How AI Could Slash Nearly Half of Global Emissions
Research from the London School of Economics and Political Science (LSE) and Systemiq says that AI offers significant potential in addressing climate change challenges across major industries like power, food and transport.
The research finds that AI has the potential to drastically reduce emissions, nearly half of global CO₂ contributions, by 2035 – suggesting that AI could cut global emissions by 5.4 GtCO₂e yearly, surpassing its own energy demands.
“From enabling smarter logistics to optimising energy grids, AI is already driving efficiency,” said Sophie Graham, Chief Sustainability Officer at IFS, on LinkedIn.
“But its impact goes further, equipping us with powerful advances in forecasting and early detection for severe weather events, critical to long-term resilience.
“IFS Industrial AI has a clear role to play in this transition, supporting asset-intensive industries adapting to a low-carbon future. Now is a pivotal moment to scale AI responsibly and equitably - especially where it can deliver the greatest climate value.”
Why AI matters in the climate transition
AI’s role in sustainability is pivotal due to its wide-ranging applicability and scalability.
It can function not only as a technological tool but also facilitate systemic shifts toward economic growth and emissions reduction.
The study utilises a bottom-up approach, examining AI’s potential in the power, food (specifically meat and dairy) and mobility sectors (such as light road vehicles).
In the power sector, AI-driven grid management and enhancements could reduce emissions by up to 1.8 GtCO₂e annually.
In the food sector, AI’s support in transitioning to alternative proteins might lead to 0.9-3.0 GtCO₂e in savings each year.
In transport, AI contributions to shared mobility and EV adoption could cut emissions by as much as 0.6 GtCO₂e annually.
AI in the power sector
The power sector, a major contributor to greenhouse gas emissions, can also greatly benefit from AI by integrating renewable energy optimally.
Handling the variability of solar and wind power needs precise real-time management, which AI can assist with, forecasting energy supply from renewables and matching it with demand.
Notably, Google DeepMind’s AI technology has enhanced wind energy’s value by 20% by decreasing reliance on back-up power.
Furthermore, AI improvements in operations can boost the efficiency of wind and solar plants, resulting in more emissions-free energy with reduced carbon output per generated unit.
The environmental impacts of AI
While AI presents substantial decarbonisation potential, its deployment has environmental costs.
Data centres, which are fundamental to AI operations, demand significant energy, especially for cooling.
With expanding AI usage, data centres will see increased electricity demand.
Yet, the LSE and Systemiq study suggests the emissions reductions AI could foster outweigh the additional emissions from data centre energy consumption for AI applications.
The expected surplus of reductions could range between 3.3-5.4 GtCO₂e, overshadowing the potential increase from data centre operations, predicted to be between 0.4-1.6 GtCO₂e.
More broadly, the study emphasises the need for policymakers to cultivate favourable environments for AI deployment, fund research and development and align AI applications with public good priorities.
