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Prediction of land use and optimization of water environmental carrying capacity on Xiamen Island based on socio-economic and water environmental coordination objectives

承载能力 环境科学 人均 约束(计算机辅助设计) 环境经济学 障碍物 环境资源管理 土地利用 用水 占用率 计算机科学 环境工程 系统动力学 持续性 可持续发展 限制 水平衡 空格(标点符号) 空间规划 生态学 概念模型 参数统计 边界(拓扑) 校长(计算机安全) 环境规划
作者
Guotao Li,Lin Cheng,Gong Liu,Zhi Zheng
出处
期刊:Sustainable Cities and Society [Elsevier BV]
卷期号:134: 106922-106922
标识
DOI:10.1016/j.scs.2025.106922
摘要

• A new DPSIR-SD-VCA integrated framework for assessing WECC was developed. • Predicted the dynamic changes of indicators under four coordination objectives. • Revealed the significant role of LUCC in determining WECC. • Identified the per capita water area occupancy rate as the main obstacle factor. • Optimizing the proportion and structure of water space helps to improve the WECC. Water environment carrying capacity (WECC) is a critical indicator for evaluating the coordination between socioeconomic development and the water environment. However, existing WECC assessments overlook the system’s complex causal couplings under time-varying conditions and, within spatial allocation, the applicability of planning-defined land-use conversion boundaries and the integrity of spatial units, thereby limiting interpretability and reliability. Accordingly, this study develops an integrated DPSIR-SD-VCA framework. First, we construct a comprehensive indicator system for WECC using the Driving forces-Pressure-State-Impact-Response (DPSIR) conceptual model and employ a system dynamics (SD) model to dynamically portray indicator results and trends under multi-scenario, time-varying conditions. We then couple a vector-based cellular automata (VCA) model—incorporating topological neighborhood relations and a vector patch-growth mechanism—to spatialize the time-varying indicators more realistically. Finally, through spatial autocorrelation, the coupling coordination degree model, and the obstacle degree model, we identify each sub-district’s WECC spatial characteristics, level of coordinated development, and principal obstacles. Results indicate universal improvement in WECC by 2035, ranked ecological and economic balance (EEB) > ecological environmental protection (EEP) > business as usual (BAU) > social economic development (SED). Per capita water surface area (X 1 ) emerges as the dominant constraint on WECC enhancement. Land transitions between residential land, commercial land, public management-services land, green land, and water bodies constitute the principal land-use/land-cover change (LUCC) drivers underpinning spatial heterogeneity in WECC. A pattern of higher WECC values in the south and lower values in the north is evident, motivating context-specific strategies in land-use restructuring, ecological restoration, and industrial upgrading.
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