Mapping the Landscape of Artificial Intelligence for Climate Resilience in Rural Areas: A Bibliometric Study
DOI:
https://doi.org/10.58524/3akfsk52Keywords:
Artificial Intelligence, Bibliometric Analysis, Climate Change, Machine Learning, Rural AreasAbstract
Rural areas are particularly vulnerable to climate-related risks, yet limited access to locally available environmental data can constrain effective mitigation and adaptation strategies. This study maps the global research landscape on the application of Artificial Intelligence (AI) to climate resilience in rural areas through a bibliometric analysis of 254 documents indexed in the Scopus database. Biblioshiny and VOSviewer were used to examine publication trends, geographic contributions, keyword co-occurrence patterns, and thematic evolution. The findings show a sharp increase in publications after 2021, with China, the United States, and Saudi Arabia among the leading contributors. The intellectual structure of the field is primarily organized around three interconnected themes: Machine Learning, climate change, and Remote Sensing. Thematic analysis further indicates a transition from relatively conventional land-use and environmental monitoring approaches toward more advanced applications involving Deep Learning, carbon-emission analysis, decarbonization strategies, and site-level environmental monitoring. These developments suggest that AI-based climate resilience research is becoming increasingly interdisciplinary and application-oriented. By specifically isolating rural areas as the unit of analysis, this study complements broader bibliometric studies that commonly examine agriculture, climate change, or AI at a more general level. The findings provide an evidence-based overview of the current knowledge structure, emerging themes, and research directions that may support future interdisciplinary research and the responsible development of AI-based climate adaptation strategies for rural communities.
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