基于多时期交叉效率模型的中国省际碳排放效率及影响因素分析

doi:10.16381/j.cnki.issn1003-207x.2024.0626

Abstract

Abstract:

As the world’s largest carbon emitter， China is facing particularly severe pressure on carbon reduction. Improving the carbon emissions efficiency is an inevitable choice to obtain a win-win situation between promoting economic growth and reducing carbon emissions. Consequently， numerous scholars conduct extensive research on carbon emission efficiency and its influencing factors. Due to the unique advantages of data envelopment analysis （DEA） in measuring the efficiency of decision-making units （DMUs） with multiple inputs and outputs， a lot of DEA extension approaches are proposed to measure carbon emission efficiency. However， most of these methods either focus solely on comparability between DMU efficiencies or on comparability of efficiencies over time， with fewer addressing both aspects. This results in biased measurements of carbon emission efficiency across multiple time periods. To measure multi-period carbon emission efficiency scientifically and effectively， a multi-period cross-efficiency model with undesirable outputs is constructed to measure the carbon emission efficiency of 30 provinces in China from 2006 to 2021. Furthermore， recognizing the significant spatial spillover effect of inter-provincial carbon emission efficiency in China， a panel spatial lag model is employed to examine the impact of various factors， such as industrial structure change and technological innovation， on carbon emission efficiency. The results show that there are large inter-provincial differences in carbon emission efficiency of China’s provinces， which display a significant right skewed distribution from an overall perspective， that is， the carbon emission efficiency of most provinces is relatively low. From the regional perspective， the carbon emission efficiency in the eastern region of China is the highest， followed by the central and western regions， and the gap in carbon emission efficiency among the three regions is increasing year by year； In terms of dynamic evolution trend， the carbon emission efficiency of the whole country， the eastern and central regions showed a fluctuating upward trend during the sample period， while the western region showed a downward trend due to the impact of the rough mode in the western development. The spatial autocorrelation test shows that there is a significant positive spatial spillover effect on carbon emission efficiency. Factors such as industrial structure upgrading， technological innovation， population density and external development show a significant positive impact on carbon emission efficiency， whereas the energy consumption structure and production factor structure exhibit a significant negative impact on carbon emission efficiency. Therefore， China should make concerted efforts in industrial structure adjustment， energy structure optimization， technological innovation breakthroughs and regional cooperation to effectively promote a win-win situation for energy conservation and carbon reduction， as well as economic growth.

Key words: carbon emission efficiency, data envelopment analysis, multi-period cross-efficiency, spatial lag model

CLC Number:

F124.6

Zeshui Xu,Mei Chang,Xunjie Gou. An Analysis of Inter-provincial Carbon Emission Efficiency and Its Influencing Factors in China: A Multi-period Cross-efficiency Approach[J]. Chinese Journal of Management Science, 2026, 34(6): 356-368.

Figures/Tables 6

DMU	目标DMU				最终交叉效率 $E ¯ o (o = 1, ⋯, n)$
DMU	$D M U 1$	$D M U 2$	$⋯$	$D M U n$	最终交叉效率 $E ¯ o (o = 1, ⋯, n)$
$D M U 1$	$E 11$	$E 12$	$⋯$	$E 1 n$	$E ¯ 1 = ∑ d = 1 n E 1 d / n$
$D M U 2$	$E 21$	$E 22$	$⋯$	$E 2 n$	$E ¯ 2 = ∑ d = 1 n E 2 d / n$
$⋮$	$⋮$	$⋮$	$⋱$	$⋮$	$⋮$
$D M U n$	$E n 1$	$E n 2$	$⋯$	$E n n$	$E ¯ n = ∑ d = 1 n E n d / n$

变量	模型1	模型2	模型3	模型4
变量	$W d$	$W e$	$W d e 1$	$W d e 2$
$W T F C E$	0.4883^*** (3.919)	0.1165^*** (2.751)	0.2212^***(3.282)	0.5940^***(4.952)
$I S$	0.5993^***(10.575)	0.5964^*** (10.116)	0.5794^***(9.744)	0.5777^***(10.219)
$K L$	-0.2375^***(-7.632)	-0.1962^***(-6.823)	-0.2572^***(-7.217)	-0.2696^***(-8.263)
$E S$	-0.0848^**(-2.590)	-0.0957^***(-2.888)	-0.1019^***(-3.072)	-0.0888^***(-2.741)
$P D$	0.0696^***(2.700)	0.0580^**(2.205)	0.0736^***(2.834)	0.0703^***(2.753)
$I N$	0.1165^***(3.439)	0.1312^***(3.872)	0.1490^***(4.388)	0.1203^***(3.608)
$E E$	0.1077^***(14.358)	0.1035^***(13.160)	0.1036^***(13.389)	0.1054^***(14.140)
$F T$	0.0762^*** (3.941)	0.0794^***(4.074)	0.0967^***(4.771)	0.0847^***(4.419)
_cons	-0.2140^***(-3.609)	-0.0572(-1.375)	-0.1128^**(-2.422)	-0.2492^***(-4.346)
调整R²	0.9601	0.9594	0.9597	0.9609

References 32

[1]	Chang M， Xu Z， Gou X. Does advanced human capital structure improve carbon emission performance in China？ Empirical research from a spatial spillover perspective［J］. Journal of Cleaner Production， 2024， 481： 144152.
[2]	李金铠，马静静，魏伟. 中国八大综合经济区能源碳排放效率的区域差异研究［J］. 数量经济技术经济研究， 2020， 37（6）： 109-129.
	Li J K， Ma J J， Wei W. Study on regional differences of energy carbon emission efficiency in eight economic areas of China［J］. The Journal of Quantitative & Technical Economics， 2020， 37（6）： 109-129.
[3]	Ferreira A， Pinheiro M D， de Brito J， et al. Combined carbon and energy intensity benchmarks for sustainable retail stores［J］. Energy， 2018， 165： 877-889.
[4]	Vujović T， Petković Z， Pavlović M， et al. Economic growth based in carbon dioxide emission intensity［J］. Physica A： Statistical Mechanics and Its Applications， 2018， 506： 179-185.
[5]	Fan M， Shao S， Yang L. Combining global Malmquist–Luenberger index and generalized method of moments to investigate industrial total factor CO₂ emission performance： A case of Shanghai （China）［J］. Energy Policy， 2015， 79： 189-201.
[6]	Chen Y， Wong C W Y， Yang R， et al. Optimal structure adjustment strategy， emission reduction potential and utilization efficiency of fossil energies in China［J］. Energy， 2021， 237： 121623.
[7]	Li Z， Meng N， Yao X. Sustainability performance for China’s transportation industry under the environmental regulation［J］. Journal of Cleaner Production， 2017， 142： 688-696.
[8]	刘海英，王钰. 基于历史法和零和DEA方法的用能权与碳排放权初始分配研究［J］. 中国管理科学， 2020， 28（9）： 209-220.
	Liu H Y， Wang Y. Research on initial allocation of energy-consuming right and CO₂-emission right based on historical method and ZSG-DEA method［J］. Chinese Journal of Management Science， 2020， 28（9）： 209-220.
[9]	王星，张乾翔. 经济增长压力下金融集聚对碳排放效率的影响［J］. 中国人口·资源与环境， 2022， 32（3）： 11-20.
	Wang X， Zhang Q X. Impact of financial agglomeration on carbon emission efficiency under the economic growth pressure［J］. China Population， Resources and Environment， 2022， 32（3）： 11-20.
[10]	Zhou P， Ang B W， Wang H. Energy and CO₂ emission performance in electricity generation： A non-radial directional distance function approach［J］. European Journal of Operational Research， 2012， 221（3）： 625-635.
[11]	Teng X， Liu F P， Chiu Y H. The change in energy and carbon emissions efficiency after afforestation in China by applying a modified dynamic SBM model［J］. Energy， 2021， 216： 119301.
[12]	陈晓红，易国栋，刘翔. 基于三阶段SBM-DEA模型的中国区域碳排放效率研究［J］. 运筹与管理， 2017， 26（3）： 115-122.
	Chen X H， Yi G D， Liu X. Analysis of the low carbon economy efficiency in China： Based on a method of three stage SBM-DEA model with undesirable outputs［J］. Operations Research and Management Science， 2017， 26（3）： 115-122.
[13]	邵帅，范美婷，杨莉莉. 经济结构调整、绿色技术进步与中国低碳转型发展——基于总体技术前沿和空间溢出效应视角的经验考察［J］. 管理世界， 2022， 38（2）： 46-69+4-10.
	Shao S， Fan M T， Yang L L. Economic restructuring， green technical progress， and low-carbon transition development in China： An empirical investigation based on the overall technology frontier and spatial spillover effect［J］. Journal of Management World， 2022， 38（2）： 46-69+4-10.
[14]	Zheng H， Wu Y， He H， et al. Urbanization and urban energy eco-efficiency： A meta-frontier super EBM analysis based on 271 cities of China［J］. Sustainable Cities and Society， 2024， 101： 105089.
[15]	程幼明，王慧颖，张孝琪. 基于群决策考虑属性效用一致性的DEA他评交叉效率公共权重排序法［J］. 控制与决策， 2021， 36（9）： 2279-2289.
	Cheng Y M， Wang H Y， Zhang X Q. A common-weight ranking method for DEA peer-efficiency based on group decision-making and considering the consistency of attribute utility［J］. Control and Decision， 2021， 36（9）： 2279-2289.
[16]	Kao C， Liu S T. Multi-period efficiency measurement in data envelopment analysis： The case of Taiwan residents commercial banks［J］.Omega，2014， 47： 90-98.
[17]	刘志华，徐军委，张彩虹. 科技创新、产业结构升级与碳排放效率——基于省际面板数据的PVAR分析［J］. 自然资源学报， 2022， 37（2）： 508-520.
	Liu Z H， Xu J W， Zhang C H. Technological innovation， industrial structure upgrading and carbon emissions efficiency： An analysis based on PVAR model of panel data at provincial level［J］.Journal of Natural Resources， 2022， 37（2）： 508-520.
[18]	Xu L， Fan M， Yang L， et al. Heterogeneous green innovations and carbon emission performance： Evidence at China’s city level［J］. Energy Economics， 2021， 99： 105269.
[19]	Zhang W， Liu X， Zhao S， et al. Does green finance agglomeration improve carbon emission performance in China？ A perspective of spatial spillover［J］. Applied Energy， 2024， 358： 122561.
[20]	Zhang W， Liu X， Wang D， et al. Digital economy and carbon emission performance： Evidence at China’s city level［J］. Energy Policy， 2022， 165： 112927.
[21]	Zhu Y， Yang F， Wei F， et al. Measuring environmental efficiency of the EU based on a DEA approach with fixed cost allocation under different decision goals［J］. Expert Systems with Applications， 2022， 208： 118183.
[22]	Dakpo K H， Jeanneaux P， Latruffe L. Modelling pollution-generating technologies in performance benchmarking： Recent developments， limits and future prospects in the nonparametric framework［J］. European Journal of Operational Research， 2016， 250（2）： 347-359.
[23]	Du J， Chen Y， Huang Y. A modified malmquist-luenberger productivity index： Assessing environmental productivity performance in China［J］. European Journal of Operational Research， 2018， 269（1）： 171-187.
[24]	Tone K， Tsutsui M. Dynamic DEA： A slacks-based measure approach［J］. Omega， 2010， 38（3-4）： 145-156.
[25]	Tavana M， Izadikhah M， Di Caprio D， et al. A new dynamic range directional measure for two-stage data envelopment analysis models with negative data［J］. Computers & Industrial Engineering， 2018， 115： 427-448.
[26]	郭海红，刘新民. 中国农业绿色全要素生产率时空演变［J］. 中国管理科学， 2020， 28（9）： 66-75.
	Guo H H， Liu X M. Time-space evolution of China’s agricultural green total factor productivity［J］. Chinese Journal of Management Science， 2020， 28（9）： 66-75.
[27]	单豪杰. 中国资本存量K的再估算： 1952—2006年［J］. 数量经济技术经济研究， 2008， 25（10）： 17-31.
	Shan H J. Reestimating the capital stock of China： 1952-2006［J］. The Journal of Quantitative & Technical Economics， 2008， 25（10）： 17-31.
[28]	Liu Z， Guan D， Wei W， et al. Reduced carbon emission estimates from fossil fuel combustion and cement production in China［J］. Nature， 2015， 524（7565）： 335-338.
[29]	Dong F， Li X， Long R， et al. Regional carbon emission performance in China according to a stochastic frontier model［J］. Renewable and Sustainable Energy Reviews， 2013， 28： 525-530.
[30]	Parent O， LeSage J P. Using the variance structure of the conditional autoregressive spatial specification to model knowledge spillovers［J］. Journal of Applied Econometrics， 2008， 23（2）： 235-256.
[31]	干春晖，郑若谷，余典范. 中国产业结构变迁对经济增长和波动的影响［J］. 经济研究， 2011， 46（5）： 4-16+31.
	Gan C H， Zheng R G， Yu D F. An empirical study on the effects of industrial structure on economic growth and fluctuations in China［J］. Economic Research Journal， 2011， 46（5）： 4-16+31.
[32]	何维达，温家隆，张满银. 数字经济发展对中国绿色生态效率的影响研究——基于双向固定效应模型［J］. 经济问题， 2022（1）： 1-8+30.
	He W D， Wen J L， Zhang M Y. Research on the impact of digital economy development on China’s green ecological efficiency： Based on two-way fixed effects model［J］. On Economic Problems， 2022（1）： 1-8+30.

省份	本文模型		Kao和Liu的模型
省份	碳排放效率	排名	碳排放效率	排名
北京	0.6847	2	0.7443	4
天津	0.5764	4	0.7425	5
河北	0.3024	27	0.3656	29
山西	0.2942	28	0.4107	23
内蒙古	0.3290	25	0.4119	22
辽宁	0.4117	13	0.4781	14
吉林	0.4107	14	0.4480	18
黑龙江	0.5043	9	0.5769	11
上海	0.7649	1	0.8806	1
江苏	0.5364	6	0.6165	9
浙江	0.5210	8	0.6024	10
安徽	0.3678	20	0.4451	20
福建	0.5320	7	0.6174	8
江西	0.3992	17	0.4846	13
山东	0.4273	11	0.4711	15
河南	0.3738	18	0.4452	19
湖北	0.3714	19	0.4106	24
湖南	0.4105	15	0.4613	16
广东	0.6786	3	0.7719	3
广西	0.3653	21	0.4318	21
海南	0.5586	5	0.7076	7
重庆	0.4551	10	0.5148	12
四川	0.4040	16	0.4585	17
贵州	0.2613	30	0.2904	30
云南	0.3405	24	0.3874	26
陕西	0.3464	22	0.3879	25
甘肃	0.3405	23	0.3867	27
青海	0.4180	12	0.8688	2
宁夏	0.3197	26	0.7309	6
新疆	0.2848	29	0.3667	28

An Analysis of Inter-provincial Carbon Emission Efficiency and Its Influencing Factors in China: A Multi-period Cross-efficiency Approach

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