Artificial intelligence’s (AI) climate benefits risk being overstated as surging data centre emissions and water consumption threaten to outpace efficiency gains, says a new report from GlobalData.
While predictive AI can deliver measurable reductions across energy, agriculture, and buildings, the environmental cost of generative AI remains substantial, with limited evidence that its climate benefits can offset its footprint.
GlobalData’s latest report – Artificial Intelligence for Climate Mitigation – reveals that AI can support targeted emissions-reduction initiatives, but it will not fix the climate crisis on its own. The report warns that the surge of carbon emissions and water consumption from AI development and use threatens to outpace the efficiency gains delivered by the technology.
Aoife McGurk, senior analyst in GlobalData Strategic Intelligence, comments: “AI can support climate mitigation, but it will never be the whole solution. For AI technology to have a net positive impact on the planet, the efficiencies it generates must outweigh the environmental harm caused by data centres and inference.”
While AI advocates point to potential efficiency gains across energy networks, many high-profile assertions about generative AI’s climate mitigation potential lack proper, peer-reviewed evidence. GlobalData emphasises that where there are proven climate benefits from AI, they stem overwhelmingly from traditional predictive AI rather than power-hungry generative AI or large frontier models.
McGurk continues: “Predictive AI can help improve efficiency and reduce resource use across key sectors, through renewable grid optimisation, sustainable agriculture, and building energy use management. Generative AI, on the other hand, produces significantly higher emissions, and there is little evidence that it mitigates climate change.”
GlobalData specifically cautions companies against leaning on advanced generative AI tools (such as large language models) for corporate sustainability strategies.
Beyond its heavy carbon and water footprints, generative AI introduces significant compliance and reputational hazards. Relying on generative systems for reporting and compliance creates risk of greenwashing if models hallucinate emissions data or sustainability policies.
Rather than viewing AI as an automatic climate mitigation enabler, GlobalData advises business leaders to assess the AI use case on two rigorous criteria: does the AI deployment deliver a tangible reduction in greenhouse gas (GHG) emissions or ecosystem degradation; and will the environmental harm caused by this AI deployment be less than its intended benefit? If the answer to either question is no, the AI deployment risks either being ineffective or having a net negative impact on climate mitigation efforts.
McGurk concludes: “If a deployment of generative or agentic AI would deliver significant climate mitigation benefits, companies should do what they can to limit the environmental harm caused by using these systems. Strict safeguards – such as using small language models, automatic model triage, carbon-aware computing, edge infrastructure, emissions budgets, algorithmic efficiency improvements, and novel data centre cooling – can prevent AI-enabled ESG strategies from inadvertently accelerating the climate crisis.”