数字农科院2.0

Review of simulations on land use change: a methodology based on bibliometric analysis

文献类型： 外文期刊

作者： Qingquan Sun;Lexuan Huang;Han Meng;Liang Chi;Jianzhai Wu;Xiangyang Zhou

作者机构：

关键词： bibliometrics;land use change;models;prediction;simulation

期刊名称： Frontiers in Sustainable Food Systems

ISSN： 2571-581X

年卷期： 2025 年 9 卷

页码：

收录情况： SCIE（2025版）

摘要： Introduction: Land use change simulation is crucial for understanding global environmental changes and guiding sustainable land management. This study conducts a bibliometric analysis of 2,147 Web of Science articles from 1988 to 2023 to summarize research trends, thematic evolutions, and future directions in land use change modeling. Methods: Using Biblioshiny tools, the study applies quantitative analytics, co-citation network mapping, and keyword clustering. Results: The research reveals three developmental phases. From 1988 to 2000 (62 articles), foundational models like CLUE and CA were developed. During 2001–2016 (1,039 articles), there were advancements in coupled models and multi-scenario simulations. From 2017 to 2023 (1,046 articles), the focus shifted to integrative frameworks linking land dynamics, ecosystem services, and climate feedbacks. Annual publication outputs increased from 5 to 149, showing exponential growth. Key research themes involve computational modeling, spatiotemporal dynamics analysis, and environmental impact assessment. Recent trends highlight “river-basin,” “multi-source data fusion,” and “geographically weighted models,” indicating a move toward basin-scale simulations, machine learning integration, and policy-oriented scenarios. China, the U.S., and Germany lead in research output, with top institutions including Beijing Normal University and the Chinese Academy of Sciences. China and the U.S. have strong domestic collaborations, while European countries have higher international collaboration ratios. Discussion: The analysis points out research gaps, such as limited integration of socio-economic drivers and insufficient cross-scale modeling. Future research should focus on developing hybrid frameworks combining process-based and data-driven models, leveraging multi-source data for accuracy, and designing scenario-based models for sustainable development goals, especially in river basins and urbanizing regions.

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