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Guided by the “Dual Carbon” strategy, identifying pathways for the coordinated development of carbon reduction and economic growth-towards the goal of common prosperity-has emerged as a critical challenge in China's modernization process.Using national panel data from 2000 to 2022 and provincial panel data for 30 provinces from 2000 to 2021,this study employs the Tapio decoupling model and the Logarithmic Mean Divisia Index(LMDI) decomposition method from the perspective of environmental equity.It systematically assesses the decoupling relationship between carbon emissions and economic growth at both the national and provincial levels, and further explores the key drivers and underlying factors contributing to regional disparities in decoupling status.The findings indicate, firstly, a “weak decoupling” relationship between carbon emissions and economic growth nationwide, characterized by significant regional variations.Specifically, less developed provinces exhibit higher decoupling elasticity, generally experiencing faster growth in carbon emissions compared to economically developed regions.Secondly, factors such as energy structure, energy consumption intensity, per capita GDP,and population size significantly influence carbon decoupling elasticities, with these variables showing distinctly different impacts across various regions.Lastly, from a spatial distribution perspective, there exist notable imbalances in carbon governance efficiency and environmental equity in contemporary China.Less developed regions face dual pressures of “economic development and emission reduction” during the low-carbon transition process, potentially exacerbating regional imbalances and leading to further polarization.This study expands the theoretical analysis of carbon decoupling by integrating perspectives on environmental equity and policy coordination mechanisms, thereby providing theoretical foundations and policy recommendations for formulating region-specific carbon reduction policies and achieving the coordinated advancement of economic development and environmental protection.
[1] 陈诗一,马倩倩.经济增长与环境公平——基于中国开发区政策的研究[J].学术月刊,2023,55(7):31-45.
[2] 于潇,林建鑫.公众环境关注是否促进减污降碳协同增效?[J].西安财经大学学报,2024,37(3):55-67.
[3] MIAO A K,YUAN Y,WU H,et al.Pathway for China’s provincial carbon emission peak:a case study of the Jiangsu Province[J].Energy,2024,298:131417.
[4] 戴其文,李毅,代嫣红,等.环境正义研究前沿及其启示[J].自然资源学报,2021,36(11):2938-2954.
[5] 李博英,王璇子.碳排放强度对居民消费价格指数的影响研究[J].统计与信息论坛,2022,37(10):65-74.
[6] 杨志安,胡博.财政分权的碳排放效应:空间溢出与机制检验[J].东北大学学报(社会科学版),2024,26(3):34-42.
[7] 陈慧灵,高子恒,王振波.省级尺度工业碳排放影响因素及碳转移格局[J].生态学报,2023,43(14):1-13.
[8] WU W Q,MA X,ZHANG Y,et al.A novel conformable fractional non-homogeneous grey model for forecasting carbon dioxide emissions of BRICS countries[J].Science of the total environment,2020,707:135447.
[9] WANG Q,LI Y F,LI R R.Rethinking the environmental Kuznets curve hypothesis across 214 countries:the impacts of 12 economic,institutional,technological,resource,and social factors[J].Humanities and social sciences communications,2024,11(1):1-19.
[10] 王芳,曹一鸣,陈硕.反思环境库兹涅茨曲线假说[J].经济学(季刊),2019,19(1):81-100.
[11] 李达,林龙圳,林震,等.黄河流域生态保护和高质量发展的EKC检验[J].生态学报,2021,41(10):3965-3974.
[12] SHUAI C,SHEN L,JIAO L,et al.Identifying key impact factors on carbon emission:evidences from panel and time-series data of 125 countries from 1990 to 2011[J].Applied energy,2017,187:310-325.
[13] 徐宇曦,陈一欣,苏杰,等.环境正义视角下公园绿地空间配置的公平性评价——以南京市主城区为例[J].应用生态学报,2022,33(6):1589-1598.
[14] 刘自敏,李莉,李兴.中国碳市场的价格有效性评估与优化设计研究[J].统计与信息论坛,2025,40(1):77-91.
[15] 魏丽莉,侯宇琦,曹昊煜.中国城市碳排放绩效:动态分解、空间差异与影响因素[J].统计与信息论坛,2024,39(2):69-83.
[16] 胡健.中国推动能源革命的动因、实践进展与未来取向[J].西安财经大学学报,2024,37(5):90-102.
[17] 闫妹雅,杨国涛,张淼.“双控”和“双碳”目标下中国省域能源消费指标分配[J].统计与信息论坛,2023,38(5):118-128.
[18] HE K H,MI Z F,ZHANG J,et al.The polarizing trend of regional CO2 emissions in China and its implications[J].Environmental science & technology,2023,57(11):4406-4414.
[19] 尹碧波,邝萍.智慧城市的碳减排效应研究:基于双重机器学习的因果推断[J].统计与信息论坛,2025,40(3):73-86.
[20] 赵曼仪,王科.减污降碳协同效应综合评估的研究综述与展望[J].中国人口·资源与环境,2024,34(2):58-69.
[21] DUAN H Y,SUN X H,SONG J N,et al.Peaking carbon emissions under a coupled socioeconomic-energy system:evidence from typical developed countries[J].Resources,conservation and recycling,2022,187:106641.
[22] 朱长征,杨莎,刘鹏博,等.中国交通运输业碳达峰时间预测研究[J].交通运输系统工程与信息,2022,22(6):291-299.
[23] 张金良,贾凡.中国火电行业多模型碳达峰情景预测[J].电力建设,2022,43(5):18-28.
[24] TAPIO P.Towards a theory of decoupling:degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001[J].Transport policy,2005,12(2):137-151.
[25] ANG B W.Decomposition analysis for policymaking in energy:which is the preferred method?[J].Energy policy,2004,32(9):1131-1139.
[26] 王良栋,吴乐英,陈玉龙,等.经济平稳增长下黄河流域相关省区碳达峰时间及峰值水平[J].资源科学,2021,43(11):2331-2341.
[27] 马晓君,陈瑞敏,苏衡.中国工业行业能源消耗的驱动因素与脱钩分析[J].统计与信息论坛,2021,36(3):70-81.
[28] 袁路,潘家华.Kaya恒等式的碳排放驱动因素分解及其政策含义的局限性[J].气候变化研究进展,2013,9(3):210-215.
[29] 胡剑波,罗志鹏,李峰.“碳达峰”目标下中国碳排放强度预测——基于LSTM和ARIMA-BP模型的分析[J].财经科学,2022,407(2):89-101.
[30] YAZDI S K,FALAHATPARVAR F.The effects of urbanization and renewable energy on CO2 emissions:a panel data[J].International journal of renewable energy sources,2017,2:121-127.
[31] BALSALOBRE-LORENTE D,SHAHBAZ M,ROUBAUD D,et al.How economic growth,renewable electricity and natural resources contribute to CO2 emissions?[J].Energy policy,2018,113:356-367.
[32] YORK R,ROSA E A,DIETZ T.STIRPAT,IPAT and ImPACT:analytic tools for unpacking the driving forces of environmental impacts[J].Ecological economics,2003,46(3):351-365.
Basic Information:
DOI:10.20207/j.cnki.1007-3116.20250804.001
China Classification Code:F124;X22
Citation Information:
[1]LI Boying.Synergistic Effects of Carbon Mitigation and Economic Growth in China: Measurement and Regional Disparities[J].Journal of Statistics and Information,2025,40(08):14-25.DOI:10.20207/j.cnki.1007-3116.20250804.001.
Fund Information:
国家自然科学基金青年项目“基于碳排放时空关联性的我国各省区碳减排及其影响机制研究”(42301341); 中央高校基本科研业务费专项资金资助;同济大学中国特色社会主义理论研究中心资金资助