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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.''
As described in the paper "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>
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]].</blockquote>Many DAC technologies are in early stages of commercialization<ref name=":0" /><ref name=":1" />, 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.
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