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<p>The growing frequency of climate change-related extreme weather events has made greenhouse gas reduction a global priority. In this context, CO<sub>2</sub> emissions from the energy industry (CETEI) represent approximately 17% of China’s national total, with pronounced regional imbalances between energy production and consumption. Using panel data of the energy industry across 30 Chinese provinces/municipalities from 2007 to 2021, this study employs the SBM-DEA model to measure energy efficiency and performs primary regional division via fuzzy C-means clustering. Given that a single energy efficiency indicator is insufficient to fully characterize the regional heterogeneity of carbon emissions, symbolic regression is introduced to screen six carbon emission-influencing factors (i.e., the proportion of new energy generation, per capita carbon emissions, the proportion of coal, per capita output value, energy production, and industrial structure) from two dimensions: carbon reduction capability (CR) and economic growth (EG). On this basis, secondary clustering is performed, and the final clustering results are derived from the cross-coupling of the outcomes of the two clustering processes. The Dagum Gini coefficient decomposition method is further used to identify the internal cause of spatial differentiation in CETEI. Results show that China’s energy industry can be categorized into four regional clusters: high-efficiency Regions A (Beijing et al., dominated by per capita carbon emissions) and B (Hainan, Tianjin et al., core factor: industrial structure); low-efficiency Regions C (Anhui et al., key constraint: new energy proportion) and D (Fujian et al., decisive factor: industrial structure). Spatial disparities in CETEI fluctuate, with the largest internal gap in Region A and the smallest in Region B. Inter-regional differences are the primary contributor to national disparities (45% contribution rate). These findings provide a scientific basis for formulating differentiated regional carbon emission reduction policies in China.</p>

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Investigation of provincial clustering and spatial differences: Insights from the CO2 of China’s energy industry

  • 期刊论文

  • 发表日期：

    2026年3月14日

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  • Zhiqun Zhang,
  • Haiying Liu,
  • Yan Lin,
  • Xianzhe Cai,
  • Pengcheng Han,
  • Dianwu Wang

摘要

The growing frequency of climate change-related extreme weather events has made greenhouse gas reduction a global priority. In this context, CO2 emissions from the energy industry (CETEI) represent approximately 17% of China’s national total, with pronounced regional imbalances between energy production and consumption. Using panel data of the energy industry across 30 Chinese provinces/municipalities from 2007 to 2021, this study employs the SBM-DEA model to measure energy efficiency and performs primary regional division via fuzzy C-means clustering. Given that a single energy efficiency indicator is insufficient to fully characterize the regional heterogeneity of carbon emissions, symbolic regression is introduced to screen six carbon emission-influencing factors (i.e., the proportion of new energy generation, per capita carbon emissions, the proportion of coal, per capita output value, energy production, and industrial structure) from two dimensions: carbon reduction capability (CR) and economic growth (EG). On this basis, secondary clustering is performed, and the final clustering results are derived from the cross-coupling of the outcomes of the two clustering processes. The Dagum Gini coefficient decomposition method is further used to identify the internal cause of spatial differentiation in CETEI. Results show that China’s energy industry can be categorized into four regional clusters: high-efficiency Regions A (Beijing et al., dominated by per capita carbon emissions) and B (Hainan, Tianjin et al., core factor: industrial structure); low-efficiency Regions C (Anhui et al., key constraint: new energy proportion) and D (Fujian et al., decisive factor: industrial structure). Spatial disparities in CETEI fluctuate, with the largest internal gap in Region A and the smallest in Region B. Inter-regional differences are the primary contributor to national disparities (45% contribution rate). These findings provide a scientific basis for formulating differentiated regional carbon emission reduction policies in China.