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''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 [https://en.wikipedia.org/wiki/Carbon_dioxide_removal Wikipedia page] on this topic.''
''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 [https://en.wikipedia.org/wiki/Carbon_dioxide_removal Wikipedia page] on this topic.''


Negative Emission Technologies (NETs), often referred to as Carbon Dioxide Removal (CDR), aim to artificially remove carbon dioxide (CO<sub>2</sub>) from the atmospere<ref>{{Cite journal|last=Minx|first=Jan C|last2=Lamb|first2=William F|last3=Callaghan|first3=Max W|last4=Fuss|first4=Sabine|last5=Hilaire|first5=Jérôme|last6=Creutzig|first6=Felix|last7=Amann|first7=Thorben|last8=Beringer|first8=Tim|last9=de Oliveira Garcia|first9=Wagner|last10=Hartmann|first10=Jens|last11=Khanna|first11=Tarun|date=2018-05-21|title=Negative emissions—Part 1: Research landscape and synthesis|url=https://iopscience.iop.org/article/10.1088/1748-9326/aabf9b|journal=Environmental Research Letters|language=en|volume=13|issue=6|pages=063001|doi=10.1088/1748-9326/aabf9b|issn=1748-9326}}</ref>, in addition to the natural removal of the atmospheric CO<sub>2</sub> by the natural carbon sinks (such as land and ocean)<ref>IPCC, 2018: Annex I: Glossary [Matthews, J.B.R. (ed.)]. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In Press</ref>. NETs are not a substitution for climate mitigation and reducing global emission rate, but can be used together with mitigation efforts to speed up the reduction of emissions and reaching the net-zero emission targets sooner, depending on the [[Emission scenarios|emission scenario]]. The mitigation pathways consistent with reaching the 1.5 °C target (reported by the IPCC Special Report on 1.5 Degrees<ref name=":3">Rogelj, J., D. Shindell, K. Jiang, S. Fifita, P. Forster, V. Ginzburg, C. Handa, H. Kheshgi, S. Kobayashi, E. Kriegler, L. Mundaca, R. Séférian, and M.V. Vilariño, 2018: Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)].</ref>) entail low to moderate levels of CDR (up to 1000 PgC removed; IPCC SR1.5 Chapter 2<ref>Rogelj, J., D. Shindell, K. Jiang, S. Fifita, P. Forster, V. Ginzburg, C. Handa, H. Kheshgi, S. Kobayashi, E. Kriegler, L. Mundaca, R. Séférian, and M.V. Vilariño, 2018: Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)].</ref>). NETs are also an underlying assumption of overshoot scenarios -where a given temperature target is temporarily exceeded and then returned to with the aid of negative emission. While global mean temperature has shown to be largely reversible in response to artifical carbon dioxide removal, other components of climate change (such as sea-level rise, ocean acidification, and other terrestrial and marine ecosystem changes) are not easily reversible on human time-scales, even if extremely large amounts of NETs were implemented<ref>{{Cite journal|last=Jones|first=C D|last2=Ciais|first2=P|last3=Davis|first3=S J|last4=Friedlingstein|first4=P|last5=Gasser|first5=T|last6=Peters|first6=G P|last7=Rogelj|first7=J|last8=van Vuuren|first8=D P|last9=Canadell|first9=J G|last10=Cowie|first10=A|last11=Jackson|first11=R B|date=2016-09-01|title=Simulating the Earth system response to negative emissions|url=https://doi.org/10.1088/1748-9326/11/9/095012|journal=Environmental Research Letters|language=en|volume=11|issue=9|pages=095012|doi=10.1088/1748-9326/11/9/095012|issn=1748-9326}}</ref><ref>{{Cite journal|last=Tokarska|first=Katarzyna B|last2=Zickfeld|first2=Kirsten|date=2015-09-01|title=The effectiveness of net negative carbon dioxide emissions in reversing anthropogenic climate change|url=https://doi.org/10.1088/1748-9326/10/9/094013|journal=Environmental Research Letters|language=en|volume=10|issue=9|pages=094013|doi=10.1088/1748-9326/10/9/094013|issn=1748-9326}}</ref><ref>{{Cite journal|last=Hofmann|first=M.|last2=Mathesius|first2=S.|last3=Kriegler|first3=E.|last4=Vuuren|first4=D. P. van|last5=Schellnhuber|first5=H. J.|date=2019-12-06|title=Strong time dependence of ocean acidification mitigation by atmospheric carbon dioxide removal|url=https://www.nature.com/articles/s41467-019-13586-4|journal=Nature Communications|language=en|volume=10|issue=1|pages=5592|doi=10.1038/s41467-019-13586-4|issn=2041-1723}}</ref>.
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 CO<sub>2</sub> from the atmosphere than we release <ref>{{Cite journal|last=Fuss|first=Sabine|last2=Canadell|first2=Josep G.|last3=Peters|first3=Glen P.|last4=Tavoni|first4=Massimo|last5=Andrew|first5=Robbie M.|last6=Ciais|first6=Philippe|last7=Jackson|first7=Robert B.|last8=Jones|first8=Chris D.|last9=Kraxner|first9=Florian|last10=Nakicenovic|first10=Nebosja|last11=Le Quéré|first11=Corinne|date=2014-10|title=Betting on negative emissions|url=https://www.nature.com/articles/nclimate2392|journal=Nature Climate Change|language=en|volume=4|issue=10|pages=850–853|doi=10.1038/nclimate2392|issn=1758-6798}}</ref><ref>{{Cite journal|last=Gasser|first=T.|last2=Guivarch|first2=C.|last3=Tachiiri|first3=K.|last4=Jones|first4=C. D.|last5=Ciais|first5=P.|date=2015-08-03|title=Negative emissions physically needed to keep global warming below 2 °C|url=https://www.nature.com/articles/ncomms8958|journal=Nature Communications|language=en|volume=6|issue=1|pages=1–7|doi=10.1038/ncomms8958|issn=2041-1723}}</ref>. Although there has been significant progress in negative emissions research <ref name=":0">{{Cite book|last=National Academies of Sciences|first=Engineering|url=https://www.nap.edu/catalog/25259/negative-emissions-technologies-and-reliable-sequestration-a-research-agenda|title=Negative Emissions Technologies and Reliable Sequestration: A Research Agenda|date=2018-10-24|isbn=978-0-309-48452-7|language=en}}</ref><ref name=":1">{{Cite web|last=ICEF|first=|date=|title=Direct Air Capture of Carbon Dioxide: ICEF Roadmap 2018|url=https://www.icef-forum.org/|url-status=live|archive-url=|archive-date=|access-date=2020-09-12|website=www.icef-forum.org}}</ref><ref>{{Cite web|title=ShieldSquare Captcha|url=http://stacks.iop.org/1748-9326/13/i=6/a=063001?key=crossref.9b8e1db79e5bb89326008b4b6859ede0|access-date=2020-09-12|website=stacks.iop.org|language=en}}</ref><ref>{{Cite journal|last=Fuss|first=Sabine|last2=Lamb|first2=William F.|last3=Callaghan|first3=Max W.|last4=Hilaire|first4=Jérôme|last5=Creutzig|first5=Felix|last6=Amann|first6=Thorben|last7=Beringer|first7=Tim|last8=Garcia|first8=Wagner de Oliveira|last9=Hartmann|first9=Jens|last10=Khanna|first10=Tarun|last11=Luderer|first11=Gunnar|date=2018-05|title=Negative emissions—Part 2: Costs, potentials and side effects|url=https://doi.org/10.1088%2F1748-9326%2Faabf9f|journal=Environmental Research Letters|language=en|volume=13|issue=6|pages=063002|doi=10.1088/1748-9326/aabf9f|issn=1748-9326}}</ref><ref>{{Cite web|title=ShieldSquare Captcha|url=http://stacks.iop.org/1748-9326/13/i=6/a=063003?key=crossref.a329c88fc7b90b61b136cf0c66c67240|access-date=2020-09-12|website=stacks.iop.org|language=en}}</ref>, the actual CO<sub>2</sub> 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.
[[File:NETs hr.jpg|alt=|thumb|Different groups of negative emission technologies<ref>{{Cite journal|last=Minx|first=Jan C|last2=Lamb|first2=William F|last3=Callaghan|first3=Max W|last4=Bornmann|first4=Lutz|last5=Fuss|first5=Sabine|date=2017-03-01|title=Fast growing research on negative emissions|url=https://iopscience.iop.org/article/10.1088/1748-9326/aa5ee5|journal=Environmental Research Letters|language=en|volume=12|issue=3|pages=035007|doi=10.1088/1748-9326/aa5ee5|issn=1748-9326}}</ref> (Source: Figure 1 from Jan C Minx ''et al'' 2017 ''Environ. Res. Lett.'' '''12''' 035007)]]
Different groups of negative emission technologies<ref>{{Cite journal|last=Minx|first=Jan C|last2=Lamb|first2=William F|last3=Callaghan|first3=Max W|last4=Bornmann|first4=Lutz|last5=Fuss|first5=Sabine|date=2017-03-01|title=Fast growing research on negative emissions|url=https://iopscience.iop.org/article/10.1088/1748-9326/aa5ee5|journal=Environmental Research Letters|volume=12|issue=3|pages=035007|doi=10.1088/1748-9326/aa5ee5|issn=1748-9326}}</ref>:


* [[Direct Air Capture]] (DAC) with sequestration of the captured CO<sub>2</sub> in underground geologic formations.
Some of the most commonly known negative emissions technologies include nature-based solutions such as [[Forestry and Other Land Use|afforestation]] (growing more trees and storing carbon in this biomass) and [[Agriculture|regenerative farming]] practices as well as highly engineered technologies such as direct air capture (DAC) with sequestration of the captured CO<sub>2</sub> in underground geologic formations. Another commonly discussed negative emissions technology is biomass combustion with carbon capture and sequestration, described further in [[Electricity Systems]].
* [[Bioenergy carbon capture and sequestration]] (BECCS)
* [[Biochar and soil carbon sequestration]] (SDS)
* [[Forestry and Other Land Use|Afforestation]] and reforestation (growing more trees and storing carbon in this biomass)
* [[Agriculture|Regenerative farming]] practices
* [[Enhanced weathering]]
* [[Ocean fertilisation]]
Many DAC technologies are in early stages of commercialization<ref name=":0">{{Cite book|last=National Academies of Sciences|first=Engineering|url=https://www.nap.edu/catalog/25259/negative-emissions-technologies-and-reliable-sequestration-a-research-agenda|title=Negative Emissions Technologies and Reliable Sequestration: A Research Agenda|date=2018-10-24|isbn=978-0-309-48452-7|language=en}}</ref><ref name=":1">{{Cite web|last=ICEF|first=|date=|title=Direct Air Capture of Carbon Dioxide: ICEF Roadmap 2018|url=https://www.icef-forum.org/|url-status=live|archive-url=|archive-date=|access-date=2020-09-12|website=www.icef-forum.org}}</ref>, though there is still large uncertainty regarding geological storage of captured CO<sub>2</sub> on long time-scales, and deployment of negative emission technologies on large-scales and in a sustainable way is unlikely<ref>{{Cite journal|last=Minx|first=Jan C.|last2=Lamb|first2=William F.|last3=Callaghan|first3=Max W.|last4=Fuss|first4=Sabine|last5=Hilaire|first5=Jérôme|last6=Creutzig|first6=Felix|last7=Amann|first7=Thorben|last8=Beringer|first8=Tim|last9=Garcia|first9=Wagner de Oliveira|last10=Hartmann|first10=Jens|last11=Khanna|first11=Tarun|date=2018-05|title=Negative emissions—Part 1: Research landscape and synthesis|url=https://doi.org/10.1088/1748-9326/aabf9b|journal=Environmental Research Letters|language=en|volume=13|issue=6|pages=063001|doi=10.1088/1748-9326/aabf9b|issn=1748-9326}}</ref><ref>{{Cite journal|last=Fuss|first=Sabine|last2=Lamb|first2=William F|last3=Callaghan|first3=Max W|last4=Hilaire|first4=Jérôme|last5=Creutzig|first5=Felix|last6=Amann|first6=Thorben|last7=Beringer|first7=Tim|last8=de Oliveira Garcia|first8=Wagner|last9=Hartmann|first9=Jens|last10=Khanna|first10=Tarun|last11=Luderer|first11=Gunnar|date=2018-05-21|title=Negative emissions—Part 2: Costs, potentials and side effects|url=https://iopscience.iop.org/article/10.1088/1748-9326/aabf9f|journal=Environmental Research Letters|language=en|volume=13|issue=6|pages=063002|doi=10.1088/1748-9326/aabf9f|issn=1748-9326}}</ref><ref>{{Cite journal|last=Nemet|first=Gregory F|last2=Callaghan|first2=Max W|last3=Creutzig|first3=Felix|last4=Fuss|first4=Sabine|last5=Hartmann|first5=Jens|last6=Hilaire|first6=Jérôme|last7=Lamb|first7=William F|last8=Minx|first8=Jan C|last9=Rogers|first9=Sophia|last10=Smith|first10=Pete|date=2018-05-21|title=Negative emissions—Part 3: Innovation and upscaling|url=https://iopscience.iop.org/article/10.1088/1748-9326/aabff4|journal=Environmental Research Letters|language=en|volume=13|issue=6|pages=063003|doi=10.1088/1748-9326/aabff4|issn=1748-9326}}</ref>. The underlying chemical processes are fairly well understood and the design of these systems generally does not require machine learning; however, ML may be useful in designing more effective CO<sub>2</sub> sorbents. ML also may have a number of applications in CO<sub>2</sub> sequestration, namely in identifying, modeling, and monitoring CO<sub>2</sub> sequestration sites.

Many of the ML applications we discuss below are either speculative or in the early stages of development or commercialization<ref name=":2">{{Cite journal|last=Rolnick|first=David|last2=Donti|first2=Priya L.|last3=Kaack|first3=Lynn H.|last4=Kochanski|first4=Kelly|last5=Lacoste|first5=Alexandre|last6=Sankaran|first6=Kris|last7=Ross|first7=Andrew Slavin|last8=Milojevic-Dupont|first8=Nikola|last9=Jaques|first9=Natasha|last10=Waldman-Brown|first10=Anna|last11=Luccioni|first11=Alexandra|date=2019-11-05|title=Tackling Climate Change with Machine Learning|url=http://arxiv.org/abs/1906.05433|journal=arXiv:1906.05433 [cs, stat]}}</ref>.


==Machine Learning Application Areas==
==Machine Learning Application Areas==
'''Direct air capture:''' Many DAC technologies are in early stages of commercialization <ref name=":0" /><ref name=":1" />. The underlying chemical processes are fairly well understood and the design of these systems generally does not require machine learning.

⚫
One potentially promising ML application for DAC is accelerated materials discovery of new chemical sorbents that either bind to atmospheric CO2 with either greater selectivity or have lower energy input requirements.


⚫
'''Sequestering CO<sub>2</sub>:''' For carbon capture and sequestration to be effective, it must sequester CO<sub>2</sub> for hundreds or thousands of years. Thus, understanding the long-term migration of sequestered CO<sub>2</sub>, particularly in underground saline reservoir formations but also in basalts, is of critical importance. Machine learning can help speed up computationally intensive reservoir simulation models by orders of magnitude, accelerating the speed at which scientists can answer key questions.


⚫
*'''[[Accelerated Science|Accelerated materials discovery]]''' '''of new chemical sorbents:''' One potentially promising ML application for DAC is accelerated materials discovery of new chemical sorbents that either bind to atmospheric CO<sub>2</sub> with either greater selectivity or have lower energy input requirements.
Machine learning may be able to help with many aspects of CO<sub>2</sub> sequestration. First, ML can help identify and characterize potential storage locations. Oil and gas companies have had promising results using ML for subsurface imaging based on raw seismograph traces <ref>{{Cite journal|last=Araya-Polo|first=Mauricio|last2=Jennings|first2=Joseph|last3=Adler|first3=Amir|last4=Dahlke|first4=Taylor|date=2017-12-29|title=Deep-learning tomography|url=https://library.seg.org/doi/abs/10.1190/tle37010058.1|journal=The Leading Edge|volume=37|issue=1|pages=58–66|doi=10.1190/tle37010058.1|issn=1070-485X}}</ref>. These models and the data behind them could likely be repurposed to help trap CO<sub>2</sub> rather than release it. Second, ML can help monitor and maintain active sequestration sites. Noisy sensor measurements must be translated into inferences about subsurface CO<sub>2</sub> flow and remaining injection capacity <ref>{{Cite journal|last=Celia|first=M. A.|last2=Bachu|first2=S.|last3=Nordbotten|first3=J. M.|last4=Bandilla|first4=K. W.|date=2015|title=Status of CO2 storage in deep saline aquifers with emphasis on modeling approaches and practical simulations|url=https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2015WR017609|journal=Water Resources Research|language=en|volume=51|issue=9|pages=6846–6892|doi=10.1002/2015WR017609|issn=1944-7973}}</ref>; recently, <ref>{{Cite journal|last=Mo|first=Shaoxing|last2=Zhu|first2=Yinhao|last3=Zabaras|first3=Nicholas|last4=Shi|first4=Xiaoqing|last5=Wu|first5=Jichun|date=2019|title=Deep Convolutional Encoder-Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media|url=https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018WR023528|journal=Water Resources Research|language=en|volume=55|issue=1|pages=703–728|doi=10.1029/2018WR023528|issn=1944-7973}}</ref> found success using convolutional image-to-image regression techniques for uncertainty quantification in a global CO2 storage simulation study. Additionally, it is important to monitor for CO<sub>2</sub> leaks <ref>{{Cite journal|last=Moriarty|first=Dylan|last2=Dobeck|first2=Laura|last3=Benson|first3=Sally|date=2014-01-01|title=Rapid surface detection of CO2 leaks from geologic sequestration sites|url=http://www.sciencedirect.com/science/article/pii/S1876610214022425|journal=Energy Procedia|series=12th International Conference on Greenhouse Gas Control Technologies, GHGT-12|language=en|volume=63|pages=3975–3983|doi=10.1016/j.egypro.2014.11.427|issn=1876-6102}}</ref>. ML techniques have recently been applied to monitoring potential CO<sub>2</sub> leaks from wells <ref>{{Cite journal|last=Chen|first=Bailian|last2=Harp|first2=Dylan R.|last3=Lin|first3=Youzuo|last4=Keating|first4=Elizabeth H.|last5=Pawar|first5=Rajesh J.|date=2018-09-01|title=Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach|url=http://www.sciencedirect.com/science/article/pii/S0306261918307372|journal=Applied Energy|language=en|volume=225|pages=332–345|doi=10.1016/j.apenergy.2018.05.044|issn=0306-2619}}</ref>; computer vision approaches for emissions detection (see <ref>{{Cite journal|last=Chen|first=Bailian|last2=Harp|first2=Dylan R.|last3=Lin|first3=Youzuo|last4=Keating|first4=Elizabeth H.|last5=Pawar|first5=Rajesh J.|date=2018-09-01|title=Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach|url=http://www.sciencedirect.com/science/article/pii/S0306261918307372|journal=Applied Energy|language=en|volume=225|pages=332–345|doi=10.1016/j.apenergy.2018.05.044|issn=0306-2619}}</ref> and [[Greenhouse Gas Emissions Detection]]) may also be applicable.
⚫
*'''[[CO2 Migration Modeling|CO<sub>2</sub> migration modeling]]:''' For carbon capture and sequestration to be effective, it must sequester CO<sub>2</sub> for hundreds or thousands of years. Thus, understanding the long-term migration of sequestered CO<sub>2</sub>, particularly in underground saline reservoir formations but also in basalts, is of critical importance. Machine learning can help speed up computationally intensive reservoir simulation models by orders of magnitude, accelerating the speed at which scientists can answer key questions.
*'''Identification of CO<sub>2</sub> sequestration locations:''' ML can help identify and characterize potential CO<sub>2</sub> storage locations. In particular, oil and gas companies have had promising results using ML for subsurface imaging based on raw seismograph traces;<ref>{{Cite journal|last=Araya-Polo|first=Mauricio|last2=Jennings|first2=Joseph|last3=Adler|first3=Amir|last4=Dahlke|first4=Taylor|date=2017-12-29|title=Deep-learning tomography|url=https://library.seg.org/doi/abs/10.1190/tle37010058.1|journal=The Leading Edge|volume=37|issue=1|pages=58–66|doi=10.1190/tle37010058.1|issn=1070-485X}}</ref> these models and the data behind them could likely be repurposed to help trap CO<sub>2</sub> rather than release it.
*'''[[Sequestration Site Monitoring|CO<sub>2</sub> sequestration site monitoring]]:''' ML can help monitor and maintain active sequestration sites by analyzing sensor measurements, and by monitoring for CO<sub>2</sub> leaks.


==Background Readings==
==Background Readings==


* Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. <ref name=":0" />
*Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. <ref name=":0" />


* Direct Air Capture of Carbon Dioxide: ICEF Roadmap 2018 <ref name=":1" /><br />
*Direct Air Capture of Carbon Dioxide: ICEF Roadmap 2018 <ref name=":1" /><br />


==Online Courses and Course Materials==
==Online Courses and Course Materials==


* Introduction to CO<sub>2</sub> sequestration for negative emissions, lecture by Sally Benson at the International Conference on Negative CO<sub>2</sub> Emissions [[https://www.youtube.com/watch?v=lIVwbSnD0AI link]]
*Introduction to CO<sub>2</sub> sequestration for negative emissions, lecture by Sally Benson at the International Conference on Negative CO<sub>2</sub> Emissions [[https://www.youtube.com/watch?v=lIVwbSnD0AI link]]


==Conferences, Journals, and Professional Organizations==
==Community==


*[http://negativeco2emissions2020.com/ The International Conference on Negative CO2 emissions]
* Carbon 180 [[https://carbon180.org/ link]]
*[https://carbon180.org/ Carbon 180]


==Libraries and Tools==
==Libraries and Tools==
{{SectionStub}}


==Data==
==Data==
[https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/#/login?redirect=%2Fworkspaces '''IPCC SR1.5 Scenario Explorer''']- climate change migation pathways used in the IPCC SR1.5 report<ref name=":3" /> (many of the scenarios contain negative emissions).


==References==
==References==

Latest revision as of 17:21, 16 March 2021

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.

Negative Emission Technologies (NETs), often referred to as Carbon Dioxide Removal (CDR), aim to artificially remove carbon dioxide (CO2) from the atmospere[1], in addition to the natural removal of the atmospheric CO2 by the natural carbon sinks (such as land and ocean)[2]. NETs are not a substitution for climate mitigation and reducing global emission rate, but can be used together with mitigation efforts to speed up the reduction of emissions and reaching the net-zero emission targets sooner, depending on the emission scenario. The mitigation pathways consistent with reaching the 1.5 °C target (reported by the IPCC Special Report on 1.5 Degrees[3]) entail low to moderate levels of CDR (up to 1000 PgC removed; IPCC SR1.5 Chapter 2[4]). NETs are also an underlying assumption of overshoot scenarios -where a given temperature target is temporarily exceeded and then returned to with the aid of negative emission. While global mean temperature has shown to be largely reversible in response to artifical carbon dioxide removal, other components of climate change (such as sea-level rise, ocean acidification, and other terrestrial and marine ecosystem changes) are not easily reversible on human time-scales, even if extremely large amounts of NETs were implemented[5][6][7].

Different groups of negative emission technologies[8] (Source: Figure 1 from Jan C Minx et al 2017 Environ. Res. Lett. 12 035007)

Different groups of negative emission technologies[9]:

Many DAC technologies are in early stages of commercialization[10][11], though there is still large uncertainty regarding geological storage of captured CO2 on long time-scales, and deployment of negative emission technologies on large-scales and in a sustainable way is unlikely[12][13][14]. The underlying chemical processes are fairly well understood and the design of these systems generally does not require machine learning; however, ML may be useful in designing more effective CO2 sorbents. ML also may have a number of applications in CO2 sequestration, namely in identifying, modeling, and monitoring CO2 sequestration sites.

Many of the ML applications we discuss below are either speculative or in the early stages of development or commercialization[15].

Machine Learning Application Areas[edit | edit source]

  • Accelerated materials discovery of new chemical sorbents: One potentially promising ML application for DAC is accelerated materials discovery of new chemical sorbents that either bind to atmospheric CO2 with either greater selectivity or have lower energy input requirements.
  • CO2 migration modeling: For carbon capture and sequestration to be effective, it must sequester CO2 for hundreds or thousands of years. Thus, understanding the long-term migration of sequestered CO2, particularly in underground saline reservoir formations but also in basalts, is of critical importance. Machine learning can help speed up computationally intensive reservoir simulation models by orders of magnitude, accelerating the speed at which scientists can answer key questions.
  • Identification of CO2 sequestration locations: ML can help identify and characterize potential CO2 storage locations. In particular, oil and gas companies have had promising results using ML for subsurface imaging based on raw seismograph traces;[16] these models and the data behind them could likely be repurposed to help trap CO2 rather than release it.
  • CO2 sequestration site monitoring: ML can help monitor and maintain active sequestration sites by analyzing sensor measurements, and by monitoring for CO2 leaks.

Background Readings[edit | edit source]

  • Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. [10]
  • Direct Air Capture of Carbon Dioxide: ICEF Roadmap 2018 [11]

Online Courses and Course Materials[edit | edit source]

  • Introduction to CO2 sequestration for negative emissions, lecture by Sally Benson at the International Conference on Negative CO2 Emissions [link]

Conferences, Journals, and Professional Organizations[edit | edit source]

Libraries and Tools[edit | edit source]

🌎 This section is currently a stub. You can help by adding resources, as well as 1-2 sentences of context for each resource.

Data[edit | edit source]

IPCC SR1.5 Scenario Explorer- climate change migation pathways used in the IPCC SR1.5 report[3] (many of the scenarios contain negative emissions).

References[edit | edit source]

  1. ↑ Minx, Jan C; Lamb, William F; Callaghan, Max W; Fuss, Sabine; Hilaire, Jérôme; Creutzig, Felix; Amann, Thorben; Beringer, Tim; de Oliveira Garcia, Wagner; Hartmann, Jens; Khanna, Tarun (2018-05-21). "Negative emissions—Part 1: Research landscape and synthesis". Environmental Research Letters. 13 (6): 063001. doi:10.1088/1748-9326/aabf9b. ISSN 1748-9326.
  2. ↑ IPCC, 2018: Annex I: Glossary [Matthews, J.B.R. (ed.)]. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In Press
  3. ↑ 3.0 3.1 Rogelj, J., D. Shindell, K. Jiang, S. Fifita, P. Forster, V. Ginzburg, C. Handa, H. Kheshgi, S. Kobayashi, E. Kriegler, L. Mundaca, R. Séférian, and M.V. Vilariño, 2018: Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)].
  4. ↑ Rogelj, J., D. Shindell, K. Jiang, S. Fifita, P. Forster, V. Ginzburg, C. Handa, H. Kheshgi, S. Kobayashi, E. Kriegler, L. Mundaca, R. Séférian, and M.V. Vilariño, 2018: Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)].
  5. ↑ Jones, C D; Ciais, P; Davis, S J; Friedlingstein, P; Gasser, T; Peters, G P; Rogelj, J; van Vuuren, D P; Canadell, J G; Cowie, A; Jackson, R B (2016-09-01). "Simulating the Earth system response to negative emissions". Environmental Research Letters. 11 (9): 095012. doi:10.1088/1748-9326/11/9/095012. ISSN 1748-9326.
  6. ↑ Tokarska, Katarzyna B; Zickfeld, Kirsten (2015-09-01). "The effectiveness of net negative carbon dioxide emissions in reversing anthropogenic climate change". Environmental Research Letters. 10 (9): 094013. doi:10.1088/1748-9326/10/9/094013. ISSN 1748-9326.
  7. ↑ Hofmann, M.; Mathesius, S.; Kriegler, E.; Vuuren, D. P. van; Schellnhuber, H. J. (2019-12-06). "Strong time dependence of ocean acidification mitigation by atmospheric carbon dioxide removal". Nature Communications. 10 (1): 5592. doi:10.1038/s41467-019-13586-4. ISSN 2041-1723.
  8. ↑ Minx, Jan C; Lamb, William F; Callaghan, Max W; Bornmann, Lutz; Fuss, Sabine (2017-03-01). "Fast growing research on negative emissions". Environmental Research Letters. 12 (3): 035007. doi:10.1088/1748-9326/aa5ee5. ISSN 1748-9326.
  9. ↑ Minx, Jan C; Lamb, William F; Callaghan, Max W; Bornmann, Lutz; Fuss, Sabine (2017-03-01). "Fast growing research on negative emissions". Environmental Research Letters. 12 (3): 035007. doi:10.1088/1748-9326/aa5ee5. ISSN 1748-9326.
  10. ↑ 10.0 10.1 National Academies of Sciences, Engineering (2018-10-24). Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. ISBN 978-0-309-48452-7.
  11. ↑ 11.0 11.1 ICEF. "Direct Air Capture of Carbon Dioxide: ICEF Roadmap 2018". www.icef-forum.org. Retrieved 2020-09-12.
  12. ↑ Minx, Jan C.; Lamb, William F.; Callaghan, Max W.; Fuss, Sabine; Hilaire, Jérôme; Creutzig, Felix; Amann, Thorben; Beringer, Tim; Garcia, Wagner de Oliveira; Hartmann, Jens; Khanna, Tarun (2018-05). "Negative emissions—Part 1: Research landscape and synthesis". Environmental Research Letters. 13 (6): 063001. doi:10.1088/1748-9326/aabf9b. ISSN 1748-9326. Check date values in: |date= (help)
  13. ↑ Fuss, Sabine; Lamb, William F; Callaghan, Max W; Hilaire, Jérôme; Creutzig, Felix; Amann, Thorben; Beringer, Tim; de Oliveira Garcia, Wagner; Hartmann, Jens; Khanna, Tarun; Luderer, Gunnar (2018-05-21). "Negative emissions—Part 2: Costs, potentials and side effects". Environmental Research Letters. 13 (6): 063002. doi:10.1088/1748-9326/aabf9f. ISSN 1748-9326.
  14. ↑ Nemet, Gregory F; Callaghan, Max W; Creutzig, Felix; Fuss, Sabine; Hartmann, Jens; Hilaire, Jérôme; Lamb, William F; Minx, Jan C; Rogers, Sophia; Smith, Pete (2018-05-21). "Negative emissions—Part 3: Innovation and upscaling". Environmental Research Letters. 13 (6): 063003. doi:10.1088/1748-9326/aabff4. ISSN 1748-9326.
  15. ↑ 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].
  16. ↑ Araya-Polo, Mauricio; Jennings, Joseph; Adler, Amir; Dahlke, Taylor (2017-12-29). "Deep-learning tomography". The Leading Edge. 37 (1): 58–66. doi:10.1190/tle37010058.1. ISSN 1070-485X.
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