Environmental system dynamics: Current development and applications

被引:0
作者
Xin, Sihui [1 ]
Li, Zhouyuan [1 ]
Nong, Junsong [1 ]
Wu, Jiaxin [1 ]
Zou, Xinwei [1 ]
Wu, Ruijin [1 ]
Dong, Shikui [1 ]
Wu, Rongling [1 ,2 ]
Wang, Shaopeng [3 ,4 ]
机构
[1] Beijing Forestry Univ, Sch Grassland Sci, Beijing 100083, Peoples R China
[2] Beijing Inst Math Sci & Applicat, Beijing 101408, Peoples R China
[3] Peking Univ, Inst Ecol, Coll Urban & Environm Sci, Beijing 100871, Peoples R China
[4] Peking Univ, State Key Lab Vegetat Struct Funct & Construct Veg, Beijing 100871, Peoples R China
基金
中国国家自然科学基金;
关键词
System dynamics; Stock-flow analysis; Causal loop feedback; Modeling applications; SD community development; SUSTAINABLE DEVELOPMENT; LANDSCAPE; LIMITS;
D O I
10.1016/j.ecolmodel.2025.111135
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
System Dynamics (SD) has emerged as a fundamental and powerful modeling methodology for understanding and simulating the behavior of both natural and social-economic systems. This review explores the historical development, key concepts, and broad applications of SD as a fundamental modeling approach. Tracing its evolution from the its inception since 1960s, the paper outlines the four major phases of SD's growth, from its initial conceptualization, through its methodological refinements, to its widespread application in ecosystems, and socio-economic systems in recent over the half century. The review highlights the development of SD communities across the globe, including the North American school, European clusters, and the growing body of work in China, and the progress in the global collaboration. We discuss and organize the foundational paired concepts of SD, including stock-flow relationships, the structure-behavior paradigm, the cause-effect process, and the loop-feedback paradigm. We summarize the SD modeling workflow protocol in the four stages, as conceptualization, visualization, quantification, and verification. It demonstrates good practices of SD modeling in various ecological contexts, spanning population, community, landscape, and macro-ecosystem levels, while emphasizing the method's adaptability and capacity for spatial modeling. Building on an extensive literature review and bibliometric analysis, the paper synthesizes key progress in SD modeling while offering insights into future perspectives and potential advancements. It concludes by reflecting on SD's ability to address multi-scale, multi-dimensional challenges and its compatibility with emerging novel approaches. Our goal is to bridge SD with contemporary ecological modeling practices by systematically reviewing the theoretical and practical advances of SD. This review provides insights for scholars and practitioners seeking to embed SD approach to the environmental and ecological systems simulation.
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页数:16
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