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Towards COP26 targets: Characteristics and influencing factors of spatial correlation network structure on U.S. carbon emission

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  • Wang, Zhenshuang
  • Xie, Wanchen
  • Zhang, Chengyi

Abstract

The COP26 stress on countries to accelerate the transition to low-carbon energy systems. To reveal the driving mechanism of the spatial correlation network of carbon emission in the U.S. and promote the coordinated emission reduction of regional carbon emissions in the U.S., This paper investigates the evolution characteristics of the spatial correlation network structure of 50 states in U.S. carbon emission from 2013 to 2020, with the modified gravity model and social network analysis (SNA), and explores its driving factors by quadratic assignment procedure (QAP) model. The conclusions show that there is an obvious spatial correlation among interstate carbon emissions in the U.S., the network density shows a downward trend, the overall network efficiency shows an increasing trend, and the spatial correlation network presents a “core-edge” structure. Economically developed states generally have a core position in the network and play a controlling role in guiding other states to develop together with them. Among the network plates, 16 states such as Alabama, Indiana, South Carolina, New Hampshire, Kentucky, Tennessee, Maine and North Carolina have always been located in the “Broker” plate, which played the role of intermediary and bridge in the network. Massachusetts, Illinois, New Jersey, Maryland and New York, which are concentrated in the coastal and lake areas, have always been located in the “Net Benefit” plate. Wyoming, Colorado, North Dakota, Alaska, Texas and Nebraska, which are concentrated in areas with relatively abundant energy reserves, have always been located in the “Net Overflow” plate, and carbon emission show an obvious spillover effect in the network. The coastal and lake areas are the main destinations of the spatial spillover of the spatial correlation network of carbon emissions in U.S.. Geographical adjacency, population size, per capita GDP and technology level have a significant impact on the spatial correlation of carbon emissions. The spatial correlation and spillover of carbon emissions among states increase with the higher the similarity of technology level among regions.

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  • Wang, Zhenshuang & Xie, Wanchen & Zhang, Chengyi, 2023. "Towards COP26 targets: Characteristics and influencing factors of spatial correlation network structure on U.S. carbon emission," Resources Policy, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:jrpoli:v:81:y:2023:i:c:s0301420722007280
    DOI: 10.1016/j.resourpol.2022.103285
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    as
    1. César Ducruet & Laurent Beauguitte, 2014. "Network science and spatial science : Review and outcomes of a complex relationship," Post-Print hal-03246947, HAL.
    2. Feng Hao & Guizhen He & Michael Snipes, 2018. "A comparative study of the economy’s environmental impact between states in the USA and provinces in China," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 8(2), pages 132-141, June.
    3. You, Wanhai & Lv, Zhike, 2018. "Spillover effects of economic globalization on CO2 emissions: A spatial panel approach," Energy Economics, Elsevier, vol. 73(C), pages 248-257.
    4. Wang, Hongtao & Yang, Yi & Keller, Arturo A. & Li, Xiang & Feng, Shijin & Dong, Ya-nan & Li, Fengting, 2016. "Comparative analysis of energy intensity and carbon emissions in wastewater treatment in USA, Germany, China and South Africa," Applied Energy, Elsevier, vol. 184(C), pages 873-881.
    5. Jiang, Qichuan & Ma, Xuejiao, 2021. "Spillovers of environmental regulation on carbon emissions network," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    6. Kuik, Onno & Branger, Frédéric & Quirion, Philippe, 2019. "Competitive advantage in the renewable energy industry: Evidence from a gravity model," Renewable Energy, Elsevier, vol. 131(C), pages 472-481.
    7. Yang, Xinyu & Jiang, Ping & Pan, Yao, 2020. "Does China's carbon emission trading policy have an employment double dividend and a Porter effect?," Energy Policy, Elsevier, vol. 142(C).
    8. Li, Aijun & Hu, Mingming & Wang, Mingjian & Cao, Yinxue, 2016. "Energy consumption and CO2 emissions in Eastern and Central China: A temporal and a cross-regional decomposition analysis," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 284-297.
    9. Marbuah, George & Amuakwa-Mensah, Franklin, 2017. "Spatial analysis of emissions in Sweden," Energy Economics, Elsevier, vol. 68(C), pages 383-394.
    10. Prakash, Vrishab & Ghosh, Sajal & Kanjilal, Kakali, 2020. "Costs of avoided carbon emission from thermal and renewable sources of power in India and policy implications," Energy, Elsevier, vol. 200(C).
    11. Khan, Anwar & Chenggang, Yang & Hussain, Jamal & Bano, Sadia & Nawaz, AAmir, 2020. "Natural resources, tourism development, and energy-growth-CO2 emission nexus: A simultaneity modeling analysis of BRI countries," Resources Policy, Elsevier, vol. 68(C).
    12. Fei Ma & Yixuan Wang & Kum Fai Yuen & Wenlin Wang & Xiaodan Li & Yuan Liang, 2019. "The Evolution of the Spatial Association Effect of Carbon Emissions in Transportation: A Social Network Perspective," IJERPH, MDPI, vol. 16(12), pages 1-23, June.
    13. Huang, Junbing & Li, Xinghao & Wang, Yajun & Lei, Hongyan, 2021. "The effect of energy patents on China's carbon emissions: Evidence from the STIRPAT model," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    14. Zhu, Zhi-Shuang & Liao, Hua & Cao, Huai-Shu & Wang, Lu & Wei, Yi-Ming & Yan, Jinyue, 2014. "The differences of carbon intensity reduction rate across 89 countries in recent three decades," Applied Energy, Elsevier, vol. 113(C), pages 808-815.
    15. Neumayer, Eric, 2002. "Can natural factors explain any cross-country differences in carbon dioxide emissions?," Energy Policy, Elsevier, vol. 30(1), pages 7-12, January.
    16. Wang, Keying & Wu, Meng & Sun, Yongping & Shi, Xunpeng & Sun, Ao & Zhang, Ping, 2019. "Resource abundance, industrial structure, and regional carbon emissions efficiency in China," Resources Policy, Elsevier, vol. 60(C), pages 203-214.
    17. Auffhammer, Maximilian & Carson, Richard T., 2008. "Forecasting the path of China's CO2 emissions using province-level information," Journal of Environmental Economics and Management, Elsevier, vol. 55(3), pages 229-247, May.
    18. Li, Yingzhu & Su, Bin & Dasgupta, Shyamasree, 2018. "Structural path analysis of India's carbon emissions using input-output and social accounting matrix frameworks," Energy Economics, Elsevier, vol. 76(C), pages 457-469.
    19. Xinhua Zhu & Qianli Wang & Peifeng Zhang & Yunjiang Yu & Lingling Xie, 2021. "Optimizing the spatial structure of urban agglomeration: based on social network analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(2), pages 683-705, April.
    20. Liu, S. & Xiao, Q., 2021. "An empirical analysis on spatial correlation investigation of industrial carbon emissions using SNA-ICE model," Energy, Elsevier, vol. 224(C).
    21. César Ducruet & Laurent Beauguitte, 2014. "Spatial Science and Network Science: Review and Outcomes of a Complex Relationship," Networks and Spatial Economics, Springer, vol. 14(3), pages 297-316, December.
    22. Siyao Li & Qiaosheng Wu & You Zheng & Qi Sun, 2021. "Study on the Spatial Association and Influencing Factors of Carbon Emissions from the Chinese Construction Industry," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    23. De Oliveira-De Jesus, Paulo M., 2019. "Effect of generation capacity factors on carbon emission intensity of electricity of Latin America & the Caribbean, a temporal IDA-LMDI analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 516-526.
    24. McNeil, Michael A. & Feng, Wei & de la Rue du Can, Stephane & Khanna, Nina Zheng & Ke, Jing & Zhou, Nan, 2016. "Energy efficiency outlook in China’s urban buildings sector through 2030," Energy Policy, Elsevier, vol. 97(C), pages 532-539.
    25. Jansuwan, Sarawut & Chen, Anthony & Xu, Xiangdong, 2021. "Analysis of freight transportation network redundancy: An application to Utah’s bi-modal network for transporting coal," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 154-171.
    26. Hu, Ying & Yu, Yang & Mardani, Abbas, 2021. "Selection of carbon emissions control industries in China: An approach based on complex networks control perspective," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    27. Browne, David & O'Regan, Bernadette & Moles, Richard, 2012. "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," Ecological Economics, Elsevier, vol. 83(C), pages 97-107.
    28. José Miguel Barrios & Willem W. Verstraeten & Piet Maes & Jean-Marie Aerts & Jamshid Farifteh & Pol Coppin, 2012. "Using the Gravity Model to Estimate the Spatial Spread of Vector-Borne Diseases," IJERPH, MDPI, vol. 9(12), pages 1-19, November.
    29. Kuishuang Feng & Steven J. Davis & Laixiang Sun & Klaus Hubacek, 2015. "Drivers of the US CO2 emissions 1997–2013," Nature Communications, Nature, vol. 6(1), pages 1-8, November.
    30. Qian, Xianhang & Wang, Ying & Zhang, Guangli, 2018. "The spatial correlation network of capital flows in China: Evidence from China's High-Value Payment System," China Economic Review, Elsevier, vol. 50(C), pages 175-186.
    31. Liu, Jiaguo & Li, Sujuan & Ji, Qiang, 2021. "Regional differences and driving factors analysis of carbon emission intensity from transport sector in China," Energy, Elsevier, vol. 224(C).
    32. O’ Mahony, Tadhg & Zhou, Peng & Sweeney, John, 2012. "The driving forces of change in energy-related CO2 emissions in Ireland: A multi-sectoral decomposition from 1990 to 2007," Energy Policy, Elsevier, vol. 44(C), pages 256-267.
    33. Wu, Linfei & Sun, Liwen & Qi, Peixiao & Ren, Xiangwei & Sun, Xiaoting, 2021. "Energy endowment, industrial structure upgrading, and CO2 emissions in China: Revisiting resource curse in the context of carbon emissions," Resources Policy, Elsevier, vol. 74(C).
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