The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards.

The 2030 Agenda for Sustainable Development—including 17 interconnected Sustainable Development Goals (SDGs) and 169 targets—is a global plan of action for people, planet and prosperity. SDG7 calls for action to ensure access to affordable, reliable, sustainable and modern energy for all. Here we characterize synergies and trade-offs between efforts to achieve SDG7 and delivery of the 2030 Agenda as a whole. We identify 113 targets requiring actions to change energy systems, and published evidence of relationships between 143 targets (143 synergies, 65 trade-offs) and efforts to achieve SDG7. Synergies and trade-offs exist in three key domains, where decisions about SDG7 affect humanity’s ability to: realize aspirations of greater welfare and well-being; build physical and social infrastructures for sustainable development; and achieve sustainable management of the natural environment. There is an urgent need to better organize, connect and extend this evidence, to help all actors work together to achieve sustainable development.

Energy access metrics are needed to track the progress towards providing sustainable energy for all. This paper presents advancements in the development of the Multidimensional Energy Poverty Index (MEPI), as well as results and analysis for a number of developing countries. The MEPI is a composite index designed to shed light on energy poverty by assessing the services that modern energy provides. The index captures both the incidence and intensity of energy poverty. It provides valuable insights–allowing the analysis of determinants of energy poverty–and, subsequently insights into policy efficacy. Building on previous work, this paper presents results obtained as a result of both increased data availability and enhanced methodology. Specifically, this analysis (i) includes an increased number of countries, and (ii) tracks the evolution of energy poverty over time of energy poverty in selected countries is reported.

The rapidly increasing demand for generative artificial intelligence (AI) models requires extensive server installation with sustainability implications in terms of the compound energy–water–climate impacts. Here we show that the deployment of AI servers across the United States could generate an annual water footprint ranging from 731 to 1,125 million m³ and additional annual carbon emissions from 24 to 44 Mt CO₂-equivalent between 2024 and 2030, depending on the scale of expansion. Other factors, such as industry efficiency initiatives, grid decarbonization rates and the spatial distribution of server locations within the United States, drive deep uncertainties in the estimated water and carbon footprints. We show that the AI server industry is unlikely to meet its net-zero aspirations by 2030 without substantial reliance on highly uncertain carbon offset and water restoration mechanisms. Although best practices may reduce emissions and water footprints by up to 73% and 86%, respectively, their effectiveness is constrained by current energy infrastructure limitations. These findings underscore the urgency of accelerating the energy transition and point to the need for AI companies to harness the clean energy potential of Midwestern states. Coordinating efforts of private actors and regulatory interventions would ensure the competitive and sustainable development of the AI sector.