城市尺度下长三角区域碳排放效率时空演化及影响因素研究

宋青; 李超群; 陈骏宇

doi:10.12302/j.issn.1000-2006.202212021

宋青, 李超群, 陈骏宇

南京林业大学学报（自然科学版） ›› 2023, Vol. 47 ›› Issue (6) : 251-262.

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南京林业大学学报（自然科学版） ›› 2023, Vol. 47 ›› Issue (6) : 251-262. DOI: 10.12302/j.issn.1000-2006.202212021

研究论文

城市尺度下长三角区域碳排放效率时空演化及影响因素研究

宋青 ¹ ,
李超群 ¹ ,
陈骏宇 ¹^,²^,^*

作者信息 +

Spatio-temporal evolution and influencing factors of carbon emission efficiency in the Yangtze River Delta region at the city scale

SONG Qing ¹ ,
LI Chaoqun ¹ ,
CHEN Junyu ¹^,²^,^*

Author information +

文章历史 +

摘要

【目的】探究长三角地区碳排放效率时空特征及影响因素,更好地为长三角地区根据自身实际发展情况制定有针对性的碳减排措施,促进区域的协调发展。【方法】基于非期望产出的SBM(slacks-based measure)模型测度长三角地区41个城市的碳排放效率,结合探索性空间分析方法对长三角地区碳排放效率的时空特征进行探究,从规模、结构、技术3个维度出发,将空间因素考虑在内构建空间计量模型并深入分析长三角地区碳排放效率的影响因素。【结果】长三角地区41个城市碳排放效率存在较大差异,不均衡现象明显;碳排放效率高值区主要分布在上海市和江苏省,碳排放效率低值区集中分布在安徽省,空间差异特征明显;碳排放效率在空间上表现出较强的正相关性,空间聚集态势明显且空间集聚主要以H-H和L-L类型集聚为主;经济发展规模、产业结构对于碳排放效率的提升有显著的抑制作用,外商直接投资对于碳排放效率有明显的正向驱动作用。【结论】空间杜宾模型(SDM)的效应分解结果表明,注重经济发展质量、调整产业结构是提升长三角地区城市碳排放效率的重要途径。

Abstract

【Objective】The study explored the spatio-temporal characteristics and influencing factors of carbon emission efficiency in the Yangtze River Delta, to better formulate carbon emission reduction measures for the region according to its actual development conditions, and to promote the coordinated development of the region.【Method】Based on the slacks-based measure(SBM) model of undesired output, the carbon emission efficiency of 41 cities in the Yangtze River Delta were measured and the spatiotemporal characteristics of carbon emission efficiency in the Yangtze River Delta were analyzed through exploratory spatial data analysis. Constructing a spatial econometric model with spatial factors from the three dimensions of scale, structure and technology allowed analysis of the influencing factors of carbon emission efficiency in the Yangtze River Delta. 【Result】Large differences in carbon emission efficiency were evident among 41 cities in the Yangtze River Delta. Imbalance was obvious. The high-value areas of carbon emission efficiency were mainly distributed in the Shanghai and Jiangsu provinces, and the areas with low carbon emission efficiency were concentrated in the Anhui Province, with obvious spatial differences. Carbon emission efficiency showed a strong positive correlation in space, spatial agglomeration was obvious, and spatial agglomeration was mainly H-H and L-L. The scale of economic development and industrial structure had a significant inhibitory effect on the improvement of carbon emissions efficiency, and foreign direct investment had an obvious positive driving effect on carbon emissions efficiency. 【Conclusion】 The effect decomposition results of Spatial Doberman model shows that focusing on the quality of economic development and adjusting the industrial structure is an important way to improve the carbon emission efficiency of cities in the Yangtze River Delta.

导出引用

宋青, 李超群, 陈骏宇. 城市尺度下长三角区域碳排放效率时空演化及影响因素研究[J]. 南京林业大学学报（自然科学版）. 2023, 47(6): 251-262 https://doi.org/10.12302/j.issn.1000-2006.202212021

SONG Qing, LI Chaoqun, CHEN Junyu. Spatio-temporal evolution and influencing factors of carbon emission efficiency in the Yangtze River Delta region at the city scale[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2023, 47(6): 251-262 https://doi.org/10.12302/j.issn.1000-2006.202212021

中图分类号： F205

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

[1]	International Energy Agency (IEA). CO₂ emissions from fuel combustion 2017[M]. Paris: OECD Publishing, 2017. 本文引用 [1]

[2]	LIU F, TANG L, LIAO K C, et al. Spatial distribution and regional difference of carbon emissions efficiency of industrial energy in China[J]. Sci Rep, 2021, 11(1):19419.DOI:10.1038/s41598-021-98225-z. 本文引用 [1]

[3]	魏楚. 中国城市CO₂边际减排成本及其影响因素[J]. 世界经济, 2014, 37(7):115-141. WEI C. Marginal cost of CO₂ emission reduction in China and its influencing factors[J]. J World Econ, 2014, 37(7):115-141.DOI:10.19985/j.cnki.cassjwe.2014.07.007. 本文引用 [1]

[4]	冯冬, 李健. 京津冀区域城市二氧化碳排放效率及减排潜力研究[J]. 资源科学, 2017, 39(5):978-986. FENG D, LI J. Research of the carbon dioxide emission efficiency and reduction potential of cities in the Beijing-Tianjin-Hebei region[J]. Resour Sci, 2017, 39(5):978-986.DOI:10.18402/resci.2017.05.17. 本文引用 [1]

[5]	ZHOU D Q, MENG F Y, BAI Y, et al. Energy efficiency and congestion assessment with energy mix effect: the case of APEC countries[J]. J Clean Prod, 2017, 142:819-828.DOI:10.1016/j.jclepro.2016.08.166. 本文引用 [1]

[6]	FENG C, ZHANG H, HUANG J B. The approach to realizing the potential of emissions reduction in China: an implication from data envelopment analysis[J]. Renew Sustain Energy Rev, 2017, 71:859-872.DOI:10.1016/j.rser.2016.12.114. 本文引用 [1]

[7]	PAN C M, MING D K. The analysis of carbon emission efficiency and affecting factors in China[J]. Environmental and Resource Economics Review, 2017, 26(2):205-227. 本文引用 [1]

[8]

李慧, 李玮, 姚西龙. 中国省域全要素碳排放效率空间特征与动态收敛性研究[J]. 科技管理研究, 2019, 39(19):98-103.

, LI

, YAO

X L

. Spatial characteristics and dynamic convergence research on provincial total factor carbon emissions efficiency in China[J]. Sci Technol Manag Res, 2019, 39(19):98-103.DOI:10.3969/j.issn.1000-7695.2019.19.013.

本文引用 [1]

[9]

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

. Analysis of the low carbon economy efficiency in China: based on a method of three- stage SBM-DEA model with undesirable outputs[J]. Oper Res Manag Sci, 2017, 26(3):115-122.DOI:10.12005/orms.2017.0065.

本文引用 [1]

[10]	关伟, 王舒, 许淑婷. 出口贸易碳排放效率时空特征与影响因素:以中国沿海省份为例[J]. 生态经济, 2019, 35(1):30-36. GUAN W, WANG S, XU S T. Spatial-temporal characteristics and affecting factors of export trade carbon emission: a case study in the coastal provinces of China[J]. Ecol Econ, 2019, 35(1):30-36. 本文引用 [1]

[11]

郗永勤, 吉星. 我国工业行业碳排放效率实证研究:考虑非期望产出SBM超效率模型与DEA视窗方法的应用[J]. 科技管理研究, 2019, 39(17):53-62.

Y Q

, JI

. An empirical study on carbon emission efficiency of industrial industry in China: considering the super efficiency model of unexpected output SBM and the application of DEA window method[J]. Sci Technol Manag Res, 2019, 39(17):53-62.DOI:10.3969/j.issn.1000-7695.2019.17.008.

本文引用 [1]

[12]

邵海琴, 王兆峰. 长江经济带旅游业碳排放效率的综合测度与时空分异[J]. 长江流域资源与环境, 2020, 29(8):1685-1693.

SHAO

H Q

, WANG

Z F

. Comprehensive measurement of carbon emissions efficiency of tourism and its spatio-temporal differentiation in the Yangtze River Economic Belt[J]. Resour Environ Yangtze Basin, 2020, 29(8):1685-1693.DOI:10.11870/cjlyzyyhj202008001.

本文引用 [1]

[13]	王诚, 王晓娟. 长江经济带碳排放效率:测度与分析[J]. 统计与决策, 2022, 38(17):82-85. WANG C, WANG X J. Carbon emission efficiency in Yangtze River Economic Belt: measurement and analysis[J]. Stat Decis, 2022, 38(17):82-85.DOI:10.13546/j.cnki.tjyjc.2022.17.016. 本文引用 [1]

[14]

蒋培培, 王远, 罗进, 等. 长江与黄河流域碳排放效率时空演变特征及路径识别探究[J]. 环境科学研究, 2022, 35(7):1743-1751.

JIANG

P P

, WANG

, LUO

, et al. Comparative study of spatial-temporal evolution and growth path of carbon emissions efficiency in Yangtze River basin and Yellow River basin[J]. Res Environ Sci, 2022, 35(7):1743-1751.DOI:10.13198/j.issn.1001-6929.2022.04.06.

本文引用 [1]

[15]	岳立, 雷燕燕, 王杰. 中国省域旅游业碳排放效率时空特征及影响因素分析[J]. 统计与决策, 2020, 36(16):69-73. YUE L, LEI Y Y, WANG J. Spatial-temporal characteristics and influencing factors of carbon emission efficiency of tourism in China province[J]. Stat Decis, 2020, 36(16):69-73.DOI:10.13546/j.cnki.tjyjc.2020.16.015. 本文引用 [1]

[16]

袁长伟, 张帅, 焦萍, 等. 中国省域交通运输全要素碳排放效率时空变化及影响因素研究[J]. 资源科学, 2017, 39(4):687-697.

YUAN

C W

, ZHANG

, JIAO

, et al. Temporal and spatial variation and influencing factors research on total factor efficiency for transportation carbon emissions in China[J]. Resour Sci, 2017, 39(4):687-697.DOI:10.18402/resci.2017.04.10.

本文引用 [1]

[17]	李健, 马晓芳, 苑清敏. 区域碳排放效率评价及影响因素分析[J]. 环境科学学报, 2019, 39(12):4293-4300. LI J, MA X F, YUAN Q M. Evaluation and influencing factors’ analysis of regional carbon emission efficiency[J]. Acta Sci Circumstantiae, 2019, 39 (12):4293-4300.DOI:10.13671/j.hjkxxb.2019.0309. 本文引用 [1]

[18]

蔺雪芹, 边宇, 王岱. 京津冀地区工业碳排放效率时空演化特征及影响因素[J]. 经济地理, 2021, 41(6):187-195.

LIN

X Q

, BIAN

, WANG

. Spatiotemporal evolution characteristics and influencing factors of industrial carbon emission efficiency in Beijing-Tianjin-Hebei region[J]. Econ Geogr, 2021, 41(6):187-195.DOI:10.15957/j.cnki.jjdl.2021.06.021.

本文引用 [1]

[19]	徐英启, 程钰, 王晶晶, 等. 中国低碳试点城市碳排放效率时空演变与影响因素[J]. 自然资源学报, 2022, 37(5):1261-1276. XU Y Q, CHENG Y, WANG J J, et al. Spatio-temporal evolution and influencing factors of carbon emission efficiency in low carbon city of China[J]. J Nat Resour, 2022, 37(5):1261-1276.DOI:10.31497/zrzyxb.20220511. 本文引用 [1]

[20]

李建豹, 黄贤金, 揣小伟, 等. 长三角地区碳排放效率时空特征及影响因素分析[J]. 长江流域资源与环境, 2020, 29(7):1486-1496.

J B

, HUANG

X J

, CHUAI

X W

, et al. Spatio-temporal characteristics and influencing factors of carbon emissions efficiency in the Yangtze River Delta region[J]. Resour Environ Yangtze Basin, 2020, 29(7):1486-1496.DOI:10.11870/cjlyzyyhj202007002.

本文引用 [1]

[21]	中共中央国务院. 中共中央国务院印发《长江三角洲区域一体化发展规划纲要》[EB/OL].(2019-12-01)[2022-12-28]. https://www.gov.cn/gongbao/content/2019/content_5462503.htm. https://www.gov.cn/gongbao/content/2019/content_5462503.htm 本文引用 [1]

[22]	吴建新, 郭智勇. 基于连续性动态分布方法的中国碳排放收敛分析[J]. 统计研究, 2016, 33(1):54-60. WU J X, GUO Z Y. Research on the convergence of carbon dioxide emissions in China: a continuous dynamic distribution approach[J]. Stat Res, 2016, 33(1):54-60.DOI:10.19343/j.cnki.11-1302/c.2016.01.008. 本文引用 [1]

[23]	TONE K. A slacks-based measure of efficiency in data envelopment analysis[J]. Eur J Oper Res, 2001, 130(3):498-509.DOI: 10.1016/s0377-2217(99)00407-5. 本文引用 [1]

[24]

郭文慧, 吴佩林, 王玎. 山东省碳排放效率与影响因素分析:基于非期望产出的SBM模型的实证研究[J]. 东岳论丛, 2013, 34(5):172-176.

GUO

W H

, WU

P L

, WANG

. Analysis of carbon emission efficiency and influencing factors in Shandong Province:an empirical study based on SBM model of unexpected output[J]. Dongyue Tribune, 2013, 34(5):172-176.DOI: 10.15981/j.cnki.dongyueluncong.2013.05.001.

本文引用 [1]

[25]	郭炳南, 林基. 基于非期望产出SBM模型的长三角地区碳排放效率评价研究[J]. 工业技术经济, 2017, 36(1):108-115. GUO B N, LIN J. Research on efficiency of carbon emissions in Yangtze River Delta based on the SBM-undesirable model[J]. Ind Technol Econ, 2017, 36(1):108-115.DOI:10.3969/j.issn.1004-910X.2017.01.014. 本文引用 [1]

[26]	张军, 吴桂英, 张吉鹏. 中国省际物质资本存量估算:1952—2000[J]. 经济研究, 2004, 39(10):35-44. ZHANG J, WU G Y, ZHANG J P. The estimation of China’s provincial capital stock:1952—2000[J]. Econ Res J, 2004, 39(10):35-44. 本文引用 [1]

[27]	GETIS A, ORD J K. The analysis of spatial association by use of distance statistics[J]. Geogr Anal, 2010, 24(3):189-206.DOI: 10.1111/j.1538-4632.1992.tb00261.x. 本文引用 [1]

[28]	EHRLICH P, HOLDEN J. One-dimensional economy[J]. Bulletin of the Atomic Scientists, 1972, 28:16-27. 本文引用 [1]

[29]	GROSSMAN G M, KRUEGER A B. Environmental impacts of a north American free trade agreement[R]. Cambridge: National Bureau of Economic Research, 1991. 本文引用 [1]

[30]

宁论辰, 郑雯, 曾良恩. 2007—2016年中国省域碳排放效率评价及影响因素分析:基于超效率SBM-Tobit模型的两阶段分析[J]. 北京大学学报(自然科学版), 2021, 57(1):181-188.

NING

L C

, ZHENG

, ZENG

L E

. Research on China’s carbon dioxide emissions efficiency from 2007 to 2016:based on two stage super efficiency SBM model and Tobit model[J]. Acta Sci Nat Univ Pekin, 2021, 57(1):181-188.DOI:10.13209/j.0479-8023.2020.111.

本文引用 [1]

[31]	柳涛. 黄河流域城市碳排放效率测度及影响因素分析[J]. 产业创新研究, 2022(17):45-47. LIU T. Measurement of urban carbon emission efficiency in the Yellow River basin and analysis of its influencing factors[J]. Ind Innov, 2022(17):45-47. 本文引用 [1]

[32]

宋杰鲲, 梁璐璐, 牛丹平, 等. 山东省地市碳排放效率测度、影响因素与提升对策[J]. 中国石油大学学报(社会科学版), 2018, 34(1):15-21.

SONG

J K

, LIANG

L L

, NIU

D P

, et al. Evaluation,influencing factors and promotion countermeasures of cities’ carbon emissions efficiencies in Shandong Province[J]. J China Univ Petrol (Ed Soc Sci), 2018, 34(1):15-21.DOI:10.13216/j.cnki.upcjess.2018.01.0003.

本文引用 [1]

[33]

孙焱林, 何莲, 温湖炜. 异质性视角下中国省域碳排放效率及其影响因素研究[J]. 工业技术经济, 2016, 35(4):117-123.

SUN

Y L

, HE

, WEN

H W

. Research on inter-provincial carbon emissions efficiency and its determinants in China based on provincial heterogeneity perspective[J]. J Ind Technol Econ, 2016, 35(4):117-123.DOI:10.3969/j.issn.1004-910X.2016.04.015.

本文引用 [1]

[34]	尹妮妮, 王川川, 闫鹏程. “一带一路”省域碳排放效率及驱动因素研究[J]. 煤炭工程, 2022, 54(9):182-186. YIN N N, WANG C C, YAN P C. Provincial carbon emission efficiency and driving factors along “the Belt and Road” in China under carbon peak and carbon neutrality[J]. Coal Eng, 2022, 54(9):182-186.DOI:10.11799/ce202209032. 本文引用 [1]

[35]	平智毅, 吴学兵, 吴雪莲. 长江经济带碳排放效率的时空差异及其影响因素分析[J]. 生态经济, 2020, 36(3):31-37. PING Z Y, WU X B, WU X L. Spatial-temporal differences and its influencing factors of carbon emission efficiency in the Yangtze River Economic Belt[J]. Ecol Econ, 2020, 36(3):31-37. 本文引用 [1]