TY - JOUR

T1 - Artificial Intelligence for Alleviating Energy Poverty: Pathways Toward Sustainable and Renewable Energy Transitions

AU - Wang, Q.

AU - Sun, T.

AU - Li, R.

N1 - Export Date: 29 March 2026; Cited By: 9; Correspondence Address: Q. Wang; School of Economics and Management, China University of Petroleum (East China), Qingdao, China; email: wangqiang7@upc.edu.cn; R. Li; School of Economics and Management, China University of Petroleum (East China), Qingdao, China; email: lirr@upc.edu.cn

PY - 2026

Y1 - 2026

N2 - Energy poverty remains a critical barrier to sustainable development, particularly in underdeveloped and emerging economies. This study investigates how artificial intelligence (AI) can alleviate energy poverty and promote the transition to renewable energy. Using panel data from 89 countries covering the period 2010–2022, we developed a comprehensive AI index through a projection pursuit model optimized by the sparrow search algorithm. Fixed-effects models reveal that a 1% rise in AI development reduces energy poverty by 0.461% on average. The effect is strongest in countries with moderate poverty but weaker in those with very low or very high levels. Grouped regressions show that AI may worsen energy poverty in low- and middle-income nations, and significantly relieve it in high-income ones. Furthermore, nonlinear threshold effects indicate that AI's benefits strengthen when coupled with renewable energy transition and sound governance but decline in resource-dependent economies. Overall, this study highlights AI's potential to advance equitable, sustainable energy systems and contributes to achieving Sustainable Development Goal 7. Policymakers should therefore promote inclusive AI infrastructure, expand renewable energy integration, and ensure that technological progress aligns with equitable energy access goals. © 2025 ERP Environment and John Wiley & Sons Ltd.

AB - Energy poverty remains a critical barrier to sustainable development, particularly in underdeveloped and emerging economies. This study investigates how artificial intelligence (AI) can alleviate energy poverty and promote the transition to renewable energy. Using panel data from 89 countries covering the period 2010–2022, we developed a comprehensive AI index through a projection pursuit model optimized by the sparrow search algorithm. Fixed-effects models reveal that a 1% rise in AI development reduces energy poverty by 0.461% on average. The effect is strongest in countries with moderate poverty but weaker in those with very low or very high levels. Grouped regressions show that AI may worsen energy poverty in low- and middle-income nations, and significantly relieve it in high-income ones. Furthermore, nonlinear threshold effects indicate that AI's benefits strengthen when coupled with renewable energy transition and sound governance but decline in resource-dependent economies. Overall, this study highlights AI's potential to advance equitable, sustainable energy systems and contributes to achieving Sustainable Development Goal 7. Policymakers should therefore promote inclusive AI infrastructure, expand renewable energy integration, and ensure that technological progress aligns with equitable energy access goals. © 2025 ERP Environment and John Wiley & Sons Ltd.

KW - artificial intelligence

KW - energy poverty

KW - governance

KW - natural resource dependence

KW - renewable energy transition

KW - SDG 7

KW - sustainable development

KW - alternative energy

KW - energy resource

KW - governance approach

KW - natural resource

KW - Sustainable Development Goal

UR - https://www.mendeley.com/catalogue/3cf6118a-8e9e-3336-9412-344658d53a89/

U2 - 10.1002/sd.70388

DO - 10.1002/sd.70388

M3 - статья

VL - 34

SP - 1324

EP - 1347

JO - Sustainable Development

JF - Sustainable Development

SN - 0968-0802

IS - S2

ER -