Spatial correlation and influencing factors of carbon emission efficiency in the Yangtze River Delta city cluster

doi:10.12302/j.issn.1000-2006.202305002

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (6): 217-228.doi: 10.12302/j.issn.1000-2006.202305002

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Spatial correlation and influencing factors of carbon emission efficiency in the Yangtze River Delta city cluster

QUAN Tianshu(), ZHANG Hui, XU Yuyun^*()

College of Economics and Management, Nanjing Forestry University, Nanjing 210037,China

Received:2023-05-04 Revised:2023-06-24 Online:2024-11-30 Published:2024-12-10
Contact: XU Yuyun E-mail:quantianshu@njfu.edu.cn;xuyuyun@njfu.edu.cn

Abstract

Abstract:

【Objective】In the context of the “Carbon Peaking and Carbon Neutrality” goal, determining the spatial network pattern of carbon emission efficiency and its influencing factors in the Yangtze River Delta city cluster will promote the construction of low-carbon ecological cities and establish an inclusive green growth mechanism.【Method】Based on the slack-based measured directional distance function model, the global Malmquist-Luenberger index, and the kernel density estimation method, accurate measurements were obtained and an analysis of the spatial and temporal evolution characteristics of the carbon emission efficiency level of the Yangtze River Delta urban city cluster was conducted for the period of 2008 to 2020. The spatial correlation structure and factors influencing the carbon emission efficiency of the Yangtze River Delta city cluster were visualized by a modified gravity model and social network analysis.【Result】(1) The dynamic efficiency of carbon emissions was found to be generally increasing, but there were significant inter-regional differences and a spatial diffusion effect. (2) Based on network density, correlations and network efficiency, the network stability of carbon emission efficiency in the Yangtze River Delta cities was considered to be high, but the network centrality displayed unbalanced characteristics. (3) There were obvious gradient characteristics among the spatial network segments of carbon emission efficiency in the Yangtze River Delta city cluster. (4) Government macro-control, environmental regulation, industrial structure, external development, green innovation, and new urbanization were the main driving factors of the evolution of spatially linked networks.【Conclusion】Promoting the carbon emission efficiency of the Yangtze River Delta city cluster through mutual cooperation and learning among cities, bringing into play the spatial radiation effect of core cities, optimizing industrial structure, and improving the level of new urbanization could promote the integrated ecological and green spatial development of the Yangtze River Delta city cluster.

Key words: carbon emission efficiency, Yangtze River Delta city cluster, social network analysis, second-order assignment procedure

CLC Number:

F293.1
X22

QUAN Tianshu, ZHANG Hui, XU Yuyun. Spatial correlation and influencing factors of carbon emission efficiency in the Yangtze River Delta city cluster[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(6): 217-228.

Figures/Tables 11

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板块内部比例关系 proportional relationships within the block	板块接收到的关系比例 proportion of relationships received by the block
板块内部比例关系 proportional relationships within the block	≈0	>0
≥(g_k-1)(g-1)	双向溢出板块	净受益板块
<(g_k-1)(g-1)	净溢出板块	经纪人板块

城市 city	效率水平efficiency level				城市 city	效率水平efficiency level
城市 city	2007—2008	2011—2012	2015—2016	2019—2020	城市 city	2007—2008	2011—2012	2015—2016	2019—2020
上海Shanghai	1.003	1.002	0.995	1.012	绍兴Shaoxing	0.996	1.001	0.996	1.008
南京Nanjing	1.000	1.021	0.996	1.009	金华Jinhua	0.999	0.998	0.988	1.009
无锡Wuxi	0.996	1.003	0.996	1.005	舟山Zhoushan	0.989	1.002	0.998	1.083
常州Changzhou	1.004	0.998	0.991	1.005	台州Taizhou	0.991	0.998	0.992	1.011
苏州Suzhou	0.994	0.999	0.992	1.011	合肥Hefei	0.997	1.010	1.001	1.008
南通Nantong	1.000	1.000	1.001	1.008	芜湖Wuhu	0.993	1.014	0.994	1.005
盐城Yancheng	0.983	1.003	0.965	1.006	马鞍山Ma’anshan	0.999	1.016	0.998	1.008
扬州Yangzhou	0.992	1.004	0.995	1.013	铜陵Tongling	0.998	1.009	1.005	1.011
镇江Zhenjiang	0.996	0.998	0.994	1.000	安庆Anqing	1.002	1.004	0.991	1.003
泰州Taizhou	0.995	1.005	0.996	1.011	滁州Chuzhou	0.976	1.017	0.997	0.979
杭州Hangzhou	0.997	0.999	0.997	1.005	池州Chizhou	0.992	1.005	1.023	1.092
宁波Ningbo	0.995	1.000	0.992	1.008	宣城Xuancheng	0.994	1.000	1.000	0.986
嘉兴Jiaxing	0.997	0.997	0.993	1.009	均值mean	0.995	1.004	0.995	1.012
湖州Huzhou	0.996	1.002	0.994	1.002

年份 year	网络密度 network density	关联性分析relevance analysis
年份 year	网络密度 network density	关联度 relatedness	网络等级度 network rating	网络效率 network efficiency	最近上限 recent ceiling
2008	0.294	1	0.148	0.660	0.997
2009	0.295	1	0.148	0.657	0.997
2010	0.295	1	0.148	0.657	0.997
2011	0.295	1	0.148	0.657	0.997
2012	0.294	1	0.148	0.660	0.997
2013	0.297	1	0.148	0.653	0.997
2014	0.294	1	0.148	0.660	0.997
2015	0.295	1	0.148	0.657	0.997
2016	0.294	1	0.148	0.660	0.997
2017	0.294	1	0.148	0.657	0.997
2018	0.294	1	0.148	0.660	0.997
2019	0.294	1	0.148	0.657	0.997
2020	0.289	1	0.148	0.663	0.997

城市 city	点度中心度 degree centrality			中间中心度 betweenness centrality	接近中心度 closeness centrality
城市 city	出度 out-degree	入度 in-degree	中心度 centrality	中心度 centrality	中心度 centrality
安庆Anqing	8	18	18	48.135	78.125
池州Chizhou	8	15	15	37.344	71.429
苏州Suzhou	10	14	14	56.318	69.444
上海Shanghai	8	14	14	35.286	69.444
滁州Chuzhou	8	14	14	58.859	69.444
无锡Wuxi	8	14	14	42.952	69.444
宁波Ningbo	7	13	13	25.353	67.568
宣城Xuancheng	7	11	12	40.353	64.103
杭州Hangzhou	7	10	11	58.585	64.103
扬州Yangzhou	10	1	10	5.446	62.500
舟山Zhoushan	5	10	10	12.599	62.500
盐城Yancheng	9	10	10	37.088	60.976
泰州Taizhou	9	1	9	2.467	60.976
台州Taizhou	8	6	9	20.490	60.976
金华Jinhua	8	6	9	20.490	60.976
南京Nanjing	6	6	8	12.723	55.556
湖州Huzhou	8	0	8	0	59.524
马鞍山Ma’anshan	7	0	7	0	58.140
南通Nantong	7	1	7	2.022	58.140
芜湖Wuhu	6	3	6	4.110	51.020
镇江Zhenjiang	6	2	6	1.965	52.083
铜陵Tongling	6	3	6	4.020	51.020
常州Changzhou	6	5	6	8.735	52.083
合肥Hefei	6	5	6	7.193	51.020
绍兴Shaoxing	5	5	5	5.415	52.083
嘉兴Jiaxing	5	1	5	1.052	52.083
均值mean	7	7	10	21.115	60.952

板块 block	接收关系数number of receiving relation					溢出强度 spillover intensity	接受强度 acceptance intensity	期望内部关系比例/% expected internal relationship ratio	实际内部关系比例/% actual internal relationship ratio	板块特征 block characteristics
板块 block	板块1 block1	板块2 block2	板块3 block3	板块4 block4	成员数 number of members	溢出强度 spillover intensity	接受强度 acceptance intensity	期望内部关系比例/% expected internal relationship ratio	实际内部关系比例/% actual internal relationship ratio	板块特征 block characteristics
1	0	1	44	5	7	50	80	24	0	主受益
2	2	1	21	6	5	29	14	16	3.3	经纪人
3	51	6	0	1	8	58	79	28	0	双向溢出
4	27	7	14	2	6	48	12	20	4.0	净溢出

Spatial correlation and influencing factors of carbon emission efficiency in the Yangtze River Delta city cluster

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变量 variable	2020		2016		2012		2008
变量 variable	系数 coefficient	显著性 sig.	系数 coefficient	显著性 sig.	系数 coefficient	显著性 sig.	系数 coefficient	显著性 sig.
x_GOV	0.392	0.001	0.314	0.001	0.217	0.001	0.200	0.001
x_ER	0.025	0.004	0.001	0.555	0.025	0.002	0.008	0.080
x_IND	0.626	0.001	0.731	0.001	0.740	0.001	0.858	0.001
x_FDI	0.011	0.209	0.014	0.087	0.001	0.345	-0.038	0.002
x_GIN	0.189	0.001	0.125	0.009	0.224	0.001	0.017	0.440
x_URBAN	0.154	0.001	0.083	0.002	0.030	0.001	0.013	0.110

[1]	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.
[2]	SHI Shumin, WANG Nan, LI Zhaxijie. Social network analysis of dominance behavior and social structure of Crossoptilon crossoptilon in breeding season [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2019, 43(03): 137-144.