@article{CreutzigAcemogluBaietal., author = {Creutzig, Felix and Acemoglu, Daron and Bai, Xuemei and Edwards, Paul N. and Hintz, Marie Josefine and Kaack, Lynn and Kilkis, Siir and Kunkel, Stefanie and Luers, Amy and Milojevic-Dupont, Nikola and Rejeski, Dave and Renn, J{\"u}rgen and Rolnick, David and Rosol, Christoph and Russ, Daniela and Turnbull, Thomas and Verdolini, Elena and Wagner, Felix and Wilson, Charlie and Zekar, Aicha and Zumwald, Marius}, title = {Digitalization and the Anthropocene}, series = {Annual Review of Environment and Resources}, volume = {47}, journal = {Annual Review of Environment and Resources}, doi = {10.1146/annurev-environ-120920-100056}, pages = {479 -- 509}, abstract = {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.}, language = {en} } @article{KaackDontiStrubelletal., author = {Kaack, Lynn and Donti, Priya L. and Strubell, Emma and Kamiya, George and Creutzig, Felix and Rolnick, David}, title = {Aligning artificial intelligence with climate change mitigation}, series = {Nature Climate Change}, volume = {12}, journal = {Nature Climate Change}, doi = {10.1038/s41558-022-01377-7}, pages = {518 -- 527}, abstract = {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.}, language = {en} } @article{KaackRolnickDontietal., author = {Kaack, Lynn and Rolnick, David and Donti, Priya L. and Kochanski, Kelly and Lacoste, Alexandre and Sankaran, Kris and Ross, Andrew S. and Milojevic-Dupont, Nikola and Jaques, Natasha and Waldman-Brown, Anna and Luccioni, Alexandra S. and Maharaj, Tegan and Sherwin, Evan D. and Mukkavilli, Karthik and Kording, Konrad P. and Gomes, Carla P. and Ng, Andrew Y. and Hassabis, Demis and Platt, John C. and Creutzig, Felix and Chayes, Jennifer and Bengio, Yoshua}, title = {Tackling Climate Change with Machine Learning}, series = {ACM Computing Surveys}, volume = {55}, journal = {ACM Computing Surveys}, number = {2}, doi = {10.1145/3485128}, pages = {1 -- 96}, abstract = {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.}, language = {en} }