Optimizing China's carbon quota allocation for peak emissions: A novel AMLC framework tailored to regional dynamics

As China advances its carbon reduction efforts, the equitable and efficient allocation of initial carbon quotas among provinces has become increasingly critical. This study proposes a novel two-stage Adjusting Measures to Local Conditions (AMLC) framework that integrates regional development disparities and marginal abatement costs into the quota allocation process. Employing a PSO-LSSVM model for carbon emission estimation and a hierarchical clustering algorithm, this study categorizes 30 Chinese provinces into six distinct types, facilitating a regionally adaptive allocation strategy. Key findings include: (1) Between 2003 and 2022, carbon emissions exhibited a structural shift from energy-abundant, carbon-exporting provinces with inefficient industrial structures to energy-deficient, carbon-importing provinces, intensifying regional disparities. (2) Equity-based allocation schemes tend to favor regions with abundant carbon sinks, yet all provinces in the sixth category experience quota deficits; conversely, efficiency-based allocation disproportionately benefits initially efficient regions, exacerbating interprovincial disparities. (3) The AMLC framework enables 17 provinces across all clusters to achieve quota surpluses while significantly enhancing national green productivity, demonstrating its superiority in promoting high-quality economic development. These findings provide valuable insights for policymakers in designing macroeconomic strategies that balance equity and efficiency in carbon quota distribution, thereby supporting China's transition toward peak emissions and sustainable growth.