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 dioxyde removal as a whole, please see the [https://en.wikipedia.org/wiki/Carbon_dioxide_removal Wikipedia page] on this topic.'' |
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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. |
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. |
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