中国煤炭资源型城市碳减排的分类调控OACHSSCD

The effective low-carbon transition for coal resource-based cities holds strategic significance for the national coordinating energy security,economic development,and social stability.This study examines 45 prefecture-level coal-resources-based cities in China,categorizes them into four types:growing,mature,declining,and regenerating.The super-efficiency EBM-GML model is employed to analyze static and dynamic carbon emission efficiency from 2006 to 2020.Additionally,the GA-BP neural network model combined with scenario analysis is utilized to forecast the carbon emission trends of these cities through 2035.Results indicate:1)Overall static efficiency remains low,with mature cities showing an initial rise followed by decline,while the other three categories exhibit upward trends.Dynamic efficiency demonstrates fluctuating characteristics,with growth and regeneration cities experiencing greater volatility.2)Scenario projections indicate that while regenerative cities continue to see declining carbon emissions,the other three categories show increasing trends,with growing cities experiencing the fastest growth rate.Findings reveal that combined development scenarios can achieve effective emission reductions,with greater reduction potential in growing and regenerative cities.Based on this,we propose that coal resource-based cities require differentiated emission reduction pathways through refined classification and regulation.This approach provides effective decision-making support for the low-carbon transition of more coal resource-based cities globally.