分类施策、梯次有序推进我国地区“双碳”战略：基于碳排放空间差异的结构分解及聚类研究

doi:10.16381/j.cnki.issn1003-207x.2023.0876

Abstract

Abstract:

Due to different resource endowments， industrial division and development stages， CO2 emissions pattern in different regions of China show spatial heterogeneity. The Action Plan for Carbon Peaking before 2030 points out that all regions should take measures by category and take orderly steps to achieve carbon peak based on their own economic and social development stages and resource and environmental endowments. By analyzing the key driving factors of carbon emissions in China's 30 provinces and cities and identifying their differences， a classification of Chinese provinces and cities is proposed， and strategies for each type of provinces and cities are put forward to reach carbon peaking and carbon neutrality in the future.In order to propose a scientific and reasonable spatial classification， the spatial differences and key drivers of carbon emissions in 30 provinces of China （except Tibet， Hong Kong， Macao and Taiwan） are quantified by constructing a multi-regional spatial structure decomposition analysis model. Based on the results of spatial structure decomposition analysis， a cluster analysis of 30 provinces in China is made， so as to identify the carbon emission characteristics of each type of provinces， and key strategies for achieving “dual carbon” goal in various provinces are put forward.The results show that there are six key factors leading to the spatial differences of carbon emissions among provinces in China， which are sectoral structure， demand allocation， final demand， energy intensity， production structure and energy mix effects. Based on the contribution of the above drivers to the spatial differences of carbon emissions among provinces in China， 30 provinces are classified into five categories through cluster analysis， i.e. growth-driven provinces， efficiency-driven provinces， balanced-developing provinces， extensive-growth provinces and provinces with great transformation potential. Although growth-driven provinces have a high level of carbon emissions， their energy intensity， energy mix and sectoral structure are better than the national average level， and economic growth is the main driver of the high level of carbon emissions in these provinces. The carbon emission level of efficiency-driven provinces is generally low， as a result of the higher energy efficiency and better energy mix in these provinces. The level of carbon emissions in the balanced-developing provinces is in the middle of all provinces in China， and the effects of various drivers are also in the middle of the whole country. The level of carbon emissions in extensive-growth provinces is high， as a result of low efficiency and unreasonable structure in these provinces. The provinces with great transformation potential have the lowest carbon emission level with rich renewable energy sources and thus large transition potential.The contributions of this paper are as follows. First， the spatial differences of carbon emissions among provinces in China are identied. Second， the drivers of the spatial differences of carbon emissions among provinces in China are analyzed. Third， it provides a methodology for the classification of provinces in China for the ‘Action Plan for Peak Carbon by 2030’， and the key strategies to achieve the "dual carbon" goal for various provinces are further proposed.

Key words: China's carbon emissions, spatial differences, driving factors, spatial structural decomposition analysis, cluster analysis, dual carbon

CLC Number:

F205

Hongguang Nie,Cairui Jiang,Jianlei Mo. Achieving China's Regional Dual-carbon Goals by Adopting Differentiated Policies in Different Regions: Structural Decomposition and Clustering Analysis Based on Spatial Differences in Carbon Emissions in China[J]. Chinese Journal of Management Science, 2025, 33(11): 310-320.

Figures/Tables 5

References 39

[1]	Sachs J， Kroll C， Lafortune G， et al. Sustainable development report 2021［M］. Cambridge： Cambridge University Press， 2021.
[2]	IPCC. Climate change 2022： Impacts， adaptation and vulnerability［M］. Cambridge： Cambridge University Press， 2022.
[3]	韩立群. 碳中和的历史源起、各方立场及发展前景［J］. 国际研究参考， 2021（7）： 29-36+44.
	Han L Q. Carbon neutrality： Historical origins， positions of various parties and prospects［J］. International Study Reference， 2021（7）： 29-36+44.
[4]	Net Zero Tracker. Global Net Zero Coverage［EB/OL］.（2002-12-19）［2003-04-15］..
[5]	Le Quéré C， Andrew R M， Friedlingstein P， et al. Global carbon budget 2018［J］. Earth System Science Data， 2018， 10（4）： 2141-2194.
[6]	中共中央国务院. 关于完整准确全面贯彻新发展理念做好碳达峰碳中和工作的意见［N］. 人民日报， 2021-10-25（001）.
	The Central Committee of the Communist Party of China and the State Council. Opinions on fully， accurately， and comprehensively implementing the new development philosophy to achieve carbon peaking and carbon neutrality［N］. People’s Daily， 2021-10-25（001）.
[7]	Zhang W， Li K， Zhou D， et al. Decomposition of intensity of energy-related CO₂ emission in Chinese provinces using the LMDI method［J］. Energy Policy， 2016， 92： 369-381.
[8]	Li A， Zhang A， Zhou Y， et al. Decomposition analysis of factors affecting carbon dioxide emissions across provinces in China［J］. Journal of Cleaner Production， 2017， 141： 1428-1444.
[9]	Wang H， Ang B W， Su B. A multi-region structural decomposition analysis of global CO₂ emission intensity［J］. Ecological Economics， 2017， 142： 163-176.
[10]	Wang M， Feng C. Decomposition of energy-related CO₂ emissions in China： An empirical analysis based on provincial panel data of three sectors［J］. Applied Energy， 2017， 190： 772-787.
[11]	Ang B W， Xu X Y， Su B. Multi-country comparisons of energy performance： The index decomposition analysis approach［J］.Energy Economics，2015， 47： 68-76.
[12]	Su B， Ang B W. Multi-region comparisons of emission performance： The structural decomposition analysis approach［J］. Ecological Indicators， 2016， 67： 78-87.
[13]	Ang B W， Su B， Wang H. A spatial-temporal decomposition approach to performance assessment in energy and emissions［J］.Energy Economics，2016，60： 112-121.
[14]	Nie H， Kemp R， Vivanco D F， et al. Structural decomposition analysis of energy-related CO₂ emissions in China from 1997 to 2010［J］. Energy Efficiency， 2016， 9（6）： 1351-1367.
[15]	Cellura M， Longo S， Mistretta M. Application of the structural decomposition analysis to assess the indirect energy consumption and air emission changes related to Italian households consumption［J］. Renewable and Sustainable Energy Reviews， 2012， 16（2）： 1135-1145.
[16]	Lee K， Oh W. Analysis of CO₂ emissions in APEC countries： A time-series and a cross-sectional decomposition using the log mean Divisia method［J］. Energy Policy， 2006， 34（17）： 2779-2787.
[17]	Sun J W. An analysis of the difference in CO₂ emission intensity between Finland and Sweden［J］. Energy， 2000， 25（11）： 1139-1146.
[18]	Wu F， Huang N， Zhang Q， et al. Multi-province comparison and typology of China’s CO₂ emission： A spatial–temporal decomposition approach［J］. Energy， 2020， 190： 116312.
[19]	Li H， Zhao Y， Qiao X， et al. Identifying the driving forces of national and regional CO₂ emissions in China： Based on temporal and spatial decomposition analysis models［J］. Energy Economics， 2017， 68： 522-538.
[20]	Guevara Z， Henriques S， Sousa T. Driving factors of differences in primary energy intensities of 14 European countries［J］. Energy Policy， 2021， 149： 112090.
[21]	王鹏，冯相昭，王敏，等. 我国省域碳排放特征识别及类型划分［J］. 环境与可持续发展， 2021， 46（3）： 31-36.
	Wang P， Feng X Z， Wang M， et al. The characteristics of carbon emission and its classification at provincial level in China［J］. Environment and Sustainable Development， 2021， 46（3）： 31-36.
[22]	王思博，庄贵阳，窦晓铭. 中国省域碳达峰梯次划分与差异化排放路径——基于碳排放与经济发展双重视角的考察［J］. 武汉大学学报（哲学社会科学版）， 2023， 76（3）： 136-150.
	Wang S B， Zhuang G Y， Dou X M. Tiered division of peak carbon emissions and differentiated emission paths among provinces in China based on the dual perspectives of carbon emissions and economic development［J］. Wuhan University Journal （Philosophy & Social Science）， 2023， 76（3）： 136-150.
[23]	邓吉祥，刘晓，王铮. 中国碳排放的区域差异及演变特征分析与因素分解［J］. 自然资源学报， 2014， 29（2）： 189-200.
	Deng J X， Liu X， Wang Z. Characteristics analysis and factor decomposition based on the regional difference changes in China’s CO₂ emission［J］. Journal of Natural Resources， 2014， 29（2）： 189-200.
[24]	Mi Z， Meng J， Guan D， et al. Chinese CO₂ emission flows have reversed since the global financial crisis［J］. Nature Communications， 2017， 8： 1712.
[25]	Gao C， Tao S， He Y， et al. Effect of population migration on spatial carbon emission transfers in China［J］. Energy Policy， 2021， 156： 112450.
[26]	李国志，李宗植. 中国二氧化碳排放的区域差异和影响因素研究［J］. 中国人口·资源与环境， 2010， 20（5）： 22-27.
	Li G Z， Li Z Z. Regional difference and influence factors of China’s carbon dioxide emissions［J］. China Population， Resources and Environment， 2010， 20（5）： 22-27.
[27]	宋德勇，徐安. 中国城镇碳排放的区域差异和影响因素［J］. 中国人口·资源与环境， 2011， 21（11）： 8-14.
	Song D Y， Xu A. Regional difference and influencial factors of China’s urban carbon emissions［J］. China Population， Resources and Environment， 2011， 21（11）： 8-14.
[28]	王灿，张雅欣. 碳中和愿景的实现路径与政策体系［J］. 中国环境管理， 2020， 12（6）： 58-64.
	Wang C， Zhang Y X. Implementation pathway and policy system of carbon neutrality vision［J］. Chinese Journal of Environmental Management， 2020， 12（6）： 58-64.
[29]	何建坤. 碳达峰碳中和目标导向下能源和经济的低碳转型［J］. 环境经济研究， 2021， 6（1）： 1-9.
	He J K. Low carbon transformation of energy and economy aiming for the peaking of carbon emission and carbon neutrality［J］. Journal of Environmental Economics， 2021， 6（1）： 1-9.
[30]	Dietzenbacher E， Los B. Structural decomposition techniques： Sense and sensitivity［J］. Economic Systems Research， 1998， 10（4）： 307-324.
[31]	Liu F L， Ang B W. Eight methods for decomposing the aggregate energy-intensity of industry［J］. Applied Energy， 2003， 76（1-3）： 15-23.
[32]	Shan Y， Liu J， Liu Z， et al. New provincial CO₂ emission inventories in China based on apparent energy consumption data and updated emission factors［J］. Applied Energy， 2016， 184： 742-750.
[33]	Shan Y， Guan D， Zheng H， et al. China CO₂ emission accounts 1997-2015［J］. Scientific Data， 2018， 5： 170201.
[34]	Shan Y， Huang Q， Guan D， et al. China CO₂ emission accounts 2016-2017［J］. Scientific Data， 2020， 7： 54.
[35]	Guan Y， Shan Y， Huang Q， et al. Assessment to China’s recent emission pattern shifts［J］. Earth’s Future， 2021， 9（11）： e2021EF002241.
[36]	国家统计局. 中国第三产业统计年鉴2018［M］. 北京：中国统计出版社，2018.
	National Bureau of Statistics. China statistical yearbook of the tertiery industry 2018 ［M］. Beijing： China Statistics Press， 2018.
[37]	Su B， Ang B W. Attribution of changes in the generalized Fisher index with application to embodied emission studies［J］. Energy， 2014， 69： 778-786.
[38]	Cellura M， Longo S， Mistretta M. Application of the structural decomposition analysis to assess the indirect energy consumption and air emission changes related to Italian households consumption［J］. Renewable and Sustainable Energy Reviews， 2012， 16（2）： 1135-1145.
[39]	Su B， Ang B W. Structural decomposition analysis applied to energy and emissions： Some methodological developments［J］. Energy Economics， 2012， 34（1）： 177-188.

Achieving China's Regional Dual-carbon Goals by Adopting Differentiated Policies in Different Regions: Structural Decomposition and Clustering Analysis Based on Spatial Differences in Carbon Emissions in China

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 39

Related Articles 12

Metrics

Comments

Recommended 0

[1]	Mingzhen Song, Teng Ma, Lingcheng Kong, Jiaping Xie. Under Cascade Utilization of Power Battery the Lease Strategy Selection of Energy Storage Power Station in Wind Power Enterprises: Capacity Charging or Two-part Charging? [J]. Chinese Journal of Management Science, 2025, 33(9): 135-147.
[2]	LI Ye, DING Yuan-ping. Construction of Linear Time-varying Parameters DLDGM(1,N) Model Based on Driving Factors Control [J]. Chinese Journal of Management Science, 2022, 30(3): 221-229.
[3]	LIU Xue-wen. Study on the Optimization of Investor Sentiment Measure Indicators in Chinese Stock Market [J]. Chinese Journal of Management Science, 2019, 27(1): 22-33.
[4]	XIE Rui, WANG Zhen-guo, ZHANG Bin-bin. Study on Driving Factors and Critical Supply Chain Paths of CO₂ Emissions in China [J]. Chinese Journal of Management Science, 2017, 25(10): 119-129.
[5]	HE Zhi-yi, CHEN Zheng-hui. Research on the Consumption Fluctuation Characteristics and Customer Segmentation of Natural Gas Customer [J]. Chinese Journal of Management Science, 2015, 23(8): 132-138.
[6]	TIAN Jun, LI Li-fang, BAI Jian, FENG Geng-zhong. Modular Design of Emergency Task Executive Processes Based on DSM [J]. Chinese Journal of Management Science, 2014, 22(8): 100-107.
[7]	LIU Xue-wen, OUYANG Mei-chen, XU Jie. Systematic Approach Construction Building of the Selection,Combination and Evaluation of Activity Cost Driver [J]. Chinese Journal of Management Science, 2014, 22(11): 72-78.
[8]	LIU Xue-wen, OUYANG Mei-chen, XU Jie. Systematic Approach Construction Building of the Selection,Combination and Evaluation of Activity Cost Driver [J]. Chinese Journal of Management Science, 2014, 22(11): 72-78.
[9]	LIU Ze-shuang, WANG Xing-hong, LI Wan-xu. The Evaluation and Practical Examples of Whole Benefits of Waterpower Enterprises [J]. Chinese Journal of Management Science, 2002, (4): 95-100.
[10]	LI Cong-zhu, JIANG Tie-jun. A Study of the Methods on Stock Sample can be Obtained for Computing Index [J]. Chinese Journal of Management Science, 2002, (1): 11-16.
[11]	ZHU Jia, JI Lei, CHI Hong, CHEN Jian-ming. Multivariate Analysis for the Assessment of Saving Offices of Bank [J]. Chinese Journal of Management Science, 2001, (3): 68-73.
[12]	Wang Yuanyue. A Phasic Research on Direct Foreign Investment in China [J]. Chinese Journal of Management Science, 1997, (3): 27-31.