Abstract
The objective of this study is to identify the spatiotemporal change law and the leading factors of industrial carbon emission decoupling. Based on the industrial carbon emission level of the Yangtze River Delta urban agglomeration (YRDUA) from 2006 to 2020, the spatiotemporal heterogeneity was explored with the help of the spatial Markov chain, the Tapio decoupling model was used to analyze its decoupling state from the industrial economy, and its driving factors were decomposed using the Kaya identity and logarithmic mean Divisia index (LMDI) model. The results show that (1) in 51.9% of the YRDUA's cities, the industrial carbon emission situation was stable, the emission reduction observation area (medium carbon) occupied a dominant position, and the emission reduction key area (relatively high carbon) gradually decreased. (2) Industrial carbon emissions had spatial overflow and path dependency characteristics, and the probability of carbon emission type transfer maintaining the original state reached 80.0%. From 2006 to 2011, the average probability of the downward migration of high-carbon cities was 5.0%. From 2011 to 2020, the average probability of the upward transfer of low-carbon cities was 9.4%. (3) The negative decoupling rate of carbon emissions in the YRDUA experienced a transition from 3.7% to 44.4% and then back to 7.4%, showing spatial imbalance. Unsatisfactory decoupling cities were concentrated along the Yangtze River and in coastal areas. (4) The promoting efficiency of energy intensity, carbon emission coefficient, and employment structure was gradually strengthened, and the carbon-increasing effect of labor input was gradually weakened. (5) The decoupling mode of heavy difficult cities is dominated by the three-factor balanced type, which is jointly affected by industrial production, labor input, and carbon emission coefficient. The findings in this study can provide inspiration for industrial carbon emission reduction in megalopolises.
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