Spatial correlation network structure and influencing factors of carbon emission in urban agglomeration
ZHENG Hang1, YE A-zhong1,2
1. School of Economics and Management, Fuzhou University, Fuzhou 350116, China; 2. Research Center of Fujian Economic High Quality Development in Fuzhou University Based on Social Science Planning of Fujian Province, Fuzhou 350116, China
Abstract：Based on the method of social network analysis (SNA) and quadratic assignment procedure (QAP), the paper conducted a research with regard to the spatial correlation and influencing factors of carbon emissions in urban agglomerations of Pearl River Delta with the data of prefecture-level cities in Pearl River Delta urban agglomerations during 2001 and 2019. As the result suggested, the spatial correlation of carbon emissions in PRD urban agglomerations presented a complex network structure, and the closeness of spatial correlation changed periodically, showing the feature of "fluctuating according to policy". The spatial correlation network of carbon emissions showed a significant core-edge distribution pattern. The economically developed cities such as Guangzhou and Shenzhen were at the core of the network, playing the role of "intermediary" and "bridge", while the underdeveloped cities such as Huizhou and Jiangmen were at the edge of the network and had weak ability to control and influence the network. The spatial correlation network of carbon emissions could be divided into four sectors: net benefit, net spillover, two-way spillover and broker. The expansion of differences in economic development level, energy use efficiency, technological level and environmental protection intensity promoted the formation of spatial correlation relationship of carbon emissions. The analysis above would be helpful for decision-makers to define emission reduction responsibilities and emission reduction targets for cities in the PEARL River Delta urban agglomeration, and provided references for formulating fairer and more targeted coordinated emission reduction plans for urban agglomeration.
Shan Y L, Guan D B, Liu J H, et al. Methodology and applications of city level CO2 emission accounts in China [J]. Journal of Cleaner Production, 2017,161:1215-1225.
[2]
Zhuang M, Sheng J C, Webber M, et al. Measuring water use performance in the cities along China’s South-North water transfer project [J]. Applied Geography, 2018,98:184-200.
[3]
刘华军,刘传明,孙亚男.中国能源消费的空间关联网络结构特征及其效应研究 [J]. 中国工业经济, 2015,(5):83-95. Liu H J, Liu C M, Sun Y N. Spatial correlation network structure of energy consumption and its effect in China [J]. China Industrial Economics, 2015,(5):83-95.
[4]
汪 浩,陈操操,潘 涛,等.县域尺度的京津冀都市圈CO2排放时空演变特征 [J]. 环境科学, 2014,33(1):385-393. Wang H, Chen C C, Pan T, et al. County scale characteristics of CO2 emission's spatial-temporal evolution in the Beijing-Tianjin-Hebei metropolitan region [J]. Environmental Science, 2014,33(1):385-393.
[5]
于 博,杨 旭,吴相利.哈长城市群县域碳排放空间溢出效应及影响因素研究——基于NPP-VIIRS夜间灯光数据的实证 [J]. 环境科学学报, 2020,40(2):697-706. Yu B, Yang X, Wu X L. Study on spatial spillover effects and influencing factors of carbon emissions in county areas of Ha-Chang city group: Evidence from NPP-VIIRS nightlight data [J]. Acta Scientiae Circumstantiae, 2020,40(2):697-706.
[6]
陈操操,蔡博峰,孙 粉,等.京津冀与长三角城市群碳排放的空间聚集效应比较 [J]. 中国环境科学, 2017,37(11):4371-4379. Chen C C, Cai B F, Sun F, et al. Spatial agglomeration effects of carbon dioxide emissions between Beijing-Tianjin-Hebei region and Yangtze River delta region [J]. China Environmental Science, 2017, 37(11):4371-4379.
[7]
Browne D, O'Regan B, Moles R. Comparison of energy flow accounting, energy flow metabolism ratio analysis and ecological footprinting as tools for measuring urban sustainability: A case-study of an Irish city-region [J]. Ecological Economics, 2012,83(6):97-107.
[8]
孙 慧,刘媛媛.中国区际碳排放差异与损益偏离现象分析 [J]. 管理评论, 2016,28(10):89-96. Sun H, Liu Y Y. Analysis on inter-regional differences of carbon emissions and loss and profit deviation in China [J]. Management Review, 2016,28(10):89-96.
[9]
You W H, Lv Z K. Spillover effects of economic globalization on CO2 emissions: A spatial panel approach [J]. Energy Economics, 2018, 73:248-257.
[10]
武 红.中国省域碳减排:时空格局,演变机理及政策建议——基于空间计量经济学的理论与方法 [J]. 管理世界, 2015,(11):3-10. Wu H. China's provincial carbon emission reduction: temporal and spatial pattern, evolution mechanism and policy suggestions [J]. Management World, 2015,(11):3-10.
[11]
Wang Y, Zhao T, Wang J, et al. Spatial analysis on carbon emission abatement capacity at provincial level in China from 1997 to 2014: An empirical study based on SDM model [J]. Atmospheric Pollution Research, 2019,10:97-104.
[12]
Wang B, Sun Y F, Wang Z H. Agglomeration effect of CO2 emissions and emissions reduction effect of technology: A spatial econometric perspective based on China’s province-level data [J]. Journal of Cleaner Production, 2018,204:96-106.
[13]
Wang F, Gao M N, Liu J, et al. The spatial network structure of China’s regional carbon emissions and its network effect [J]. Energies, 2018,11(10):1-14.
[14]
孙亚男,刘华军,刘传明,等.中国省际碳排放的空间关联性及其效应研究——基于SNA的经验考察 [J]. 上海经济研究, 2016,(2):82-92. Sun Y N, Liu H J, Liu C M, et al. Research on spatial association of provinces carbon emissions and its effects in China [J]. Shanghai Journal of Economics, 2016,(2):82-92.
[15]
杨桂元,吴 齐,涂 洋.中国省际碳排放的空间关联及其影响因素研究——基于社会网络分析方法 [J]. 商业经济与管理, 2016,(4): 56-68,78. Yang G Y, Wu Q, Tu Y. Research of China's regional carbon emission spatial correlation and its determinants: Based on the method of social network analysis [J]. Journal of Business Economics, 2016,(4):56- 68,78.
[16]
张 翼.基于空间关联网络结构的中国省域协同碳减排研究 [J]. 统计与信息论坛, 2017,32(2):63-69. Zhang Y. Study on Chinese provincial collaborative carbon reduction based on spatial correlation network structure [J]. Journal of Statistics and Information, 2017,32(2):63-69.
[17]
张德钢,陆远权.中国碳排放的空间关联及其解释——基于社会网络分析法 [J]. 软科学, 2017,31(4):15-18. Zhang D G, Lu Y Q. Study on the spatial correlation and explanation of carbon emission in China [J]. Soft Science, 2017,31(4):15-18.
[18]
吴佳勉,赵巧芝,孙圣仪.基于SNA方法的区域间碳排放空间关联网络特征测度研究 [J]. 统计与管理, 2019,(10):35-38. Wu J M, Zhao Q Z, Sun S Y. Research on characteristic measurement of interregional carbon emission spatial correlation network based on SNA method [J]. Statistics and Management, 2019,(10):35-38.
[19]
王 凯,张淑文,甘 畅,等.我国旅游业碳排放的空间关联性及其影响因素 [J]. 环境科学研究, 2019,32(6):938-947. Wang K, Zhang S W, Gan C. Spatial correlation of carbon emissions in tourism industry and its influencing factors in China [J]. Research of Environmental Sciences, 2019,32(6):938-947.
[20]
Ma F, Wang Y, Yuen K F, et al. The evolution of the spatial association effect of carbon emissions in transportation: A social network perspective [J]. International Journal of Environmental Research and Public Health, 2019,16(12):2154-2176.
[21]
庞庆华,李 涵,杨田田.长江经济带碳排放的空间关联性及其影响因素 [J]. 科技管理研究, 2019,39(15):246-251. Pang Q H, Li H, Yang T T. Spatial correlation of carbon emissions in Yangtze River Economic Zone and its influencing factors [J]. Science and Technology Management Research, 2019,39(15):246-251.
[22]
何艳秋,陈 柔,朱思宇,等.中国农业碳排放空间网络结构及区域协同减排 [J]. 江苏农业学报, 2020,36(5):1218-1228. He Y Q, Chen R, Zhu S Y, et al. Spatial network structure of agricultural carbon emission in China and regional collaborative emission reduction [J]. Jiangsu Journal of Agricultural Sciences, 2020, 36(5):1218-1228.
[23]
王晓平,冯 庆,宋金昭.成渝城市群碳排放空间关联结构演化及影响因素 [J]. 中国环境科学, 2020,40(9):4123-4134. Wang X P, Feng Q, Song J Z. The spatial association structure evolution of carbon emissions in Chengdu-Chongqing urban agglomeration and its influence mechanism [J]. China Environmental Science, 2020,40(9):4123-4134.
[24]
Bai C, Zhou L, Xia M, et al. Analysis of the spatial association network structure of China's transportation carbon emissions and its driving factors [J]. Journal of Environmental Management, 2020,253: 1-12.
[25]
He Y, Lan X, Zhou Z, et al. Analyzing the spatial network structure of agricultural greenhouse gases in China [J]. Environmental Science and Pollution Research, 2021,28(7):7929-7944.
[26]
李 敬,陈 澍,万广华,等.中国区域经济增长的空间关联及其解释——基于网络分析方法 [J]. 经济研究, 2014,49(11):4-16. Li J, Chen S, Wan G H, et al. Study on the spatial correlation and explanation of regional economic growth in China——Based on analytic network process [J]. Economic Research Journal, 2014,49(11): 4-16.
[27]
Rödder W, Brenner D, Kulmann F. Entropy based evaluation of net structures—deployed in social network analysis [J]. Expert Systems with Applications, 2014,41:7968-7979.
[28]
Schröter B, Hauck J, Hackenberg I, et al. Bringing transparency into the process: social network analysis as a tool to support the participatory design and implementation process of payments for ecosystem services [J]. Ecosystem Services, 2018,34:206-217.
[29]
Zhang G L, Zhang N, Liao W M. How do population and land urbanization affect CO2emissions under gravity center change? A spatial econometric analysis [J]. Journal of Cleaner Production, 2018, 202:510-523.
[30]
Maharani W, Gozali A A. Collaborative social network analysis and content-based approach to improve the marketing strategy of SMEs in Indonesia [J]. Procedia Computer Science, 2015,59:373-381.
[31]
Rios V, Gianmoena L. Convergence in CO2emissions: A spatial economic analysis with cross-country interactions [J]. Energy Economics, 2018,75:222-238.
[32]
Balado-Naves R, Baños-Pino J F, Mayor M. Do countries influence neighboring pollution? A spatial analysis of the EKC for CO2 emissions [J]. Energy Policy, 2018,123:266-279.
[33]
Commoner B. Making Peace with the Planet [M]. New York: Pantheon Books, 1990.
[34]
Brooks B, Hogan B, Ellison N, et al. Assessing structural correlates to social capital in Facebook ego networks [J]. Social Networks, 2014, 38:1-15.
[35]
Badi S, Wang L S, Pryke S. Relationship marketing in Guanxi networks: A social network analysis study of Chinese construction small and medium-sized enterprises [J]. Industrial Marketing Management, 2017,60:204-218.
[36]
Boorman S A, White H C. Social structure from multiple networks. ii. role structures [J]. American Journal of Sociology, 1976,81(6):293- 299.
[37]
Wasserman S, Faust K. Social network analysis: Methods and applications [J]. Contemporary sociological, 1994,91:219-220.
[38]
He Y Y, Wei Z X, Liu G Q, et al. Spatial network analysis of carbon emissions from the electricity sector in China [J]. Journal of Cleaner Production, 2020,262:1-13.
[39]
邵海琴,王兆峰.中国交通碳排放效率的空间关联网络结构及其影响因素 [J]. 中国人口·资源与环境, 2021,31(4):32-41. Shao H Q, Wang Z F. Spatial network structure of transportation carbon emissions efficiency in China and its influencing factors [J]. China Population, Resources and Environment, 2021,31(4):32-41.
[40]
孟祥宁,张 林.中国装备制造业绿色全要素生产率增长的演化轨迹及动力 [J]. 经济与管理研究, 2018,39(1):105-115. Meng X N, Zhang L. Evolutionary trail and dynamics of green total factor productivity growth in China's equipment manufacturing industry [J]. Research on Economics and Management, 2018, 39(1):105-115.
[41]
郑石明.政治周期,五年规划与环境污染——以工业二氧化硫排放为例 [J]. 政治学研究, 2016,(2):80-94,127-128. Zheng S M. Political cycle, Five-Year plan and environmental pollution——with empirical analysis of industrial sulfur dioxide emissions [J]. CASS Journal of Political Science, 2016,(2):80-94, 127-128.
[42]
Cao X, Kostka G, Xu X. Environmental political business cycles: the case of PM2.5 air pollution in Chinese prefectures [J]. Environmental Science and Policy, 2019,93:92-100.
[43]
Song J Z, Feng Q, Wang X P, et al. Spatial association and effect evaluation of CO2 emissions in the Chengdu-Chongqing urban agglomeration: Quantitative evidence from social network analysis [J]. Sustainability, 2019,11(1):1-19.
[44]
Li X T, Feng D, Li J, et al. Research on the spatial network characteristics and synergetic abatement effect of the carbon emissions in Beijing–Tianjin–Hebei urban agglomeration [J]. Sustainability, 2019,11(5):1444-1458.
