Can artificial intelligence enable environmental sustainability? Evidence from a resource-dependent economy

Artificial intelligence (AI) is increasingly positioned as a transformative tool for addressing environmental challenges through energy optimization, environmental monitoring, renewable-energy integration, and data-driven decision-making. Yet empirical evidence demonstrating whether growing AI capacity translates into measurable environmental improvements at the national level remains limited, particularly in resource-dependent economies. This study investigates the relationship between AI capacity, economic growth, and environmental sustainability using Qatar as an analytical case over the period 2002–2023. A composite AI capacity index is developed to capture the structural conditions enabling AI adoption through three dimensions: digital infrastructure, institutional capacity, and economic capability.

A progressive econometric framework is employed to distinguish direct, controlled, nonlinear, and indirect relationships between AI capacity and environmental performance. The baseline model initially identifies a positive and statistically significant association between AI capacity and CO₂ emissions (β = 0.168, p < 0.001). However, after controlling for economic structure, the AI effect becomes statistically insignificant (β = −0.069, p = 0.367), while GDP emerges as the dominant determinant of emissions. A nonlinear specification provides evidence of a developing Environmental Kuznets Curve, indicating that the relationship between economic growth and environmental pressure begins to flatten at high income levels.

Importantly, AI capacity exhibits a positive, although modest, association with renewable-energy consumption (β = 0.019, p ≈ 0.078), revealing a potential indirect pathway through which AI can support environmental sustainability. The findings therefore challenge technology-centric assumptions that AI adoption automatically generates environmental benefits. Instead, AI emerges as an enabling infrastructure whose environmental contribution depends on its integration with renewable energy, low-carbon infrastructure, economic diversification, and effective environmental governance. The study provides evidence for policymakers seeking to align digital transformation with environmental and energy-transition strategies in resource-dependent economies.

Keywords: Artificial Intelligence; Environmental Sustainability; CO₂ Emissions; Renewable Energy; Energy Transition; Environmental Kuznets Curve; Qatar