Spatial Evolution and Scenario Simulation of Carbon Metabolism in Coal-Resource-Based Cities Towards Carbon Neutrality: A Case Study of Jincheng, China

As important energy suppliers in China, coal-resource-based cities are pivotal to achieving the nation’s 2060 carbon-neutrality goal. This study focused on Jincheng City, utilizing the LOW EMISSIONS ANALYSIS PLATFORM (LEAP) model to predict carbon emissions from energy consumption under various scenarios from 2020 to 2060. Then, combined with the Markov-PLUS model to map carbon emissions to land-use types, it evaluated spatial changes in carbon metabolism and analyzed carbon-transfer patterns across different land-use types. The results showed the following: (1) Across all scenarios, Jincheng’s carbon emissions exhibited an initial increase followed by a decline, with the industrial sector accounting for over 70% of total emissions. While the baseline scenario deviated from China’s carbon peaking target, the high-limit scenario achieved an early carbon peak by 2027. (2) High-negative-carbon-metabolism areas were concentrated in central urban zones and industrial parks. Notably, arable land shifted from a carbon-sink area to a carbon source area by 2060 in both the low- and high-limit scenarios. (3) In the baseline scenario, industrial and transportation land uses were the primary barriers to carbon metabolism balance. In the low-carbon scenario, the focus shifted from industrial and transportation emissions to urban construction land emissions. In the high-limit scenario, changes in urban–rural land-use relationships significantly influenced carbon metabolism balance. This study emphasizes the importance of industrial green transformation and land-use planning control to achieve carbon neutrality, and it further explores the significant impact of territorial spatial planning on the low-carbon transition of coal-resource-based cities.