Difference between revisions of "Negative Emissions Technologies"

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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.''
   
  +
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.
As described in "Tackling Climate Change with Machine Learning,"<ref>{{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><blockquote>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>.</blockquote>
+
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]].
   
== 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.
=== Understanding and improving aerosols ===
 
   
* Design
 
* Modeling
 
   
  +
'''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.
=== Engineering a planetary control system ===
 
   
  +
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.
=== Modeling impacts ===
 
   
== Background Readings ==
+
==Background Readings==
Primers
 
   
  +
* Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. <ref name=":0" />
* '''"Geoengineering the climate: History and prospect" (2000)'''<ref>{{Cite journal|last=Keith|first=David|date=2000|title=Geoengineering the climate: History and prospect|url=|journal=Annual review of energy and the environment|volume=25|pages=|via=}}</ref>: An early review of solar geoengineering and climate change.
 
* '''"Solar Geoengineering Governance" (2018)'''<ref>{{Cite journal|last=Sugiyama|first=Masahiro|last2=Ishii|first2=Atsushi|last3=Asayama|first3=Shinichiro|last4=Kosugi|first4=Takanobu|date=2018-04-26|title=Solar Geoengineering Governance|url=http://dx.doi.org/10.1093/acrefore/9780190228620.013.647|journal=Oxford Research Encyclopedia of Climate Science|doi=10.1093/acrefore/9780190228620.013.647}}</ref>: An climate science encyclopedia entry on governance of solar geoengineering systems.
 
* '''"An overview of the Earth system science of solar geoengineering" (2016''')<ref>{{Cite journal|last=Irvine|first=Peter J.|last2=Kravitz|first2=Ben|last3=Lawrence|first3=Mark G.|last4=Muri|first4=Helene|date=2016-07-14|title=An overview of the Earth system science of solar geoengineering|url=http://dx.doi.org/10.1002/wcc.423|journal=Wiley Interdisciplinary Reviews: Climate Change|volume=7|issue=6|pages=815–833|doi=10.1002/wcc.423|issn=1757-7780}}</ref>: A modern overview of solar geoengineering.
 
   
  +
* Direct Air Capture of Carbon Dioxide: ICEF Roadmap 2018 <ref name=":1" /><br />
== Online Courses and Course Materials ==
 
   
 
==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]]
=== Major conferences ===
 
   
 
==Community==
* '''AGU Fall Meeting''': A yearly conference organized by the American Geophysical Union. Website [https://www.agu.org/fall-meeting here].
 
* '''Climate Engineering Conference''': Website [https://www.ce-conference.org/ here].
 
   
  +
* Carbon 180 [[https://carbon180.org/ link]]
=== Major journals ===
 
   
 
==Libraries and Tools==
* '''Atmospheric Chemistry and Physics''': A journal of the European Geosciences Union dedicated to atmospheric chemical and physical processes. Website [https://www.atmospheric-chemistry-and-physics.net/ here].
 
* '''Earth's Future''': A journal of the American Geophysical Union dedicated to the state and future of the earth. Website [https://agupubs.onlinelibrary.wiley.com/journal/23284277 here].
 
   
 
==Data==
=== Major societies and organizations ===
 
   
 
==References==
== Libraries and Tools ==
 
  +
<references />
 
* '''The Geoengineering Model Intercomparison Project''': A gateway to solar geoengineering simulators, available [http://climate.envsci.rutgers.edu/GeoMIP/ here].
 
 
== Data ==
 
 
== References ==
 

Revision as of 15:46, 2 November 2020

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.

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 [1][2]. Although there has been significant progress in negative emissions research [3][4][5][6][7], 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 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.

Machine Learning Application Areas

Direct air capture: Many DAC technologies are in early stages of commercialization [3][4]. 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 CO2: 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.

Machine learning may be able to help with many aspects of CO2 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 [8]. These models and the data behind them could likely be repurposed to help trap CO2 rather than release it. Second, ML can help monitor and maintain active sequestration sites. Noisy sensor measurements must be translated into inferences about subsurface CO2 flow and remaining injection capacity [9]; recently, [10] 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 CO2 leaks [11]. ML techniques have recently been applied to monitoring potential CO2 leaks from wells [12]; computer vision approaches for emissions detection (see [13] and Greenhouse Gas Emissions Detection) may also be applicable.

Background Readings

  • Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. [3]
  • Direct Air Capture of Carbon Dioxide: ICEF Roadmap 2018 [4]

Online Courses and Course Materials

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

Community

Libraries and Tools

Data

References

  1. Fuss, Sabine; Canadell, Josep G.; Peters, Glen P.; Tavoni, Massimo; Andrew, Robbie M.; Ciais, Philippe; Jackson, Robert B.; Jones, Chris D.; Kraxner, Florian; Nakicenovic, Nebosja; Le Quéré, Corinne (2014-10). "Betting on negative emissions". Nature Climate Change. 4 (10): 850–853. doi:10.1038/nclimate2392. ISSN 1758-6798. Check date values in: |date= (help)
  2. Gasser, T.; Guivarch, C.; Tachiiri, K.; Jones, C. D.; Ciais, P. (2015-08-03). "Negative emissions physically needed to keep global warming below 2 °C". Nature Communications. 6 (1): 1–7. doi:10.1038/ncomms8958. ISSN 2041-1723.
  3. 3.0 3.1 3.2 National Academies of Sciences, Engineering (2018-10-24). Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. ISBN 978-0-309-48452-7.
  4. 4.0 4.1 4.2 ICEF. "Direct Air Capture of Carbon Dioxide: ICEF Roadmap 2018". www.icef-forum.org. Retrieved 2020-09-12.
  5. "ShieldSquare Captcha". stacks.iop.org. Retrieved 2020-09-12.
  6. Fuss, Sabine; Lamb, William F.; Callaghan, Max W.; Hilaire, Jérôme; Creutzig, Felix; Amann, Thorben; Beringer, Tim; Garcia, Wagner de Oliveira; Hartmann, Jens; Khanna, Tarun; Luderer, Gunnar (2018-05). "Negative emissions—Part 2: Costs, potentials and side effects". Environmental Research Letters. 13 (6): 063002. doi:10.1088/1748-9326/aabf9f. ISSN 1748-9326. Check date values in: |date= (help)
  7. "ShieldSquare Captcha". stacks.iop.org. Retrieved 2020-09-12.
  8. 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.
  9. Celia, M. A.; Bachu, S.; Nordbotten, J. M.; Bandilla, K. W. (2015). "Status of CO2 storage in deep saline aquifers with emphasis on modeling approaches and practical simulations". Water Resources Research. 51 (9): 6846–6892. doi:10.1002/2015WR017609. ISSN 1944-7973.
  10. Mo, Shaoxing; Zhu, Yinhao; Zabaras, Nicholas; Shi, Xiaoqing; Wu, Jichun (2019). "Deep Convolutional Encoder-Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media". Water Resources Research. 55 (1): 703–728. doi:10.1029/2018WR023528. ISSN 1944-7973.
  11. Moriarty, Dylan; Dobeck, Laura; Benson, Sally (2014-01-01). "Rapid surface detection of CO2 leaks from geologic sequestration sites". Energy Procedia. 12th International Conference on Greenhouse Gas Control Technologies, GHGT-12. 63: 3975–3983. doi:10.1016/j.egypro.2014.11.427. ISSN 1876-6102.
  12. Chen, Bailian; Harp, Dylan R.; Lin, Youzuo; Keating, Elizabeth H.; Pawar, Rajesh J. (2018-09-01). "Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach". Applied Energy. 225: 332–345. doi:10.1016/j.apenergy.2018.05.044. ISSN 0306-2619.
  13. Chen, Bailian; Harp, Dylan R.; Lin, Youzuo; Keating, Elizabeth H.; Pawar, Rajesh J. (2018-09-01). "Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach". Applied Energy. 225: 332–345. doi:10.1016/j.apenergy.2018.05.044. ISSN 0306-2619.