Abstract
Our study aims to map the interdisciplinary research landscape encompassing artificial intelligence, environmental science, and economics through bibliometric analysis. By using the Web of Science academic database to retrieve publications based on the most representative words from the taxonomy of each concept, we applied citation analysis, co-authorship network mapping, and keyword co-occurrence analysis using the R package bibliometrix and VOS Viewer software to assess the growth, key contributors, collaboration patterns, and thematic focuses within this multidisciplinary field. Topics including “artificial intelligence,” “machine learning,” and “deep learning” for artificial intelligence (AI), “environmental footprint,” “sustainability,” and “climate change” for life sciences, and “economic impact,” “resource allocation,” and “business impact” for economics have been used as part of PRISMA methodology to narrow down the most relevant studies for the analysis. Additionally, database coverage comparison and word mining over definitions and taxonomy have been performed as a prerequisite for a robust dataset. The results revealed a significant increase in publications over the past decade, highlighting the expanding role of AI in tackling environmental and economic challenges. Keyword analysis exposed dominant themes such as sustainability, resource management, and economic impact assessment, alongside emerging trends in AI applications for environmental conservation. Moreover, the network mapping revealed the AI techniques most applied in environmental economics research, such as predictive modeling, optimization algorithms, and data analytics. Therefore, our findings underscore the critical integration of AI with environmental science and economics, revealing a dynamic and rapidly evolving research landscape. The study highlights the necessity of interdisciplinary collaboration in leveraging AI technologies for sustainable development and economic optimization. It offers a roadmap for future research, suggesting areas where AI can significantly contribute to addressing complex global challenges, with topic partitioning and evolution over time. This bibliometric study serves as an important resource for researchers and policymakers, guiding the development of integrated strategies that involve AI’s potential in the presented domains.