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

doi:10.12302/j.issn.1000-2006.202212021

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (6): 251-262.doi: 10.12302/j.issn.1000-2006.202212021

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

SONG Qing¹(), LI Chaoqun¹, CHEN Junyu¹^,²^,^*()

1. Business School, University of Science and Technology of Suzhou, Suzhou 215009,China
2. Department of Management and Economics, Tianjin University, Tianjin 300072,China

Received:2022-12-12 Revised:2023-02-16 Online:2023-11-30 Published:2023-11-23

Abstract

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.

Key words: carbon emission efficiency, non-expected outputs, spatio-temporal characteristics, spatial Doberman model(SDM), Yangtze River Delta region

CLC Number:

F205

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.

Figures/Tables 7

Table 1

Carbon emission efficiency of cities in the Yangtze River Delta region from 2010 to 2019"

城市 city		2010		2011		2012		2013		2014		2015		2016	2017	2018	2019	均值 mean
1.上海Shanghai		1.00		1.00		1.00		1.00		1.00		1.00		1.00	1.00	1.00	1.00	1.00
2.南京Nanjing		0.30		0.42		0.48		0.44		0.45		0.49		0.53	0.89	0.72	0.93	0.57
3.无锡Wuxi		0.72		0.71		1.00		1.00		1.00		1.00		1.00	1.00	1.00	1.00	0.94
4.徐州Xuzhou		0.51		0.52		0.57		0.52		0.55		0.59		0.63	0.84	0.71	0.96	0.64
5.常州Changzhou		1.00		0.49		0.63		0.63		0.63		0.60		0.61	0.80	0.77	0.80	0.70
6.苏州Suzhou		1.00		1.00		1.00		1.00		1.00		1.00		1.00	0.82	0.81	1.00	0.96
7.南通Nantong		0.78		0.80		1.00		0.62		0.51		0.66		0.70	0.82	0.64	0.87	0.74
8.连云港Lianyungang		0.49		0.52		0.57		0.56		0.44		0.55		0.60	0.62	0.47	0.64	0.55
9.淮安Huai’an		0.39		0.41		0.44		0.42		0.42		0.45		0.47	0.68	0.55	0.81	0.50
10.盐城Yancheng		1.00		1.00		1.00		1.00		1.00		0.70		0.74	0.80	0.61	0.89	0.87
11.扬州Yangzhou		0.83		0.83		0.75		0.58		0.59		0.63		0.65	1.00	0.74	1.00	0.76
12.镇江Zhenjiang		0.59		0.58		0.63		1.00		0.83		0.85		0.90	1.00	1.00	1.00	0.84
13.泰州Taizhou	1.00		1.00		0.91		0.59		0.60		0.64		0.67		0.68	0.54	0.71	0.73
14.宿迁Suqian	0.76		0.66		0.61		0.48		0.51		0.54		0.58		0.69	0.56	0.67	0.61
15.杭州Hangzhou	0.41		0.41		0.49		0.49		0.48		0.51		0.54		0.78	0.68	0.75	0.55
16.宁波Ningbo	0.53		0.51		0.61		0.66		0.68		0.67		0.68		0.78	0.75	0.74	0.66
17.温州Wenzhou	1.00		1.00		0.86		0.94		0.82		0.76		0.74		0.72	0.65	0.71	0.82
18.嘉兴Jiaxing	0.49		0.51		0.56		0.60		0.61		0.62		0.63		0.53	0.52	0.53	0.56
19.湖州Huzhou	0.47		0.49		0.54		0.54		0.55		0.56		0.57		0.57	0.55	0.54	0.54
20.绍兴Shaoxing	0.68		0.71		0.85		0.49		0.49		0.50		0.50		0.64	0.59	0.63	0.61
21.金华Jinhua	1.00		1.00		1.00		1.00		1.00		1.00		1.00		0.71	0.63	0.67	0.90
22.衢州Quzhou	0.46		0.44		0.49		0.54		0.53		0.54		0.54		0.57	0.56	0.54	0.52
23.舟山Zhoushan	0.47		0.53		0.56		0.58		0.44		0.46		0.49		1.00	1.00	1.00	0.65
24.台州Taizhou	0.64		0.62		0.70		0.71		0.71		0.73		0.77		0.76	0.69	0.75	0.71
25.丽水Lishui	0.75		0.76		0.97		1.00		1.00		1.00		1.00		0.77	0.66	0.65	0.86
26.合肥Hefei	0.45		0.43		0.44		0.45		0.45		0.46		0.45		0.61	0.41	0.62	0.48
27.芜湖Wuhu	0.41		0.38		0.39		0.41		0.41		0.43		0.44		0.57	0.42	0.59	0.45
28.蚌埠Bengbu	0.42		0.42		0.42		0.43		0.42		0.45		0.46		0.63	0.47	0.64	0.48
29.淮南Huainan	0.29		0.30		0.38		0.38		0.37		0.37		0.39		0.52	0.42	0.47	0.39
30.马鞍山Ma’anshan	0.42		0.39		0.47		0.51		0.50		0.50		0.51		0.60	0.51	0.57	0.50
31.淮北Huaibei	0.30		0.33		0.40		0.36		0.37		0.40		0.41		0.54	0.41	0.51	0.40
32.铜陵Tongling	0.34		0.34		0.41		0.41		0.42		0.39		0.39		0.51	0.42	0.42	0.41
33.安庆Anqing	0.41		0.40		0.47		0.45		0.45		0.49		0.52		0.87	0.59	0.87	0.55
34.黄山Huangshan	0.44		0.46		0.46		0.50		0.53		0.53		0.54		0.67	0.45	0.66	0.52
35.滁州Chuzhou	0.51		0.63		0.57		0.62		0.63		0.66		0.69		0.50	0.40	0.43	0.56
36.阜阳Fuyang	0.47		0.49		0.60		0.60		0.66		0.59		0.57		0.60	0.49	0.50	0.56
37.宿州Suzhou	0.49		0.53		0.57		0.54		0.55		0.44		0.46		0.71	0.55	0.75	0.56
38.六安Lu’an	0.52		0.39		0.47		0.45		0.86		0.40		0.44		0.51	0.36	0.43	0.48
39.亳州Bozhou	0.79		0.70		0.76		0.72		0.70		0.71		0.76		0.74	0.54	0.71	0.71
40.池州Chizhou	0.42		0.39		0.40		0.39		0.39		0.52		0.56		0.48	0.40	0.44	0.44
41.宣城Xuancheng	0.68		0.41		0.40		0.42		0.63		0.65		0.67		0.50	0.38	0.43	0.52

Table 1

Fig. 1

Table 2

Spatial distribution of carbon emission efficiency in the Yangtze River Delta region from 2010 to 2019"

年份year	区间interval	城市city
2010	低碳排放效率区	淮北、淮安、蚌埠、淮南、南京、马鞍山、杭州、芜湖、铜陵、安庆、池州
	中低碳排放效率区	连云港、徐州、宿州、阜阳、六安、合肥、滁州、镇江、黄山、湖州、嘉兴、衢州、宁波、舟山
	中高碳排放效率区	亳州、宿迁、扬州、南通、无锡、宣城、绍兴、台州、丽水
	高碳排放效率区	苏州、上海、盐城、泰州、常州、金华、温州
2013	低碳排放效率区	淮北、淮安、蚌埠、淮南、六安、合肥、南京、芜湖、铜陵、安庆、池州、宣城
	中低碳排放效率区	连云港、徐州、宿州、宿迁、马鞍山、湖州、黄山、杭州、绍兴、衢州
	中高碳排放效率区	亳州、阜阳、滁州、扬州、泰州、南通、常州、嘉兴、宁波、台州、舟山
	高碳排放效率区	盐城、镇江、苏州、无锡、上海、金华、丽水、温州
2016	低碳排放效率区	宿州、淮北、蚌埠、淮安、淮南、六安、舟山、合肥、芜湖、铜陵
	中低碳排放效率区	连云港、宿迁、阜阳、南京、马鞍山、常州、安庆、池州、黄山、湖州、杭州、衢州、绍兴
	中高碳排放效率区	徐州、亳州、滁州、扬州、泰州、盐城、南通、宣城、嘉兴、宁波、台州、温州
	高碳排放效率区	镇江、无锡、苏州、上海、金华、丽水
2019	低碳排放效率区	淮北、阜阳、淮南、滁州、六安、池州、铜陵、宣城、湖州、嘉兴、衢州
	中低碳排放效率区	连云港、宿迁、蚌埠、合肥、马鞍山、芜湖、黄山、绍兴、金华、丽水
	中高碳排放效率区	亳州、宿州、淮安、泰州、常州、杭州、宁波、台州、温州
	高碳排放效率区	徐州、安庆、舟山、盐城、扬州、南京、镇江、南通、上海、无锡、苏州

Table 2

Table 3

Fig. 2

Fig. 3

Table 4

References 37

[1]	International Energy Agency (IEA). CO₂ emissions from fuel combustion 2017[M]. Paris: OECD Publishing, 2017.
[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.
[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.
[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.
[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.
[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.
[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.
[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 H, LI W, 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.
[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 X. 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.
[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.
[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.
	XI Y Q, JI X. 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.
[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.
[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.
[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 Y, LUO J, 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.
[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.
[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 S, JIAO P, 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.
[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.
[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 Y, WANG D. 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.
[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.
[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.
	LI 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.
[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.
[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.
[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.
[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 D. 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.
[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.
[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.
[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.
[28]	EHRLICH P, HOLDEN J. One-dimensional economy[J]. Bulletin of the Atomic Scientists, 1972, 28:16-27.
[29]	GROSSMAN G M, KRUEGER A B. Environmental impacts of a north American free trade agreement[R]. Cambridge: National Bureau of Economic Research, 1991.
[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 W, 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.
[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.
[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.
[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 L, 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.
[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.
[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.
[36]	ANSELIN L, FLORAX R, REY S J. Advances in spatial econometrics:methodology,tools and applications[M]. Berlin:Springer, 2004.
[37]	LESAGE J P, PACE R K. Introduction to spatial econometrics[M]. Boca Raton: CRC Press, 2009.

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年份 year	莫兰指数 Moran’s I	Z检验值 Z-test value	P
2010	0.289	3.098	0.002
2011	0.402	4.236	<0.001
2012	0.499	5.175	<0.001
2013	0.284	3.068	0.002
2014	0.153	1.770	0.077
2015	0.326	3.514	<0.001
2016	0.307	3.314	0.001
2017	0.179	2.017	0.044
2018	0.357	3.828	<0.001
2019	0.232	2.533	0.011

变量 variables	直接解释 direct explanatory		空间滞后解释 spatial lag explanatory		直接效应 direct effects		间接效应 indirect effects
变量 variables	R²	Z	R²	Z	R²	Z	R²	Z
人均GDP lnG	-1.770^***	-5.02	2.353^***	4.98	-1.737^***	-4.83	2.359^***	4.95
人口规模lnP_s	0.210	0.22	0.288	1.04	0.019	0.21	0.285	1.01
产业结构I_s	-0.872^***	-3.12	0.488	0.84	-0.841^***	-3.15	0.521	0.90
能源消费结构S	-0.203	-1.53	-0.429	-1.41	-0.210	-1.63	-0.436	-1.37
R&D经费支出lnR_D	-0.007	-0.55	-0.065^**	-2.07	-0.008	-0.62	-0.068^**	-2.07
外商直接投资lnF	0.059^**	2.19	-0.046	-0.99	0.059^**	2.29	-0.043	-0.87

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

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