Deploying artificial intelligence for climate change adaptation

Walter Leal Filho, Tony Wall, Serafino Afonso Rui Mucova, Gustavo J. Nagy, Abdul-Lateef Balogun, Johannes M. Luetz, Artie W. Ng, Marina Kovaleva, Fardous Mohammad Safiul Azam, Fátima Alves, Zeus Guevara, Newton R Matandirotya, Antonis Skouloudis, Asaf Tzachor, Krishna Malakar and Odhiambo Gandhi

Technological Forecasting and Social Change, 2022, vol. 180, issue C

Abstract: Artificial Intelligence (AI) is believed to have a significant potential use in tackling climate change. This paper explores the connections between AI and climate change research as a whole and its usefulness in climate change adaptation efforts in particular. Using a systematic review of the literature on applications of AI for climate change adaptation and a questionnaire survey of a multinational and interdisciplinary team of climate change researchers, this paper shows the various means via which AI can support research on climate change in diverse regions, and contribute to efforts towards climate change adaptation. The surveyed articles are classified under nine areas, e.g., Global/Earth Related; Water-related Issues and agriculture, 95% of which are related to adaptation. The areas that have attracted the most studies about AI applications are water-related management issues (38%). In terms of the survey results, the most robust agreements were noted concerning the capacity of digitisation and AI to strengthen governance practices and afford policy coherence in climate change. Evidence gathered in the study suggests that, provided that due care is taken, the use of AI can provide a welcome support to global efforts to better understand and handle the many challenges associated with a changing climate.

Keywords: Artificial intelligence; Digital technologies; Climate change adaptation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:180:y:2022:i:c:s0040162522001949

DOI: 10.1016/j.techfore.2022.121662

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