Green and intelligent: the role of AI in the climate transition

Artificial Intelligence (AI) can play a powerful role in supporting climate action while boosting sustainable and inclusive economic growth. However, limited research exists on the potential influence of AI on the low-carbon transition.

The authors of this paper identify five impact areas through which AI can help build an effective response to climate threats:

1. Transforming complex systems
2. Innovating technology discovery and resource efficiency
3. Nudging and behavioural change
4. Modelling climate systems and policy interventions
5. Managing resilience and adaptation.

Examples of how AI can be utilised in supporting climate action include:

• The application of AI models to better design and implement policies for climate action, by generating insights and predictions around complex climate policy scenarios or monitoring the effectiveness of policy implementation.
• Supporting long-term resilience and adaptation through its ability to create large-scale simulations tracking how ecosystems might evolve.
• Improving early warning systems for extreme weather events, such as floods and wildfires, enabling governments and communities to take proactive measures to mitigate damage, saving lives and significant costs.
• Using AI to better predict investment risks and returns, improving financial decisions where information is scarcer, particularly in emerging markets where perceived risk is high, often due to limited and asymmetric information.

The authors also estimate the potential for greenhouse gas emission reductions through AI applications in three key sectors – power, food and mobility – which collectively contribute nearly half of global emissions. They find that advancements in AI in power, transport and food consumption could reduce global emissions of greenhouse gases by 3.2 to 5.4 billion tonnes of carbon-dioxide-equivalent annually by 2035. When compared with the increase in data centre-related emissions generated by all AI-related activities (not just those related to decarbonisation), the authors find that the estimated emissions reductions in only these three sectors would outweigh increases from global power consumption of data centres and AI.

In the power sector, AI can improve the efficiency of renewable energy systems by optimising grid management and increasing the load factor of solar photovoltaics and wind by as much as 20%. AI could also improve the adoption rates of alternative proteins (APs) from 8–14% in a ‘business as usual’ scenario to 18–33% in an ambitious AI scenario, and 27–50% in a highly ambitious AI scenario. These sectors are highly interconnected, hence accelerating the adoption and efficiency of low-carbon solutions will no doubt trigger technological tipping points elsewhere, resulting in cascading effects across the economy.

However, letting markets alone determine the applications and governance of AI could prove to be risky. Governments have a critical role in ensuring that AI is deployed effectively to accelerate the transition equitably and sustainably. The concept of the ‘active state’ is central to this transformation, as market forces alone may not be sufficient to drive the scale of change required and unlock the full potential of AI. Public intervention is particularly important in addressing the potential risks associated with AI, such as increased energy consumption and the exacerbation of inequalities between developed and developing countries.

The authors conclude that by fostering innovation, directing investment, and promoting international cooperation, governments can ensure that AI improves, rather than hinders, action on climate change. AI can be part of the clean growth story of the future, improve adaptation and resilience and make people happier if channelled correctly.