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 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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