摘要：The reduction of CO<sub>2</sub> emission has become one of the significant tasks to control climate change in China. This study employs Exploratory Spatial Data Analysis (ESDA) to identify the provinces in China with different types of spatiotemporal transition, and applies the Logarithmic Mean Divisia Index (LMDI) to analyze the influencing factors of industrial CO<sub>2</sub> emissions. Spatial autocorrelation of provincial industrial CO<sub>2</sub> emissions from 2003 to 2017 has been demonstrated. The results are as follows: (1) 30 provinces in China are categorized into 8 types of spatiotemporal transition, among which 24 provinces are characterized by stable spatial structure and 6 provinces show significant spatiotemporal transition; (2) For all types of spatiotemporal transition, economic scale effect is mostly contributed to industrial CO<sub>2</sub> emission, while energy intensity effect is the most crucial driving force to reduce industrial carbon dioxide emission; (3) provinces of type HH-HH, HL-HL and HL-HH are most vital for CO<sub>2</sub> emission reduction, while the potential CO<sub>2</sub> emission increase of developing provinces in LL-LL, LH-LH and LL-LH should also be taken into account. Specific measures for CO<sub>2</sub> emission reduction are suggested accordingly.
关键词：CO2 emission reduction; exploratory spatial data analysis; logarithmic mean divisia index; spatial agglomeration; spatiotemporal transition
