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<p>This research uses Chongqing, China, as a representative case study to address the challenges inherent in investigating carbon peak pathways within topographically constrained inland Chinese cities. These challenges include the lack of regional structural variables, limited flexibility in scenario design, and a scarcity of case studies focusing on western China. Employing an extended STIRPAT model, the study systematically assesses the influence of critical regional factors—such as industrial structure, energy intensity, and energy mix—on carbon emissions. To improve the accuracy of parameter estimation and mitigate multicollinearity among variables, ridge regression was applied using data from China’s Carbon Emissions Accounting Database (CEADs). Seven multi-scenario combinations were developed to project carbon emission trajectories from 2023 to 2050, followed by a comparative analysis with analogous studies conducted in Yunnan Province. The principal findings are as follows: (1) Population size, industrial structure, and energy mix constitute the primary determinants of carbon emissions in Chongqing; (2) Under the baseline scenario, carbon emissions are projected to peak in 2037, whereas adopting a “low-growth plus high-efficiency decarbonization” pathway could effectively advance the peak to 2035; (3) Relative to Yunnan—a similarly topographically constrained region in Southwest China—Chongqing exhibits more pronounced “valley industry” lock-in effects. Accordingly, mitigation strategies for Chongqing should emphasize accelerating the transformation of energy-intensive industries and enhancing regional energy coordination. This study illustrates how variations in industrial foundations lead to divergent carbon peak trajectories under comparable topographical constraints, thereby offering tailored policy insights for analogous regions.</p>

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Carbon peaking pathways for topographic-constrained megacities: multi-scenario simulations and regional comparisons based on Chongqing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月18日

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  • Lijun Liang,
  • Mengze Ma,
  • Jianglin Feng

摘要

This research uses Chongqing, China, as a representative case study to address the challenges inherent in investigating carbon peak pathways within topographically constrained inland Chinese cities. These challenges include the lack of regional structural variables, limited flexibility in scenario design, and a scarcity of case studies focusing on western China. Employing an extended STIRPAT model, the study systematically assesses the influence of critical regional factors—such as industrial structure, energy intensity, and energy mix—on carbon emissions. To improve the accuracy of parameter estimation and mitigate multicollinearity among variables, ridge regression was applied using data from China’s Carbon Emissions Accounting Database (CEADs). Seven multi-scenario combinations were developed to project carbon emission trajectories from 2023 to 2050, followed by a comparative analysis with analogous studies conducted in Yunnan Province. The principal findings are as follows: (1) Population size, industrial structure, and energy mix constitute the primary determinants of carbon emissions in Chongqing; (2) Under the baseline scenario, carbon emissions are projected to peak in 2037, whereas adopting a “low-growth plus high-efficiency decarbonization” pathway could effectively advance the peak to 2035; (3) Relative to Yunnan—a similarly topographically constrained region in Southwest China—Chongqing exhibits more pronounced “valley industry” lock-in effects. Accordingly, mitigation strategies for Chongqing should emphasize accelerating the transformation of energy-intensive industries and enhancing regional energy coordination. This study illustrates how variations in industrial foundations lead to divergent carbon peak trajectories under comparable topographical constraints, thereby offering tailored policy insights for analogous regions.