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<p>To address the inherent tension between regional development and climate targets, this study investigates the Chengdu-Chongqing Economic Circle using an integrated framework from pattern diagnosis to pathway planning. Methodologically, it assesses low-carbon performance, employs the SHAP model to unveil non-linear driving mechanisms, and integrates the CatBoost algorithm with a multi-objective optimization model to simulate optimal pathways. Key findings are fourfold: First, the region’s low-carbon development exhibits profound structural imbalances, evidenced by three distinct city clusters (stable high-efficiency, fluctuating catch-up, and persistently low-efficiency). Second, population is the primary driver of this efficiency divergence, but its impact demonstrates a significant non-linear scale threshold effect, which explains the mechanism of regional polarization. Third, future optimal emission reduction pathways are type-differentiated: leading cities can achieve endogenous reductions, with Chongqing’s net emissions projected to decrease from 140.24 Mt to 76.00 Mt, while growth-pressured cities require strong exogenous constraints. Finally, achieving multi-objective optimality by 2030 necessitates reaching coordinated targets across economic growth, land use, and energy structure, such as limiting the share of coal consumption to 26.84%. Collectively, these findings underscore the necessity of moving beyond one-size-fits-all policies towards a differentiated, system-oriented governance framework for effective regional decarbonization.</p>

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From unveiling Non-linear mechanisms to optimizing Low-Carbon pathways: A Multi-Objective approach for the Chengdu-Chongqing economic circle

  • 期刊论文

  • 发表日期：

    2026年3月2日

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  • Lu Chen,
  • Jingyi Zhao,
  • Fanjie Luo

摘要

To address the inherent tension between regional development and climate targets, this study investigates the Chengdu-Chongqing Economic Circle using an integrated framework from pattern diagnosis to pathway planning. Methodologically, it assesses low-carbon performance, employs the SHAP model to unveil non-linear driving mechanisms, and integrates the CatBoost algorithm with a multi-objective optimization model to simulate optimal pathways. Key findings are fourfold: First, the region’s low-carbon development exhibits profound structural imbalances, evidenced by three distinct city clusters (stable high-efficiency, fluctuating catch-up, and persistently low-efficiency). Second, population is the primary driver of this efficiency divergence, but its impact demonstrates a significant non-linear scale threshold effect, which explains the mechanism of regional polarization. Third, future optimal emission reduction pathways are type-differentiated: leading cities can achieve endogenous reductions, with Chongqing’s net emissions projected to decrease from 140.24 Mt to 76.00 Mt, while growth-pressured cities require strong exogenous constraints. Finally, achieving multi-objective optimality by 2030 necessitates reaching coordinated targets across economic growth, land use, and energy structure, such as limiting the share of coal consumption to 26.84%. Collectively, these findings underscore the necessity of moving beyond one-size-fits-all policies towards a differentiated, system-oriented governance framework for effective regional decarbonization.