The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations

2021/10/18 by Josh Cowls, Andreas Tsamados, Mariarosaria Taddeo +1 · 4 citations
Economics, Econometrics and Finance · Energy · Environmental Science · #Air Quality Monitoring and Forecasting #Climate Change Policy and Economics #Energy, Environment, and Transportation Policies

paper · pdf · doi:10.1007/s00146-021-01294-x

openalex publication_date 2021/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI's greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based, and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combatting climate change, while reducing its impact on the environment.

Citations

• ImageNet Large Scale Visual Recognition Challenge
• MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
• Neural Architecture Search with Reinforcement Learning
• Efficient Neural Architecture Search via Parameter Sharing
• Efficient Processing of Deep Neural Networks: A Tutorial and Survey
• Accelerating Deep Learning by Focusing on the Biggest Losers
• Quantifying the Carbon Emissions of Machine Learning
• Dota 2 with Large Scale Deep Reinforcement Learning
• MetNet: A Neural Weather Model for Precipitation Forecasting
• The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research
• On the Dangers of Stochastic Parrots
• Enabling reproducible ML and Systems research: the good, the bad, and the ugly

Cited by

• Quantifying the Climate Risk of Generative AI: Region-Aware Carbon Accounting with G-TRACE and the AI Sustainability Pyramid
• The Transhumanist Anthropocene: From the climate crisis to upgrading humanity
• The anatomy of Green AI technologies: structure, evolution, and impact
• GPT (Generative Pre-Trained Transformer)— A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions

Related

• Beyond AI as an environmental pharmakon: Principles for reopening the problem-space of machine learning's carbon footprint
• Dreaming of AI: environmental sustainability and the promise of participation
• Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges
• AI and Global Climate Change: The political economy of data and energy in geographic perspective
• AI for a Planet Under Pressure