Artificial intelligence and natural resource exploitation shaping load capacity factor in Quad economies: Role of renewable energy and green innovations

Abstract

This study examines the heterogeneous effects of artificial intelligence (AI) adoption and natural resource exploitation on load capacity factor (LCF) in the Quad economies over the period 1995–2023. After accounting for cross-sectional dependence (CD) and slope heterogeneity (SH), second-generation econometric methods are employed to estimate reliable outcomes. To capture variation across different levels of ecological capacity, the Method of Moments Quantile Regression (MMQR) is applied. The estimated findings reveal that AI adoption has a statistically significant and negative influence on LCF, with coefficients declining from −0.240 at lower quantiles to −0.305 at upper quantiles, indicating stronger adverse environmental effects at higher levels of ecological capacity. In contrast, natural resources exert a positive influence on LCF, with effects strengthening from 0.305 to 0.330 across the quantile distribution. Also, green technological innovations contribute positively to environmental sustainability, with its impact rising from 0.093 at lower quantiles to 0.249 at higher quantiles. Moreover, financial development emerges as the most influential determinant of LCF, as indicated by the largest coefficient magnitudes across quantiles, with values ranging from 1.033 to 1.406. Additionally, renewable energy displays a stronger positive relation with LCF, growing from 0.421 at the lower quantile to 0.764 at the upper quantile. These results highlight the importance of integrated strategies combining AI governance, sustainable resource management, financial expansion, and renewable energy development to enhance environmental sustainability in the Quad economies.