MCDA在城市绿地规划和国家统计局中的实践评述

IF 2.3 Q3 ENVIRONMENTAL SCIENCES Blue-Green Systems Pub Date : 2023-11-03 DOI:10.2166/bgs.2023.132
Morgane Bousquet, Martijn Kuller, Sandrine Lacroix, Peter A. Vanrolleghem
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引用次数: 0

摘要

为了应对与可持续发展相关的多重挑战,土地利用规划政策越来越多地考虑绿色空间和基于自然的解决方案。国家统计局带来的多重好处使得使用多标准决策分析(MCDA)对其使用的最佳平衡至关重要。MCDA提供了一个方法目录,允许构建具有多个目标的问题,并帮助采用最佳解决方案。然而,国家统计局规划是一门新学科,研究仍在进行中,以使这种做法更普遍。我们对MCDA-NBS工具和实践进行了重要的文献回顾。我们根据Web of Science数据库中系统评价和元分析的首选报告项目(PRISMA)方法进行了文献研究,我们选择了2000年至2022年间关于该主题的124篇论文。我们通过研究这些实践在哪里应用,为什么和如何实施,以及谁参与其中,为NBS和绿色空间规划提供了最先进的MCDA方法。我们发现,在全球北方,研究通常是在参与标准加权阶段的专家的帮助下,在GIS-MCDA工具的帮助下,对单个案例进行研究,通常集成直接排名方法或AHP方法。
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A critical review of MCDA practices in planning of urban green spaces and NBS
Abstract Green spaces and nature-based solutions (NBS) are increasingly considered by land-use planning policies to respond to the multiple challenges related to sustainable development. The multiple benefits brought by NBS make the use of multicriteria decision analysis (MCDA) essential to optimally balance their use. MCDA offers a catalog of methods allowing to structure problems with multiple objectives and to help adopt the optimal solution. However, NBS planning is a recent discipline and research is still ongoing to make this practice more common. We carried out a critical literature review on MCDA-NBS tools and practices. We conducted our literature research following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method on the Web of Science database and we selected 124 papers on the subject between 2000 and 2022. We present a state-of-the-art MCDA approach for NBS and green space planning by looking at where these practices are applied, why and how this process is conducted, and who is involved in it. We found that studies are usually conducted in the global North on a single case study with the help of experts involved in the criteria weighting phase and the help of GIS-MCDA tools often integrating a direct ranking method or the AHP method.
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来源期刊
Blue-Green Systems
Blue-Green Systems Multiple-
CiteScore
8.70
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0.00%
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