Model Construction and Scenario Analysis for Carbon Dioxide Emissions from Energy Consumption in Jiangsu Province: Based on the STIRPAT Extended Model

Ying Liu, Lvhan Yang, Meng Wu (), Jinxian He (), Wenqiang Wang, Yunpeng Li, Renjiang Huang, Dongfang Liu and Heyao Tan
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Ying Liu: Jiangsu Mineral Resources and Geological Design and Research Institute, China National Administration of Coal Geology, Xuzhou 221006, China
Lvhan Yang: Sichuan Tianshengyuan Environmental Services Co., Ltd., Chengdu 610213, China
Meng Wu: Jiangsu Mineral Resources and Geological Design and Research Institute, China National Administration of Coal Geology, Xuzhou 221006, China
Jinxian He: Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221008, China
Wenqiang Wang: Jiangsu Mineral Resources and Geological Design and Research Institute, China National Administration of Coal Geology, Xuzhou 221006, China
Yunpeng Li: Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221008, China
Renjiang Huang: Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221008, China
Dongfang Liu: Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221008, China
Heyao Tan: Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221008, China

Sustainability, 2025, vol. 17, issue 19, 1-17

Abstract: Against the backdrop of China’s “dual carbon” strategy (carbon peaking and carbon neutrality), provincial-level carbon emission research is crucial for the implementation of related policies. However, existing studies insufficiently cover the driving mechanisms and scenario prediction for energy-importing provinces. This study can provide theoretical references for similar provinces in China to conduct research on carbon dioxide emissions from energy consumption. The carbon dioxide emissions from energy consumption in Jiangsu Province between 2000 and 2023 were calculated using the carbon emission coefficient method. The Tapio decoupling index model was adopted to evaluate the decoupling relationship between economic growth and carbon dioxide emissions from energy consumption in Jiangsu. An extended STIRPAT model was established to predict carbon dioxide emissions from energy consumption in Jiangsu, and this model was applied to analyze the emissions under three scenarios (baseline scenario, low-carbon scenario, and enhanced low-carbon scenario) during 2024–2030. The results show the following: (1) During 2000–2023, the carbon dioxide emissions from energy consumption in Jiangsu Province ranged from 215.22428 million tons to 783.94270 million tons, with an average of 549.96280 million tons. (2) The decoupling status between carbon dioxide emissions from energy consumption and economic development in Jiangsu was dominated by weak decoupling, accounting for 91.304%, while a small proportion (8.696%) of expansive coupling was also observed. (3) Under the baseline scenario, the carbon dioxide emissions from energy consumption in Jiangsu in 2030 will reach 796.828 million tons; under the low-carbon scenario, the emissions will be 786.355 million tons; and under the enhanced low-carbon scenario, the emissions will be 772.293 million tons. Furthermore, countermeasures and suggestions for reducing carbon dioxide emissions from energy consumption in Jiangsu are proposed, mainly including strengthening the guidance of policies and institutional systems, optimizing the energy consumption structure, intensifying technological innovation efforts, and enhancing government promotion and publicity.

Keywords: Jiangsu province; CO 2 emissions from energy consumption; predictive model; STIRPAT model; carbon peak (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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