Changes in the atmosphere have impacts on the ground. The expected societal impacts of climate change include prolonged ecological and socioeconomic stresses as well as brief, but severe, societal disruptions. For example, impacts could include both gradual decreases in crop yield and localized food shortages. If we can anticipate climate impacts well enough, then we can prepare for them by asking:
- How do we reduce vulnerability to climate impacts?
- 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.
- Providing annotation: Extracting actionable information or labels from unstructured raw data.
- Promoting exchange: Making it easier to share resources and information to pool and reduce risk.
Satellite imagery are used for ecological and social observation. Some public sources include,
- awesome-satellite-imagery-datasets: A github repository of accessible satellite imagery data.
There have also been competitions revolving around climate change adaptation issues,
- DroughtWatch revolves around drought monitoring in Kenya.
- Promoting Digital Financial Services in Tanzania describes an attempt to mobile money effort to improve financial inclusion and resilience.
- The IBM Malaria Challenge is a competition around Improved disease surveillance and response, which is motivated by the spread of vector borne disease resulting from climate change.
This competition describes an attempt to use mobile money effort to improve financial inclusion and resilience.
Improved disease surveillance and response is an important part of adaptation – here is one competition with this goal in mind.
Methods and Software
- Quinn, J. et al. Computational sustainability and artificial intelligence in the developing world (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)
- Shi, L. et al. Roadmap towards justice in urban climate adaptation research. (2016)
Journals and conferences
- PLOS Responding to Climate Change
- ACM Compass
- AI for Good Global Summit
- Lancet Health and Climate Change
Societies and organizations
Past and upcoming events
- Quinn, John; Frias-Martinez, Vanessa; Subramanian, Lakshminarayan (2014-09-29). "Computational Sustainability and Artificial Intelligence in the Developing World". AI Magazine. 35 (3): 36. doi:10.1609/aimag.v35i3.2529. ISSN 0738-4602.
- Shi, Linda; Chu, Eric; Anguelovski, Isabelle; Aylett, Alexander; Debats, Jessica; Goh, Kian; Schenk, Todd; Seto, Karen C.; Dodman, David; Roberts, Debra; Roberts, J. Timmons (2016-05-25). "Erratum: Corrigendum: Roadmap towards justice in urban climate adaptation research". Nature Climate Change. 6 (6): 634–634. doi:10.1038/nclimate3034. ISSN 1758-678X.