Leal Filho, W, Wall, T, Rui Mucova, SA, Nagy, GJ, Balogun, A-L, Luetz, JM, Ng, AW, Kovaleva, M, Azam, FMS, Alves, F, Guevara, Z, Matandirotya, NR, Skouloudis, A, Tzachor, A, Malakar, K and Gandhi, O (2022) Deploying artificial intelligence for climate change adaptation. Technological Forecasting and Social Change, 180. ISSN 0040-1625

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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.

Item Type:	Article
Uncontrolled Keywords:	10 Technology, 14 Economics, 15 Commerce, Management, Tourism and Services
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences T Technology > T Technology (General)
Divisions:	Business & Management (new Sep 19)
Publisher:	Elsevier
Date Deposited:	19 Apr 2022 10:21
Last Modified:	19 Apr 2022 10:21
DOI or Identification number:	10.1016/j.techfore.2022.121662
URI:	https://researchonline.ljmu.ac.uk/id/eprint/16667

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