Artificial intelligence, machine learning and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022

Applying artificial intelligence (AI), machine learning (ML), and big data to natural resource management (NRM) is revolutionizing how natural resources are managed. To gain more insights into the domain, we use 394 Scopus-indexed documents to explore the thematic evolution and explore future research directions. We found that the topics related to AI, ML, and big data for natural resource management have increased significantly since 2012. While “Remote Sensing” is the most productive journal, S. Alqadhi and J. Mallick are the most contributing authors, and the United States has been the most contributing country. While the keywords “sustainable development” and “remote sensing” have been growing steadily since 1975, “natural resource modeling” and “machine learning” have been more popular during the last few years. The thematic analysis reveals that the existing literature is concentrated around four clusters, and the content analysis of the clusters uncovers 15 future research agendas. These research agendas include the development of efficient strategies for NRM, understanding the role of AI and ML in natural resource management, leveraging data-driven methods for decision-making, and developing models for interdisciplinary and cross-sectoral approaches. The study provides important implications of using technology in NRM. These technologies help policymakers create effective policies, improves assessment and decision-making, and optimizes resource use. These advancements benefit society by increasing access to essential resources in a fair manner, and they have positive impacts on both the public and private sectors, enabling evidence-based policymaking and responsible resource extraction. Collaboration and investment in these technologies are crucial for achieving sustainable development and preserving natural resources for future generations.