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China's regional imbalance in electricity demand, power and water pricing - From the perspective of electricity-related virtual water transmission

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  • Yan, Xia
  • Jie, Wu
  • Minjun, Shi
  • Shouyang, Wang
  • Zhuoying, Zhang

Abstract

This paper investigates the linkage between regional electricity pricing and water resources based on an accounting of the electricity-related virtual water (VW) transmissions in the framework of interregional input-output model. This study finds out that the water resource conditions of the provinces with scarce water resources but rich energy resources are worsening due to power generation and thus the power generation's impacts on water resources in these regions should be seriously addressed across its entire life cycle. This study confirms the coexistence of the transmission of low-priced electricity to the regions with higher electricity prices and the hidden transmission of high-priced water resources to the regions with rich water resource endowment and lower water prices through electricity-related VW transmissions, which can aggravate the “invisible imbalance” of the spatial allocation of water resources. This study also emphasizes that China's regional electricity pricing mechanisms should fully involve the external costs of water to stimulate energy enterprises' willingness to control water use. The results of this study can provide scientific references for joint management of water and energy, and thus are conducive to policy formulations relevant to electricity pricing reforms in China.

Suggested Citation

  • Yan, Xia & Jie, Wu & Minjun, Shi & Shouyang, Wang & Zhuoying, Zhang, 2022. "China's regional imbalance in electricity demand, power and water pricing - From the perspective of electricity-related virtual water transmission," Energy, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:energy:v:257:y:2022:i:c:s0360544222016784
    DOI: 10.1016/j.energy.2022.124775
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    3. Hasan Murat Ertuğrul & Mustafa Tevfik Kartal & Serpil Kılıç Depren & Uğur Soytaş, 2022. "Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models," Energies, MDPI, vol. 15(20), pages 1-17, October.

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