Bridging digital innovation and energy justice: The role of artificial intelligence in advancing energy equity

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Type of the article: Research Article

Abstract
Global progress toward universal access to affordable, reliable, and clean energy has stalled, with over two billion people still lacking access to clean cooking, and affordability pressures are rising. AI is emerging as an energy-intensive technology and a potential enabler of more equitable energy systems. This paper assesses whether AI vibrancy contributes to advancing energy equity across countries while accounting for differences in economic capacity. The study employs a balanced panel of 36 countries from 2017 to 2023 (252 observations), drawing on the Global AI Vibrancy Tool, World Bank Open Data, and the World Energy Council’s Energy Trilemma Index. Box–Cox transformations were applied to address skewness, and panel econometric models (fixed and random effects) with robust standard errors were estimated. The FE model shows no significant within-country effect of AI vibrancy on energy equity (R² = 0.012). The RE model indicates a positive association: a one-unit increase in the AI vibrancy score results in an improvement of 0.00165 in the energy equity index (p < 0.01). At the same time, GDP per capita exerts a strong and highly significant effect (p < 0.001). The RE model explains 12.4% of the overall variation in energy equity. After correcting for heteroscedasticity and cross-sectional dependence, GDP per capita remains significant, whereas the effect of AI vibrancy weakens to marginal significance (p ≈ 0.09). Country-specific effects further reveal systematic over- and under-performance beyond what AI vibrancy and income predict, underscoring the critical role of governance and institutional quality in shaping energy equity outcomes.

Acknowledgment
The article was prepared as a part of the MSCA4Ukraine project 06030419, European Union’s Horizon 2020 Research and Innovation Programme. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union, the European Research Executive Agency, or the MSCA4Ukraine Consortium. Neither the European Union nor the European Research Executive Agency, nor the MSCA4Ukraine Consortium as a whole, nor any individual member institutions of the MSCA4Ukraine Consortium can be held responsible for them.

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    • Figure 1. The Box-Cox likelihood profile for y
    • Figure 2. The Box–Cox likelihood profile for x1
    • Figure 3. The effect of applying Box–Cox transformations to the Vibrancy Score (x1) and the Energy Equity Index (y)
    • Table 1. Descriptive statistics of variables
    • Table 2. Fixed effects and random effects estimates for Energy Equity Index
    • Table 3. RE model with robust standard errors
    • Table 4. Estimated country effects from the random effects model
    • Conceptualization
      Oxana Kirichok, Yuliia Orlovska, Gulnara Zhanseitova, Alvina Oriekhova, Denys Babaiev, Oleksii Havrylenko, Tetiana Vasylieva
    • Resources
      Oxana Kirichok
    • Writing – original draft
      Oxana Kirichok, Yuliia Orlovska, Gulnara Zhanseitova, Alvina Oriekhova, Denys Babaiev, Oleksii Havrylenko, Tetiana Vasylieva
    • Writing – review & editing
      Oxana Kirichok, Yuliia Orlovska, Gulnara Zhanseitova, Alvina Oriekhova, Denys Babaiev, Oleksii Havrylenko, Tetiana Vasylieva
    • Funding acquisition
      Yuliia Orlovska
    • Visualization
      Gulnara Zhanseitova, Oleksii Havrylenko
    • Validation
      Alvina Oriekhova, Oleksii Havrylenko
    • Software
      Denys Babaiev, Oleksii Havrylenko
    • Data curation
      Oleksii Havrylenko
    • Formal Analysis
      Oleksii Havrylenko
    • Investigation
      Oleksii Havrylenko
    • Methodology
      Oleksii Havrylenko
    • Project administration
      Tetiana Vasylieva
    • Supervision
      Tetiana Vasylieva