General Resources

This is the approved revision of this page, as well as being the most recent.

As described in the paper "Tackling Climate Change with Machine Learning":

Climate change is one of the greatest problems society has ever faced, with increasingly severe consequences for humanity as natural disasters multiply, sea levels rise, and ecosystems falter. While no silver bullet, machine learning (ML) can be an invaluable tool in fighting climate change via a wide array of applications and techniques. [...] Despite the growth of movements applying ML and AI to problems of societal and global good, there remains the need for a concerted effort to identify how these tools may best be applied to tackle climate change. Many ML practitioners wish to act, but are uncertain how. On the other side, many fields have begun actively seeking input from the ML community.

Machine Learning Application AreasEdit

Please refer to the specific topics and application areas listen in the pages of our Wikipedia, a list of which can be found on the Main Page.

Background ReadingsEdit

On the problem of climate changeEdit

On how to tackle climate changeEdit

Online Courses and Course MaterialsEdit

  • UN Climate Change E-Learning Portal : a central website to help countries and individuals achieve climate change action both through general climate literacy and applied skills development

CommunityEdit

Societies and organizations

  • AI, People & Planet: an initiative that aims to explore how rapid technological change like AI might both support and undermine transformations to sustainability.
  • Climate Informatics: a network of scientists working at the intersection of computer science and climate science.
  • Open Climate Fix: a non-profit research and development lab focused on reducing greenhouse gas emissions.
  • Computational Sustainability Network: a network of Interdisciplinary, multi-investigator research teams that are focusing on cross-cutting computational topics such as optimization, dynamical models, big data, machine learning, and citizen science, applied to sustainability challenges including conservation, poverty mitigation and renewable energy.
  • ClimateAction.tech: A global community of tech professionals using their skills, expertise and platforms to support solutions to the climate crisis.

Libraries and ToolsEdit

DataEdit

ReferencesEdit

  1. Steffen, Will; Rockström, Johan; Richardson, Katherine; Lenton, Timothy M.; Folke, Carl; Liverman, Diana; Summerhayes, Colin P.; Barnosky, Anthony D.; Cornell, Sarah E.; Crucifix, Michel; Donges, Jonathan F. (2018-08-14). "Trajectories of the Earth System in the Anthropocene". Proceedings of the National Academy of Sciences. 115 (33): 8252–8259. doi:10.1073/pnas.1810141115. ISSN 0027-8424. PMID 30082409.
  2. Rockström, Johan; Steffen, Will; Noone, Kevin; Persson, Åsa; Chapin, F. Stuart; Lambin, Eric F.; Lenton, Timothy M.; Scheffer, Marten; Folke, Carl; Schellnhuber, Hans Joachim; Nykvist, Björn (2009-09). "A safe operating space for humanity". Nature. 461 (7263): 472–475. doi:10.1038/461472a. ISSN 1476-4687. Check date values in: |date= (help)
  3. Lenton, T. M.; Held, H.; Kriegler, E.; Hall, J. W.; Lucht, W.; Rahmstorf, S.; Schellnhuber, H. J. (2008-02-07). "Tipping elements in the Earth's climate system". Proceedings of the National Academy of Sciences. 105 (6): 1786–1793. doi:10.1073/pnas.0705414105. ISSN 0027-8424.
  4. Mann, Michael E.; Bradley, Raymond S.; Hughes, Malcolm K. (1998-04). "Global-scale temperature patterns and climate forcing over the past six centuries". Nature. 392 (6678): 779–787. doi:10.1038/33859. ISSN 1476-4687. Check date values in: |date= (help)
  5. Davis, Steven J.; Lewis, Nathan S.; Shaner, Matthew; Aggarwal, Sonia; Arent, Doug; Azevedo, Inês L.; Benson, Sally M.; Bradley, Thomas; Brouwer, Jack; Chiang, Yet-Ming; Clack, Christopher T. M. (2018-06-29). "Net-zero emissions energy systems". Science. 360 (6396). doi:10.1126/science.aas9793. ISSN 0036-8075. PMID 29954954.
  6. Geels, Frank W.; Sovacool, Benjamin K.; Schwanen, Tim; Sorrell, Steve (2017-09-22). "Sociotechnical transitions for deep decarbonization". Science. 357: 1242–1244. doi:10.1126/science.aao3760. ISSN 1095-9203.
  7. Creutzig, Felix; Roy, Joyashree; Lamb, William F.; Azevedo, Inês M. L.; Bruine de Bruin, Wändi; Dalkmann, Holger; Edelenbosch, Oreane Y.; Geels, Frank W.; Grubler, Arnulf; Hepburn, Cameron; Hertwich, Edgar G. (2018-04). "Towards demand-side solutions for mitigating climate change". Nature Climate Change. 8 (4): 260–263. doi:10.1038/s41558-018-0121-1. ISSN 1758-6798. Check date values in: |date= (help)
  8. Sterner, Thomas; Barbier, Edward B.; Bateman, Ian; van den Bijgaart, Inge; Crépin, Anne-Sophie; Edenhofer, Ottmar; Fischer, Carolyn; Habla, Wolfgang; Hassler, John; Johansson-Stenman, Olof; Lange, Andreas (2019-01). "Policy design for the Anthropocene". Nature Sustainability. 2 (1): 14–21. doi:10.1038/s41893-018-0194-x. ISSN 2398-9629. Check date values in: |date= (help)
  9. Stern, Nicholas (2008-04-01). "The Economics of Climate Change". American Economic Review. 98 (2): 1–37. doi:10.1257/aer.98.2.1. ISSN 0002-8282.
  10. Carbon Pricing Leadership Coalition (2017). "CPLC Leadership Report - 2017" (PDF). https://www.carbonpricingleadership.org/. External link in |website= (help)
  11. Rolnick, David; Donti, Priya L.; Kaack, Lynn H.; Kochanski, Kelly; Lacoste, Alexandre; Sankaran, Kris; Ross, Andrew Slavin; Milojevic-Dupont, Nikola; Jaques, Natasha; Waldman-Brown, Anna; Luccioni, Alexandra (2019-11-05). "Tackling Climate Change with Machine Learning". arXiv:1906.05433 [cs, stat].