Exploring spatial characteristics of city-level CO2 emissions in China and their influencing factors from global and local perspectives

In China, cities are the basic units for implementing CO2 abatement policies. However, few studies have comprehensively explored the spatial characteristics of CO2 emissions (CEs) and their influencing factors at the city level from different perspectives. After collecting spatial data from 280 Chinese prefecture-level cities for 2005, 2012, and 2015, this work firstly uncovered the overall and local spatial characteristics of CEs by adopting spatial auto correlation analysis. Then, five influencing factors, including the total resident population (POP), per capita GDP (PCGDP), energy intensity (EI), the proportion of secondary industry (SI), and climate factor-heating degree days (HDD), were examined using global and local regression models. The analyses revealed that (1) CEs presented spatial agglomeration features from global and local perspectives, indicating spatial association between neighboring cities; and (2) POP, PCGDP, EI, and HDD had statistically significant spatial correlations with CEs, and their effect sizes were as follows: PCGDP > POP > EI > HDD. More importantly, the impacts of these influencing factors on CEs varied across cities, exhibiting obvious spatial heterogeneity. According to these findings, local governments should strengthen coordination and cooperation with their surrounding cities to promote regional synergistic action on emission reduction. In addition, policymakers should also design differentiated abatement policies based on regional characteristics and differences instead of applying similar policies to all cities. (C) 2020 Published by Elsevier B.V.

Automated summary

The study reveals that CEs exhibit spatial agglomeration features both globally and locally, indicating spatial association between neighboring cities. Furthermore, POP, PCGDP, EI, and HDD have significant spatial correlations with CEs, with the effect sizes in the order of PCGDP > POP > EI > HDD. The impacts of these influencing factors on CEs vary across cities, showing obvious spatial heterogeneity.