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Decoding China's low-carbon resilience: A discrete numerical integration approach to spatiotemporal dynamics and influencing factors
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DOI：10.1016/j.jclepro.2026.149060.png)
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Abstract
En 中文
Under the dual pressures of global climate change and economic transformation, balancing economic development with ecological protection has become a critical global challenge. Low-carbon resilience (LCR), as a core indicator for measuring a country's ability to maintain sustainable development during low-carbon transitions, provides a perspective for evaluating the efficacy of China's coordinated economic-ecological development. This study develops an LCR evaluation model that integrates total factor carbon productivity with numerical discrete integration and applies it to 30 Chinese provinces from 2004 to 2024. Additionally, kernel density estimation, the gravity center migration model, the Moran's I index, the Dagum Gini coefficient, and the Geodetector model are employed to explore the spatiotemporal evolution characteristics and influencing factors of the LCR. The results show that China's LCR followed an N-shaped (rise–decline–rise) evolution between 2004 and 2024. Eastern China consistently maintains the highest LCR, whereas Western China remains the lowest; the Central and Northeastern regions occupy intermediate positions. The center of gravity for the LCR is concentrated in central-eastern China and undergoes a two-stage shift: first moving northwestward and then turning northeastward. Overall, regional disparities in the LCR initially narrowed and then widened again, with interregional differences and the spatial overlapping of high- and low-LCR provinces identified as the primary contributors. Technological progress, foreign trade activity, and energy efficiency are core influencing factors of LCR, whereas the economic development level, environmental regulation intensity, and green credit level serve as potential influencing factors. This study provides an analytical framework for assessing LCR and offers insights for low-carbon governance.
Keywords:
Low-carbon resilience
Discrete numerical integration
Spatiotemporal dynamics
Influencing factors
Journal
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10
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4.6W
Citations:
36.8W
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Cited Papers