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 Areas[edit | edit source]
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 Readings[edit | edit source]
On the problem of climate change[edit | edit source]
- Trajectories of the Earth System in the Anthropocene (2018) : a seminal article that explores the risk that self-reinforcing feedbacks could push the Earth System toward a planetary warming.
- A safe operating space for humanity (2009)  : a paper by argue Johan Rockström and colleagues that aims to identify and quantify planetary boundaries that prevent human activities from causing unacceptable environmental change.
- Tipping elements in the Earth’s climate system (2008) : an examination of the critical threshold at which a tiny perturbation can qualitatively alter the state or development of global temperatures.
- Global-scale temperature patterns and climate forcing over the past six centuries (1998) : an article that identifies greenhouse gases as the dominant forcing during the twentieth century, resulting in the side of mean annual temperatures globally.
On how to tackle climate change[edit | edit source]
- Net-zero emissions energy systems (2018) : a primer on the potential of creating energy systems that do not add any CO2 to the atmosphere, identifying challenges and opportunities for the field.
- Sociotechnical transitions for deep decarbonization (2017) : an in-depth survey of different "sociotechnical systems" such as electricity, heat, buildings, manufacturing, and transport, etc., and their potential for acceleration of low-carbon transitions.
- Towards demand-side solutions for mitigating climate change (2018): an article that presents a multi-disciplinary approach to identify demand-side climate solutions and to assess their mitigation potential.
- Policy design for the Anthropocene (2019): a study that examines the complexities of designing policies that can keep Earth warming to a minimum.
- The economics of climate change (2008) : a complex analysis of the economics of climate change, the scientific and economic challenges that come up, and ways in which economics can be used to spur global action.
- Report of the High-Level Commission on Carbon Prices (2017) : a report aiming to identify corridors of carbon prices that can be used to guide the design of carbon-pricing instruments and other climate policies.
- Tackling Climate Change with Machine Learning (2019): a review paper identifying high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields.
Online Courses and Course Materials[edit | edit source]
- 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
Community[edit | edit source]
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 Tools[edit | edit source]
Data[edit | edit source]
References[edit | edit source]
- 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.
- 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:
- 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.
- 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:
- 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.
- 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.
- 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:
- 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:
- 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.
- Carbon Pricing Leadership Coalition (2017). "CPLC Leadership Report - 2017" (PDF). https://www.carbonpricingleadership.org/. External link in
- 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].