{"title":"食物-水-能-环境综合评价模型的研究进展:文献计量分析","authors":"Songhua Huan , Liwen Liu","doi":"10.1016/j.egycc.2025.100215","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":72914,"journal":{"name":"Energy and climate change","volume":"6 ","pages":"Article 100215"},"PeriodicalIF":5.6000,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research progress of integrated assessment models in food-water-energy-environment analysis: A bibliometric analysis\",\"authors\":\"Songhua Huan , Liwen Liu\",\"doi\":\"10.1016/j.egycc.2025.100215\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":72914,\"journal\":{\"name\":\"Energy and climate change\",\"volume\":\"6 \",\"pages\":\"Article 100215\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2025-09-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy and climate change\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S266627872500042X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy and climate change","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266627872500042X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
摘要
食物-水-能源-环境(FWEE)分析在理解资源短缺、可持续性和气候变化等相互关联的挑战方面发挥着至关重要的作用。然而,这些挑战通常是复杂和相互依赖的,使得单个模型很难捕捉到它们的全部动态。综合评估模型(iam)已经成为解决这一问题的一种很有前途的方法。因此,在FWEE框架内的iam已经成为一个主要的研究焦点,引起了相当大的学术关注。尽管如此,目前的研究是碎片化的,缺乏一个有凝聚力的框架。为了填补这一空白,我们使用Web of Science核心馆藏的数据进行了文献计量分析,回顾了1990年至2024年在FWEE领域的IAMs研究。利用VOSviewer和CiteSpace进行数据可视化和评估,绘制了FWEE中IAMs的研究格局。本研究采用了共现分析、聚类分析和突发分析等多种技术:(1)确定关键研究趋势、期刊和领域;(2)绘制主要国家、合作伙伴、知名作者和机构的地图;(3)突出基础知识体系,重点关注模型开发、新兴技术和研究方法,从早期的理论分析转向新兴技术和政策分析的实证研究;(4)指出能源技术、社会成本和技术变革等当前研究热点,并提供研究演变和质量分布概况。最后,我们建议未来的研究重点是推进面向FWEE的人工智能系统的发展,特别是与人工智能的融合。本研究为现有研究提供了一个全面的框架,为未来的理论探索和创新应用提供了有价值的见解。
Research progress of integrated assessment models in food-water-energy-environment analysis: A bibliometric analysis
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.