Decision support system for green roofs investments in residential buildings

被引:33
|
作者
Teotonio, Ines [1 ]
Cabral, Marta [1 ]
Cruz, Carlos Oliveira [2 ]
Silva, Cristina Matos [2 ]
机构
[1] Univ Lisbon, Inst Super Tecn, CERIS, Av Rovisco Pais, P-1049001 Lisbon, Portugal
[2] Univ Lisbon, Inst Super Tecn, CERIS, Dept Civil Engn & Architecture & Georesources, Av Rovisco Pais, P-1049001 Lisbon, Portugal
关键词
Decision making; Green roofs; Investors preferences; Multicriteria analysis; Residential buildings; Sustainable development; MULTICRITERIA; SELECTION;
D O I
10.1016/j.jclepro.2019.119365
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
When designing green roofs, decision-makers continually face the difficult task of balancing benefits against costs. The use of decision analysis methods is essential in complex decision-making processes including different perspectives, multiple objectives, and uncertainty. This is the case when choosing between green roof systems, since different stakeholders show diverse concerns, and each solution has a different cost and performance. One of the most used methods in decision analysis is multicriteria analysis. The present study aims to adapt existing multicriteria decision models for the context of green roofs installation. The proposed methodology is based on the MACBETH method (Measuring Attractiveness by a Categorical Based Evaluation Technique) and determines the green roof option with the best trade-off between costs and benefits in agreement with the preferences of the users/investors. The paper presents the application to a real case study in Lisbon, Portugal, comparing the installation of 6 different green roofs over a parking lot. The methodology application identifies the intensive green roof as best solution classifying with a score of 69.43 out of 100. The conclusions on the best option remained robust in the sensitivity and robustness analysis. This approach supports the decision-making process of green roofs and enables robust and informed decisions on urban planning, while optimizing buildings retrofitting. (C) 2019 Elsevier Ltd. All rights reserved.
引用
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页数:23
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