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The spatiotemporal characteristics of electrical energy supply-demand and the green economy outlook of Guangdong Province, China

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  • Yao, Huizong
  • Zang, Chuanfu

Abstract

The GDP of Guangdong Province exceeded 10 trillion RMB in 2019. The province has the largest economy in China and also the greatest consumption of electrical energy, the most important driving force for its economic development. However, Guangdong Province has a high degree of external dependence and large internal differences, and consequently, its distribution of electrical energy demand is uneven and its regional growth is unbalanced. Exploration of Guangdong’s electrical energy supply and demand structure and elucidation of the rules governing spatial and temporal changes in its electrical energy systems are highly important. This paper collects data on the electrical energy supply and demand in Guangdong Province from 2000 to 2018, employs ArcGIS spatial analysis and Moran Index and β-convergence test. The main results were as follows. Self-sufficiency for electricity in Guangdong dropped from 100% in 2000 to 69% in 2018. The remainder of electricity was imported. Guangdong mainly relies on thermal electric generation, while other import provinces with the exception of Guizhou mainly rely on hydropower. The proportion of renewable energy fell from 11.6% in 2000 to 5.0% in 2018. The demand for electrical energy in Guangdong is very large, more than half of this demand is from secondary industry. Other modern industries had the highest growth rate in electricity consumption. Electrical energy mainly flows to the Guangdong-Hong Kong-Macao Greater Bay Area, and the increased demand is also mainly concentrated there. The absolute differences in electrical consumption among cities has increased, showing a distinct polarization phenomenon. This paper provides a reference for solving electrical energy supply-demand problems, ensuring the sustainable development of the economy and promoting the green economic transformation of Guangdong.

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  • Yao, Huizong & Zang, Chuanfu, 2021. "The spatiotemporal characteristics of electrical energy supply-demand and the green economy outlook of Guangdong Province, China," Energy, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:energy:v:214:y:2021:i:c:s0360544220319988
    DOI: 10.1016/j.energy.2020.118891
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    References listed on IDEAS

    as
    1. Fallahi, Firouz & Voia, Marcel-Cristian, 2015. "Convergence and persistence in per capita energy use among OECD countries: Revisited using confidence intervals," Energy Economics, Elsevier, vol. 52(PA), pages 246-253.
    2. Mishra, Vinod & Smyth, Russell, 2017. "Conditional convergence in Australia's energy consumption at the sector level," Energy Economics, Elsevier, vol. 62(C), pages 396-403.
    3. T. W. Swan, 1956. "ECONOMIC GROWTH and CAPITAL ACCUMULATION," The Economic Record, The Economic Society of Australia, vol. 32(2), pages 334-361, November.
    4. Chong, ChinHao & Liu, Pei & Ma, Linwei & Li, Zheng & Ni, Weidou & Li, Xu & Song, Shizhong, 2017. "LMDI decomposition of energy consumption in Guangdong Province, China, based on an energy allocation diagram," Energy, Elsevier, vol. 133(C), pages 525-544.
    5. Akram, Vaseem & Rath, Badri Narayan & Sahoo, Pradipta Kumar, 2020. "Stochastic conditional convergence in per capita energy consumption in India," Economic Analysis and Policy, Elsevier, vol. 65(C), pages 224-240.
    6. Liddle, Brantley, 2009. "Electricity intensity convergence in IEA/OECD countries: Aggregate and sectoral analysis," Energy Policy, Elsevier, vol. 37(4), pages 1470-1478, April.
    7. Sheldon, Seth & Hadian, Saeed & Zik, Ory, 2015. "Beyond carbon: Quantifying environmental externalities as energy for hydroelectric and nuclear power," Energy, Elsevier, vol. 84(C), pages 36-44.
    8. Liebensteiner, Mario & Wrienz, Matthias, 2020. "Do Intermittent Renewables Threaten the Electricity Supply Security?," Energy Economics, Elsevier, vol. 87(C).
    9. Anandarajah, Gabrial & Strachan, Neil, 2010. "Interactions and implications of renewable and climate change policy on UK energy scenarios," Energy Policy, Elsevier, vol. 38(11), pages 6724-6735, November.
    10. Lukanov, Boris R. & Krieger, Elena M., 2019. "Distributed solar and environmental justice: Exploring the demographic and socio-economic trends of residential PV adoption in California," Energy Policy, Elsevier, vol. 134(C).
    11. Hong, Jingke & Gu, Jianping & Liang, Xin & Liu, Guiwen & Shen, Geoffrey Qiping & Tang, Miaohan, 2019. "Spatiotemporal investigation of energy network patterns of agglomeration economies in China: Province-level evidence," Energy, Elsevier, vol. 187(C).
    12. Kais Saidi & Sami Hammami, 2016. "Economic growth, energy consumption and carbone dioxide emissions: recent evidence from panel data analysis for 58 countries," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(1), pages 361-383, January.
    13. Jahangir Alam, Mohammad & Ara Begum, Ismat & Buysse, Jeroen & Van Huylenbroeck, Guido, 2012. "Energy consumption, carbon emissions and economic growth nexus in Bangladesh: Cointegration and dynamic causality analysis," Energy Policy, Elsevier, vol. 45(C), pages 217-225.
    14. Sharifzadeh, Mahdi & Lubiano-Walochik, Helena & Shah, Nilay, 2017. "Integrated renewable electricity generation considering uncertainties: The UK roadmap to 50% power generation from wind and solar energies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 385-398.
    15. Mohammadi, Hassan & Ram, Rati, 2012. "Cross-country convergence in energy and electricity consumption, 1971–2007," Energy Economics, Elsevier, vol. 34(6), pages 1882-1887.
    16. Headley, Alexander J. & Copp, David A., 2020. "Energy storage sizing for grid compatibility of intermittent renewable resources: A California case study," Energy, Elsevier, vol. 198(C).
    17. Urban, Frauke & Geall, Sam & Wang, Yu, 2016. "Solar PV and solar water heaters in China: Different pathways to low carbon energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 531-542.
    18. Lin, Jiang & Kahrl, Fredrich & Yuan, Jiahai & Chen, Qixin & Liu, Xu, 2019. "Economic and carbon emission impacts of electricity market transition in China: A case study of Guangdong Province," Applied Energy, Elsevier, vol. 238(C), pages 1093-1107.
    Full references (including those not matched with items on IDEAS)

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