The impacts of artificial intelligence on environmental sustainability and human well-being
Artificial intelligence (AI) is increasingly being described as a transformative general-purpose technology, yet its impacts on environmental sustainability and human well-being remain poorly understood. Here, we conduct a systematic review of 1,291 studies selected from 6,655 records to map how the literature assesses these impacts. We find that current research provides a fragmented and uneven account of AI's consequences for human well-being and the environment. Environmental studies focus narrowly on energy use and CO2 emissions (72%) and rarely consider systemic effects (11%), while well-being studies are predominantly conceptual and overlook subjective well-being, cognitive capabilities, and upstream supply-chain impacts. Strikingly, 82% of environmental studies portray AI's impacts as positive, but this finding is driven by the large number of application-level studies. Well-being analyses show a near-even split (44% positive; 46% negative). However, this split masks differences across well-being dimensions: impacts on income and health are generally expected to be positive, whereas impacts on inequality, social cohesion, and employment are expected to be negative. Based on our findings, we identify important priorities for future research: environmental assessments should consider systemic effects and indicators beyond energy and CO2, while well-being research should prioritise empirical analysis. More fundamentally, research must consider environmental sustainability and human well-being together, recognising that AI's impacts are deeply interconnected. Towards this aim, we extend an existing three-level framework for the climate impacts of AI to include broader environmental impacts and human well-being, providing a unified basis to assess AI impacts and guide its development towards environmental sustainability and human flourishing.