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

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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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