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Negative Emissions Technologies: Difference between revisions
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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.''
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.
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
==
'''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.
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.
==
* 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 />
== Online Courses and Course Materials ==▼
== Community ==▼
* 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]]
* Carbon 180 [[https://carbon180.org/ link]]
▲== Libraries and Tools ==
<references />
▲== Data ==
▲== References ==
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