Research on the Spatial Network Characteristics and Synergetic Abatement Effect of the Carbon Emissions in Beijing–Tianjin–Hebei Urban Agglomeration

Sustainability - Tập 11 Số 5 - Trang 1444
Xintao Li1, Dong Feng1, Jian Li1,2, Zaisheng Zhang1
1College of Management and Economics, Tianjin University, Tianjin 300072, China
2Research Center for Circular Economy and Enterprise Sustainable Development, Tianjin University of Technology, Tianjin 300384, China

Tóm tắt

Based on the carbon emission data in the Beijing–Tianjin–Hebei urban agglomeration from 2007 to 2016, this paper used the method of social network analysis (SNA) to investigate the spatial correlation network structure of the carbon emission. Then, by constructing the synergetic abatement effect model, we calculated the synergetic abatement effect in the cities and we empirically examined the influence of the spatial network characteristics on the synergetic abatement effect. The results show that the network density first increased from 0.205 in 2007 to 0.263 in 2014 and then decreased to 0.205 in 2016; the network hierarchy fluctuated around 0.710, and the minimum value of the network efficiency was 0.561, which indicates that the network hierarchy structure is stern and the network has good stability. Beijing and Tianjin are in the center of the carbon emission spatial network and play important “intermediary” and “bridge” roles that can have better control over other carbon emission spatial spillover relations between the cities, thus the spatial network of carbon emissions presents a typical “center–periphery” structure. The synergetic abatement effect increased from −2.449 in 2007 to 0.800 in 2011 and then decreased to −1.653 in 2016; the average synergetic effect was −0.550. This means that the overall synergetic level has a lot of room to grow. The carbon emission spatial network has a significant influence on the synergetic abatement effect, while increasing the network density and the network hierarchy. Decreasing the network efficiency will significantly enhance the synergetic abatement effect.

Từ khóa


Tài liệu tham khảo

Li, 2018, Historical growth in total factor carbon productivity of the Chinese industry—A comprehensive analysis, J. Clean. Prod., 170, 471, 10.1016/j.jclepro.2017.09.145

People.cn (2018, December 15). Greenhouse Gas Bulletin: Global Carbon Dioxide Concentrations Hit a New High in 2016. Available online: http://world.people.com.cn/n1/2017/1031/c1002-29618565.html.

Tan, 2016, China’s regional CO2 emissions reduction potential: A study of Chongqing city, Appl. Energy, 162, 1345, 10.1016/j.apenergy.2015.06.071

IEA (2014). CO2 Emissions from Fuel Combustion 2013, IEA.

Chen, 2018, Quo Vadis? Major players in global coal consumption and emissions reduction, Transform. Bus. Econ., 17, 112

Zhao, 2017, Saving forests through development? Fuelwood consumption and the energy-ladder hypothesis in rural Southern China, Transform. Bus. Econ., 16, 199

Zhang, 2012, Panel estimation for urbanization energy consumption and CO2 emissions: A regional analysis in China, Energy Policy, 49, 488, 10.1016/j.enpol.2012.06.048

Zhuang, 2018, Measuring water use performance in the cities along China’s South-North water transfer project, Appl. Geogr., 98, 184, 10.1016/j.apgeog.2018.07.020

Zhu, 2017, CO2 emissions from the industrialization and urbanization processes in the manufacturing center Tianjin in China, J. Clean. Prod., 168, 867, 10.1016/j.jclepro.2017.08.245

Yamaji, 1993, A study on economic measures for CO2 reduction in Japan, Energy Policy, 21, 123, 10.1016/0301-4215(93)90134-2

Ang, 1999, Is the energy intensity being a less useful indicator than the carbon factor in the study of climate change?, Energy Policy, 27, 943, 10.1016/S0301-4215(99)00084-1

Bohm, 1994, Fairness in a tradable permit treaty for carbon emission reductions in Europe and the former Soviet Union, Environ. Resour. Econ., 4, 219, 10.1007/BF00692325

Janssen, 1995, Allocation of fossil CO2 emission rights quantifying cultural perspectives, Ecol. Econ., 13, 65, 10.1016/0921-8009(94)00058-4

Zhao, 2017, Research on spatial and temporal evolution of carbon emission intensity and its transition mechanism, China Popul. Resour. Environ., 27, 84

Jiang, 2017, Provincial-level carbon emission drivers and emission reduction strategies in China: Combining multi-layer LMDI decomposition with hierarchical clustering, J. Clean. Prod., 169, 178, 10.1016/j.jclepro.2017.03.189

Jorgenson, 2017, Income Inequality and Carbon Emissions in the United States: A State-level Analysis, 1997–2012, Ecol. Econ., 134, 40, 10.1016/j.ecolecon.2016.12.016

Zhao, 2017, Allocation of carbon emissions among industries/sectors: Emissions intensity reduction constrained approach, J. Clean. Prod., 142, 3083, 10.1016/j.jclepro.2016.10.159

Grunewald, 2014, Decomposing inequality in CO2 emissions: The role of primary energy carriers and economic sectors, Ecol. Econ., 100, 183, 10.1016/j.ecolecon.2014.02.007

Marbuah, 2017, Spatial analysis of emissions in Sweden, Energy Econ., 68, 383, 10.1016/j.eneco.2017.10.003

Wu, 2015, Analysis on Chinese Provincial Carbon Emission Reduction: Spatial-temporal Patterns, Evolution Mechanisms and Policy Recommendations: Based on the Theory and Method of Spatial Econometrics, Manag. World, 11, 3

Wang, 2018, Agglomeration effect of CO2 emissions and emissions reduction effect of technology: A spatial econometric perspective based on China’s province-level data, J. Clean. Prod., 204, 96, 10.1016/j.jclepro.2018.08.243

Yan, 2017, Carbon emission efficiency and spatial clustering analyses in China’s thermal power industry: Evidence from the provincial level, J. Clean. Prod., 156, 518, 10.1016/j.jclepro.2017.04.063

You, 2018, Spillover effects of economic globalization on CO2 emissions: A spatial panel approach, Energy Econ., 73, 248, 10.1016/j.eneco.2018.05.016

Sun, 2016, Analysis on inter-regional differences of carbon emissions loss and profit deviation in China, Manag. Rrev., 10, 89

Wang, 2019, Spatial analysis on carbon emission abatement capacity at provincial level in China from 1997 to 2014: An empirical study based on SDM model, Atmos. Pollut. Res., 10, 97, 10.1016/j.apr.2018.06.003

Zhang, 2018, Study of carbon metabolic processes and their spatial distribution in the Beijing-Tianjin-Hebei urban agglomeration, Sci. Total Environ., 7, 1630, 10.1016/j.scitotenv.2018.07.033

Wang, F., Gao, M.N., Liu, J., and Fan, W.N. (2018). The spatial network structure of China’s regional carbon emissions and its network effect. Energies, 11.

Sun, 2016, Research on spatial association of provinces carbon emissions and its effects in China, Shanghai Econ. Res., 2, 82

Yang, 2016, Researches of China’s regional carbon emission spatial correlation and its determinants: Based on the method of social network analysis, J. Bus. Econ., 4, 56

Chen, 2018, Estimation and factor decomposition of carbon emissions in China’s tourism sector, Probl. Ekorozw., 13, 91

Li, J.C., Xiang, Y.W., Jia, H.Y., and Chen, L. (2018). Analysis of total factor energy efficiency and its influencing factors on key energy-intensive industries in the Beijing-Tianjin-Hebei region. Sustainability, 10.

Yang, 2015, Assessing green development efficiency of municipalities and provinces in China integrating models of super-efficiency DEA and Malmquist index, Sustainability, 7, 4492, 10.3390/su7044492

Li, 2014, Study on the spatial correlation and explanation of regional economic growth in China-Based on analytic network process, Econ. Res., 11, 4

Hauck, 2018, Bringing transparency into the process: Social network analysis as a tool to support the participatory design and implementation process of Payments for Ecosystem Services, Ecosyst. Serv., 34, 206, 10.1016/j.ecoser.2018.03.007

Brenner, 2014, Entropy based evaluation of net structures—Deployed in Social Network Analysis, Expert Syst Appl., 41, 7968, 10.1016/j.eswa.2014.06.049

Zhang, 2018, How do population and land urbanization affect CO2 emissions under gravity center change? A spatial econometric analysis, J. Clean. Prod., 202, 510, 10.1016/j.jclepro.2018.08.146

Maharani, 2015, Collaborative Social Network Analysis and Content-based Approach to Improve the Marketing Strategy of SMEs in Indonesia, Procedia Comput. Sci., 59, 373, 10.1016/j.procs.2015.07.540

Rios, 2018, Convergence in CO2 emissions: A spatial economic analysis with cross-country interactions, Energy Econ., 75, 222, 10.1016/j.eneco.2018.08.009

Mayor, 2018, Do countries influence neighbouring pollution? A spatial analysis of the EKC for CO2 emissions, Energy Policy, 123, 266, 10.1016/j.enpol.2018.08.059

Badi, 2017, Relationship marketing in Guanxi networks: A social network analysis study of Chinese construction small and medium-sized enterprises, Ind. Mark. Manag., 60, 204, 10.1016/j.indmarman.2016.03.014

Brooks, 2014, Assessing structural correlates to social capital in Facebook ego networks, Soc. Networks, 38, 1, 10.1016/j.socnet.2014.01.002

Zhang, 2018, How does foreign trade influence China’s carbon productivity? Based on panel spatial lag model analysis, Struct. Chang. Econ. Dyn., 47, 171, 10.1016/j.strueco.2018.08.008

Warner, 2012, Hegemony and Asymmetry: Multiple-chessboard Games on Transboundary Rivers, Int. Environ. Agreem., 12, 215, 10.1007/s10784-012-9177-y

Zhang, 2018, Does Industrial Agglomeration Mitigate Fossil CO2 Emissions? An Empirical Study with Spatial Panel Regression model, Energy Procedia, 152, 731, 10.1016/j.egypro.2018.09.237

Li, 2018, Spatial Spillover Effects of Industrial Carbon Emissions in China, Energy Procedia, 152, 679, 10.1016/j.egypro.2018.09.230

Cerqueira, 2009, Measuring the Determinants of Business Cycle Synchronization Using a Panel Approach, Econ. Lett., 102, 106, 10.1016/j.econlet.2008.11.016

Can, 2017, The impact of economic complexity on carbon emissions: Evidence from France, Environ. Sci. Pollut. Res., 24, 16364, 10.1007/s11356-017-9219-7

Han, 2018, Carbon emission analysis and evaluation of industrial departments in China: An improved environmental DEA cross model based on information entropy, J. Environ. Manag., 205, 298, 10.1016/j.jenvman.2017.09.062

Ikeda, 2017, Stable economic agglomeration patterns in two dimensions: Beyond the scope of central place theory, J. Reg. Sci., 57, 132, 10.1111/jors.12290

Neves, 2017, Is energy consumption in the trans-port sector hampering both economic growth and the reduction of CO2 emissions? A disaggregated energy consumption analysis, Transp. Policy, 59, 64, 10.1016/j.tranpol.2017.07.004

Riti, 2017, Decoupling CO2 emission and economic growth in China: Is there consistency in estimation results in analyzing environmental Kuznets curve?, J. Clean. Prod., 166, 1448, 10.1016/j.jclepro.2017.08.117

Zhang, 2016, Regulating effects of climate, net primary productivity, and nitrogen on carbon sequestration rates in temperate wet-lands, Northeast China, Ecol. Indic., 70, 114, 10.1016/j.ecolind.2016.05.041

Tao, 2015, Effects of land use and cover change on terrestrial carbon stocks in urbanized areas: A study from Changzhou, China, J. Clean. Prod., 103, 651, 10.1016/j.jclepro.2014.07.055

Criado, 2011, Convergence in per capita CO2 emissions: A robust distributional approach, Resour. Energy Econ., 33, 637, 10.1016/j.reseneeco.2011.01.003

Aichele, 2015, Kyoto and carbon leakage: An empirical analysis of the carbon content of bilateral trade, Rev. Econ. Stat., 97, 104, 10.1162/REST_a_00438