长三角城市群碳排放效率的空间关联网络及其影响因素

doi:10.12302/j.issn.1000-2006.202305002

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南京林业大学学报（自然科学版） ›› 2024, Vol. 48 ›› Issue (6): 217-228.doi: 10.12302/j.issn.1000-2006.202305002

长三角城市群碳排放效率的空间关联网络及其影响因素

权天舒(), 张晖, 许玉韫^*()

南京林业大学经济管理学院,江苏南京 210037

收稿日期:2023-05-04 修回日期:2023-06-24 出版日期:2024-11-30 发布日期:2024-12-10
通讯作者: *许玉韫(xuyuyun@njfu.edu.cn),讲师。
作者简介:
权天舒(quantianshu@njfu.edu.cn),博士生。
基金资助:
国家社会科学基金项目(21BGL167);国家社会科学基金项目(21&ZD101)

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

摘要/Abstract

摘要：

【目的】在“双碳”目标背景下,探索长三角城市群碳排放效率的空间网络格局及其影响因素,为推进低碳生态城市建设和建立包容性绿色增长机制提供依据。【方法】基于SBM-DDF模型、全局Malmquist-Luenberger指数以及核密度估计法,对2008—2020年长三角城市群碳排放效率水平进行测度和时空演变特征分析,并通过修正引力模型与社会网络分析法可视化了长三角城市群碳排放效率空间关联结构及影响因素。【结果】①长三角城市群碳排放动态效率总体呈上升态势,但地区间差异显著且表现出一定的空间扩散效应。②从网络密度、关联性、网络效率等3个方面看,长三角城市碳排放效率的网络稳定性较高,但网络中心度呈非均衡特征。③长三角城市群碳排放效率空间网络板块间具有明显的梯度特征。④政府宏观调控、环境规制、产业结构、对外开放、绿色创新、新型城镇化水平是推动空间关联网络演变的主要驱动机制。【结论】通过城市间相互合作与学习、发挥核心城市的空间辐射效应、优化产业结构、提高新型城市化水平等方式有助于长三角城市群碳排放效率的提升,进而推进长三角城市群生态绿色空间一体化发展。

关键词: 碳排放效率, 长三角城市群, 社会网络分析, 二次指派程序

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

中图分类号:

F293.1
X22

权天舒,张晖,许玉韫. 长三角城市群碳排放效率的空间关联网络及其影响因素[J]. 南京林业大学学报（自然科学版）, 2024, 48(6): 217-228.

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 (Natural Science Edition), 2024, 48(6): 217-228.DOI: 10.12302/j.issn.1000-2006.202305002.

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参考文献 40

[1]	ZHANG R J, TAI H, CHENG K T, et al. Carbon emission efficiency network formation mechanism and spatial correlation complexity analysis:taking the Yangtze River Economic Belt as an example[J]. Sci Total Environ, 2022,841:156719.DOI: 10.1016/j.scitotenv.2022.156719.
[2]	张伟丽, 郝智娟, 王伊斌, 等. 城市群人口流动空间网络及影响因素[J]. 地理科学, 2023, 43(1):72-81.
	ZHANG W L, HAO Z J, WANG Y B, et al. Spatial network and influencing factors of population flow in urban agglomeration[J]. Sci Geogr Sin, 2023, 43(1):72-81.DOI: 10.13249/j.cnki.sgs.2023.01.008.
[3]	冯薇, 赵荣钦, 谢志祥, 等. 碳中和目标下土地利用碳排放效率及其时空格局:以黄河流域72个地级市为例[J]. 中国土地科学, 2023, 37(1):102-113.
	FENG W, ZHAO R Q, XIE Z X, et al. Land use carbon emission efficiency and its spatial-temporal pattern under carbon neutral target:a case study of 72 cities in the Yellow River basin[J]. China Land Sci, 2023, 37(1):102-113.DOI: 10.11994/zgtdkx.20230109.100514.
[4]	徐英启, 程钰, 王晶晶. 中国资源型城市碳排放效率时空演变与绿色技术创新影响[J]. 地理研究, 2023, 42(3):878-894.
	XU Y Q, CHENG Y, WANG J J. The impact of green technological innovation on the spatiotemporal evolution of carbon emission efficiency of resource-based cities in China[J]. Geogr Res, 2023, 42(3):878-894.DOI: 10.11821/dlyj020220256.
[5]	胡剑波, 闫烁, 王蕾. 中国出口贸易隐含碳排放效率及其收敛性[J]. 中国人口·资源与环境, 2020, 30(12):95-104.
	HU J B, YAN S, WANG L. Efficiency and convergence of China’s export trade embodied carbon emissions[J]. China Popul Resour Environ, 2020, 30(12):95-104.DOI: 10.12062/cpre.20200621.
[6]	聂永有, 姚清宇. 长三角地区生产性服务业集聚与碳排放效率:基于SDM与PTR模型的实证检验[J]. 工业技术经济, 2022, 41(6):111-119.
	NIE Y Y, YAO Q Y. Agglomeration of producer services and carbon emission efficiency in the Yangtze River Delta: empirical analysis of SDM and PTR model[J]. J Ind Technol Econ, 2022, 41(6):111-119.DOI: 10.3969/j.issn.1004-910X.2022.06.014.
[7]	邵帅, 范美婷, 杨莉莉. 经济结构调整、绿色技术进步与中国低碳转型发展:基于总体技术前沿和空间溢出效应视角的经验考察[J]. 管理世界, 2022(2):46-69.
	SHAO S, FAN M T, YANG L L. Economic restructuring,green technical progress,and low-carbon transition development in China:an empirical investigation based on the overall technology frontier and spatial spillover effect[J]. J Manag World, 2022(2):46-69.
[8]	吕雁琴, 范天正, 张晋宁. 中国交通运输碳排放效率的时空异质性及影响因素研究[J]. 生态经济, 2023, 39(3):13-22.
	LYU Y Q, FAN T Z, ZHANG J N. Spatiotemporal characteristics and influencing factors of China’s transport sector carbon emissions efficiency[J]. Ecol Econ, 2023, 39(3):13-22.
[9]	BALAGUER J, CANTAVELLA M. The role of education in the Environmental Kuznets Curve: evidence from Australian data[J]. Energy Econ, 2018, 70:289-296.DOI: 10.1016/j.eneco.2018.01.021.
[10]	CHU X, JIN Y Y, WANG X, et al. The evolution of the spatial-temporal differences of municipal solid waste carbon emission efficiency in China[J]. Energies, 2022, 15(11):3987.DOI: 10.3390/en15113987.
[11]	WANG R, WANG Q Z, YAO S L. Evaluation and difference analysis of regional energy efficiency in China under the carbon neutrality targets:insights from DEA and Theil models[J]. J Environ Manage, 2021,293:112958.DOI: 10.1016/j.jenvman.2021.112958.
[12]	HOANG V N, COELLI T. Measurement of agricultural total factor productivity growth incorporating environmental factors:a nutrients balance approach[J]. J Environ Econ Manag, 2011, 62(3):462-474.DOI: 10.1016/j.jeem.2011.05.009.
[13]	郭海红, 刘新民. 中国农业绿色全要素生产率的时空分异及收敛性[J]. 数量经济技术经济研究, 2021, 38(10):65-84.
	GUO H H, LIU X M. Spatial and temporal differentiation and convergence of China’s agricultural green total factor productivity[J]. J Quant Tech Econ, 2021, 38(10):65-84.DOI: 10.13653/j.cnki.jqte.2021.10.004.
[14]	胡安俊, 孙久文. 碳排放的产业空间版图、省际转移与中国碳达峰[J]. 经济纵横, 2022(5):73-82.
	HU A J, SUN J W. The industrial and spatial maps,transfer among provinces of carbon emissions and suggestions on peak of carbon emissions in China[J]. Econ Rev J, 2022(5):73-82.DOI: 10.16528/j.cnki.22-1054/f.202205073.
[15]	ZHANG Y J, LIU Z, QIN C X, et al. The direct and indirect CO₂ rebound effect for private cars in China[J]. Energy Policy, 2017, 100:149-161.DOI: 10.1016/j.enpol.2016.10.010.
[16]	朱于珂, 高红贵, 徐运保. 双向FDI协调发展如何降低区域CO₂排放强度?——基于企业绿色技术创新的中介效应与政府质量的调节作用[J]. 软科学, 2022, 36(2):86-94.
	ZHU Y K, GAO H G, XU Y B. How does coordinated development of two-way FDI reduce regional CO₂ emission intensity?—Based on the mediating effect of enterprise green technology innovation and the moderating effect of government quality[J]. Soft Sci, 2022, 36(2):86-94.DOI: 10.13956/j.ss.1001-8409.2022.02.13.
[17]	刘承毅, 李欣. 环境规制对高碳制造业绿色低碳发展的影响:基于数字技术的调节效应[J]. 首都经济贸易大学学报, 2023, 25(3):18-31.
	LIU C Y, LI X. The impact of environmental regulation on the green and low-carbon development of high carbon manufacturing industry: based on the regulatory effect of digital technology[J]. J Cap Univ Econ Bus, 2023, 25(3):18-31.DOI: 10.13504/j.cnki.issn1008-2700.2023.03.002.
[18]	佘硕, 王巧, 张阿城. 技术创新、产业结构与城市绿色全要素生产率:基于国家低碳城市试点的影响渠道检验[J]. 经济与管理研究, 2020, 41(8):44-61.
	SHE S, WANG Q, ZHANG A C. Technological innovation,industrial structure and urban GTFP: channel test based on national low-carbon city pilots[J]. Res Econ Manag, 2020, 41(8):44-61.DOI: 10.13502/j.cnki.issn1000-7636.2020.08.004.
[19]	刘赢时, 田银华, 罗迎. 产业结构升级、能源效率与绿色全要素生产率[J]. 财经理论与实践, 2018, 39(1):118-126.
	LIU Y S, TIAN Y H, LUO Y. Upgrading of industrial structure,energy efficiency,green total factor productivity[J]. Theory Pract Finance Econ, 2018, 39(1):118-126.DOI: 10.16339/j.cnki.hdxbcjb.2018.01.018.
[20]	王璇, 侯正, 方勇. 双向FDI、环境规制与碳生产率[J]. 经济与管理研究, 2022, 43(12):50-64.
	WANG X, HOU Z, FANG Y. Two-way FDI,environmental regulation,and carbon productivity[J]. Res Econ Manag, 2022, 43(12):50-64.DOI: 10.13502/j.cnki.issn1000-7636.2022.12.004.
[21]	杨浩昌, 钟时权, 李廉水. 绿色技术创新与碳排放效率:影响机制及回弹效应[J]. 科技进步与对策, 2023, 40(8):99-107.
	YANG H C, ZHONG S Q, LI L S. Green technology innovation and carbon emission efficiency:an impact mechanism analysis and the rebound effect[J]. Sci Technol Prog Policy, 2023, 40(8):99-107.DOI: 10.6049/kjjbydc.2023010061.
[22]	王玉娟, 江成涛, 蒋长流. 新型城镇化与低碳发展能够协调推进吗?——基于284个地级及以上城市的实证研究[J]. 财贸研究, 2021, 32(9):32-46.
	WANG Y J, JIANG C T, JIANG C L. Can new-type urbanization and low-carbon economy be pushed coordinately?—Empirical research based on 284 prefecture-level cities[J]. Finance Trade Res, 2021, 32(9):32-46.DOI: 10.19337/j.cnki.34-1093/f.2021.09.003.
[23]	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.
[24]	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.
[25]	LI H Q, LU Y, ZHANG J, et al. Trends in road freight transportation carbon dioxide emissions and policies in China[J]. Energy Policy, 2013, 57:99-106.DOI: 10.1016/j.enpol.2012.12.070.
[26]	LONG Y, LIU L C, YANG B. Different types of environmental concerns and heterogeneous influence on green total factor productivity:evidence from Chinese provincial data[J]. J Clean Prod, 2023,428:139295.DOI: 10.1016/j.jclepro.2023.139295.
[27]	时朋飞, 耿飚, 李星明, 等. 长江经济带旅游业环境生产率测度、空间分异及驱动机制研究[J]. 中国软科学, 2022(3):78-87,111.
	SHI P F, GENG B, LI X M, et al. Measurement,spatial heterogeneity and driving mechanism of environment total factor productivity in tourism:a case study of the Yangtze River Economic Belt in China[J]. China Soft Sci, 2022(3):78-87,111.DOI: 10.3969/j.issn.1002-9753.2022.03.008.
[28]	汤放华, 汤慧, 孙倩, 等. 长江中游城市集群经济网络结构分析[J]. 地理学报, 2013, 68(10):1357-1366.
	TANG F H, TANG H, SUN Q, et al. Analysis of the economic network structure of urban agglomerations in the middle Yangtze River[J]. Acta Geogr Sin, 2013, 68(10):1357-1366.DOI: 10.11821/dlxb201310005.
[29]	SU Y, YU Y Q. Spatial association effect of regional pollution control[J]. J Clean Prod, 2019, 213:540-552.DOI: 10.1016/j.jclepro.2018.12.121.
[30]	周莹莹, 程宝栋, 尤薇佳, 等. 全球原木贸易网络结构及其危机传播的仿真分析[J]. 南京林业大学学报(自然科学版), 2022, 46(5):192-200.
	ZHOU Y Y, CHENG B D, YOU W J, et al. Simulation analysis of global log trade network structure and crisis propagation[J]. J Nanjing Fore Univ (Nat Sci Ed), 2022, 46 (5): 192-200.DOI:10.12302/j.issn.1000-2006.202101040.
[31]	赵林, 曹乃刚, 韩增林, 等. 中国生态福利绩效空间关联网络演变特征与形成机制[J]. 自然资源学报, 2022, 37(12):3183-3200.
	ZHAO L, CAO N G, HAN Z L, et al. Evolution characteristics and formation mechanism of spatial correlation network of ecological well-being performance in China[J]. J Nat Resour, 2022, 37(12):3183-3200.DOI: 10.31497/zrzyxb.20221211.
[32]	仇实, 于强, 刘泓君, 等. 基于生态环境质量评价的酒泉市生态空间网络优化研究[J]. 南京林业大学学报(自然科学版): 2024, 48(2):199-208.
	QIU S, YU Q, LIU H J, et al. Optimization of ecological space network in Jiuquan City based on ecological environment quality evaluation[J]. J Nanjing For Univ (Nat Sci Ed), 2024, 48(2):199-208.DOI:10.12302/j.issn.1000-2006.202204070.
[33]	HUANG Q Y, WONG D W S. Activity patterns,socioeconomic status and urban spatial structure:what can social media data tell us?[J]. Int J Geogr Inf Sci, 2016, 30(9):1873-1898.DOI: 10.1080/13658816.2016.1145225.
[34]	BAI C Q, FENG C, DU K R, et al. Understanding spatial-temporal evolution of renewable energy technology innovation in China: evidence from convergence analysis[J]. Energy Policy, 2020,143:111570.DOI: 10.1016/j.enpol.2020.111570.
[35]	王婧, 杜广杰. 中国城市绿色创新空间关联网络及其影响效应[J]. 中国人口·资源与环境, 2021, 31(5):21-27.
	WANG J, DU G J. Spatial association network of green innovation in Chinese cities and its impact effect[J]. China Popul Resour Environ, 2021, 31(5):21-27.DOI: 10.12062/cpre.20201007.
[36]	韦施威, 杜金岷, 潘爽. 数字经济如何促进绿色创新:来自中国城市的经验证据[J]. 财经论丛, 2022(11):10-20.
	WEI S W, DU J M, PAN S. How does digital economy promote green innovation:empirical evidence from Chinese cities[J]. Collect Essays Finance Econ, 2022(11):10-20.DOI: 10.13762/j.cnki.cjlc.20220308.001.
[37]	周亮, 车磊, 孙东琪. 中国城镇化与经济增长的耦合协调发展及影响因素[J]. 经济地理, 2019, 39(6):97-107.
	ZHOU L, CHE L, SUN D Q. The coupling coordination development between urbanization and economic growth and its influencing factors in China[J]. Econ Geogr, 2019, 39(6):97-107.DOI: 10.15957/j.cnki.jjdl.2019.06.011.
[38]	张明斗, 张震. 长三角城市群城市经济韧性的空间关联网络研究[J]. 地理与地理信息科学, 2023, 39(1):69-79.
	ZHANG M D, ZHANG Z. Spatial correlation network of urban economic resilience in the Yangtze River Delta urban agglomeration[J]. Geogr Geo Inf Sci, 2023, 39(1):69-79.DOI: 10.3969/j.issn.1672-0504.2023.01.010.
[39]	王小华, 杨玉琪, 罗新雨, 等. 中国经济高质量发展的空间关联网络及其作用机制[J]. 地理学报, 2022, 77(8):1920-1936.
	WANG X H, YANG Y Q, LUO X Y, et al. The spatial correlation network and formation mechanism of China’s high-quality economic development[J]. Acta Geogr Sin, 2022, 77(8):1920-1936.DOI: 10.11821/dlxb202208006.
[40]	陈红霞, 雷佳. 中国省际金融科技发展的空间关联网络及影响因素分析[J]. 城市发展研究, 2023, 30(1):112-122.
	CHEN H X, LEI J. Spatial correlation network and influencing factors of inter-provincial financial technology development in China[J]. Urban Dev Stud, 2023, 30(1):112-122.DOI: 10.3969/j.issn.1006-3862.2023.01.014.

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

长三角城市群碳排放效率的空间关联网络及其影响因素

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]	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.
[2]	石树敏,王楠,李扎西姐. 白马鸡繁殖期等级行为和社群结构的社会网络分析[J]. 南京林业大学学报（自然科学版）, 2019, 43(03): 137-144.