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<p>With the intensification of climate change, scientifically assessing regional exposure to extreme weather events and strengthening adaptive capacity have become critical tasks for all countries. Using panel data from 30 Chinese provinces from 2012 to 2022, this study measures provincial climate change vulnerability across the three dimensions of exposure, sensitivity, and adaptive capacity by applying the entropy weight method with 15 indicators. The spatio-temporal dynamics of vulnerability are further examined with the Dagum Gini coefficient, and Markov state transition model, while the core driving factors are identified through the XGBoost-SHAP approach. The results indicate that: (1) overall provincial vulnerability has declined, mainly due to improvements in adaptive capacity; eastern coastal provinces exhibit consistently lower vulnerability, while western provinces show faster reductions; (2) inter-level mobility remains limited, and provincial gaps are stable, displaying characteristics of “club convergence”; (3) cross-regional transvariation is the primary source of vulnerability differences, with intra- and inter-regional disparities playing smaller roles; and (4) digitalization, energy efficiency, and public environmental awareness are key determinants that significantly mitigate vulnerability. These findings provide empirical evidence for enhancing climate change adaptation governance and formulating region-specific adaptation strategies in China.</p>

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Spatio-temporal dynamics and drivers of climate change vulnerability in China: an entropy weight and XGBoost-SHAP analysis

  • 期刊论文

  • 发表日期：

    2026年7月8日

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  • Zhe Zhang,
  • Shengzhen Ding

摘要

With the intensification of climate change, scientifically assessing regional exposure to extreme weather events and strengthening adaptive capacity have become critical tasks for all countries. Using panel data from 30 Chinese provinces from 2012 to 2022, this study measures provincial climate change vulnerability across the three dimensions of exposure, sensitivity, and adaptive capacity by applying the entropy weight method with 15 indicators. The spatio-temporal dynamics of vulnerability are further examined with the Dagum Gini coefficient, and Markov state transition model, while the core driving factors are identified through the XGBoost-SHAP approach. The results indicate that: (1) overall provincial vulnerability has declined, mainly due to improvements in adaptive capacity; eastern coastal provinces exhibit consistently lower vulnerability, while western provinces show faster reductions; (2) inter-level mobility remains limited, and provincial gaps are stable, displaying characteristics of “club convergence”; (3) cross-regional transvariation is the primary source of vulnerability differences, with intra- and inter-regional disparities playing smaller roles; and (4) digitalization, energy efficiency, and public environmental awareness are key determinants that significantly mitigate vulnerability. These findings provide empirical evidence for enhancing climate change adaptation governance and formulating region-specific adaptation strategies in China.