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

摘要/Abstract

摘要：

本文首先针对现有研究在多时期碳排放效率测算上的有偏测度问题，提出一个具有非期望产出的多时期交叉效率模型，对2006-2021年我国30个省份的碳排放效率进行更加准确的测度分析。进而，采用面板空间滞后模型，在控制碳排放效率空间溢出效应的基础上，考察产业结构变迁和科技创新等多种因素对碳排放效率的影响。研究发现：我国各省份的碳排放效率存在较大的省际差异，整体呈现显著的右偏态分布，即大多数省份的碳排放效率偏低；分区域来看，我国东部地区的碳排放效率最高，其后依次为中部和西部地区，且各区域的碳排放效率差距逐年增大；动态演变趋势上，样本期间，全国及东部、中部地区的碳排放效率呈现波动上升趋势，而西部地区的碳排放效率整体呈现波动下降趋势；空间自相关检验显示，碳排放效率存在显著的正向空间溢出效应；产业结构升级、科技创新、人口密度和对外开放对碳排放效率表现出显著的促增效应，而能源消费结构、要素结构对碳排放效率具有显著的负向影响。因此，我国各区域应在产业结构调整、能源结构优化、科技创新突破和区域协调合作等方面协同努力，以有效推动节能降碳与经济增长双赢。

关键词: 碳排放效率, DEA模型, 多时期交叉效率, 空间滞后模型

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

中图分类号:

F124.6

徐泽水,常梅,缑迅杰. 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[J]. 中国管理科学, 2026, 34(6): 356-368.

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.

图/表 6

图1

表1

DMU的交叉效率矩阵"

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 $E ¯ o (o = 1, ⋯, n)$
$D M U 1$	$E 11$	$E 12$	$⋯$	$E 1 n$	$E ¯ 1 = ∑ d = 1 n E 1 d / n$
$D M U 2$	$E 21$	$E 22$	$⋯$	$E 2 n$	$E ¯ 2 = ∑ d = 1 n E 2 d / n$
$⋮$	$⋮$	$⋮$	$⋱$	$⋮$	$⋮$
$D M U n$	$E n 1$	$E n 2$	$⋯$	$E n n$	$E ¯ n = ∑ d = 1 n E n d / n$

表1

表2

图2

图3

表3

回归模型参数估计结果"

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

表3

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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