Fossil-fuel dependence and sustainable development: quantile evidence on AI, renewable energy, and genuine savings

This study investigates how fossil fuel consumption, artificial intelligence (AI), renewable energy, and genuine savings shape sustainable development in 20 highly fossil-fuel-dependent economies from 1990 to 2022. Moving beyond the conventional focus on environmental degradation, it evaluates sustainable development through the Sustainable Development Index, which measures human development relative to ecological overshoot. The study employs the method of moments quantile regression to identify how these relationships vary across the Sustainable Development Index distribution. It also examines whether the fossil fuel–sustainable development association varies with genuine savings and the renewable energy–sustainable development association with AI capability. The results show that fossil fuel consumption impedes sustainable development across all quantiles, with a stronger adverse effect at higher quantiles. Conversely, AI is positively associated with sustainable development, particularly at lower and middle quantiles, while genuine savings significantly weakens the detrimental effect of fossil fuel dependence. Renewable energy shows a negative direct association with the SDI, which is the effect at low AI levels in the interaction model; its interaction with AI is positive and statistically significant, indicating that the association of renewable energy with sustainable development is more favorable where AI is higher. The results are robust to the global financial crisis and to bidirectional causality; the AI–renewable energy interaction is more sensitive to the identification strategy. The results suggest that expanding renewable energy alone may be insufficient; reducing fossil fuel dependence, strengthening AI-enabled renewable systems, and improving genuine savings are jointly associated with more favorable sustainable development outcomes.