Moghayedi, A.; Michell, K. and Awuzie, B. O. (2026) Analysis of the drivers and barriers influencing artificial intelligence for tackling climate change challenges. Smart and Sustainable Built Environment, 15(3), pp. 1277-1312. ISSN 2046-6099
Abstract
Purpose – Facilities management (FM) organizations are pivotal in enhancing the resilience of buildings against climate change impacts. While existing research delves into the adoption of digital technologies by FM organizations, there exists a gap regarding the specific utilization of artificial intelligence (AI) to address climate challenges. This study aims to investigate the drivers and barriers influencing the adoption and utilization of AI by South African FM organizations in mitigating climate change challenges. Design/methodology/approach – This study focuses on South Africa, a developing nation grappling with climate change's ramifications on its infrastructure. Through a combination of systematic literature review and an online questionnaire survey, data was collected from representatives of 85 professionally registered FM organizations in South Africa. Analysis methods employed include content analysis, Relative Importance Index (RII), and Total Interpretative Structural Modeling (TISM). Findings – The findings reveal that regulatory compliance and a responsible supply chain serve as critical drivers for AI adoption among South African FM organizations. Conversely, policy constraints and South Africa's energy crisis emerge as major barriers to AI adoption in combating climate change challenges within the FM sector. Originality/value – This study contributes to existing knowledge by bridging the gap in understanding how AI technologies are utilized by FM organizations to address climate challenges, particularly in the context of a developing nation like South Africa. The research findings aim to inform policymakers on fostering a conducive environment for FM organizations to harness AI in fostering climate resilience in built assets.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence; barriers; climate change; drivers; facilities management; influencing |
| Index terms: | built asset, relative importance index, methodology, developing nation, digital technology, artificial intelligence, climate change, survey, regulatory compliance, facilities management, South Africa, systematic literature review, questionnaire, content analysis, climate resilience, structural modelling |
| Subjects: | analytical methods, data analysis and analytics, research methods, Geography, climate science, asset management, risk assessment, management, artificial intelligence, environmental policy, data collection methods, research evaluation and metrics, computing systems, development economics |
| Topics: | Research Practice, Engineering Principles, International Construction, Business Strategy, Geographical Context, Digital Applications, Sustainability, Risk Management |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here