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Is energy aid allocated fairly? A global energy vulnerability perspective

Author

Listed:
  • Kangyin Dong

    (University of International Business and Economics [Beijing, China])

  • Qingzhe Jiang

    (University of International Business and Economics [Beijing, China])

  • Yang Liu

    (University of International Business and Economics [Beijing, China])

  • Zhiyang Shen

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique, IÉSEG School Of Management [Puteaux])

  • Michael Vardanyan

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

Abstract

The escalating climate change crisis is causing many developing countries to become more vulnerable to energy-related challenges, highlighting the issue of fairness in allocating energy aid. Given this context, it is important to ensure countries that face higher energy vulnerabilities receive a greater share of energy aid funding. This study aims to assess the impact of energy vulnerability on energy aid allocation using a double-hurdle model and a panel of 124 countries from 2002 to 2019. We rely on an index of energy vulnerability to understand the differences in the amount of aid available to different energy-vulnerable recipients, and study the transmission mechanisms underlying this relationship. Our results suggest that the allocation of energy aid has been largely fair. Specifically, energy-vulnerable countries are more likely to be selected as recipients of energy aid during the selection stage, and subsequently receive a greater share of this aid during the allocation stage. However, this effect is heterogeneous and asymmetric. Donors tend to allocate energy aid to countries characterized by relatively low quality of government, low income levels, and limited access to energy assistance. Furthermore, disparities exist in the impact of energy vulnerability on different types of energy support. Specifically, countries with higher energy vulnerability receive a greater proportion of energy policy and energy distribution aid, but are allocated a relatively small share of aid to their non-renewable energy-generation sectors. Additionally, the relationship between energy vulnerability and renewable energy generation aid is not statistically significant. Our results highlight the importance of considering energy vulnerability when allocating energy assistance, and have both scholarly and practical significance. Our findings also have important policy implications for donors by providing guidance on how to promote a fairer allocation of energy aid.

Suggested Citation

  • Kangyin Dong & Qingzhe Jiang & Yang Liu & Zhiyang Shen & Michael Vardanyan, 2024. "Is energy aid allocated fairly? A global energy vulnerability perspective," Post-Print hal-04277505, HAL.
    • Handle: RePEc:hal:journl:hal-04277505
    DOI: 10.1016/j.worlddev.2023.106409
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    Citations

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    Cited by:

    1. Wu, Sihong & Michailova, Snejina & Fan, Di, 2025. "Legitimacy under pressure: Energy firms’ expansion in countries with weak environmental performance," World Development, Elsevier, vol. 185(C).
    2. Zhao, Jun & Dong, Kangyin & Dong, Xiucheng, 2024. "How does energy poverty eradication affect global carbon neutrality?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    3. Hashemizadeh, Ali & Ju, Yanbing & Abadi, Faezeh Zareian Baghdad, 2024. "Policy design for renewable energy development based on government support: A system dynamics model," Applied Energy, Elsevier, vol. 376(PB).
    4. Nepal, Rabindra & Zhao, Xiaomeng & Liu, Yang & Dong, Kangyin, 2024. "Can green finance strengthen energy resilience? The case of China," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    5. Liu, Yang & Dong, Kangyin & Taghizadeh-Hesary, Farhad & Dong, Xiucheng, 2024. "How do minerals affect the global energy transition? Metallic versus non-metallic mineral," Resources Policy, Elsevier, vol. 92(C).
    6. Borojo, Dinkneh Gebre & Yushi, Jiang & Gong, Xueting & Zhang, Hongyu & Miao, Miao, 2024. "The heterogeneous impacts of climate finance on energy efficiency and renewable energy production in developing countries," Renewable Energy, Elsevier, vol. 236(C).
    7. Deng, Youyi & Dong, Kangyin, 2024. "How does the coal-to-gas policy mitigate carbon emissions? The role of fintech development," Resources Policy, Elsevier, vol. 89(C).
    8. Liu, Yang & Dong, Kangyin & Dong, Xiucheng & Taghizadeh-Hesary, Farhad, 2024. "Towards a sustainable electricity industry in China: An appraisal of the efficacy of environmental policies," Utilities Policy, Elsevier, vol. 86(C).
    9. Zhu, Qingyuan & Sun, Chenhao & Xu, Chengzhen & Geng, Qianqian, 2025. "The impact of artificial intelligence on global energy vulnerability," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 15-27.
    10. Liu, Yang & Wu, Ailing & Wang, Jianda & Taghizadeh-Hesary, Farhad & Dong, Xiucheng, 2024. "Green growth in the global south: How does metallic minerals affect GTFP enhancement?," Resources Policy, Elsevier, vol. 88(C).
    11. Manuel A. Zambrano-Monserrate & Gaetano Perone, 2025. "Adaptive capacity to climate change: Asymmetric effects of energy aid and governance quality," Climatic Change, Springer, vol. 178(9), pages 1-25, September.
    12. Liu, Yang & Dong, Kangyin & Nepal, Rabindra, 2025. "Can sustainable financing facilitate the energy justice transformation? Evidence from developing countries in Asia," Global Finance Journal, Elsevier, vol. 64(C).
    13. Liu, Yang & Dong, Kangyin & Nepal, Rabindra, 2024. "How does climate vulnerability affect the just allocation of climate aid funds?," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 298-317.
    14. Wang, Bo & Wang, Jianda & Dong, Kangyin & Nepal, Rabindra, 2024. "How does artificial intelligence affect high-quality energy development? Achieving a clean energy transition society," Energy Policy, Elsevier, vol. 186(C).

    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • F35 - International Economics - - International Finance - - - Foreign Aid
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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