The impacts of artificial intelligence on environmental sustainability and human well-being

TL;DR

This systematic review analyzes 1,291 studies to assess AI's impacts on environmental sustainability and human well-being, revealing a focus on energy use, CO2 emissions, and mostly positive environmental impacts, while well-being effects are mixed and underexplored.

Contribution

It provides a comprehensive synthesis of AI's environmental and social impacts, highlighting gaps in systemic and subjective well-being assessments, and offers directions for future research.

Findings

Most environmental studies focus on energy and emissions.

Majority of impact assessments portray AI impacts as positive.

Well-being impacts are mixed, with concerns on inequality and social cohesion.

Abstract

Artificial Intelligence (AI) is changing the world, but its impacts on the environment and human well-being remain uncertain. We conducted a systematic literature review of 1,291 studies selected from 6,655 records, identifying the main impacts of AI and how they are assessed. The evidence reveals an uneven landscape: 72% of environmental studies focus narrowly on energy use and CO2 emissions, while only 11% consider systemic effects. Well-being research is largely conceptual and overlooks subjective dimensions. Strikingly, 83% of environmental studies portray AI's impacts as positive, while well-being analyses show a near-even split overall (44% positive; 46% negative). However, this split masks differences across well-being dimensions. While the impacts of AI on income and health are expected to be positive, its impacts on inequality, social cohesion, and employment are expected to be negative. Based on our findings, we suggest several areas for future research. Environmental assessments should incorporate water, material, and biodiversity impacts, and apply a full life-cycle perspective, while well-being research should prioritise empirical analyses. Evaluating AI's overall impact requires accounting for computing-related, application-level, and systemic impacts, while integrating both environmental and social dimensions. Bridging these gaps is essential to understand the full scope of AI's impacts and to steer its development towards environmental sustainability and human flourishing.