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Exploring the spatiotemporal evolution of energy intensity in China by visual technology of the GIS

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  • Shi, Xiaohui
  • Chu, Junhui
  • Zhao, Changyi

Abstract

Fully understanding the spatiotemporal evolution of energy intensity is the key to formulating regional heterogeneous energy policies in China. In this paper, the relative variation of energy intensity among provinces in China from 2003 to 2018 is captured by the Geographic Information System (GIS) to analyze the spatiotemporal evolution of regional energy intensity and the spatiotemporal evolution of spatial correlations between regions. The results show that the energy intensity in eastern, central, western, and northeastern China shows a downward trend. Furthermore, the main geographical distribution area of energy intensity is in the Yellow River Basin.The spatial distribution of energy intensity showed a trend of dispersion with increasing ellipse area from 3.611×106 km2 to 3.861×106 km2 and generally shifted westward, with the weighted average center from (110∘20’E, 35∘9’N) to (108∘42’E, 35∘26’N). The spatial aggregation of energy intensity in China is significant, forming high energy intensity clusters mainly in the western region and low energy intensity clusters mainly in the eastern coastal region. The pattern of the spatial distribution of energy intensity in China shows spatial locking and path dependence. Our findings provide a more comprehensive perspective of the spatiotemporal evolution of energy intensity in China.

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  • Shi, Xiaohui & Chu, Junhui & Zhao, Changyi, 2021. "Exploring the spatiotemporal evolution of energy intensity in China by visual technology of the GIS," Energy, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:energy:v:228:y:2021:i:c:s0360544221008999
    DOI: 10.1016/j.energy.2021.120650
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    Cited by:

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    2. Chong, Cheng Tung & Fan, Yee Van & Lee, Chew Tin & Klemeš, Jiří Jaromír, 2022. "Post COVID-19 ENERGY sustainability and carbon emissions neutrality," Energy, Elsevier, vol. 241(C).
    3. Wang, Kai-Hua & Zhao, Yan-Xin & Su, Yun Hsuan & Lobonţ, Oana-Ramona, 2023. "Energy security and CO2 emissions: New evidence from time-varying and quantile-varying aspects," Energy, Elsevier, vol. 273(C).
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    5. Wang, Na & Fu, Xiaodong & Wang, Shaobin, 2022. "Spatial-temporal variation and coupling analysis of residential energy consumption and economic growth in China," Applied Energy, Elsevier, vol. 309(C).
    6. Yan, Junna & Li, Yingzhu & Su, Bin & Ng, Tsan Sheng, 2022. "Contributors and drivers of Chinese energy use and intensity from regional and demand perspectives, 2012-2015-2017," Energy Economics, Elsevier, vol. 115(C).

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