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<p>Understanding the factors that drive interprovincial energy transfers is vital for optimizing energy allocation and supporting low-carbon transition strategies. In this study, we integrated a multi-regional input–output model with a gravity model to identify the asymmetrical effects of export- and import-side characteristics on the direction and volume of embodied energy flows in China. The combined model incorporates distance to estimate energy kilometers and the associated carbon footprint (CF). Using this model, we simulated energy transfers and CF changes for 2018–2022 based on the regression results. The results show that (1) energy transfers are mainly driven by export-side characteristics, with flows moving from regions with higher GDP, urbanization rates, energy supply, and energy intensity to those with lower levels, among which energy intensity is the dominant factor. (2) The total energy flow increased by 30.22% during 2015–2022, and improvements in energy intensity and industrial layout during 2020–2022 effectively moderated CF growth. (3) Scenarios in which the energy intensity of the three main exporting provinces (Inner Mongolia, Hebei, and Shanxi) was reduced by 0.2%, 0.4%, and 0.6% also showed the CF decreasing by 0.03%, 0.06%, and 0.09%, respectively. On the basis of this study, we recommend reducing energy intensity in upstream provinces through adjustments to primary energy prices and taxes, as well as establishing an ecological compensation mechanism, optimizing industrial spatial layouts, and promoting balanced urbanization.</p>

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Drivers of interprovincial energy transfers: integrating multi-regional input–output and gravity model with distance

  • 期刊论文

  • 发表日期：

    2026年4月16日

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  • Ling Yan,
  • Takehiko Murayama,
  • Shigeo Nishikizawa,
  • Kultip Suwanteep,
  • Xiangyu Zheng,
  • Jiarui Li

摘要

Understanding the factors that drive interprovincial energy transfers is vital for optimizing energy allocation and supporting low-carbon transition strategies. In this study, we integrated a multi-regional input–output model with a gravity model to identify the asymmetrical effects of export- and import-side characteristics on the direction and volume of embodied energy flows in China. The combined model incorporates distance to estimate energy kilometers and the associated carbon footprint (CF). Using this model, we simulated energy transfers and CF changes for 2018–2022 based on the regression results. The results show that (1) energy transfers are mainly driven by export-side characteristics, with flows moving from regions with higher GDP, urbanization rates, energy supply, and energy intensity to those with lower levels, among which energy intensity is the dominant factor. (2) The total energy flow increased by 30.22% during 2015–2022, and improvements in energy intensity and industrial layout during 2020–2022 effectively moderated CF growth. (3) Scenarios in which the energy intensity of the three main exporting provinces (Inner Mongolia, Hebei, and Shanxi) was reduced by 0.2%, 0.4%, and 0.6% also showed the CF decreasing by 0.03%, 0.06%, and 0.09%, respectively. On the basis of this study, we recommend reducing energy intensity in upstream provinces through adjustments to primary energy prices and taxes, as well as establishing an ecological compensation mechanism, optimizing industrial spatial layouts, and promoting balanced urbanization.