Aligning artificial intelligence with climate change mitigation

Citation: Kaack, L. H., Donti, P. L., Strubell, E., Kamiya, G., Creutzig, F., & Rolnick, D. (2022). Aligning artificial intelligence with climate change mitigation. Nature Climate Change, 12, 518–527. https://doi.org/10.1038/s41558-022-01377-7Main Takeaways:Three Layers of AI's Climate Footprint: The authors propose a framework that splits machine learning's climate impact into three distinct categories — the energy and hardware emissions of computing itself, the immediate effects of specific ML applications, and the broader system-level changes that ML induces across society. The categories that are easiest to measure (like the electricity used to train a model) are likely not the ones with the largest effects, which is why most current discussions of "AI and climate" capture only a sliver of the real picture.Computing Is a Small Slice — For Now: The entire global ICT sector accounts for roughly 1.4% of global greenhouse gas emissions, and AI workloads are only a fraction of that. But the trajectory is steep: at Facebook, ML training compute has been growing about 150% per year and inference compute about 105% per year, far outpacing efficiency gains. Even striking efficiency wins — like Google's TPU being 30–80 times more energy-efficient than contemporary CPUs or GPUs — can be swamped by raw growth in demand.The "Internet of Cows" Problem: ML is a general-purpose tool, which means it's just as good at accelerating oil and gas exploration or scaling up cattle farming (an industry already responsible for about 9% of global emissions) as it is at forecasting solar power or optimizing data center cooling. Whether AI is net-positive or net-negative for the climate is genuinely undetermined, and depends on which applications get funded, deployed, and regulated.System-Level Effects May Dwarf Everything Else: The largest climate impacts of AI may come not from training runs or even individual applications, but from how ML reshapes society — through rebound effects (efficiency gains that drive more consumption), technological lock-in (autonomous cars entrenching private vehicle travel over transit and rail), and ML-powered recommender systems that boost demand for emissions-intensive goods. These effects are the hardest to quantify but potentially the most consequential, and the authors argue they need to be built into climate scenario modeling — something the IEA, EIA, and IPCC's Shared Socioeconomic Pathways largely don't do today.