''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.''
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>.
== Machine Learning Application Areas ==
== Background Readings ==
== Online Courses and Course Materials ==
== Community ==
== Libraries and Tools ==
== Data ==