Spatial-temporal evolution and driving forces of provincial carbon footprints in China: An integrated EE-MRIO and WA-SDA approach

Abstract

The reduction of carbon footprint (CF) induced by cross-province trade is equally important for China with unbalanced economic development under the background of “double carbon” and “dual circulation” strategy. Based on the environmentally-extended multi-regional input-output (EE-MRIO) and weighted average structural decomposition analysis (WA-SDA) model, this study explored the distribution characteristics and spatial-temporal evolution process of three-perspective CFs (By province, Per capita, and Per ¥GDP) at the provincial level, and accurately revealed the socio-economic driving factors of provincial CFs from 2007 to 2017. Here we show that China's CF increased from 6151.4 Mt. in 2007 to 8778.6 Mt. in 2017, with an average annual growth rate of 3.6%. With the socioeconomic development, the gravity centers of CFs and per capita CFs were gradually shifting from eastern affluent provinces and populous municipalities to underdeveloped western China, while the provinces with relatively low carbon footprint intensities (CFIs) were still concentrated in affluent eastern coastal areas. It will be conductive to the overall improvement of national emission mitigation effective through introducing the advanced production technologies and scientific consumption awareness of developed provinces to eastern China in the future. Meanwhile, the corresponding control measures on CF reduction should pay attention to the changes of per capita consumption and population especially for economically-developed populous regions such as Shandong, Jiangsu, Guangdong, and Zhejiang. For Inner Mongolia, the bidirectional adjustment of emission intensity and production structure will be necessary for its CF reduction. Moreover, the continuous optimization of consumption structure will be expected to contribute more impacts on CF reduction under the demand-side reform. The obtained results and policy implications are expected to provide corresponding and holistic reference for decision-makers to develop more efficient carbon mitigation and management policies.