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Climate Change Adaptation: Difference between revisions

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* How do we support rapid recovery from climate-induced disruptions?
 
A wide variety of strategies have been put forward, from robust power grids to food shortage prediction, and while this is good news for society, it can be overwhelming for an ML practitioner hoping to contribute. Fortunately, a few critical needs tend to recur across strategies – it is by meeting these needs that machine learning has the greatest potential to support societal adaptation [8, 16, 515]. From a high level, these involve
 
* Sounding alarms: Identifying and prioritizing the areas of highest risk, by using evidence of risk from historical data.
== Recommended Readings ==
 
* Quinn, J. et al. Computational sustainability and artificial intelligence in the developing world<ref>{{Cite journal|last=Quinn|first=John|last2=Frias-Martinez|first2=Vanessa|last3=Subramanian|first3=Lakshminarayan|date=2014-09-29|title=Computational Sustainability and Artificial Intelligence in the Developing World|url=http://dx.doi.org/10.1609/aimag.v35i3.2529|journal=AI Magazine|volume=35|issue=3|pages=36|doi=10.1609/aimag.v35i3.2529|issn=0738-4602}}</ref> (2014).
* Gomes, C. et al., Computational sustainability: Computing for a better world and a sustainable future. (2019)
* Agrawal, A., and Perrin, N. Climate adaptation, local institutions and rural livelihoods. (2009)
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