Research progress of integrated assessment models in food-water-energy-environment analysis: A bibliometric analysis

• The research dynamics of integrated assessment models (IAMs) in food-water-energy-environment (FWEE) from 1990 to 2024 is investigated. • The productivity of IAMs in FWEE distributed among countries, institutions, and authors is collected. • The knowledge base, seminal literature, and hot topics of IAMs in FWEE are found. • Potential directions of AI combined IAMs in FWEE have been discussed. The Food-Water-Energy-Environment (FWEE) analysis plays a crucial role in understanding the interconnected challenges of resource scarcity, sustainability, and climate change. However, these challenges are often complex and interdependent, making it difficult for a single model to capture their full dynamics. Integrated Assessment Models (IAMs) have emerged as a promising approach to address this issue. As a result, IAMs within the FWEE framework have become a major research focus, attracting considerable academic attention. Despite this, current research is fragmented and lacks a cohesive framework. To fill this gap, we conducted a bibliometric analysis using data from the Web of Science Core Collection, reviewing IAMs research in the FWEE domain from 1990 to 2024. Utilizing VOSviewer and CiteSpace for data visualization and assessment, we mapped the research landscape of IAMs in FWEE. Our study employed various techniques, such as co-occurrence analysis, clustering, and burst analysis, to: (1) identify key research trends, journals, and domains; (2) map the leading countries, their collaborations, and prominent authors and institutions; (3) highlight the foundational knowledge system, focusing on model development, emerging technologies, and research methods, noting a shift from early theoretical analyses to empirical studies on emerging technologies and policy analysis; and (4) pinpoint current research hotspots, including energy technology, social costs, and technical change, while providing an overview of research evolution and quality distribution. Finally, we suggest that future studies focus on advancing IAMs development for FWEE, especially with the integration of artificial intelligence. This study offers a comprehensive framework of existing research, providing valuable insights for future theoretical exploration and innovative applications.