• CO2 emissions
• Spatio-temporal characteristics
• Spatial autocorrelation
• GTWR model
• China