TY - JOUR A1 - Creutzig, Felix A1 - Acemoglu, Daron A1 - Bai, Xuemei A1 - Edwards, Paul N. A1 - Hintz, Marie Josefine A1 - Kaack, Lynn A1 - Kilkis, Siir A1 - Kunkel, Stefanie A1 - Luers, Amy A1 - Milojevic-Dupont, Nikola A1 - Rejeski, Dave A1 - Renn, Jürgen A1 - Rolnick, David A1 - Rosol, Christoph A1 - Russ, Daniela A1 - Turnbull, Thomas A1 - Verdolini, Elena A1 - Wagner, Felix A1 - Wilson, Charlie A1 - Zekar, Aicha A1 - Zumwald, Marius T1 - Digitalization and the Anthropocene JF - Annual Review of Environment and Resources N2 - Great claims have been made about the benefits of dematerialization in a digital service economy. However, digitalization has historically increased environmental impacts at local and planetary scales, affecting labor markets, resource use, governance, and power relationships. Here we study the past, present, and future of digitalization through the lens of three interdependent elements of the Anthropocene: (a) planetary boundaries and stability, (b) equity within and between countries, and (c) human agency and governance, mediated via (i) increasing resource efficiency, (ii) accelerating consumption and scale effects, (iii) expanding political and economic control, and (iv) deteriorating social cohesion. While direct environmental impacts matter, the indirect and systemic effects of digitalization are more profoundly reshaping the relationship between humans, technosphere and planet. We develop three scenarios: planetary instability, green but inhumane, and deliberate for the good. We conclude with identifying leverage points that shift human–digital–Earth interactions toward sustainability. Y1 - 2022 U6 - https://doi.org/10.1146/annurev-environ-120920-100056 VL - 47 SP - 479 EP - 509 ER - TY - JOUR A1 - Kaack, Lynn A1 - Donti, Priya L. A1 - Strubell, Emma A1 - Kamiya, George A1 - Creutzig, Felix A1 - Rolnick, David T1 - Aligning artificial intelligence with climate change mitigation JF - Nature Climate Change N2 - There is great interest in how the growth of artificial intelligence and machine learning may affect global GHG emissions. However, such emissions impacts remain uncertain, owing in part to the diverse mechanisms through which they occur, posing difficulties for measurement and forecasting. Here we introduce a systematic framework for describing the effects of machine learning (ML) on GHG emissions, encompassing three categories: computing-related impacts, immediate impacts of applying ML and system-level impacts. Using this framework, we identify priorities for impact assessment and scenario analysis, and suggest policy levers for better understanding and shaping the effects of ML on climate change mitigation. Y1 - 2022 U6 - https://doi.org/10.1038/s41558-022-01377-7 VL - 12 SP - 518 EP - 527 ER - TY - JOUR A1 - Kaack, Lynn A1 - Rolnick, David A1 - Donti, Priya L. A1 - Kochanski, Kelly A1 - Lacoste, Alexandre A1 - Sankaran, Kris A1 - Ross, Andrew S. A1 - Milojevic-Dupont, Nikola A1 - Jaques, Natasha A1 - Waldman-Brown, Anna A1 - Luccioni, Alexandra S. A1 - Maharaj, Tegan A1 - Sherwin, Evan D. A1 - Mukkavilli, Karthik A1 - Kording, Konrad P. A1 - Gomes, Carla P. A1 - Ng, Andrew Y. A1 - Hassabis, Demis A1 - Platt, John C. A1 - Creutzig, Felix A1 - Chayes, Jennifer A1 - Bengio, Yoshua T1 - Tackling Climate Change with Machine Learning JF - ACM Computing Surveys N2 - Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change. Y1 - 2022 UR - https://dl.acm.org/doi/10.1145/3485128 U6 - https://doi.org/10.1145/3485128 VL - 55 IS - 2 SP - 1 EP - 96 ER -