Negative Emissions Technologies
This page is about the intersection of negative emissions technologies and machine learning in the context of climate change mitigation. For an overview of carbon dioxide removal as a whole, please see the Wikipedia page on this topic.
As described in "Tackling Climate Change with Machine Learning,"
Even if we could cut emissions to zero today, we would still face significant climate consequences from greenhouse gases already in the atmosphere. Eliminating emissions entirely may also be tricky, given the sheer diversity of sources (such as airplanes and cows). Instead, many experts argue that to meet critical climate goals, global emissions must become net-negative—that is, we must remove more CO2 from the atmosphere than we release [444, 445]. Although there has been significant progress in negative emissions research [446–450], the actual CO2 removal industry is still in its infancy. As such, many of the ML applications we outline in this section are either speculative or in the early stages of development or commercialization. Some of the most commonly known negative emissions technologies include nature-based solutions such as afforestation (growing more trees and storing carbon in this biomass) and regenerative farming practices (discussed further in <afolu>) as well as highly engineered technologies such as direct air capture (DAC) with sequestration of the captured CO2 in underground geologic formations. Another commonly discussed negative emissions technology is biomass combustion with carbon capture and sequestration, described further in <electricity systems>.
- 1 Machine Learning Application Areas
- 2 Background Readings
- 3 Online Courses and Course Materials
- 4 Community
- 5 Libraries and Tools
- 6 Data
- 7 References
Machine Learning Application Areas[edit | edit source]
Understanding and improving aerosols[edit | edit source]
Engineering a planetary control system[edit | edit source]
Modeling impacts[edit | edit source]
Background Readings[edit | edit source]
- "Geoengineering the climate: History and prospect" (2000): An early review of solar geoengineering and climate change.
- "Solar Geoengineering Governance" (2018): An climate science encyclopedia entry on governance of solar geoengineering systems.
- "An overview of the Earth system science of solar geoengineering" (2016): A modern overview of solar geoengineering.
Online Courses and Course Materials[edit | edit source]
Community[edit | edit source]
Major conferences[edit | edit source]
- AGU Fall Meeting: A yearly conference organized by the American Geophysical Union. Website here.
- Climate Engineering Conference: Website here.
Major journals[edit | edit source]
- Atmospheric Chemistry and Physics: A journal of the European Geosciences Union dedicated to atmospheric chemical and physical processes. Website here.
- Earth's Future: A journal of the American Geophysical Union dedicated to the state and future of the earth. Website here.
Major societies and organizations[edit | edit source]
Libraries and Tools[edit | edit source]
- The Geoengineering Model Intercomparison Project: A gateway to solar geoengineering simulators, available here.
Data[edit | edit source]
References[edit | edit source]
- 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].
- Keith, David (2000). "Geoengineering the climate: History and prospect". Annual review of energy and the environment. 25.
- Sugiyama, Masahiro; Ishii, Atsushi; Asayama, Shinichiro; Kosugi, Takanobu (2018-04-26). "Solar Geoengineering Governance". Oxford Research Encyclopedia of Climate Science. doi:10.1093/acrefore/9780190228620.013.647.
- Irvine, Peter J.; Kravitz, Ben; Lawrence, Mark G.; Muri, Helene (2016-07-14). "An overview of the Earth system science of solar geoengineering". Wiley Interdisciplinary Reviews: Climate Change. 7 (6): 815–833. doi:10.1002/wcc.423. ISSN 1757-7780.