Mapping carbon emission networks in China: insights from province-level spatial differentiation

Abstract

In the current global economic downturn and energy transition period, how to better coordinate the differences in carbon emission footprints among sub-regions has become an emerging issue. With the Gini decomposition method, social network analysis, and difference-in-differences estimation, this study explores the spatial differentiation of China’s province-level carbon emission footprint from 2000 to 2021. The findings of this study indicate that: (1) The Gini-based carbon emission footprint index shows an overall upward trend, revealing the constantly expanding differences among provinces. By comparison, the crude oil difference between the low-carbon pilot and non-pilot provinces is evident, reaching more than 0.15; (2) The carbon emission footprint spatial correlation network structure shows strong spillover characteristics. Provinces with higher network centrality have better structural holes, maintaining closer relationships with surrounding provinces. Those pilot provinces have a comparative advantage regarding social network position, as they have more effective mutual node connections; and (3) China’s low-carbon pilot policy can effectively reduce carbon emissions, with a certain reduction effect of -17.433 in comparison. Industrial rational transformation and green innovation performance are essential in this emission reduction process. At the crossroads of sustainable development, it will be incredibly beneficial to speed up the green transformation by enhancing the coordinated development of regional characteristics.

Keywords

Carbon emission footprint / spatial differentiation / Gini decomposition method / social network analysis / difference-in-differences

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