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Mapping China's photovoltaic power geographies: Spatial-temporal evolution, provincial competition and low-carbon transition

Author

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  • Liao, Maolin
  • Zhang, Ze
  • Jia, Jin
  • Xiong, Jiao
  • Han, Mengyao

Abstract

As the climate change effects of traditional energy consumption are more pronounced, renewable energy has become increasingly essential in meeting electricity demands and mitigating carbon emissions. Based on the spatial autocorrelation analysis and carbon emission avoided analysis, this study depicts the photovoltaic power geographies, analyzes the spatial-temporal characteristics, and measures the carbon emission reduction potentials of China's photovoltaic power installation by province. Five stages of the photovoltaic power installation including Full operation, Partial operation, Announced construction, Permitted construction, and Under construction are classified. Overall, the provinces with higher installed capacities and emission reduction potentials are mainly distributed in the North and Northwest China, including Inner Mongolia, Qinghai, Ningxia, and Hebei. From the spatial autocorrelation analysis, Inner Mongolia, Shaanxi, and Shanxi belong to the high-high cluster, while most provinces in South China are among the low-low cluster. In terms of cumulative carbon emission reduction, Inner Mongolia, Gansu, Qinghai, Ningxia, Shaanxi, and Xinjiang are main contributors. By comparing the spatial and temporal evolution, geographical characteristics, and low-carbon reduction of photovoltaic power installation in China's provinces and regions, this study provides quantitative supports and feasible suggestions for the achievement of low-carbon targets and sustainable development of China's photovoltaic industry.

Suggested Citation

  • Liao, Maolin & Zhang, Ze & Jia, Jin & Xiong, Jiao & Han, Mengyao, 2022. "Mapping China's photovoltaic power geographies: Spatial-temporal evolution, provincial competition and low-carbon transition," Renewable Energy, Elsevier, vol. 191(C), pages 251-260.
  • Handle: RePEc:eee:renene:v:191:y:2022:i:c:p:251-260
    DOI: 10.1016/j.renene.2022.03.068
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