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Decision support system for green roofs investments in residential buildings
Journal Pre-proof Decision support system for green roofs investments in residential buildings Inês Teotónio, Marta Cabral, Carlos Oliveira Cruz, Cristina Matos Silva PII:
S0959-6526(19)34235-0
DOI:
https://doi.org/10.1016/j.jclepro.2019.119365
Reference:
JCLP 119365
To appear in:
Journal of Cleaner Production
Received Date: 24 January 2019 Revised Date:
14 November 2019
Accepted Date: 17 November 2019
Please cite this article as: Teotónio Inê, Cabral M, Cruz CO, Silva CM, Decision support system for green roofs investments in residential buildings, Journal of Cleaner Production (2019), doi: https:// doi.org/10.1016/j.jclepro.2019.119365. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Decision support system for green roofs investments in residential buildings
Inês Teotónio PhD Student, CERIS, Instituto Superior Técnico, University of Lisbon Av. Rovisco Pais, 1049-001 Lisbon, Portugal E-mail: [email protected] [Corresponding author]
Marta Cabral PhD Student, CERIS, Instituto Superior Técnico, University of Lisbon Av. Rovisco Pais, 1049-001 Lisbon, Portugal E-mail: [email protected]
Carlos Oliveira Cruz Assistant Professor, Department of Civil Engineering and Architecture and Georesources, CERIS, Instituto Superior Técnico, University of Lisbon Av. Rovisco Pais, 1049-001 Lisbon, Portugal E-mail: [email protected]
Cristina Matos Silva Assistant Professor, Department of Civil Engineering and Architecture and Georesources, CERIS, Instituto Superior Técnico, University of Lisbon Av. Rovisco Pais, 1049-001 Lisbon, Portugal E-mail: [email protected]
Declarations of interest: none
12.396 words
1 2 3
Abstract
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When designing green roofs, decision-makers continually face the difficult task of
5
balancing benefits against costs. The use of decision analysis methods is essential
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in complex decision-making processes including different perspectives, multiple
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objectives, and uncertainty. This is the case when choosing between green roof
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systems, since different stakeholders show diverse concerns, and each solution has
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a different cost and performance. One of the most used methods in decision
10
analysis is multicriteria analysis. The present study aims to adapt existing
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multicriteria decision models for the context of green roofs installation. The
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proposed methodology is based on the MACBETH method (Measuring
13
Attractiveness by a Categorical Based Evaluation Technique) and determines the
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green roof option with the best trade-off between costs and benefits in agreement
15
with the preferences of the users/investors. The paper presents the application to a
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real case study in Lisbon, Portugal, comparing the installation of 6 different green
17
roofs over a parking lot. The methodology application identifies the intensive
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green roof as best solution classifying with a score of 69.43 out of 100. The
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conclusions on the best option remained robust in the sensitivity and robustness
20
analysis. This approach supports the decision-making process of green roofs and
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enables robust and informed decisions on urban planning, while optimizing
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buildings retrofitting.
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Keywords: decision making; green roofs; investors preferences; multicriteria
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analysis; residential buildings; sustainable development. 1
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1.
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Green roofs provide several ecosystem services, therefore, have been increasing their
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market share over the past years (Berardi et al., 2014). The reasons for the adoption of
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these systems depend mainly on the type of user/investor, type of building, and
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social/economic context (European Commission, 2014). For example, private and
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public investors have different concerns and objectives (Nurmi et al., 2016; Tomalty et
35
al., 2010). Private investors, such as property owners, value more the possibility of
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having an additional space for leisure or increased thermal comfort in their properties.
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While, public investors (e.g. municipalities) give greater importance to air quality
38
improvement and other environmental benefits. To accurately evaluate the solutions, it
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is necessary to consider the preferences of each stakeholder.
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Green roofs provide many benefits, not only on a global or urban scale but also for
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buildings (Berardi et al., 2014; Bianchini and Hewage, 2012; Kohler, 2018). So, their
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application should be analysed as a multiple objective process. In addition, different
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systems have distinct performances and costs, which sometimes makes it challenging to
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select the optimal and most effective solution (Teotónio et al., 2018). For these reasons,
45
decision-makers continually face the difficult task of balancing benefits against costs
46
while being challenged with the risks of realizing the benefits (Cruz et al., 2017;
47
Phillips and Bana e Costa, 2007).
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Decision analysis is a technique for analysing complex decisions with multiple, and
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usually conflicting, objectives, and uncertainty (Parnell, 2009). Structured processes are
50
used to support decision-makers and to clear the final choice. Multicriteria decision
51
analysis (MCDA) is one of the most common methods in decision analysis, which has 2
Introduction
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been extensively used to evaluate and compare options involving the achievement of
53
multiple objectives (Franco and Montibeller, 2010). The application of MCDA to green
54
roof projects is appropriate and will enable decision-makers (e.g., users, owners,
55
governments) to make more robust and informed decisions.
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The aim of the present study is two-fold: to adapt an existing multicriteria decision
57
model to a green roof installation context and to validate it through a real case study.
58
This will allow exploring the potentials of incorporating users/investors preferences in
59
the decision-making process, while evaluating the different trade-offs between green
60
roofs life-cycle costs and benefits.
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Like many other European cities, Lisbon has been putting efforts towards
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environmental sustainability "with particular focus on establishing green infrastructure,
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or connected networks of green space, to counteract the effects of climate change …"
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(European Commission, 2019a, 2019b). Lisbon was acknowledged with the European
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Green Capital Award for 2020 having demonstrated several initiatives for the creation
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of green corridors and new green spaces to promote citizens’ quality of life and protect
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biodiversity (European Commission, 2019a, 2019b). The installation of green roofs,
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even though limited, had also a contribution. Wastewater treatment plant of Alcântara is
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one of the best-known green roofs in Lisbon.
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To validate the methodology, this study evaluates a real case study which considers the
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installation of a green roof in a new residential building, in Lisbon, Portugal. The
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research adopts the perspective of the private decision-maker, one of the directors of the
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company responsible for the real estate investment. The perspective of other
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stakeholders, such as public decision-makers or the users, is not considered in this case,
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although the methodology is transferable to any decision-maker.
3
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The application of the multicriteria method was carried out through face-to-face
77
interviews between two decision analysts and a decision-maker. A preliminary meeting
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promoted the definition of the problem and construction of the decision model. This
79
required the determination of value functions incorporating the decision-maker
80
judgments. After the application of the model, a final meeting allowed the validation of
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the results.
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This paper is organized as follows. Section 2 presents an overview of the existing
83
literature. Section 3 presents the multicriteria decision model for green roofs. Section 4
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describes the application of the methodology to a real case study and section 5 presents
85
a discussion of results. The final section closes the study with the main conclusions.
86 87
2.
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Few studies using MCDA have been conducted to support the decision-making process
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of evaluating and/or comparing different typologies of green roofs. Several authors
90
applied MCDA methods to assess existing barriers to the widespread installation of
91
green roofs and determine the importance of design characteristics influencing their
92
choice.
93
Rosasco and Perini (2019) considered the economic, environmental, social and
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performance aspects of green roofs. They ranked these criteria according to their
95
importance for the system selection. The most relevant was the performance of the
96
system, including elements such as thermal insulation, roof protection, and systems
97
weight. The second most relevant were the environmental criteria, followed by
98
economic criteria and social criteria. The authors identified the building scale analysis,
99
i.e., a single roof, as the reason for the low impact on social aspects. This proved that
100
MCDA approaches are case-by-case sensitive. The installation and maintenance costs,
4
Literature Review
101
air quality, thermal insulation, and health effects were the most important sub-criteria
102
within each group.
103
Sangkakool et al. (2018) conducted a research with the same purpose. However, the
104
authors applied a mixed approach combining strengths, weaknesses, opportunities, and
105
threats (i.e., SWOT analysis) to structure the model. Both the studies of Rosasco and
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Perini (2019) and Sangkakool et al. (2018) used the Analytical Hierarchy Process
107
(AHP) to weight the criteria. Experts identified urban heat island mitigation and lack of
108
financial incentives as the criteria with greater contribution to the analysis. The possible
109
damage and leakage of the system, aesthetics and urban quality, social responsibility
110
concerns were considered the least relevant.
111
Brudermann and Sangkakool (2017) used the same hybrid approach to identify the key
112
factors to promote the application of green roofs in European temperate climates. The
113
strengths and opportunities had a higher impact than negative SWOT categorized
114
criteria (i.e., weaknesses and threats). The positive results revealed that green roofs have
115
a great potential in temperate climates. Sub-criteria as reduced flood risk, environmental
116
benefits, life quality and aesthetics, and green policies in cities received the highest
117
scores.
118
Naing et al. (2017) used the MCDA method to identify the potential of existing
119
buildings for green roof retrofitting. The authors took into consideration the buildings
120
physical, economic, and social aspects and the green roof to be installed. Results show
121
that the buildings physical aspects had the highest impact on the analysis.
122
All the studies involved a panel of stakeholders with reasonable sample sizes and
123
distinct work fields and backgrounds to include different perspectives on the decision
124
problem. Most authors have experienced disagreement between stakeholders, since
125
several groups showed distinct perceptions and assigned different priorities to the
5
126
criteria. For example, in the study of Rosasco and Perini (2019), academics valued more
127
the economics aspects of green roofs while professional gave more importance to social
128
aspects. In line with this, academics defended that costs, even though significant, are
129
outweighed by the economic benefits such as tax incentives and increase on property
130
value. Professionals, however, reported the opposite. The study of Naing et al. (2017)
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presented different conclusions with academics valuing more social aspects and experts
132
giving more importance to economic criteria.
133
When evaluating roof solutions, the research of Guzmán-Sánchez et al. (2018) and
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Rosasco and Perini, (2019) showed that green roofs are preferable to traditional
135
solutions. Supporting this type of analysis, Mahdiyar et al. (2019) developed a decision
136
support system to select the best green roof type to install in residential buildings. The
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validation of the model through three case studies presented changes in the best option
138
according to the priorities of the project. This proved, once again, that MCDA is case-
139
sensitive and specific project requirements have a significant impact on the decision-
140
making. For this reason, weighting criteria can generate different outcomes making it
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hard to draw valuable and global conclusions on this topic.
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Table 1 presents the main characteristics of existing MCDA applied to green roofs,
143
considering the type of MCDA approach, criteria and sub-criteria evaluated,
144
stakeholders and sample size.
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6
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Table 1. Principal characteristics of existing MCDA applied to green roofs context Reference
Evaluated solutions
MCDA approach
Total criteria
Total subcriteria
Rosasco and Perini, 2019
Green and traditional roofs
Analytic Hierarchy Process
4
17
Sangkakool et al., 2018
Green roofs
Analytic Hierarchy Process
4
14
Brudermann and Sangkakool, 2017
Green roofs
Analytic Hierarchy Process
Mahdiyar et al., 2019
Mahdiyar et al., 2018
GuzmánSanchéz et al., 2018
Naing et al., 2017
Green roofs (extensive, intensive and semiintensive)
Green roofs
Green roofs and other flat roofs
Green roofs (extensive)
Fuzzy Delphi method and Cybernetic Fuzzy Analytic Network Process Enhanced Fuzzy Delphi method Analytic Hierarchy Process and the Technique for Order of Preference by Similarity to Ideal Solution
Analytic Hierarchy Process
4
13
Scoring method
Stakeholders
Pairwise comparison by attributing a score based on a 1-9 scale Pairwise comparison by attributing a score based on a 1-9 scale Pairwise comparison by attributing a score based on a 1-9 scale
Sample size
Judgments collection Cross-group discussion (brainstorming technique led by a moderator) Structured interviews and questionnaires during conferences
Architects, engineers, academics
30
Architects, urban planners, academics, city representatives, users
46
Architects, urban planners, academics, city representatives, users
30
Semi-structured interviews and questionnaires
-
12
pairwise comparison in a scale 1,3, 5, 7, 9
Architects, civil engineers, contractors and landscape planners/designers
33
Questionnaires
4
19
pairwise comparison in a scale 0, 1, 2, 3, 4
Architects, landscape planners, contractors, academics
28
Questionnaires
16
Pairwise comparison in a 4-level scale: very important, important, slightly important and unimportant
Experts
23
Questionnaires
18
Pairwise comparison by attributing a score based on a 1-9 scale
Academics and experts (research consultants, architects, surveyors and ecologists working on green roofs)
30
Questionnaires
-
3
147 148
Table 2 specifies the sub-criteria included in each study.
149
7
150
Table 2. Sub-criteria included
Sub-criteria
Rosasco and Perini, 2019 (1) Installation costs
Costs
(2) Maintenance and disposal costs
(3) Tax incentives Incentives and policies
in existing MCDA applied to green roofs
Sangkakool et al., 2018 (1) Investment volume
(2) Maintenance requirements
(3) Lack of subsidies (4) Favorable regulations and policies
Brudermann and Sangkakool, 2017
(1) Higher implementation and maintenance costs
Mahdiyar et al., 2019
Mahdiyar et al., 2018
Structural considerations
(5) Recycle materials (6) Roof protection
(5) Cheap synthetic grass (6) Possible damage / leakage
(7) Weight of the system
Naing et al., 2017
(1) Life cycle costs
(1) Access to machinery and materials to site, and (2) existing use of buildings (3) Access to maintenance and (4) utilities, and (5) size of usable roof area
(1) Additional required initial cost
(2) Maintenance costs
(1) Maintenance costs
(3) Payback period
(2) Payback period (3) Net present value
(2) Green policies in cities (3) Legal and political constraints
(4) Green building certificate award achievement (2) Embodied carbon (3) Embodied energy (4) Recycled materials
(4) Embodied energy and carbon emission System sustainability
Guzmán-Sanchéz et al., 2018
(4) Possible damage
(5) Structural and static challenges
(5) Probable damage (6) Roof slope
(4) Structural considerations
Probable increasing in the size of (7) beam and (8) column
(5) Roof protection (6) Type of structure and loading capacity, and (7) roof slope
(6) Dead load
Probable changes in the (9) foundation and (10) lateral load resistance system (8) Height of building, (9) availability of sunlight, and (10) wind considerations (11) Rainfall and irrigation and, (12) waterproofing (13) stormwater discharge point of building (14) Safety consideration, and (15) fire risk
Physical considerations
Aesthetics and recreation
(8) Real state benefit (9) Building aesthetics Recreation included in building aesthetics
(10) Urban aesthetic (11) Health effects
Social impacts
Sound insulation Climate regulation and other environmental benefits
8
(6) Aesthetics
(7) Aesthetics / urban quality
(6) Life quality and aesthetics
(8) Lack of skills and knowledge
(7) Lack of knowledge (8) public acceptance and environmental awareness
(9) Social responsibility concerns
(12) Energy savings (heating and cooling) Thermal insulation
(5) Increase in property value
(10) Green building trends
(9) Scepticism of potential adopters
(11) Energy savings
(10) Energy savings
(11) Higher property value (12) Aesthetic aspects
(7) Recreational space
(13) Recreational space provision
(8) Type of access: public or private
(14) Type of accessibility
(9) Energy savings
(15) Energy savings (8) Thermal insulation (9) Albedo coefficient
(14) Acoustic noise reduction
(10) Noise absorption
(16) Noise absorption
(10) Noise control (11) Carbon sequestration
(12) Urban heat island mitigation (13) Climate
(11) Climate
(16) Public potential accessibility
(17) Likelihood for restrictions on planning and building permit requirements
(13) Thermal insulation properties
(15) Air quality
(7) Social use
(17) Climate
change (14) Environmental benefits Stormwater management
(16) Runoff
change (12) Environmental benefits (13) Flood risk reduction
(11) Environmental benefits (12) Water management
(18) Environmental benefits (19) Stormwater retention
(12) Runoff attenuation (13) Water purification
(17) Suitability to location (14) Solar power (15) Relative humidity control (16) Biodiversity and agricultural productivity
Others
(18) Opportunities for food production
151 152
There are also other studies comparing green roofs and competing alternatives in
153
specific contexts. These use multi-objective optimization to select solutions that
154
improve flow and flood management (e.g., Alves et al., 2018; Chow et al., 2014;
155
Radinja et al., 2019), control water pollution (e.g., Di Matteo et al., 2017; Liquete et al.,
156
2016) and enhance biodiversity (e.g., Snep et al., 2009). In these cases, the defined
157
evaluating criteria are adjusted to the particularities of each decision problem and the
158
analysis is more limited (e.g., included criteria) in comparison to green roofs analysis.
159 160
3.
Methodology
161
Decision analysis is characterized by several model types distinguished by their
162
handling of uncertainty and multiple criteria. Among the evaluation processes, this
163
study adopts the multicriteria value method, which is the best to evaluate different
164
options considering multiple objectives, as it is the case of green roofs (Phillips, 2005).
165
The MCDA method includes the following main steps: (i) problem definition and
166
structuring; (ii) model structuring and use; (iii) impact assessment and analysis,
167
including the development of action plans (Bana e Costa and Beinat, 2005; Bana e
168
Costa et al., 2008; Belton and Stewart, 2001).
169
process, this sequence can be translated into a 4-stage decision support process based on
170
the studies of Bana e Costa et al. (2012) and Franco and Montibeller (2010). Figure 1
To structure the decision-making
9
171
presents the methodology applied in this study. The main steps of the proposed
172
methodology are outlined in sections 3.1. to 3.4.
173
Figure 1. Structuring the problem using MCDA methodology
174 175 176 177
3.1.
178
The problem definition and structuring consist of characterizing the decision context,
179
establishing the analysis boundaries and its scope. It is essential that during this process
180
the decision problem for selecting a green roof and the primary motivations for the
181
installation of such solution (i.e., strategic objectives) become clear and transparent
182
(Bana e Costa and Beinat, 2005). The decision context has a great influence on the
183
decision problem and should be clearly identified. For example, whether it is a small or
184
a largescale project (e.g., greening a single building vs a city), how it affects the actors
185
involved, the people benefiting from the project or even its impacts on the environment.
186
The strategic objectives are more specific. There can be multiple reasons to install a
187
green roof, such as to preserve biodiversity, improve the thermal behaviour of a
188
building or reduce the temperature of a city (Berardi et al., 2014) and those can change
189
the way the decision problem is addressed.
190
The decision-makers play an important role since they have the power to affect the
191
decision. Sampling procedure involves the identification of the key players, for
192
example, stakeholders, experts and professionals, users, and other actors that might be
193
affected by the project and whose perspective should be considered when taking a
194
decision. The definition of their level of intervention in the process allows identifying
195
their value-systems and possible conflicts that can emerge (Franco and Montibeller,
196
2010). There is a common belief that groups outperform individuals in decision-making 10
Structuring the Problem
197
processes since there is a higher number of perspectives and share of information.
198
However, group decision biases, such as group thinking, are also more likely to happen
199
(Mateus et al., 2017).
200
Lastly, the type of evaluation model to be constructed in order to address the problem is
201
essential. According to Belton and Stewart (2001), the model should focus on technical
202
modelling and analytical features, but mostly on supporting the decision-maker. There
203
are several MCDA methods, such as: ELimination Et Choix Traduisant la REalitè
204
(ELECTRE); Preference Ranking Organization Method for Enrichment Evaluations
205
(PROMETHEE); Multi-Attribute Utility Theory (MAUT); Analytic Network Process
206
(ANP); Analytic Hierarchy Process (AHP); Case-Based Reasoning (CBR); Data
207
Envelopment Analysis (DEA); Fuzzy Set Theory; Simple Multi-Attribute Rating
208
Technique (SMART); Goal Programming; Technique for Order of Preference by
209
Similarity to Ideal Solution (TOPSIS); Simple Additive Weighting (SAW); and
210
MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique).
211
These methods differ in multiple aspects, such as the implementation procedure, outputs
212
type, necessary input level, type of decision-making problems it can cover, number of
213
criteria it allows and the type of indicators, the handling of the participatory process of
214
stakeholders, efforts by decision-makers, and the need to include a decision aid
215
specialist for technical support (Guarini et al., 2018; Velasquez and Hester, 2013). This
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study adopts the additive value model using the MACBETH method as a decision-aid
217
approach to multicriteria value measurement (Bana e Costa et al., 2012). This method is
218
based on a pairwise comparison procedure to determine the value of the alternatives
219
while applying a non-numerical questioning procedure to attribute numerical scores.
220
This is the critical distinction between MACBETH and other MCDA methods that use a
11
221
pairwise comparison procedure involving the attribution of numerical judgments and,
222
sometimes, leading to mistakes (Bana e Costa et al., 2012).
223
This study uses M-MACBETH software to reproduce the methodology. This decision
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support system is designed to be used by a decision analyst following the constructivist
225
principles of process consultation, implying the sharing of information with the
226
decision-maker during the entire process.
227 228
3.2.
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The MCDA model structure consists of three main tasks: i) the identification of decision
230
alternatives, i.e., green roof options; ii) the representation of the objectives in a value
231
tree (i.e., fundamental points of view - FPV), and; iii) the definition of attributes to
232
measure the achievement of those objectives, i.e., descriptors of performance – DP
233
(Franco and Montibeller, 2010).
234
When choosing a green roof system, there are three technical solutions that can be
235
distinguished: i) extensive, ii) semi-intensive, and iii) intensive, according to their main
236
characteristics, especially the type of vegetation, use, and maintenance (FLL, 2008).
237
The FPV are the values that actors consider relevant to the evaluation of the green roof
238
options and can consist of a single or several evaluation criteria. They should be
239
complete, non-redundant, decomposable, and consensual (Franco and Montibeller,
240
2010). There are two basic model-structuring strategies to define the FPV: bottom-up
241
and top-down. In each one of those strategies, the value true is built by aggregation and
242
desegregation of the points of view, respectively. The difference is that in the bottom-up
243
model, the FPV definition is based on the characteristics differentiating the green roof
244
options and that are relevant to meet decision-makers' values. Those characteristics are
245
then grouped, composing the FPV. In the top-down model is the inverse. First one
12
Structuring the Model
246
should establish the objectives, and only then select the underlying objectives and FPV
247
(Franco and Montibeller, 2010). This paper evaluates the installation of green roofs and,
248
therefore, the options to be addressed are known in advance. However, the main focus
249
of this methodology is the values/preferences of the decision-makers. For this reason,
250
this study adopts the top-down model-structuring strategy. Note that applications to case
251
studies may include other solutions apart from green roofs that also meet the objectives.
252
Suppose the installation of nature-based solutions (including rain gardens, street trees,
253
green roofs and walls, and others) with the objective to cover a neighbourhood with
254
green. In such cases, one should use the bottom-up model.
255
The top-down model-structuring strategy starts with the definition of the objectives,
256
separating the mean from the end objectives and, finally, identifying the fundamental
257
objectives, i.e., FPV (Franco and Montibeller, 2010). For example, Table 2 in section 2
258
presents the criteria included in existing MCDA applied to green roofs, which are
259
organized by their nature. Costs include installation, maintenance, disposal costs; social
260
impacts involve life quality, aesthetics, social responsibility concerns, lack of
261
knowledge, scepticism of potential adopters; and climate regulation and other
262
environmental benefits cover air quality, urban heat island effect, climate change, and
263
others. These sets of criteria represent potential FPV.
264
The FPV largely depends on the stakeholders' profile. While structuring the model, the
265
decision-makers should select the most relevant evaluation criteria according to the
266
strategic objectives. These should include the costs and benefits of green roofs
267
considering the particularities of the project. For example, the benefit of well-being and
268
improvement of individuals' work performance are particularly important in office
269
buildings and schools. In another context, improving the aesthetics and the availability
270
of recreational spaces in buildings are crucial for real estate investors. While the air
13
271
quality improvement and mitigation of urban heat are more significant for large-scale
272
public investors. Error! Reference source not found. Table 3 organizes the green
273
roofs costs and benefits (Berardi et al., 2014) that might be decisive when evaluating the
274
installation of green roofs to be considered in the FPV definition. Questions like “Does
275
this point of view contain many components, i.e., evaluation criteria?”, “Which ones?”
276
stimulate the discussion and help the FPV definition.
277
Table 3. Costs and benefits of green roofs
278
Economic Costs
Social and Environmental
• Installation costs • Maintenance costs • Replacement/Disposal costs
Key benefits
• Incentive policies (e.g., tax reduction)
• Mitigation of urban heat island effect
• Real estate value (increasing aesthetics
• Rainwater management (reduction and
and recreational space) • Durability (roof longevity)
delay) • Improvement of public health and quality
of life • Landscape and urban aesthetics
Other benefits
• Thermal comfort and reduction of energy
consumption
• Ecological preservation and promotion of
biodiversity
• Fire security
• Higher levels of productivity at work
• Weight of the system (building’s loading
• Lower risk of flooding
capacity)
• Urban sound absorption
• Possibility of rooftop farming
• Improvement of air quality
• Efficiency of photovoltaic panels (PV’s)
• Improvement of drained pluvial water
• Sound insulation
quality
279 280
Green roofs installation can be technically challenging in some situations, so screening
281
criteria should be initially defined to eliminate solutions that do not fulfil essential
282
requirements. For example, the budget available for investment is typically a constraint
283
and has a significant influence on the final decision. Installation conditions can also be a
284
limitation. Rehabilitation interventions are a good example since buildings are not
14
285
structurally prepared to support the additional weight of the green roof. In such cases,
286
the choice of the system can be limited to lightweight extensive green roofs.
287
Structuring the model also include key issues of interactive value modelling such as the
288
definition and operationalization of the evaluation criteria through DP, which are
289
ordered sets of plausible impact levels associated with each evaluation criterion. DP can
290
be a qualitative or quantitative measure of the extent to which each green roof solution
291
satisfies a specific condition, i.e., performance scale (Bana e Costa et al., 2018).
292
According to Bana e Costa et al. (2012), M-MACBETH software scores the
293
performance of the alternatives through the more appropriate of two different
294
comparison methods:
295
i) direct method, directly comparing the green roof alternatives (two at a time);
296
ii) indirect method, comparing pre-defined performance levels that can be
297
quantitative or qualitative.
298
The DP of each criterion should be described in the most unambiguous manner possible
299
to ensure a clear interpretation. Each criterion is associated with a performance scale
300
that consists of a carefully described and ordered set of impact levels to cover an
301
adequate range of performances. Performance scales can be built upon existing
302
studies/data on green roofs costs and their performance (Bana e Costa et al., 2012).
303
A possible DP is the fire security of green roofs based on technical data with functional
304
importance (Zinco, 2016). The DP consists of four impact levels that evaluate “the
305
extent to which the green roof can retard the spread of flames from the roof to the
306
interior of the building” according to the British Standard on external fire exposure roof
307
tests (BS 476: Part 3, 2004). It uses a national standard because the existing studies on
308
green roofs performance to fire exposure cover those tests. However, if further research
309
is done, analysts and decision-makers can change/adapt the proposed DP and the
15
310
corresponding impact levels. In fact, “Green infrastructure” is a new field of research
311
and current studies are showing in particular new ways of quantifying their benefits.
312
Therefore, this methodology can, and must, be redesigned and updated, anytime, based
313
on other standards (e.g., using current green roof guidelines (ASTM) for the United
314
States).
315 316
Table 4. Constructed performance scale for fire security criterion
317
Level
Description
When the roof is tested for external fire exposure with burning brands, wind and supplementary radiant heat, according to BS 476: Part 3: 2004, there is … Broof
… no penetration within 60 minutes
Croof
… no penetration within 30 minutes
D/Eroof
… penetration within 30 minutes
Froof
… no performance determined
318 319
Finally, the decision-makers need to identify reference levels for each criterion in each
320
set of performance levels to help a proper criteria interpretation and determine the
321
corresponding value functions. The two reference levels, good and neutral, correspond
322
to the satisfactory level of attractiveness and the level in which the performance was
323
neither attractive nor unattractive, respectively (Bana e Costa et al., 2012).
324 325
3.3.
326
To accurately compare the green roof options under consideration it is necessary to
327
develop formal models of the decision-makers’ preferences and respective value trade-
328
offs (Belton and Stewart, 2001). These models translate value functions that define each
329
criterion or their weight, respectively, enabling to transform qualitative inputs into value
330
scores (Bana e Costa et al., 2006). To achieve this, MACBETH builds upon the
16
Construction of the Model
331
decision-makers' qualitative judgments about the difference in attractiveness between
332
two elements at a time, either on performance levels or reference levels. The decision-
333
makers must be confronted with the results of the model at each stage, testing for
334
consistency and adjusting their judgments, if necessary (Bana e Costa and Chagas,
335
2004).
336
According to Bana e Costa and Chagas (2004), to determine the value functions first,
337
neutral and good performance levels are anchored with scores of 0 and 100,
338
respectively. Then, the MACBETH method compares and ranks the performance or
339
reference levels, establishing a preference scale, and assigns the final scores to each
340
level. For that, the decision-makers must measure the difference in attractiveness
341
between the several levels. The construction of judgment matrices involves the
342
following steps, as suggested by Bana e Costa and Chagas (2004):
343
1) Decision-makers judge the difference of attractiveness between the higher
344
and lower level of each DP. Then between the second-highest and the lower
345
level, and so on until the last column of the judgments matrix is completed;
346
2) The first row of the matrix is completed, followed by the second diagonal and
347
then the remaining cells.
348
The decision-makers should give qualitative judgments using the MACBETH
349
difference scale with seven categories of difference of attractiveness: null, very weak,
350
weak, moderate, strong, very strong and extreme. In case of indecision or indifference,
351
it is possible to choose more than one consecutive categories (e.g., strong-extreme). In
352
the presence of inconsistencies, the decision-makers must be warned and informed
353
about possible changes in the judgments (Bana e Costa and Chagas, 2004).
354
Finally, weighting the criteria follows a similar procedure. First, all the criteria are
355
ordered according to their importance to the decision-making process, using the
17
356
MACBETH simple ranking process. During this process, the decision-makers are
357
actually ranking the importance of the swings from the neutral to the good performance
358
level on each criterion. For this purpose, the decision analysts can ask the following
359
question “Consider that there is one green roof option with a neutral performance in all
360
the criteria. Which criterion would have the most important improvement if you could
361
change the green roof performance from neutral to good in just one criterion?”. The
362
same question applies for the second most important criterion and so on. After ranking
363
the criteria, the decision-makers need to give qualitative judgments on the weights of
364
each criterion comparing each pair of swings (Bana e Costa and Chagas, 2004).
365
Based on all judgments, the M-MACBETH software determines the final value
366
functions and weights the criteria. At the end of this process, the analyst should present
367
the final models to the decision-makers for validation. The software allows adjustments
368
on the final values within a range compatible with the judgments (Bana e Costa and
369
Chagas, 2004).
370 371
3.4.
372
After structuring and constructing the model, the application provides the necessary
373
information to discuss the problem and sustain the green roof choice . As indicated in
374
section 3.1, the present study uses the M-MACBETH software to obtain results. Bana e
375
Costa et al. (2012) provide detailed information on the mathematical formulation of this
376
software. A summary is presented above.
377
According to Bana e Costa et al. (2012), testing the consistency of the decision-makers'
378
judgments consists of verifying the following conditions 1–3. Let
379
> 2 evaluating elements, e.g., performance or reference levels;
380
of
18
Application of the Model
such that
is more or equally attractive as
be a finite set of and
two elements
and ( ) and ( ) be the scores
, = 0, … , 6 the seven MACBETH
381
assigned to those elements, respectively; and
382
categories of difference in attractiveness: null ( ), very weak ( ), weak ( ), moderate
383
( ), strong ( ), very strong ( ) and extreme ( ). Then, ( , )
384
represents a MACBETH judgment of difference in attractiveness between
385
expressed by the single category
and ( , )
∪ …∪
( = 0, … , 6) and
( , = 1, … , 6 ! "ℎ <
386
) represents a MACBETH judgment expressed by a subset of categories from Ci to Cs,
387
in cases of indecision. Bana e Costa et al. (2012) state that there are three possible
388
scenarios. If the decision-makers judge
389
then those elements are coincident: ∀ , : ( , )
with null difference of attractiveness,
⟹ ( ) = ( )
390
If the decision-makers identify
391
than : ∀ , : ( , )
and
(1)
as more attractive than , then
( = 1, … , 6)
and ∀ , : ( , )
∪ …∪
( , = 1, … , 6 with < )
⟹ ( )> ( )
(2)
and ! are more attractive than
and ,, respectively, and the difference of
392
Finally, if
393
attractiveness between
394
and ,, then the difference between their scores is equally greater: ∀ , : ( , )
has a higher score
is greater than the difference of attractiveness between !
and
∪ …∪
and ∀ !, , : (!, ,)
( , = 1, … , 6 with < )
∪ …∪
with , , ´, ´ (1, … 6), ≤ , and ´ ≤ ´ > ´ ⟹ ( ) − ( ) > (!) − (,)
(3)
395
After verifying the consistency of all the judgments, MACBETH builds a numerical
396
continuous value scale of each criterion, i.e., scoring scales. Following Bana e Costa et
397
al. (2012), this scoring process is obtained by solving the linear programming problem
398
presented above, where
3
and
4
are two elements of
such that they are at least as 19
399
attractive and at most equally attractive to any other element of
400
linear problem provides the estimation of the minimum of
401
following constraints 4–7. The linear programming problem is provided below,
402
following the guidelines of Bana e Costa et al. (2012).
403
An arbitrary zero is fixed: (
404
4
(
3
) subject to the
= 0)
(4)
If the difference of attractiveness between and ∀ ( , )
405
, respectively. The
is null, they score equally:
: ( ) − ( ) = 0
(5)
Then, ∀ ( , )
∪ …∪
with , 51, 2, 3, 4, 5, 69 and ≤ : ( ) − ( ) ≥
(6)
406
Both constraints guarantee that the ranking of the elements is kept. The preservation of
407
the order inherent of the judgments provided by the decision-makers is ensured by: ∀ ( , )
∪ …∪
and ∀ (!, ,)
´
∪ …∪
´
with , , ´, ´ 51, 2, 3, 4, 5, 69,
≤ , ´ ≤ ´ and > ´: ; ( ) − ( )< − ; (!) − (,)< ≥ − ′
(7)
408
Bana e Costa et al. (2012) indicate that the resulting basic MACBETH value scale for
409
each criterion is then transformed to an anchored scale where
410
(
4)
(
3)
= 100 and
= 0, corresponding to the good and neutral performance levels, respectively. The
411
performance of the green roof options is then scored for each criterion to determine the
412
partial values. Finally, the combination of partial values functions with the weighting
413
criteria measures the overall attractiveness of each option and allows to discuss global
414
results to assess the best value trade-offs. Belton and Stewart (2001) shows a model
415
requiring additive aggregation, as presented in Equation 8: A
>(?) = @ ! B
20
(?)
(8)
416
where >(?) is the global attractiveness of option ?; ! is the weighting coefficient
417
(represents the relative importance of criterion );
418
option ? for the criterion and C is the total number of DP.
419
Bana e Costa et al. (2018) recommend a sensitivity analysis on the criteria weight to
420
visualize the extent to which the model's recommendations change for different
421
judgments. Also, a robustness analysis is important to study the influence of diverse
422
levels of scarcity, imprecision and uncertainty on conclusions.
(?) is the partial value function of
423 424
4.
Case Study
425
The case study is a real residential project in Lisbon, Portugal. This project was part of a
426
residential condominium with six building plots, three of them already build. A
427
Portuguese company promoting turnkey investments assumed the construction and
428
management of one building with 50 luxury apartments. This section describes the
429
methodology application to this case study, in order to determine the best green roof
430
option to install in this building. This chapter follows the first 3 stages identified in the
431
previous chapter (sections 3.1. to 3.3.). The application of the model (section 3.4.) to
432
this case study is presented in the next chapter, results and discussion (section 5).
433 434
4.1.
435
Decision Problem: The company responsible for the residential project works mainly
436
for a premium market. For this reason, the designers planned the green roof installation
437
on the ground floor, above the parking lot. Figure 2Error! Reference source not
438
found. shows a top view including a green roof above the parking lot (represented in
439
green). The decision problem focused on choosing the best green roof solution to install.
440
Structuring the Problem
Figure 2. Green roof’s location proposal (top view) 21
441 442
Strategic Objectives: The decision problem had to deal with specific characteristics of
443
the project. For example, the company operates in areas related to real estate and
444
renewable energy. Therefore,
445
sustainability, as reducing building’s energy consumption. The apartments design phase
446
considers the application of materials with good thermal insulation properties (e.g.,
447
insulated glass, also known as double glazing), energy efficient equipment and 38
448
photovoltaic panels (PV) on the roof. The condominium included a large green area
449
open to the public during the day. Considering these design features, the main strategic
450
objectives of the green roof installation were: i) to improve the building's aesthetics to
451
be consistent with the surrounding landscape ii) to improve building's services and iii)
452
to enhance the project environmental commitment. This is in line with a multiple
453
objective decision-making context. So, the company decided to conduct a MCDA to
454
support their decision.
aimed to incorporate high standard solutions for
455 456
Decision-Makers: The company responsible for the acquisition of the two building plots
457
and construction of the residential project was the main investor and, consequently,
458
played a crucial role in the decision-making process. The company operated together
459
with other investors (e.g., design and construction companies) so this was a collective
460
decision that should be approved by all stakeholders. All parties agreed to select one of
461
three company directors as the interlocutor in the process. The role of the decision-
462
maker was to give assistance along the decision model development and to represent the
463
beliefs and judgments of all stakeholders during the meetings with the decision analysts.
464
The director nominee had an active role in the company with experience in investments
22
465
and large projects and, most importantly, was the person responsible for the project
466
activities management and for the connections between all the companies.
467 468
4.2.
Structuring the Model
469
Decision Alternatives/Options: The decision-maker defined options that could be
470
installed based on the three green roof types indicated in section 3.2: extensive, semi-
471
intensive and intensive. The decision-maker believed that it was also important to
472
distinguish between accessible and non-accessible systems. In total, the analysis
473
considered six green roof typologies.
474
It is important to underline that, in this case, green roof systems were the only options
475
that could meet the objectives defined in section 3.2. However, besides green roofs,
476
additional alternatives could be evaluated to meet other decision problems with different
477
objectives and stakeholders.
478 479
Fundamental Points of View:
480
In the initial phase of the preliminary meeting, the decision-maker reflected on which
481
lines of analysis should be considered to evaluate the green roof alternatives. Three FPV
482
took into consideration the costs incurred during the green roofs lifecycle and also the
483
direct benefits to the building's users which fall into two main categories: improvement
484
of building services and provision of additional benefits. Table 5 defines the final three
485
FPV.
486
Table 5. Fundamental points of view
487 FPV 1. Costs 2. Added value
Description Total amount of capital that the decision-maker is going to invest/spend to have the system properly installed and maintained. Additional benefits that can be obtained from the green roof system such as
23
higher visual impact and additional rentable space on the building. Enhancement of the building’s performance owing to the installation of a
3. Building services
green roof, in terms of additional comfort and security of the users.
488 489
Evaluation criteria: The decision-maker detailed the FPV in a set of six evaluation
490
criteria. The analysts questioned the relevance of all green roofs costs and benefits listed
491
inError! Reference source not found. Table 3. The decision-maker agreed that large-
492
scale social and environmental benefits should not be included as they are only
493
significant for large green areas. Also, the decision-maker excluded the capacity of
494
growing agricultural products in the roof since it did not serve the purpose of the
495
project. The same happened with the rainwater harvesting benefit given the risk of
496
incompatibility with the already approved details of the building's project. The increase
497
in PV's performance of the 7th floor was not possible since the green roof was expected
498
for the ground floor. Table 6 presents the final evaluation criteria.
499
Table 6. Evaluation criteria
500 Criterion Costs
Description
Measurement units
NPV (net present value) of installation and maintenance costs
€//m2 *
over the life cycle of the green roof alternative.
Added value
Impact on the visual appearance of the building, which may
(aesthetics)
affect the average market value of the real estate.
Added value
Provision of additional recreational space that can be enjoyed
(recreational space)
by the building’s users.
Fire security
Prevention of risk of fire spreading, according to the
Qualitative classification
classification scale of the European Standard.
(in terms of speed of fire
Qualitative judgments
Qualitative judgments
penetration) Thermal insulation
Reduction of thermal conductivity of the roof and consequently
% (energy needs)
reduction of energy needs of the property, in percentage. Sound insulation
Reduction of sound transmission from the exterior to the interior of the building, in decibels (dB), increasing the
dB (noise reduction)
comfort of the building’s users.
501
*
502
rate for maintenance prices of 20%). NPV was calculated as follows: ∑IJB
503
benefits in the year ", respectively,
NPV was calculated considering the installation cost in the year 0; annual maintenance costs (assuming an inflation
24
EFG HF
( 3 )F
is the private discount rate of 4%, and
, where
J
correspond to costs and
is the period of analysis of 10 years.
504 505 506
Note that a 10-year period is valid for private investors since they aim for fastest returns on their investments. However, longer periods can be assumed for public investors (typically 40 to 50 years as used by Cruz et al., 2017 and Teotónio et al., 2018).
507 508
Figure 3 shows the value tree of the decision model, showing the hierarchic structure of
509
the selected evaluation criteria. The decision-maker, together with the analysts, revised
510
and discussed the final value tree and concluded that there was no double counting
511
between criteria. This means that the project characteristics were not being evaluated
512
more than once. Note that the model considered separately the increase of the building's
513
sound and thermal insulation. Although both these criteria affect buildings insulation,
514
they do not depend from one another concerning the stakeholders preferences.
515
Figure 3. Value tree of the evaluation criteria
516 517 518
Stakeholders valued environmental/sustainability issues and aesthetics, minimizing the
519
costs influence in the decision-making. Therefore, costs were not considered as
520
screening criterion.
521 522
Descriptors of performance: DP made operational six criteria in order to appraise green
523
roofs attractiveness. The performance scales for costs, thermal insulation, and sound
524
insulation criteria were defined as quantitative. The indirect comparison method was
525
defined as follows:
526 527 528 529
− Costs, corresponds to the expected NPV, measured by a seven-level scale (20 to 380 €/m2 investment) (Cype, 2016); − Thermal insulation is measured by a four-level scale (decrease of 0 to 30%) (Ascione et al., 2013; Silva et al., 2016);
25
− Sound insulation is defined by a four-level scale (4 to 10 dB) (Connelly and
530
Hodgson, 2013; Grant et al., 2003; Lagström, 2004).
531 532
Both added value criteria (aesthetics and recreation) were scored through direct
533
comparison. Fire security was the only criterion defined through a qualitative
534
performance scale using the indirect comparison method. This process is defined in
535
section 3.2.
536
Finally, the decision-maker created good and neutral reference levels for each criterion.
537
In this context the analysts asked the following questions regarding the fire security
538
criterion: “Considering the typical energy needs of residential buildings, identify the
539
percentage of consumption reduction that would be neither attractive nor unattractive”
540
and “please identify the percentage of satisfactory reduction that would be considered
541
as an attractive building service improvement”. A similar process was applied to the
542
remaining five criteria. Figure 4 presents the performance scale results for each
543
criterion, with the neutral and good performance highlighted in blue and green,
544
respectively. Figure 4 also shows green roof options ordered with good and neutral
545
reference levels in terms of their performance in the added value in terms of aesthetics
546
and recreation. This sorting contributed to the definition of the DP of both criteria.
547
Figure 4. Performance profile of evaluation criteria
548 549 550
The design of the building included strict requirements concerning the thermal
551
insulation and fire security. Since no benefits arise from improving those parameters,
552
the decision-maker assumed the good performance level on the highest possible level of
553
the performance scale. The neutral performance level was associated with the second-
554
highest level in thermal insulation criterion, i.e., 20% energy consumption reduction. A
26
555
neutral requirement of fire penetration in the building of 30 minutes was attributed to
556
fire security, i.e., a D/Eroof classification.
557
Future functional problems of sound insulation might arise due to less demanding
558
specifications of the project. Any additional benefit concerning this issue would be
559
valuable. For this reason, the decision-maker defined neutral and good performances in
560
the lowest possible performance levels. This formulation allowed small contributions of
561
sound insulation to be more valued than the ones regarding thermal insulation and fire
562
security.
563
The investment cost was not the main concern for the company. However, during the
564
meeting, the decision-maker revealed that the company would only invest between 140
565
and 200 €/m2 in a 10-year period. This statement established good and neutral reference
566
levels to the costs.
567
From the decision-maker point of view, the aesthetics of any green roof ranked between
568
good and neutral reference performance levels. These systems had a clear distinction
569
concerning recreation aspects. Accessible green roofs ranked above the neutral level
570
and inaccessible green roofs ranked below the neutral level, since they do not provide
571
additional recreational areas.
572 573
4.3.
Construction of the Model
574
The decision-maker gave a consistent set of judgments to build the MCDA model and
575
allow the determination of value functions for each criterion and weight of those
576
criteria. Tables 1 to 5 of Supplemental Information (SI) present the related judgments
577
introduced in M-MACBETH software. The questioning procedure during this stage
578
followed the costs example. The analysts asked the decision-maker to define which was
579
“the difference between a green roof that costs 20 €/m2 and a green roof that costs
27
580
380 €/m2, over a period of 10 years”. The analysts asked similar questions to judge the
581
difference in attractiveness between each two pairs of impact levels for the remaining
582
pairs of DP of the cost criterion (please see SI - Table 1).
583
SI Table 2 (b) presents the matrix of MACBETH judgments for sound insulation.
584
Thermal insulation shows similar judgments in the MACBETH matrix (SI Table 2 a)
585
and, therefore, they should show similar value functions. To fill in the tables, the
586
analysts asked questions like “which is the difference in overall value between a green
587
roof that has no influence on the thermal comfort of the building’s users and a green
588
roof that is able to reduce the energy needs in terms of thermal comfort by 30%?”.
589
About the aesthetic and recreation criteria, the decision-maker compared each pair of
590
green roof solutions instead of the performance levels (SI Tables 4 and 5). As expected,
591
there was no difference in the aesthetic value between the same type of systems
592
depending on whether they are accessible or not, i.e., the difference of attractiveness is
593
“null”.
594
The weighting criteria followed a similar procedure. Table 7 presents the criteria
595
ordered from the one with most to less impact as follows: aesthetics, sound insulation,
596
costs, thermal insulation, fire security, and recreational value. Table 7 also shows the
597
judgments on the weights comparing each pair of swings. To build the judgement
598
matrix, the decision-maker answered questions such as “which is the difference in
599
overall value between a green roof that can decrease the noise in building’s interior by
600
10 dB and has the minimal performance in the remaining criteria, and a green roof that
601
can decrease the energy consumption of the building by 30% and has the minimal
602
performance in the remaining criteria?”. No judgmental inconsistencies were found by
603
the software. In the end, the decision-maker revised the inputs and validated the
604
judgmental statements.
28
605
Table 7. Judgments matrix of weighting coefficients
606
Aesthetics Aesthetics
Sound Very Weak
Sound Costs
Costs
Thermal
Fire
Recreation
Lower
Very Weak
Weak
Moderate
Strong
Very Strong
Very Weak
Weak
Moderate
Strong
Very Strong
Weak
Moderate
Strong
Very Strong
Weak
Moderate
Strong
Weak
Moderate
Thermal Fire Recreation
Weak
Lower
607 608
5.
Results and Discussion
609
This section presents and discusses the results of the model application to the case
610
study, following the specifications of section 3.4. Section 5.1. discusses the MACBETH
611
numerical value functions resulting from the qualitative matrices of judgments. Section
612
5.2. presents the performance scores of the green roof options for each criterion and
613
debates on the best global value trade-offs. Sections 5.3. and 5.4. address the sensitivity
614
and robustness analysis. Section 5.5. provides an overview of the main findings,
615
limitations of the study and future developments.
616 617
5.1.
Performance Scales and Weighting Criteria
618
The set of judgments introduced in the MACBETH software (section 4.3) allowed
619
determining the value scales for each criterion. Value scales represent the decision-
620
maker judgments as measures of attractiveness in a numerical scale interval. Figures 5
621
to 10, display the MACBETH scale axis for each criterion that traduces the proportion
622
of the differences in attractiveness allowing to compare the value intervals of the DP.
623
The value scale for the cost showed an extreme difference of attractiveness on values
624
above 140 €/m2. These give rise to the value function presented in Figure 5, suggesting
625
that the maximum fair investment for the decision-maker is between 140 and 200 €/m2. 29
626
Validation of value function by the decision-maker confirms an approximate maximum
627
value of 180 €/m2.
628
Figure 5. Scale of value function of the descriptor Cost
629 630 631
The value scale of aesthetic criterion in Figure 6 shows that there is no difference
632
between systems with different accessibilities, as the scores are similar. This means that
633
for the decision-maker an accessible green roof has the same aesthetic value as an
634
inaccessible green roof. Intensive and semi-intensive solutions present more similar
635
scores when compared to the extensive ones, proving that the latter has substantially
636
lower visual impact.
637
Figure 6. Scale of value function of the descriptor Aesthetics
638 639 640
The same happens to the recreation criterion (Figure 7). According to the
641
decision-maker judgments all inaccessible solutions (extensive, semi-intensive and
642
intensive green roofs) have the same attractiveness and have an extreme difference of
643
attractiveness for the remaining options (accessible green roofs). This suggests that such
644
solutions add no recreational value to the building, being scored with much lower unit
645
values than the neutral performance level. The added value of accessible intensive green
646
roofs is significantly high, yet lower than the satisfactory performance level. The added
647
value of extensive and semi-intensive green roofs is low.
648
Figure 7. Scale of value function of the descriptor Recreation
649 650
30
651
Figure 8 presents the linear scale of the fire security value function. It reveals equal
652
differences of attractiveness between the levels of the DP, which can be related to the
653
significant requirements considered in the building’s design phase, e.g., sprinklers, fire
654
protection closures, fire extinguishers, and others.
655 656
Figure 8. Scale of value function of the descriptor Fire security
657 658
Figure 9 shows an almost linear value function of the thermal insulation criterion,
659
revealing a lower difference of attractiveness between the desirable scenarios of 20%
660
and 30%energy consumption decrease , in comparison to a much higher difference
661
between the levels above the neutral performance level. This relates to the significant
662
thermal requirements already provided by the project.
663 664
Figure 9. Scale of value function of the descriptor Thermal insulation
665 666
The optimal solution of the MACBETH linear problem might not be unique. For sound
667
insulation, for example, other possible value scales would also verify constraints 4–7. In
668
such cases, MACBETH software allows the selection of some elements and the
669
readjustment of their position within an interval verifying those constraints. Figure 10
670
illustrates adjustments made by the decision-maker in the proportions of the scale
671
intervals of sound insulation. Those result in a final linear scale with equal differences
672
of attractiveness between the DP.
673 674
Figure 10. Scale of value function of the descriptor Sound insulation
675
31
676
After determining the value scales for each criterion, the judgments introduced in the
677
MACBETH software also allow determining the importance of each criterion. Figure 11
678
presents the weights of the criteria as percentages. Aesthetics, sound, and costs have
679
similar importance, which is significantly higher than the remaining. Since the aesthetic
680
value is the primary purpose for the installation of a green roof in the residential project,
681
it has the most difference in attractiveness. Sound insulation is the second selected
682
criterion due to (i) the building’s proximity to a public access area, (ii) the large glazed
683
areas and (iii) the noise from the 7th floor equipment. The investment costs are likely to
684
have some weight in the final decision (3rd selected criterion).
685
The project was already strict on thermal insulation and fire security, so any additional
686
benefit would have low impact. The recreational value is considered as having the
687
lowest contribution for the evaluation model, as the final use for the green roof is left to
688
the future property owners.
689
Figure 11. Bar chart of weighting coefficients
690 691 692
5.2.
693
Each green roof option is evaluated for each criterion, determining their partial values
694
on those criteria. For that, their performance level is scored with a numerical value
695
according to the corresponding criterion scale.
696
Extensive green roofs have lower aesthetic impact than intensive ones as their substrate
697
thickness limits the vegetation variety. Their installation cost is significantly higher in
698
contrast to traditional roof solutions, yet lower when compared to semi-intensive or
699
intensive green roofs. Extensive systems require minimal irrigation and maintenance.
700
Therefore, the vegetation may be left to its natural growing and degradation process
701
which can also have a negative impact on the system aesthetics. Intensive green roofs 32
Global Attractiveness of each Option
702
are typically accessible and may include decorative elements, such as benches and
703
vessels, and, for this reason, are often compared to public gardens. Their greater
704
thickness supports a wider variety of vegetation which allows to improve the roof visual
705
impact. However intensive solutions usually have higher maintenance and installation
706
costs than extensive ones. Semi-intensive green roofs are intermediate accessible
707
solutions (FLL, 2008). The above mentioned green roof systems have different
708
characteristics and present different performances in terms of the evaluation criteria. To
709
allow the evaluation of the recreational criteria, each system is distinctly separated in
710
terms of accessibility/use. Figure 12 distinguishes the six green roof solutions
711
considered in this decision-making problem.
712 713 714
Figure 12. Green roof options
715
Table 8 defines the performance profiles of each green roof option, assigning a DP level
716
to each option. Green roofs costs and benefits are evaluated according to existing
717
studies/data on their performance, as explained below.
718
Extensive green roofs present the lowest 10-year NPV, while the intensive ones present
719
the highest. Those values increase slightly if the roof is accessible (CYPE, 2016). There
720
is evidence that when properly maintained, particularly with regular irrigation, a green
721
roof with a minimal substrate depth of 5 cm and a maximum limit of organic materials
722
delays the fire propagation by at least 30 minutes, which is equivalent to a Broof
723
classification. Otherwise, the existence of dry vegetation may increase the fire hazard
724
(Zinco, 2016). Taking this into account, intensive and semi-intensive green roofs can be
725
classified as Broofs. The same does not apply to extensive green roofs which are
726
considered as Droofs or Eroofs, since no maintenance is required for these systems.
727 33
Table 8. Performance of each green roof option
728
Costs (€/m2) Fire Thermal (%) Sound (dB)
29.95 D/Eroof 5 4
179.2 Broof 17 6
303.88 Broof 27 10
34.95 D/Eroof 5 4
258.93 Broof 17 6
378.67 Broof 27 10
729 730
The literature also provides some contributions to the thermal and acoustic effects of
731
green roofs.
732
factors influence their performance (e.g., climate). Studies suggest that extensive, semi-
733
intensive and intensive green roofs decrease in average the annual building energy
734
consumption up to 5, 17 and 27%, respectively, in the Mediterranean climate (Ascione
735
et al, 2013; Silva et al., 2016) and decrease the sound transmission by 4, 6 and 10 dB,
736
respectively (Connelly and Hodgson, 2013; Grant et al., 2003; Lagström, 2004).
737
Accessibility does not influence these benefits, so there is no distinction between the
738
performance of accessible and inaccessible roofs.
739
With regards to the aesthetic and recreation criteria, the options performance profiles
740
are defined through comparison and ranking. Figure 4 presents this comparison.
741
Intensive green roofs have greater diversity and denser vegetation. Therefore in the
742
aesthetic criterion, intensive green roofs are closer to the best performance reference
743
level, and extensive green roofs are closer to the worse performance reference level.
744
The same happens to the recreation criterion, with accessible and inaccessible green
745
roofs being closer to the best and worst performance reference levels, respectively. This
746
classification results from the additional usable space provided by green roofs.
747
Table 9 presents partial values of each green roof option on each criterion and the
748
calculation of global attractiveness of each option. The global attractiveness is
34
However, there are still many uncertainties, considering that several
749
calculated through Equation 1 of the additive aggregation using the weighting
750
coefficient presented in Figure 11.
751
Figure 13 displays the overall scores. The intensive green roof has the best trade-off,
752
followed by the intensive accessible one. Extensive accessible and extensive have
753
negative scores, with -1.10 and -6.36, respectively. The lower and upper options
754
represent the overall lowest and highest performance on all criteria, respectively.
755
Table 9. Scores for each green roof options on each criterion
756
Global
Costs
Aesthetics
Recreation
Fire
Thermal
Sound
Upper
100.00
100.00
100.00
100.00
100.00
100.00
100.00
Intensive
69.43
-173.13
83.33
-87.50
100.00
70.00
300.00
Intensive Accessible
52.40
-297.78
83.33
87.50
100.00
70.00
300.00
Semi-int
42.93
34.67
66.67
-87.50
100.00
-60.00
100.00
Semi-int Accessible
22.20
-98.22
66.67
50.00
100.00
-60.00
100.00
Lower
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Extensive Accessible
-1.10
165.66
25.00
25.00
0.00
-275.00
0.00
Extensive
-6.36
168.78
25.00
-87.50
0.00
-275.00
0.00
Weights
0.2105
0.2456
0.0526
0.1053
0.1579
0.2281
757 758
Figure 13. Overall scores of green roof options
759 760 761
Figure 14 presents the profiles for all options, organized by decreasing order of
762
preference, allowing to visualize the contribution of each criterion. From the analysis of
763
these results it can be assumed that:
764
-
Sound insulation and fire security have a positive contribution in most options.
765
Globally, their value decreases as the options are less attractive. Both criteria
766
have no relevance in case of accessible/inaccessible extensive green roofs
767
(neutral situation).
768 769
-
Aesthetics contributes positively to all option since it improves the impact on the building’s visual appearance when compared with the status quo. The 35
770
contribution is independent of the roof accessibility. The same is not true to the
771
recreation which only applies if the roof is accessible. In these situations a
772
negative contribution is shown. -
773
Thermal insulation contributes positively to intensive accessible/inaccessible
774
green roofs and negatively to the remaining, due to the increase of systems
775
thermal conductivity. -
776
Cost contributes negatively to most options (except accessible/inaccessible
777
extensive green roofs) since the NPV of installation and maintenance costs are
778
higher than the defined upper reference.
779 780 781 782 783
Figure 14. Profiles for all green roof options ordered by decreased preference: (a) intensive; (b) intensive accessible; (c) semi-intensive; (d) semi-intensive accessible; (e) extensive accessible; (f) extensive
784 785
5.3.
786
This study performs a sensitivity analysis to incorporate the uncertain and imprecise
787
data often involved in the decision-making process. This is especially important in the
788
present case study since some of the information used in the model structure is collected
789
and adapted from previous studies (e.g., quantification of thermal and sound insulation
790
benefits).
791
This study also aims to evaluate the uncertainty on the decision-maker judgments,
792
concerning the weighting criteria where several ‘what-if’ questions may arise from a
793
challenging trade-off judgmental process. This allows assessing the robustness of the
794
alternatives global ranking to the model’s parameters variation. For example, it is
795
possible to assess if the best green roof option changes if small variations on the weight
796
of any criterion are observed. Figures 15 - 17 shows the sensitivity analysis performed 36
Sensitivity Analysis
797
in the MACBETH software. These figures vertical axis indicates global score changes
798
of each option, when a variation of the horizontal axis is considered, i.e., weight
799
variations of a specific criterion. The vertical red line corresponds to the criteria weight
800
based on the decision-maker' qualitative judgments and, therefore, corresponding to the
801
base scenario.
802
The main objective is to identify if the best green roof option changes when the weight
803
of one criterion is modified, while maintaining the proportion between the remaining
804
criteria Figure 18 shows that the model’s results are not sensitive to variations on the
805
weight of the aesthetic and fire security criteria, as the relative global scores of the
806
green roof alternatives remain unmodified. This is relevant when considering that the
807
aesthetic has the highest influence in the evaluation of the case study decision problem,
808
i.e., weigh in the model.
809
To better understand the graphs, see the following example. Figure 15 (a) presents the
810
sensitivity analysis performed to the aesthetics criterion weight. If this criterion has a
811
total contribution ( i.e., the maximum weight possible out of 100 in the horizontal axis)
812
then the remaining criteria have no contribution to the decision problem (weight
813
coefficient = 0). As seen in section 5.1 the same green roof typologies are equally
814
scored in aesthetics, showing no dependence on the systems' accessibility. This is
815
observed when the graph lines converge, proving that if aesthetics is the sole evaluating
816
criterion, there is no difference in attractiveness between accessible and inaccessible
817
green roofs. Nevertheless, the ranking of intensive, semi-intensive and extensive green
818
roofs remains the same, according to their decreasing aesthetic value. The same happens
819
to the fire security sensitivity analysis (Figure 15 b). The convergence lines show that
820
semi-intensive and intensive green roofs are equally preferable to extensive green roofs
37
821
if fire security is the only criterion. This is also in accordance with extensive green roofs
822
worse performance to fire security criterion, as shown in Table 8 performance profile.
823
Figure 15. Weights sensitivity analysis for a) aesthetics and b) fire security
824 825 826
Figure 16 shows that the weight variation of sound insulation and costs affects the
827
global attractiveness of these solutions. This is relevant since these two criteria are
828
ranked in second and third among the other weighting criteria. Nevertheless, intensive
829
green roofs remain the best option when a 10% variation is applied, which is significant.
830
To be more precise, semi-intensive green roof solutions become the best choice by
831
decreasing the weight of the sound insulation from 22,01 to 11. If its weight increases,
832
no modifications are expected. Also, changes in the best trade-off are observed with
833
small changes in costs. Semi-intensive and intensive accessible green roofs become the
834
best options when an 8% increase or a 12% decrease in costs applies respectively.
835
Figure 16. Weights sensitivity analysis for (a) sound insulation and (b) costs
836 837 838
For the remaining criteria, the conclusions are similar. Variations on the thermal
839
criterion’s weight introduce few variabilities on the results (Figure 17 a). Lastly, an 8%
840
increase on the recreation weight (Figure 17 b) makes the intensive accessible green
841
roof the best choice. Given the fact that this option outperforms the intensive green roof
842
in this particular aspect and has the same performance in the remaining criteria.
843
Figure 17. Weights sensitivity analysis for a) thermal insulation and b) recreation
844 845
38
846
Sensitivity analysis is especially important for investors since they are concerned with
847
the results robustness, especially when costs are involved. In general, the intensive
848
green roof is identified as the best solution for this decision problem and extensive
849
green roof is the worst, even when considerable variations of the weighting criteria are
850
admitted. This is because intensive green roofs outperform extensive and intensive ones
851
in all the benefits. However, their high installation and maintenance costs are critical
852
barriers to the implementation of these systems. In fact, costs variations show the
853
highest impact on the results of sensitivity analysis. Considering an extreme situation,
854
where investors are very concerned with investment values, i.e., increasing the weight
855
of costs, most solutions rank above the reference level with negative global scores of
856
attractiveness (up to value scores of -280). Extensive (accessible and inaccessible) and
857
semi-intensive (only inaccessible) green roofs are the only ones with positive outcomes
858
since they have lower life-cycle costs. In this case, decision-makers should equate
859
different options that also meet the problem objectives or reformulate the problem.
860
Just like costs, sound insulation has a great influence on results (second most important
861
evaluating criterion). In general, the sound insulation provided by intensive green roofs
862
is significantly higher than other solutions, making the results less sensitive to this
863
parameter variation. Differences between 0 and 300 in the intensive green roofs global
864
score values are observed for the entire variation interval of the weighting scale, without
865
any drop in the ranking position.
866 867
5.4.
Robustness Analysis
868
Robustness analysis aims to study which robust conclusions can be obtained for
869
different levels of information scarcity, imprecision, and uncertainty (Bana e Costa and
870
Beinat, 2005). MACBETH software organizes the information into the model in three
39
871
types: ordinal, MACBETH and cardinal. Ordinal information refers only to order,
872
excluding any attractiveness difference information (preference intensity). MACBETH
873
information includes the introspective semantic judgments in the model but ignores any
874
scoring or weighting scale compatible with those judgments. Cardinal information
875
denotes a specific scale and validation by the evaluator. The MACBETH information is
876
also organized into two sections: local and global. Local information refers to specific
877
information in a given criterion, such as the qualitative judgments required to build the
878
criteria value scales. Global information relates to information weighing the criteria,
879
affecting both the scores given to each criterion and the options partial values . This
880
means that local information affects isolated parts of the MACBETH model and global
881
information has an impact on the entire model (Bana e Costa and Beinat, 2005).
882
Results of robustness analysis can be verified through the two symbols displayed in the
883
software: Dominance: an option dominates another if it is at least as attractive as the other in all criteria and it is more attractive than others in at least one criterion. Additive dominance: an option additively dominates another if it is always found to be more attractive than the other using an additive model under a set of information constraints.
884
Several scenarios focusing on different types of information are exhaustively assessed
885
in the robustness analysis. Table 10 describes five scenarios selected to illustrate the
886
global results.
887
Table 10. Scenarios and type of information considered in the robustness analysis
888
Scenario 1 Local section
40
Ordinal
Scenario 2 Ordinal MACBETH
Scenario 3 Ordinal MACBETH
Scenario 4 Ordinal MACBETH
Scenario 5 Ordinal MACBETH
Cardinal* Global section
Ordinal
Ordinal
Ordinal MACBETH
Cardinal* Ordinal MACBETH Cardinal*
Cardinal** Ordinal MACBETH Cardinal**
889 890 891 892
* 0% degree of imprecision ** 10% degree of imprecision
893
Figure 18 shows the Scenario 1 . In this scenario is shown that by selecting the
894
robustness analysis command in the MACBETH software, the ordinal information is
895
selected by default at the local and global sections. At this level, there is not enough
896
information to determine what options are the most attractive. The same conclusion is
897
observed in Figure 19, which presents the Scenario 2, considering the MACBETH
898
information in the local section. However, in this scenario the options are reordered.
899 900 901
Figure 18. Robustness analysis – Scenario 1
902 903 904
Figure 19. Robustness analysis – Scenario 2
905 906
Figure 20 shows the Scenario 3, which also considers the MACBETH global
907
information and cardinal local information. In this analysis, a new reordering of the
908
options occurs and there are additive dominance situations. For example, it is observed
909
that the intensive option is globally more attractive than the semi-intensive accessible
910
and extensive accessible options, as well as the semi-intensive option is more attractive
911
than the extensive accessible option (situation of additive dominance).
912 913 914
Figure 20. Robustness analysis – Scenario 3
915 41
916
Figure 21 shows the Scenario 4, in which the robustness analysis considers the three
917
types of information in local and global sections. A new reordering of the options
918
occurs, which follows the order previously obtained with the application of the additive
919
model (Figure 13). In this case, the intensive green roof option dominates the
920
remaining.
921
In Figure 22 the Scenario 5 explores the variation in the degrees of imprecision of the
922
cardinal information. For a degree of imprecision of 10% in the local and global
923
information sections, a new order of the options occurs. In this case it becomes more
924
difficult to determine what options are the most attractive.
925 926
Figure 21. Robustness analysis – Scenario 4
927 928 929
Figure 22. Robustness analysis – Scenario 5
930 931 932
5.5.
933
This paper agrees with some of the main findings of other studies (see section 2). As
934
Brudermann and Sangkakool (2017), this study presents building aesthetics as the main
935
driver behind the implementation of green roofs. However, this opposes to the studies of
936
Rosasco and Perini (2019) and Sangkakool et al. (2018), ranking aesthetics as one of the
937
least relevant characteristics of green roofs. Overall, MCDA has a strong dependence on
938
the case studies specifications and, consequently, on the inputs of the analysis. For
939
example, Mahdiyar et al. (2019) examined three case studies of residential buildings and
940
identified a different green roof option for each building. Thus, it becomes clear that
941
project priorities and specific requirements have a significant impact on the decision-
42
Discussion
942
making process. This study is no exception, so it is important to consider those
943
specifications while discussing the results. This study faced some constraints and
944
singularities. The project in concern was ongoing which prevented changes in the
945
approved plans. Considering that the decision process was taken in a later project phase
946
this inhibited the addition of other benefits. As an example, the PVs performance placed
947
on the top floor could be improved if combined also with a green roof. Therefore, it is
948
recommended that the decision-making process is taken on an early project stage in
949
order to maximize its advantages.
950
Four companies with ongoing largescale projects were contacted, given the limited
951
green roof market in Portugal. From these, only one participated in this study, selecting
952
one decision-maker responsible to take into consideration the interests of all parties.
953
Future research should encourage the engagement of several stakeholders to promote
954
interaction, collective commitment, and shared understanding of the decision problem.
955
Including a large panel of stakeholders with different backgrounds allows taking into
956
account different opinions. However, it is important to stress that group decision biases
957
(such as group thinking) can make it more difficult to reach consensus between all
958
actors. This is the case of the study of Rosasco and Perini (2019) in which academics
959
and professionals assigned different weights to economic aspects of green roofs. The
960
same happens in the study of Naing et al. (2017).
961
The economic aspects seem to be a topic that raises some controversy. The Portuguese
962
property market is experiencing increasing levels of demand and rising selling prices.
963
Therefore, real estate companies operating in central Lisbon, targeting the premium
964
market, are aware of the quick return on property investment. That said, they consider
965
that are less concerned about potential investment costs and more focused on distinction
966
factors in the market, such as aesthetics and sustainability. In the present study, the
43
967
decision-maker followed this opinion, minimizing the importance of costs. However,
968
the MCDA results showed important cost contribution to green roof selection.
969
Thus, if this study is replicated for other stakeholders (e.g. potential buyers of these
970
apartments) the costs are expected to have more impact. This proves the need for
971
multicriteria analyses to support decision-making. Since people’s perception is not
972
consistent and their decisions aren´t always aligned with their stated preferences.
973
In view of the above, all studies have a common challenge of reporting comprehensive
974
case study applications. Like any other, this paper has specific particularities limiting
975
global conclusions regarding the installation of green roofs. For example, it concerns a
976
new project while most of the cases nowadays involve building's retrofitting. The
977
second scenario means different viewpoints and priorities such as the loading capacity
978
of the building.
979
Additionally, this study has only addressed private costs and benefits. This might not
980
happen for public or largescale investments, in which benefits for the society and
981
environment need to be assessed as well.
982
Also, if the building is used for other purposes, benefits can show different impacts. As
983
an example, an office building may have high energy demand due to heating and
984
cooling. Therefore, the use of green roofs could have a different impact on the building.
985
All these aspects are critical and challenging. Future research should take the context
986
(e.g. building, use, location, stakeholders) into consideration and reproduce the
987
methodology for different scenarios in order to provide more information and promote a
988
comprehensive discussion.
989
Regardless of the studies limitations and contrasting results, all authors have
990
demonstrated the advantages of installing green roofs in buildings or cities. Guzmán-
991
Sánchez et al. (2018) and Rosasco and Perini, (2019) showed that green roofs are
44
992
preferable to traditional solutions and, when economic aspects related with their
993
installation and maintenance are a major barrier to their implementation, there are
994
strategies to encourage investors to develop green areas in cities. A few examples of
995
common practices are green policies and financial incentives, which have a great
996
contribution to these decisions as suggested by Brudermann and Sangkakool (2017) and
997
Sangkakool et al. (2018).
998 999
6.
Conclusions
1000
Overall, this paper helps to structure and support decision-problems and contributes to
1001
understand investors preferences when installing green roofs in buildings. The present
1002
study adopts a multicriteria decision analysis methodology in order to calculate the
1003
attractiveness of different green roof systems by assigning numerical scores.
1004
The application of the proposed methodology to a case study of a real estate investor
1005
proves the idea that MCDA outputs strongly depend on the decision-maker preferences.
1006
This study selected the MACBETH method, as it focuses more on the stakeholders’
1007
values and less in the model outputs. Also, it motivates the involvement in the
1008
consultation process and the understanding of the decision-problem.
1009
The use of the MACBETH method in the context of green roofs installation is a step
1010
forward in literature. Other authors selected different methods such as AHP and
1011
TOPSIS. However, MACBETH proved to be an effective approach, as the decision-
1012
maker is asked to give qualitative judgments to construct the model instead of numerical
1013
values, which can lead to common mistakes. Either way, it has also some limitations.
1014
The M-MACBETH software relies on a measurement theory foundation that can raise
1015
practical issues. The linear programming of the software requires consistent judgments
1016
from the decision-maker. Individuals are naturally inconsistent due to many reasons
45
1017
such as lack of information or biased thinking and, therefore, the model outputs can be
1018
somewhat different from the original concept/preferences of the decision-maker. This
1019
whole process can be confusing. Also, the linear programming method allows the
1020
selection of multiple optimal solutions of green roofs. This is why the presence of an
1021
analyst is recommended, to help discussing the best option.
1022
This paper promotes the installation of green roofs in residential buildings comparing to
1023
a do-nothing scenario. However, these solutions may not be easily accepted by other
1024
investors due to high installation and maintenance costs. To progressively promote the
1025
integration of green roofs in urban planning it is crucial to spread the word about their
1026
benefits, raise education campaigns and implement political strategies, such as financial
1027
incentives.
1028 1029
Acknowledgements
1030
This work was supported by the FCT (Portuguese Foundation for Science and
1031
Technology) through scholarships PD/BD/135172/2017 and SFRH/BD/117717/2016
1032
and the research project GENESIS (PTDC/GESURB/29444/2017). The authors would
1033
like to thank the decision-maker for the contribution and cooperation during this
1034
research work.
1035
The authors would like to thank Maria Manso for her input in the English revision.
1036 1037
References
1038 1039 1040 1041
Alves, A., Gersonius, B., Sanchez, A., Vojinovic, Z., Kapelan, Z., 2018. Multi-criteria Approach for Selection of Green and Grey Infrastructure to Reduce Flood Risk and Increase CO-benefits. Water Resour. Manag. 32, 2505–2522. https://doi.org/10.1007/s11269-018-1943-3
1042 1043 1044
Ascione, F., Bianco, N., de’ Rossi, F., Turni, G., Vanoli, G.P. Green roofs in European climates. Are effective solutions for the energy savings in air-conditioning?. Applied Energy. 104 (2013) 845–859. doi:10.1016/j.apenergy.2012.11.068. 46
1045 1046 1047 1048
Bana e Costa, C., Chagas, M.P. A career choice problem: An example of how to use MACBETH to build a quantitative value model based on qualitative value judgments. European Journal of Operational Research. 153 (2004) 323–331. doi:10.1016/S03772217(03)00155-3.
1049 1050
Bana e Costa, C., Beinat, E. Model-structuring in public decision-aiding. London School of Economics and Political Science. London. 2005. doi:ISBN 0 7530 1701 6.
1051 1052 1053 1054
Bana e Costa, C., Fernandes, T.G., Correia, P.V.D. Prioritisation of public investments in social infrastructures using multicriteria value analysis and decision conferencing: A case study. International Transactions in Operational Research. 13 (2006) 279–297. doi:10.1111/j.1475-3995.2006.00549.x.
1055 1056 1057
Bana e Costa, C., Lourenço, J.C., Chagas, M.P., Bana e Costa, J.C. Development of Reusable Bid Evaluation Models for the Portuguese Electric Transmission Company. Decision Analysis. 5 (2008) 22–42. doi:10.1287/deca.1080.0104.
1058 1059 1060
Bana e Costa, C., De Corte, J.M., Vansnick, J.C. Macbeth. International Journal of Information Technology & Decision Making. 11 (2012) 359–387. doi:10.1142/S0219622012400068.
1061 1062
Bana e Costa, C., Lourenço, J., Oliveira, M. Model structuring & Descriptors of performance. Decision Analysis Models and Applications (Class Slides). (2018) 1–15.
1063 1064
Belton, V., Stewart, T.J. Multiple criteria decision analysis: an integrated approach. Springer. 2001.
1065 1066 1067
Berardi, U., GhaffarianHoseini, A., GhaffarianHoseini, A. State-of-the-art analysis of the environmental benefits of green roofs. Applied Energy. 115 (2014) 411–428. doi:10.1016/j.apenergy.2013.10.047.
1068 1069 1070
Bianchini, F., Hewage, K. Probabilistic social cost-benefit analysis for green roofs: A lifecycle approach. Building and Environment. 58 (2012) 152–162. doi:10.1016/j.buildenv.2012.07.005.
1071 1072 1073
Brudermann, T., Sangkakool, T., 2017. Green roofs in temperate climate cities in Europe – An analysis of key decision factors. Urban For. Urban Green. 21, 224–234. https://doi.org/10.1016/j.ufug.2016.12.008
1074 1075
BS476 : Part 3. Fire tests on building materials and structures. Classification and method of test for external fire exposure to roofs (2004).
1076 1077 1078
Chow, J.F., Savić, D., Fortune, D., Kapelan, Z., Mebrate, N., 2014. Using a systematic, multi-criteria decision support framework to evaluate sustainable drainage designs. Procedia Eng. 70, 343–352. https://doi.org/10.1016/j.proeng.2014.02.039
1079 1080
CYPE. Gerador de preços para construção civil. Portugal. CYPE Ingenieros, S.A., (2016). http://www.geradordeprecos.info/ (accessed July 12, 2016).
1081 1082 1083
Connelly, M., Hodgson, M. Experimental investigation of the sound transmission of vegetated roofs. Applied Acoustics. 74 (2013) 1136–1143. doi:10.1016/j.apacoust.2013.04.003. 47
1084 1085 1086
Cruz, C.O., Silva, C.M., Dias, P.V., Teotónio, I. Economic impact of changing thermal regulation - An application to the city of Lisbon. Energy and Buildings. 149 (2017) 354–367. doi:10.1016/j.enbuild.2017.05.030.
1087 1088 1089
Di Matteo, M., Dandy, G.C., Maier, H.R., 2017. Multiobjective Optimization of Distributed Stormwater Harvesting Systems. J. Water Resour. Plan. Manag. 143, 04017010. https://doi.org/10.1061/(asce)wr.1943-5452.0000756
1090 1091 1092
European Commission, 2014. Guide to Cost-benefit Analysis of Investment Projects: Economic appraisal tool for Cohesion Policy 2014-2020, Publications Office of the European Union. https://doi.org/10.2776/97516
1093 1094 1095
European Commission, 2019a. European Green Capital. http://ec.europa.eu/environment/europeangreencapital/lisbon-is-the-2020-europeangreen-capital-award-winner/ (accessed 6.11.19).
1096 1097 1098
European Commission, 2019b. 2020 – Lisbon. http://ec.europa.eu/environment/europeangreencapital/winning-cities/2020-lisbon/ (accessed 6.11.19).
1099 1100
FLL. Guidelines for the Planning. Construction and Maintenance of Green Roofing Green Roofing Guideline. 2008.
1101 1102 1103
Franco, L.A., Montibeller, G. Problem Structuring for Multicriteria Decision Analysis Interventions. In: J.J. Cochran (Ed.), Wiley Encyclopedia of Operations Research and Management Science, Wiley. (2010).
1104 1105
Grant, G., Engleback, L., Nicholson, B. Green Roofs: their existing status and potential for conserving biodiversity in urban areas. 2003.
1106 1107 1108
Guarini, M.R., Battisti, F., Chiovitti, A., 2018. A methodology for the selection of multi-criteria decision analysis methods in real estate and land management processes. Sustain. 10. https://doi.org/10.3390/su10020507
1109 1110 1111 1112
Guzmán-Sánchez, S., Jato-Espino, D., Lombillo, I., Diaz-Sarachaga, J.M., 2018. Assessment of the contributions of different flat roof types to achieving sustainable development. Build. Environ. 141, 182–192. https://doi.org/10.1016/j.buildenv.2018.05.063
1113 1114 1115
Kohler, N. From the design of green buildings to resilience management of building stocks. Building Research and Information. 46 (2018) 578–593. doi:10.1080/09613218.2017.1356122.
1116 1117
Lagström, J. Do Extensive Green Roofs Reduce Noise?. International Green Roof Institute. 10 (2004) 40.
1118 1119 1120
Liquete, C., Udias, A., Conte, G., Grizzetti, B., Masi, F., 2016. Integrated valuation of a nature-based solution for water pollution control. Highlighting hidden benefits. Ecosyst. Serv. 22, 392–401. https://doi.org/10.1016/j.ecoser.2016.09.011
1121 1122
Mahdiyar, A., Tabatabaee, S., Abdullah, A., Marto, A., 2018. Identifying and assessing the critical criteria affecting decision-making for green roof type selection. Sustain. 48
1123
Cities Soc. 39, 772–783. https://doi.org/10.1016/j.scs.2018.03.007
1124 1125 1126 1127
Mahdiyar, A., Tabatabaee, S., Durdyev, S., Ismail, S., Abdullah, A., Wan Mohd Rani, W.N.M., 2019. A prototype decision support system for green roof type selection: A cybernetic fuzzy ANP method. Sustain. Cities Soc. 48, 101532. https://doi.org/10.1016/j.scs.2019.101532
1128 1129 1130
Naing, Y.M., Nitivattananon, V., Shipin, O. V, 2017. Green Roof Retrofitting : Assessment of the Potential for Academic Campus. Eng. J. 21. https://doi.org/10.4186/ej.2017.21.7.57
1131 1132 1133
Nurmi,V., Votsis, A., Perrels, A., Lehvävirta, S. Green Roof Cost-Benefit Analysis: Special Emphasis on Scenic Benefits. Journal of Benefit-Cost Analysis. 7 (2016) 488– 522. doi:10.1017/bca.2016.18.
1134 1135 1136
Mateus, R.J.G., Bana e Costa, J.C., Matos, P.V., 2017. Supporting Multicriteria Group Decisions with MACBETH Tools: Selection of Sustainable Brownfield Redevelopment Actions. Gr. Decis. Negot. 26, 495–521. https://doi.org/10.1007/s10726-016-9501-y
1137
Parnell, G. Decision Analysis in One Chart. Decision Line. (2009) 20–24.
1138
Phillips, L. Decision Analysis in 2005. 2005.
1139 1140 1141
Phillips, L., Bana e Costa, C. Transparent prioritisation, budgeting and resource allocation with multi-criteria decision analysis and decision conferencing. Annals of Operations Research. 154 (2007) 51–68. doi:10.1007/s10479-007-0183-3.
1142 1143 1144
Radinja, M., Comas, J., Corominas, L., Atanasova, N., 2019. Assessing stormwater control measures using modelling and a multi-criteria approach. J. Environ. Manage. 243, 257–268. https://doi.org/10.1016/j.jenvman.2019.04.102
1145 1146 1147
Rosasco, P., Perini, K., 2019. Selection of (Green) Roof Systems: A SustainabilityBased Multi-Criteria Analysis. Buildings 9, 17. https://doi.org/10.3390/buildings9050134
1148 1149 1150
Sangkakool, T., Techato, K., Zaman, R., Brudermann, T., 2018. Prospects of green roofs in urban Thailand – A multi-criteria decision analysis. J. Clean. Prod. 196, 400– 410. https://doi.org/10.1016/j.jclepro.2018.06.060
1151 1152 1153
Silva, C.M., Gomes, M.G., Silva, M. Green Roofs Energy Performance in Mediterranean Climate. Energy and Buildings. 116 (2016) 318–320. doi:10.1016/j.enbuild.2016.01.012.
1154 1155 1156
Snep, R., Van Ierland, E., Opdam, P., 2009. Enhancing biodiversity at business sites: What are the options, and which of these do stakeholders prefer? Landsc. Urban Plan. 91, 26–35. https://doi.org/10.1016/j.landurbplan.2008.11.007
1157 1158 1159
Teotónio, I., Silva, C.M., Cruz, C.O. Eco-solutions for urban environments regeneration: The economic value of green roofs. Journal of Cleaner Production. (2018). doi:10.1016/j.jclepro.2018.07.084.
1160
Tomalty, R., Komorowski, B., Doiron, D. The Monetary Value of the Soft Benefits of 49
1161 1162 1163
Green Roofs, (2010) 114. http://www.greenroofs.org/resources/Monetary_Value_of_Soft_Benefits_of_Green_Ro ofs.pdf.
1164 1165
Velasquez, M., Hester, P.T., 2013. An Analysis of Multi-Criteria Decision Making Methods. Int. J. Oper. Res. 10, 56–66.
1166 1167
Zinco. ZinCo Green Roof Systems. (2016). http://www.zinco.pt/ (accessed April 20, 2016).
50
1168
Supplemental Information
1169
Table 1. Judgments matrix of the descriptor Cost
1170 €/m2
20
20
80
140
200
260
320
380
Moderate
Strong
Extreme
Extreme
Extreme
Extreme
Moderate
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
80 140 200 260
Extreme
320 380
1171
Table 2. Judgments matrix of the descriptor (a) Thermal insulation (b) Sound insulation
1172 1173
(a) % 30 20
(b) 30
20
10
0
dB
Weak
Moderate
Extreme
10
Moderate
Strong
8
Weak
6
10 0
10
8
6
4
Weak
Moderate
Extreme
Moderate
Strong Weak
4
1174 1175
Table 3. Judgments matrix of the descriptor Fire security Broof Broof Croof D/Eroof
Croof
D/Eroof
Froof
Weak
Moderate
Strong
Weak
Moderate Weak
Froof
1176
51
Table 4. Judgments matrix of the descriptor Aesthetics
1177
Upper
Intensive Accessible Weak
Upper Intensive Accessible
Intensive
Semi-int Accessible
Semi-int
Extensive Accessible
Extensive
Lower
Weak
Moderate
Moderate
Very strong
Very strong
Extreme
Null
Weak
Weak
Very strong
Very strong
Extreme
Weak
Weak
Very strong
Very strong
Extreme
Null
Strong
Strong
Strong
Strong
Intensive Semi-int Accessible Semi-int Extensive
Null
Accessible
Very strong Very strong Weak Weak
Extensive Lower
1178
Table 5. Judgments matrix of the descriptor Recreation
1179
Upper
Upper Intensive Accessible Semi-int Accessible Extensive Accessible Lower Intensive Semi-int Extensive
1180
52
Intensive
Semi-int
Extensive
Accessible
Accessible
Accessible
Very Weak
Moderate
Strong
Moderate
Strong
Weak
Lower
Intensive
Semi-int
Extensive
Extreme
Extreme
Extreme
Strong
Extreme
Extreme
Extreme
Moderate
Extreme
Extreme
Extreme
Weak
Extreme
Extreme
Extreme
Strong
Strong
Strong
Null
Null
Very Strong
Null
Highlights
•
Multicriteria decision model for the installation of green roofs/walls
•
Determination of the best trade-off between the underlying costs and benefits
•
Incorporation of the preferences of decision-makers in economic evaluations
•
Support for the selection of the optimal solution
•
Validation of the methodology through a case study (residential building)
Author Contribution Section
Inês Teotónio: Conceptualization; Methodology; Formal analysis; Investigation; Writing – Original Draft; Writing – Review and Editing; Visualization. Marta Cabral: Conceptualization; Methodology; Formal analysis; Investigation; Writing – Original Draft; Writing – Review and Editing; Visualization. Carlos Oliveira Cruz: Conceptualization; Validation; Writing – Review and Editing; Supervision; Funding acquisition. Cristina Matos Silva: Conceptualization; Validation; Writing – Review and Editing; Supervision; Funding acquisition
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
S0959-6526(19)34235-0
DOI:
https://doi.org/10.1016/j.jclepro.2019.119365
Reference:
JCLP 119365
To appear in:
Journal of Cleaner Production
Received Date: 24 January 2019 Revised Date:
14 November 2019
Accepted Date: 17 November 2019
Please cite this article as: Teotónio Inê, Cabral M, Cruz CO, Silva CM, Decision support system for green roofs investments in residential buildings, Journal of Cleaner Production (2019), doi: https:// doi.org/10.1016/j.jclepro.2019.119365. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Decision support system for green roofs investments in residential buildings
Inês Teotónio PhD Student, CERIS, Instituto Superior Técnico, University of Lisbon Av. Rovisco Pais, 1049-001 Lisbon, Portugal E-mail: [email protected] [Corresponding author]
Marta Cabral PhD Student, CERIS, Instituto Superior Técnico, University of Lisbon Av. Rovisco Pais, 1049-001 Lisbon, Portugal E-mail: [email protected]
Carlos Oliveira Cruz Assistant Professor, Department of Civil Engineering and Architecture and Georesources, CERIS, Instituto Superior Técnico, University of Lisbon Av. Rovisco Pais, 1049-001 Lisbon, Portugal E-mail: [email protected]
Cristina Matos Silva Assistant Professor, Department of Civil Engineering and Architecture and Georesources, CERIS, Instituto Superior Técnico, University of Lisbon Av. Rovisco Pais, 1049-001 Lisbon, Portugal E-mail: [email protected]
Declarations of interest: none
12.396 words
1 2 3
Abstract
4
When designing green roofs, decision-makers continually face the difficult task of
5
balancing benefits against costs. The use of decision analysis methods is essential
6
in complex decision-making processes including different perspectives, multiple
7
objectives, and uncertainty. This is the case when choosing between green roof
8
systems, since different stakeholders show diverse concerns, and each solution has
9
a different cost and performance. One of the most used methods in decision
10
analysis is multicriteria analysis. The present study aims to adapt existing
11
multicriteria decision models for the context of green roofs installation. The
12
proposed methodology is based on the MACBETH method (Measuring
13
Attractiveness by a Categorical Based Evaluation Technique) and determines the
14
green roof option with the best trade-off between costs and benefits in agreement
15
with the preferences of the users/investors. The paper presents the application to a
16
real case study in Lisbon, Portugal, comparing the installation of 6 different green
17
roofs over a parking lot. The methodology application identifies the intensive
18
green roof as best solution classifying with a score of 69.43 out of 100. The
19
conclusions on the best option remained robust in the sensitivity and robustness
20
analysis. This approach supports the decision-making process of green roofs and
21
enables robust and informed decisions on urban planning, while optimizing
22
buildings retrofitting.
23 24
Keywords: decision making; green roofs; investors preferences; multicriteria
25
analysis; residential buildings; sustainable development. 1
26 27 28 29
1.
30
Green roofs provide several ecosystem services, therefore, have been increasing their
31
market share over the past years (Berardi et al., 2014). The reasons for the adoption of
32
these systems depend mainly on the type of user/investor, type of building, and
33
social/economic context (European Commission, 2014). For example, private and
34
public investors have different concerns and objectives (Nurmi et al., 2016; Tomalty et
35
al., 2010). Private investors, such as property owners, value more the possibility of
36
having an additional space for leisure or increased thermal comfort in their properties.
37
While, public investors (e.g. municipalities) give greater importance to air quality
38
improvement and other environmental benefits. To accurately evaluate the solutions, it
39
is necessary to consider the preferences of each stakeholder.
40
Green roofs provide many benefits, not only on a global or urban scale but also for
41
buildings (Berardi et al., 2014; Bianchini and Hewage, 2012; Kohler, 2018). So, their
42
application should be analysed as a multiple objective process. In addition, different
43
systems have distinct performances and costs, which sometimes makes it challenging to
44
select the optimal and most effective solution (Teotónio et al., 2018). For these reasons,
45
decision-makers continually face the difficult task of balancing benefits against costs
46
while being challenged with the risks of realizing the benefits (Cruz et al., 2017;
47
Phillips and Bana e Costa, 2007).
48
Decision analysis is a technique for analysing complex decisions with multiple, and
49
usually conflicting, objectives, and uncertainty (Parnell, 2009). Structured processes are
50
used to support decision-makers and to clear the final choice. Multicriteria decision
51
analysis (MCDA) is one of the most common methods in decision analysis, which has 2
Introduction
52
been extensively used to evaluate and compare options involving the achievement of
53
multiple objectives (Franco and Montibeller, 2010). The application of MCDA to green
54
roof projects is appropriate and will enable decision-makers (e.g., users, owners,
55
governments) to make more robust and informed decisions.
56
The aim of the present study is two-fold: to adapt an existing multicriteria decision
57
model to a green roof installation context and to validate it through a real case study.
58
This will allow exploring the potentials of incorporating users/investors preferences in
59
the decision-making process, while evaluating the different trade-offs between green
60
roofs life-cycle costs and benefits.
61
Like many other European cities, Lisbon has been putting efforts towards
62
environmental sustainability "with particular focus on establishing green infrastructure,
63
or connected networks of green space, to counteract the effects of climate change …"
64
(European Commission, 2019a, 2019b). Lisbon was acknowledged with the European
65
Green Capital Award for 2020 having demonstrated several initiatives for the creation
66
of green corridors and new green spaces to promote citizens’ quality of life and protect
67
biodiversity (European Commission, 2019a, 2019b). The installation of green roofs,
68
even though limited, had also a contribution. Wastewater treatment plant of Alcântara is
69
one of the best-known green roofs in Lisbon.
70
To validate the methodology, this study evaluates a real case study which considers the
71
installation of a green roof in a new residential building, in Lisbon, Portugal. The
72
research adopts the perspective of the private decision-maker, one of the directors of the
73
company responsible for the real estate investment. The perspective of other
74
stakeholders, such as public decision-makers or the users, is not considered in this case,
75
although the methodology is transferable to any decision-maker.
3
76
The application of the multicriteria method was carried out through face-to-face
77
interviews between two decision analysts and a decision-maker. A preliminary meeting
78
promoted the definition of the problem and construction of the decision model. This
79
required the determination of value functions incorporating the decision-maker
80
judgments. After the application of the model, a final meeting allowed the validation of
81
the results.
82
This paper is organized as follows. Section 2 presents an overview of the existing
83
literature. Section 3 presents the multicriteria decision model for green roofs. Section 4
84
describes the application of the methodology to a real case study and section 5 presents
85
a discussion of results. The final section closes the study with the main conclusions.
86 87
2.
88
Few studies using MCDA have been conducted to support the decision-making process
89
of evaluating and/or comparing different typologies of green roofs. Several authors
90
applied MCDA methods to assess existing barriers to the widespread installation of
91
green roofs and determine the importance of design characteristics influencing their
92
choice.
93
Rosasco and Perini (2019) considered the economic, environmental, social and
94
performance aspects of green roofs. They ranked these criteria according to their
95
importance for the system selection. The most relevant was the performance of the
96
system, including elements such as thermal insulation, roof protection, and systems
97
weight. The second most relevant were the environmental criteria, followed by
98
economic criteria and social criteria. The authors identified the building scale analysis,
99
i.e., a single roof, as the reason for the low impact on social aspects. This proved that
100
MCDA approaches are case-by-case sensitive. The installation and maintenance costs,
4
Literature Review
101
air quality, thermal insulation, and health effects were the most important sub-criteria
102
within each group.
103
Sangkakool et al. (2018) conducted a research with the same purpose. However, the
104
authors applied a mixed approach combining strengths, weaknesses, opportunities, and
105
threats (i.e., SWOT analysis) to structure the model. Both the studies of Rosasco and
106
Perini (2019) and Sangkakool et al. (2018) used the Analytical Hierarchy Process
107
(AHP) to weight the criteria. Experts identified urban heat island mitigation and lack of
108
financial incentives as the criteria with greater contribution to the analysis. The possible
109
damage and leakage of the system, aesthetics and urban quality, social responsibility
110
concerns were considered the least relevant.
111
Brudermann and Sangkakool (2017) used the same hybrid approach to identify the key
112
factors to promote the application of green roofs in European temperate climates. The
113
strengths and opportunities had a higher impact than negative SWOT categorized
114
criteria (i.e., weaknesses and threats). The positive results revealed that green roofs have
115
a great potential in temperate climates. Sub-criteria as reduced flood risk, environmental
116
benefits, life quality and aesthetics, and green policies in cities received the highest
117
scores.
118
Naing et al. (2017) used the MCDA method to identify the potential of existing
119
buildings for green roof retrofitting. The authors took into consideration the buildings
120
physical, economic, and social aspects and the green roof to be installed. Results show
121
that the buildings physical aspects had the highest impact on the analysis.
122
All the studies involved a panel of stakeholders with reasonable sample sizes and
123
distinct work fields and backgrounds to include different perspectives on the decision
124
problem. Most authors have experienced disagreement between stakeholders, since
125
several groups showed distinct perceptions and assigned different priorities to the
5
126
criteria. For example, in the study of Rosasco and Perini (2019), academics valued more
127
the economics aspects of green roofs while professional gave more importance to social
128
aspects. In line with this, academics defended that costs, even though significant, are
129
outweighed by the economic benefits such as tax incentives and increase on property
130
value. Professionals, however, reported the opposite. The study of Naing et al. (2017)
131
presented different conclusions with academics valuing more social aspects and experts
132
giving more importance to economic criteria.
133
When evaluating roof solutions, the research of Guzmán-Sánchez et al. (2018) and
134
Rosasco and Perini, (2019) showed that green roofs are preferable to traditional
135
solutions. Supporting this type of analysis, Mahdiyar et al. (2019) developed a decision
136
support system to select the best green roof type to install in residential buildings. The
137
validation of the model through three case studies presented changes in the best option
138
according to the priorities of the project. This proved, once again, that MCDA is case-
139
sensitive and specific project requirements have a significant impact on the decision-
140
making. For this reason, weighting criteria can generate different outcomes making it
141
hard to draw valuable and global conclusions on this topic.
142
Table 1 presents the main characteristics of existing MCDA applied to green roofs,
143
considering the type of MCDA approach, criteria and sub-criteria evaluated,
144
stakeholders and sample size.
145
6
146
Table 1. Principal characteristics of existing MCDA applied to green roofs context Reference
Evaluated solutions
MCDA approach
Total criteria
Total subcriteria
Rosasco and Perini, 2019
Green and traditional roofs
Analytic Hierarchy Process
4
17
Sangkakool et al., 2018
Green roofs
Analytic Hierarchy Process
4
14
Brudermann and Sangkakool, 2017
Green roofs
Analytic Hierarchy Process
Mahdiyar et al., 2019
Mahdiyar et al., 2018
GuzmánSanchéz et al., 2018
Naing et al., 2017
Green roofs (extensive, intensive and semiintensive)
Green roofs
Green roofs and other flat roofs
Green roofs (extensive)
Fuzzy Delphi method and Cybernetic Fuzzy Analytic Network Process Enhanced Fuzzy Delphi method Analytic Hierarchy Process and the Technique for Order of Preference by Similarity to Ideal Solution
Analytic Hierarchy Process
4
13
Scoring method
Stakeholders
Pairwise comparison by attributing a score based on a 1-9 scale Pairwise comparison by attributing a score based on a 1-9 scale Pairwise comparison by attributing a score based on a 1-9 scale
Sample size
Judgments collection Cross-group discussion (brainstorming technique led by a moderator) Structured interviews and questionnaires during conferences
Architects, engineers, academics
30
Architects, urban planners, academics, city representatives, users
46
Architects, urban planners, academics, city representatives, users
30
Semi-structured interviews and questionnaires
-
12
pairwise comparison in a scale 1,3, 5, 7, 9
Architects, civil engineers, contractors and landscape planners/designers
33
Questionnaires
4
19
pairwise comparison in a scale 0, 1, 2, 3, 4
Architects, landscape planners, contractors, academics
28
Questionnaires
16
Pairwise comparison in a 4-level scale: very important, important, slightly important and unimportant
Experts
23
Questionnaires
18
Pairwise comparison by attributing a score based on a 1-9 scale
Academics and experts (research consultants, architects, surveyors and ecologists working on green roofs)
30
Questionnaires
-
3
147 148
Table 2 specifies the sub-criteria included in each study.
149
7
150
Table 2. Sub-criteria included
Sub-criteria
Rosasco and Perini, 2019 (1) Installation costs
Costs
(2) Maintenance and disposal costs
(3) Tax incentives Incentives and policies
in existing MCDA applied to green roofs
Sangkakool et al., 2018 (1) Investment volume
(2) Maintenance requirements
(3) Lack of subsidies (4) Favorable regulations and policies
Brudermann and Sangkakool, 2017
(1) Higher implementation and maintenance costs
Mahdiyar et al., 2019
Mahdiyar et al., 2018
Structural considerations
(5) Recycle materials (6) Roof protection
(5) Cheap synthetic grass (6) Possible damage / leakage
(7) Weight of the system
Naing et al., 2017
(1) Life cycle costs
(1) Access to machinery and materials to site, and (2) existing use of buildings (3) Access to maintenance and (4) utilities, and (5) size of usable roof area
(1) Additional required initial cost
(2) Maintenance costs
(1) Maintenance costs
(3) Payback period
(2) Payback period (3) Net present value
(2) Green policies in cities (3) Legal and political constraints
(4) Green building certificate award achievement (2) Embodied carbon (3) Embodied energy (4) Recycled materials
(4) Embodied energy and carbon emission System sustainability
Guzmán-Sanchéz et al., 2018
(4) Possible damage
(5) Structural and static challenges
(5) Probable damage (6) Roof slope
(4) Structural considerations
Probable increasing in the size of (7) beam and (8) column
(5) Roof protection (6) Type of structure and loading capacity, and (7) roof slope
(6) Dead load
Probable changes in the (9) foundation and (10) lateral load resistance system (8) Height of building, (9) availability of sunlight, and (10) wind considerations (11) Rainfall and irrigation and, (12) waterproofing (13) stormwater discharge point of building (14) Safety consideration, and (15) fire risk
Physical considerations
Aesthetics and recreation
(8) Real state benefit (9) Building aesthetics Recreation included in building aesthetics
(10) Urban aesthetic (11) Health effects
Social impacts
Sound insulation Climate regulation and other environmental benefits
8
(6) Aesthetics
(7) Aesthetics / urban quality
(6) Life quality and aesthetics
(8) Lack of skills and knowledge
(7) Lack of knowledge (8) public acceptance and environmental awareness
(9) Social responsibility concerns
(12) Energy savings (heating and cooling) Thermal insulation
(5) Increase in property value
(10) Green building trends
(9) Scepticism of potential adopters
(11) Energy savings
(10) Energy savings
(11) Higher property value (12) Aesthetic aspects
(7) Recreational space
(13) Recreational space provision
(8) Type of access: public or private
(14) Type of accessibility
(9) Energy savings
(15) Energy savings (8) Thermal insulation (9) Albedo coefficient
(14) Acoustic noise reduction
(10) Noise absorption
(16) Noise absorption
(10) Noise control (11) Carbon sequestration
(12) Urban heat island mitigation (13) Climate
(11) Climate
(16) Public potential accessibility
(17) Likelihood for restrictions on planning and building permit requirements
(13) Thermal insulation properties
(15) Air quality
(7) Social use
(17) Climate
change (14) Environmental benefits Stormwater management
(16) Runoff
change (12) Environmental benefits (13) Flood risk reduction
(11) Environmental benefits (12) Water management
(18) Environmental benefits (19) Stormwater retention
(12) Runoff attenuation (13) Water purification
(17) Suitability to location (14) Solar power (15) Relative humidity control (16) Biodiversity and agricultural productivity
Others
(18) Opportunities for food production
151 152
There are also other studies comparing green roofs and competing alternatives in
153
specific contexts. These use multi-objective optimization to select solutions that
154
improve flow and flood management (e.g., Alves et al., 2018; Chow et al., 2014;
155
Radinja et al., 2019), control water pollution (e.g., Di Matteo et al., 2017; Liquete et al.,
156
2016) and enhance biodiversity (e.g., Snep et al., 2009). In these cases, the defined
157
evaluating criteria are adjusted to the particularities of each decision problem and the
158
analysis is more limited (e.g., included criteria) in comparison to green roofs analysis.
159 160
3.
Methodology
161
Decision analysis is characterized by several model types distinguished by their
162
handling of uncertainty and multiple criteria. Among the evaluation processes, this
163
study adopts the multicriteria value method, which is the best to evaluate different
164
options considering multiple objectives, as it is the case of green roofs (Phillips, 2005).
165
The MCDA method includes the following main steps: (i) problem definition and
166
structuring; (ii) model structuring and use; (iii) impact assessment and analysis,
167
including the development of action plans (Bana e Costa and Beinat, 2005; Bana e
168
Costa et al., 2008; Belton and Stewart, 2001).
169
process, this sequence can be translated into a 4-stage decision support process based on
170
the studies of Bana e Costa et al. (2012) and Franco and Montibeller (2010). Figure 1
To structure the decision-making
9
171
presents the methodology applied in this study. The main steps of the proposed
172
methodology are outlined in sections 3.1. to 3.4.
173
Figure 1. Structuring the problem using MCDA methodology
174 175 176 177
3.1.
178
The problem definition and structuring consist of characterizing the decision context,
179
establishing the analysis boundaries and its scope. It is essential that during this process
180
the decision problem for selecting a green roof and the primary motivations for the
181
installation of such solution (i.e., strategic objectives) become clear and transparent
182
(Bana e Costa and Beinat, 2005). The decision context has a great influence on the
183
decision problem and should be clearly identified. For example, whether it is a small or
184
a largescale project (e.g., greening a single building vs a city), how it affects the actors
185
involved, the people benefiting from the project or even its impacts on the environment.
186
The strategic objectives are more specific. There can be multiple reasons to install a
187
green roof, such as to preserve biodiversity, improve the thermal behaviour of a
188
building or reduce the temperature of a city (Berardi et al., 2014) and those can change
189
the way the decision problem is addressed.
190
The decision-makers play an important role since they have the power to affect the
191
decision. Sampling procedure involves the identification of the key players, for
192
example, stakeholders, experts and professionals, users, and other actors that might be
193
affected by the project and whose perspective should be considered when taking a
194
decision. The definition of their level of intervention in the process allows identifying
195
their value-systems and possible conflicts that can emerge (Franco and Montibeller,
196
2010). There is a common belief that groups outperform individuals in decision-making 10
Structuring the Problem
197
processes since there is a higher number of perspectives and share of information.
198
However, group decision biases, such as group thinking, are also more likely to happen
199
(Mateus et al., 2017).
200
Lastly, the type of evaluation model to be constructed in order to address the problem is
201
essential. According to Belton and Stewart (2001), the model should focus on technical
202
modelling and analytical features, but mostly on supporting the decision-maker. There
203
are several MCDA methods, such as: ELimination Et Choix Traduisant la REalitè
204
(ELECTRE); Preference Ranking Organization Method for Enrichment Evaluations
205
(PROMETHEE); Multi-Attribute Utility Theory (MAUT); Analytic Network Process
206
(ANP); Analytic Hierarchy Process (AHP); Case-Based Reasoning (CBR); Data
207
Envelopment Analysis (DEA); Fuzzy Set Theory; Simple Multi-Attribute Rating
208
Technique (SMART); Goal Programming; Technique for Order of Preference by
209
Similarity to Ideal Solution (TOPSIS); Simple Additive Weighting (SAW); and
210
MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique).
211
These methods differ in multiple aspects, such as the implementation procedure, outputs
212
type, necessary input level, type of decision-making problems it can cover, number of
213
criteria it allows and the type of indicators, the handling of the participatory process of
214
stakeholders, efforts by decision-makers, and the need to include a decision aid
215
specialist for technical support (Guarini et al., 2018; Velasquez and Hester, 2013). This
216
study adopts the additive value model using the MACBETH method as a decision-aid
217
approach to multicriteria value measurement (Bana e Costa et al., 2012). This method is
218
based on a pairwise comparison procedure to determine the value of the alternatives
219
while applying a non-numerical questioning procedure to attribute numerical scores.
220
This is the critical distinction between MACBETH and other MCDA methods that use a
11
221
pairwise comparison procedure involving the attribution of numerical judgments and,
222
sometimes, leading to mistakes (Bana e Costa et al., 2012).
223
This study uses M-MACBETH software to reproduce the methodology. This decision
224
support system is designed to be used by a decision analyst following the constructivist
225
principles of process consultation, implying the sharing of information with the
226
decision-maker during the entire process.
227 228
3.2.
229
The MCDA model structure consists of three main tasks: i) the identification of decision
230
alternatives, i.e., green roof options; ii) the representation of the objectives in a value
231
tree (i.e., fundamental points of view - FPV), and; iii) the definition of attributes to
232
measure the achievement of those objectives, i.e., descriptors of performance – DP
233
(Franco and Montibeller, 2010).
234
When choosing a green roof system, there are three technical solutions that can be
235
distinguished: i) extensive, ii) semi-intensive, and iii) intensive, according to their main
236
characteristics, especially the type of vegetation, use, and maintenance (FLL, 2008).
237
The FPV are the values that actors consider relevant to the evaluation of the green roof
238
options and can consist of a single or several evaluation criteria. They should be
239
complete, non-redundant, decomposable, and consensual (Franco and Montibeller,
240
2010). There are two basic model-structuring strategies to define the FPV: bottom-up
241
and top-down. In each one of those strategies, the value true is built by aggregation and
242
desegregation of the points of view, respectively. The difference is that in the bottom-up
243
model, the FPV definition is based on the characteristics differentiating the green roof
244
options and that are relevant to meet decision-makers' values. Those characteristics are
245
then grouped, composing the FPV. In the top-down model is the inverse. First one
12
Structuring the Model
246
should establish the objectives, and only then select the underlying objectives and FPV
247
(Franco and Montibeller, 2010). This paper evaluates the installation of green roofs and,
248
therefore, the options to be addressed are known in advance. However, the main focus
249
of this methodology is the values/preferences of the decision-makers. For this reason,
250
this study adopts the top-down model-structuring strategy. Note that applications to case
251
studies may include other solutions apart from green roofs that also meet the objectives.
252
Suppose the installation of nature-based solutions (including rain gardens, street trees,
253
green roofs and walls, and others) with the objective to cover a neighbourhood with
254
green. In such cases, one should use the bottom-up model.
255
The top-down model-structuring strategy starts with the definition of the objectives,
256
separating the mean from the end objectives and, finally, identifying the fundamental
257
objectives, i.e., FPV (Franco and Montibeller, 2010). For example, Table 2 in section 2
258
presents the criteria included in existing MCDA applied to green roofs, which are
259
organized by their nature. Costs include installation, maintenance, disposal costs; social
260
impacts involve life quality, aesthetics, social responsibility concerns, lack of
261
knowledge, scepticism of potential adopters; and climate regulation and other
262
environmental benefits cover air quality, urban heat island effect, climate change, and
263
others. These sets of criteria represent potential FPV.
264
The FPV largely depends on the stakeholders' profile. While structuring the model, the
265
decision-makers should select the most relevant evaluation criteria according to the
266
strategic objectives. These should include the costs and benefits of green roofs
267
considering the particularities of the project. For example, the benefit of well-being and
268
improvement of individuals' work performance are particularly important in office
269
buildings and schools. In another context, improving the aesthetics and the availability
270
of recreational spaces in buildings are crucial for real estate investors. While the air
13
271
quality improvement and mitigation of urban heat are more significant for large-scale
272
public investors. Error! Reference source not found. Table 3 organizes the green
273
roofs costs and benefits (Berardi et al., 2014) that might be decisive when evaluating the
274
installation of green roofs to be considered in the FPV definition. Questions like “Does
275
this point of view contain many components, i.e., evaluation criteria?”, “Which ones?”
276
stimulate the discussion and help the FPV definition.
277
Table 3. Costs and benefits of green roofs
278
Economic Costs
Social and Environmental
• Installation costs • Maintenance costs • Replacement/Disposal costs
Key benefits
• Incentive policies (e.g., tax reduction)
• Mitigation of urban heat island effect
• Real estate value (increasing aesthetics
• Rainwater management (reduction and
and recreational space) • Durability (roof longevity)
delay) • Improvement of public health and quality
of life • Landscape and urban aesthetics
Other benefits
• Thermal comfort and reduction of energy
consumption
• Ecological preservation and promotion of
biodiversity
• Fire security
• Higher levels of productivity at work
• Weight of the system (building’s loading
• Lower risk of flooding
capacity)
• Urban sound absorption
• Possibility of rooftop farming
• Improvement of air quality
• Efficiency of photovoltaic panels (PV’s)
• Improvement of drained pluvial water
• Sound insulation
quality
279 280
Green roofs installation can be technically challenging in some situations, so screening
281
criteria should be initially defined to eliminate solutions that do not fulfil essential
282
requirements. For example, the budget available for investment is typically a constraint
283
and has a significant influence on the final decision. Installation conditions can also be a
284
limitation. Rehabilitation interventions are a good example since buildings are not
14
285
structurally prepared to support the additional weight of the green roof. In such cases,
286
the choice of the system can be limited to lightweight extensive green roofs.
287
Structuring the model also include key issues of interactive value modelling such as the
288
definition and operationalization of the evaluation criteria through DP, which are
289
ordered sets of plausible impact levels associated with each evaluation criterion. DP can
290
be a qualitative or quantitative measure of the extent to which each green roof solution
291
satisfies a specific condition, i.e., performance scale (Bana e Costa et al., 2018).
292
According to Bana e Costa et al. (2012), M-MACBETH software scores the
293
performance of the alternatives through the more appropriate of two different
294
comparison methods:
295
i) direct method, directly comparing the green roof alternatives (two at a time);
296
ii) indirect method, comparing pre-defined performance levels that can be
297
quantitative or qualitative.
298
The DP of each criterion should be described in the most unambiguous manner possible
299
to ensure a clear interpretation. Each criterion is associated with a performance scale
300
that consists of a carefully described and ordered set of impact levels to cover an
301
adequate range of performances. Performance scales can be built upon existing
302
studies/data on green roofs costs and their performance (Bana e Costa et al., 2012).
303
A possible DP is the fire security of green roofs based on technical data with functional
304
importance (Zinco, 2016). The DP consists of four impact levels that evaluate “the
305
extent to which the green roof can retard the spread of flames from the roof to the
306
interior of the building” according to the British Standard on external fire exposure roof
307
tests (BS 476: Part 3, 2004). It uses a national standard because the existing studies on
308
green roofs performance to fire exposure cover those tests. However, if further research
309
is done, analysts and decision-makers can change/adapt the proposed DP and the
15
310
corresponding impact levels. In fact, “Green infrastructure” is a new field of research
311
and current studies are showing in particular new ways of quantifying their benefits.
312
Therefore, this methodology can, and must, be redesigned and updated, anytime, based
313
on other standards (e.g., using current green roof guidelines (ASTM) for the United
314
States).
315 316
Table 4. Constructed performance scale for fire security criterion
317
Level
Description
When the roof is tested for external fire exposure with burning brands, wind and supplementary radiant heat, according to BS 476: Part 3: 2004, there is … Broof
… no penetration within 60 minutes
Croof
… no penetration within 30 minutes
D/Eroof
… penetration within 30 minutes
Froof
… no performance determined
318 319
Finally, the decision-makers need to identify reference levels for each criterion in each
320
set of performance levels to help a proper criteria interpretation and determine the
321
corresponding value functions. The two reference levels, good and neutral, correspond
322
to the satisfactory level of attractiveness and the level in which the performance was
323
neither attractive nor unattractive, respectively (Bana e Costa et al., 2012).
324 325
3.3.
326
To accurately compare the green roof options under consideration it is necessary to
327
develop formal models of the decision-makers’ preferences and respective value trade-
328
offs (Belton and Stewart, 2001). These models translate value functions that define each
329
criterion or their weight, respectively, enabling to transform qualitative inputs into value
330
scores (Bana e Costa et al., 2006). To achieve this, MACBETH builds upon the
16
Construction of the Model
331
decision-makers' qualitative judgments about the difference in attractiveness between
332
two elements at a time, either on performance levels or reference levels. The decision-
333
makers must be confronted with the results of the model at each stage, testing for
334
consistency and adjusting their judgments, if necessary (Bana e Costa and Chagas,
335
2004).
336
According to Bana e Costa and Chagas (2004), to determine the value functions first,
337
neutral and good performance levels are anchored with scores of 0 and 100,
338
respectively. Then, the MACBETH method compares and ranks the performance or
339
reference levels, establishing a preference scale, and assigns the final scores to each
340
level. For that, the decision-makers must measure the difference in attractiveness
341
between the several levels. The construction of judgment matrices involves the
342
following steps, as suggested by Bana e Costa and Chagas (2004):
343
1) Decision-makers judge the difference of attractiveness between the higher
344
and lower level of each DP. Then between the second-highest and the lower
345
level, and so on until the last column of the judgments matrix is completed;
346
2) The first row of the matrix is completed, followed by the second diagonal and
347
then the remaining cells.
348
The decision-makers should give qualitative judgments using the MACBETH
349
difference scale with seven categories of difference of attractiveness: null, very weak,
350
weak, moderate, strong, very strong and extreme. In case of indecision or indifference,
351
it is possible to choose more than one consecutive categories (e.g., strong-extreme). In
352
the presence of inconsistencies, the decision-makers must be warned and informed
353
about possible changes in the judgments (Bana e Costa and Chagas, 2004).
354
Finally, weighting the criteria follows a similar procedure. First, all the criteria are
355
ordered according to their importance to the decision-making process, using the
17
356
MACBETH simple ranking process. During this process, the decision-makers are
357
actually ranking the importance of the swings from the neutral to the good performance
358
level on each criterion. For this purpose, the decision analysts can ask the following
359
question “Consider that there is one green roof option with a neutral performance in all
360
the criteria. Which criterion would have the most important improvement if you could
361
change the green roof performance from neutral to good in just one criterion?”. The
362
same question applies for the second most important criterion and so on. After ranking
363
the criteria, the decision-makers need to give qualitative judgments on the weights of
364
each criterion comparing each pair of swings (Bana e Costa and Chagas, 2004).
365
Based on all judgments, the M-MACBETH software determines the final value
366
functions and weights the criteria. At the end of this process, the analyst should present
367
the final models to the decision-makers for validation. The software allows adjustments
368
on the final values within a range compatible with the judgments (Bana e Costa and
369
Chagas, 2004).
370 371
3.4.
372
After structuring and constructing the model, the application provides the necessary
373
information to discuss the problem and sustain the green roof choice . As indicated in
374
section 3.1, the present study uses the M-MACBETH software to obtain results. Bana e
375
Costa et al. (2012) provide detailed information on the mathematical formulation of this
376
software. A summary is presented above.
377
According to Bana e Costa et al. (2012), testing the consistency of the decision-makers'
378
judgments consists of verifying the following conditions 1–3. Let
379
> 2 evaluating elements, e.g., performance or reference levels;
380
of
18
Application of the Model
such that
is more or equally attractive as
be a finite set of and
two elements
and ( ) and ( ) be the scores
, = 0, … , 6 the seven MACBETH
381
assigned to those elements, respectively; and
382
categories of difference in attractiveness: null ( ), very weak ( ), weak ( ), moderate
383
( ), strong ( ), very strong ( ) and extreme ( ). Then, ( , )
384
represents a MACBETH judgment of difference in attractiveness between
385
expressed by the single category
and ( , )
∪ …∪
( = 0, … , 6) and
( , = 1, … , 6 ! "ℎ <
386
) represents a MACBETH judgment expressed by a subset of categories from Ci to Cs,
387
in cases of indecision. Bana e Costa et al. (2012) state that there are three possible
388
scenarios. If the decision-makers judge
389
then those elements are coincident: ∀ , : ( , )
with null difference of attractiveness,
⟹ ( ) = ( )
390
If the decision-makers identify
391
than : ∀ , : ( , )
and
(1)
as more attractive than , then
( = 1, … , 6)
and ∀ , : ( , )
∪ …∪
( , = 1, … , 6 with < )
⟹ ( )> ( )
(2)
and ! are more attractive than
and ,, respectively, and the difference of
392
Finally, if
393
attractiveness between
394
and ,, then the difference between their scores is equally greater: ∀ , : ( , )
has a higher score
is greater than the difference of attractiveness between !
and
∪ …∪
and ∀ !, , : (!, ,)
( , = 1, … , 6 with < )
∪ …∪
with , , ´, ´ (1, … 6), ≤ , and ´ ≤ ´ > ´ ⟹ ( ) − ( ) > (!) − (,)
(3)
395
After verifying the consistency of all the judgments, MACBETH builds a numerical
396
continuous value scale of each criterion, i.e., scoring scales. Following Bana e Costa et
397
al. (2012), this scoring process is obtained by solving the linear programming problem
398
presented above, where
3
and
4
are two elements of
such that they are at least as 19
399
attractive and at most equally attractive to any other element of
400
linear problem provides the estimation of the minimum of
401
following constraints 4–7. The linear programming problem is provided below,
402
following the guidelines of Bana e Costa et al. (2012).
403
An arbitrary zero is fixed: (
404
4
(
3
) subject to the
= 0)
(4)
If the difference of attractiveness between and ∀ ( , )
405
, respectively. The
is null, they score equally:
: ( ) − ( ) = 0
(5)
Then, ∀ ( , )
∪ …∪
with , 51, 2, 3, 4, 5, 69 and ≤ : ( ) − ( ) ≥
(6)
406
Both constraints guarantee that the ranking of the elements is kept. The preservation of
407
the order inherent of the judgments provided by the decision-makers is ensured by: ∀ ( , )
∪ …∪
and ∀ (!, ,)
´
∪ …∪
´
with , , ´, ´ 51, 2, 3, 4, 5, 69,
≤ , ´ ≤ ´ and > ´: ; ( ) − ( )< − ; (!) − (,)< ≥ − ′
(7)
408
Bana e Costa et al. (2012) indicate that the resulting basic MACBETH value scale for
409
each criterion is then transformed to an anchored scale where
410
(
4)
(
3)
= 100 and
= 0, corresponding to the good and neutral performance levels, respectively. The
411
performance of the green roof options is then scored for each criterion to determine the
412
partial values. Finally, the combination of partial values functions with the weighting
413
criteria measures the overall attractiveness of each option and allows to discuss global
414
results to assess the best value trade-offs. Belton and Stewart (2001) shows a model
415
requiring additive aggregation, as presented in Equation 8: A
>(?) = @ ! B
20
(?)
(8)
416
where >(?) is the global attractiveness of option ?; ! is the weighting coefficient
417
(represents the relative importance of criterion );
418
option ? for the criterion and C is the total number of DP.
419
Bana e Costa et al. (2018) recommend a sensitivity analysis on the criteria weight to
420
visualize the extent to which the model's recommendations change for different
421
judgments. Also, a robustness analysis is important to study the influence of diverse
422
levels of scarcity, imprecision and uncertainty on conclusions.
(?) is the partial value function of
423 424
4.
Case Study
425
The case study is a real residential project in Lisbon, Portugal. This project was part of a
426
residential condominium with six building plots, three of them already build. A
427
Portuguese company promoting turnkey investments assumed the construction and
428
management of one building with 50 luxury apartments. This section describes the
429
methodology application to this case study, in order to determine the best green roof
430
option to install in this building. This chapter follows the first 3 stages identified in the
431
previous chapter (sections 3.1. to 3.3.). The application of the model (section 3.4.) to
432
this case study is presented in the next chapter, results and discussion (section 5).
433 434
4.1.
435
Decision Problem: The company responsible for the residential project works mainly
436
for a premium market. For this reason, the designers planned the green roof installation
437
on the ground floor, above the parking lot. Figure 2Error! Reference source not
438
found. shows a top view including a green roof above the parking lot (represented in
439
green). The decision problem focused on choosing the best green roof solution to install.
440
Structuring the Problem
Figure 2. Green roof’s location proposal (top view) 21
441 442
Strategic Objectives: The decision problem had to deal with specific characteristics of
443
the project. For example, the company operates in areas related to real estate and
444
renewable energy. Therefore,
445
sustainability, as reducing building’s energy consumption. The apartments design phase
446
considers the application of materials with good thermal insulation properties (e.g.,
447
insulated glass, also known as double glazing), energy efficient equipment and 38
448
photovoltaic panels (PV) on the roof. The condominium included a large green area
449
open to the public during the day. Considering these design features, the main strategic
450
objectives of the green roof installation were: i) to improve the building's aesthetics to
451
be consistent with the surrounding landscape ii) to improve building's services and iii)
452
to enhance the project environmental commitment. This is in line with a multiple
453
objective decision-making context. So, the company decided to conduct a MCDA to
454
support their decision.
aimed to incorporate high standard solutions for
455 456
Decision-Makers: The company responsible for the acquisition of the two building plots
457
and construction of the residential project was the main investor and, consequently,
458
played a crucial role in the decision-making process. The company operated together
459
with other investors (e.g., design and construction companies) so this was a collective
460
decision that should be approved by all stakeholders. All parties agreed to select one of
461
three company directors as the interlocutor in the process. The role of the decision-
462
maker was to give assistance along the decision model development and to represent the
463
beliefs and judgments of all stakeholders during the meetings with the decision analysts.
464
The director nominee had an active role in the company with experience in investments
22
465
and large projects and, most importantly, was the person responsible for the project
466
activities management and for the connections between all the companies.
467 468
4.2.
Structuring the Model
469
Decision Alternatives/Options: The decision-maker defined options that could be
470
installed based on the three green roof types indicated in section 3.2: extensive, semi-
471
intensive and intensive. The decision-maker believed that it was also important to
472
distinguish between accessible and non-accessible systems. In total, the analysis
473
considered six green roof typologies.
474
It is important to underline that, in this case, green roof systems were the only options
475
that could meet the objectives defined in section 3.2. However, besides green roofs,
476
additional alternatives could be evaluated to meet other decision problems with different
477
objectives and stakeholders.
478 479
Fundamental Points of View:
480
In the initial phase of the preliminary meeting, the decision-maker reflected on which
481
lines of analysis should be considered to evaluate the green roof alternatives. Three FPV
482
took into consideration the costs incurred during the green roofs lifecycle and also the
483
direct benefits to the building's users which fall into two main categories: improvement
484
of building services and provision of additional benefits. Table 5 defines the final three
485
FPV.
486
Table 5. Fundamental points of view
487 FPV 1. Costs 2. Added value
Description Total amount of capital that the decision-maker is going to invest/spend to have the system properly installed and maintained. Additional benefits that can be obtained from the green roof system such as
23
higher visual impact and additional rentable space on the building. Enhancement of the building’s performance owing to the installation of a
3. Building services
green roof, in terms of additional comfort and security of the users.
488 489
Evaluation criteria: The decision-maker detailed the FPV in a set of six evaluation
490
criteria. The analysts questioned the relevance of all green roofs costs and benefits listed
491
inError! Reference source not found. Table 3. The decision-maker agreed that large-
492
scale social and environmental benefits should not be included as they are only
493
significant for large green areas. Also, the decision-maker excluded the capacity of
494
growing agricultural products in the roof since it did not serve the purpose of the
495
project. The same happened with the rainwater harvesting benefit given the risk of
496
incompatibility with the already approved details of the building's project. The increase
497
in PV's performance of the 7th floor was not possible since the green roof was expected
498
for the ground floor. Table 6 presents the final evaluation criteria.
499
Table 6. Evaluation criteria
500 Criterion Costs
Description
Measurement units
NPV (net present value) of installation and maintenance costs
€//m2 *
over the life cycle of the green roof alternative.
Added value
Impact on the visual appearance of the building, which may
(aesthetics)
affect the average market value of the real estate.
Added value
Provision of additional recreational space that can be enjoyed
(recreational space)
by the building’s users.
Fire security
Prevention of risk of fire spreading, according to the
Qualitative classification
classification scale of the European Standard.
(in terms of speed of fire
Qualitative judgments
Qualitative judgments
penetration) Thermal insulation
Reduction of thermal conductivity of the roof and consequently
% (energy needs)
reduction of energy needs of the property, in percentage. Sound insulation
Reduction of sound transmission from the exterior to the interior of the building, in decibels (dB), increasing the
dB (noise reduction)
comfort of the building’s users.
501
*
502
rate for maintenance prices of 20%). NPV was calculated as follows: ∑IJB
503
benefits in the year ", respectively,
NPV was calculated considering the installation cost in the year 0; annual maintenance costs (assuming an inflation
24
EFG HF
( 3 )F
is the private discount rate of 4%, and
, where
J
correspond to costs and
is the period of analysis of 10 years.
504 505 506
Note that a 10-year period is valid for private investors since they aim for fastest returns on their investments. However, longer periods can be assumed for public investors (typically 40 to 50 years as used by Cruz et al., 2017 and Teotónio et al., 2018).
507 508
Figure 3 shows the value tree of the decision model, showing the hierarchic structure of
509
the selected evaluation criteria. The decision-maker, together with the analysts, revised
510
and discussed the final value tree and concluded that there was no double counting
511
between criteria. This means that the project characteristics were not being evaluated
512
more than once. Note that the model considered separately the increase of the building's
513
sound and thermal insulation. Although both these criteria affect buildings insulation,
514
they do not depend from one another concerning the stakeholders preferences.
515
Figure 3. Value tree of the evaluation criteria
516 517 518
Stakeholders valued environmental/sustainability issues and aesthetics, minimizing the
519
costs influence in the decision-making. Therefore, costs were not considered as
520
screening criterion.
521 522
Descriptors of performance: DP made operational six criteria in order to appraise green
523
roofs attractiveness. The performance scales for costs, thermal insulation, and sound
524
insulation criteria were defined as quantitative. The indirect comparison method was
525
defined as follows:
526 527 528 529
− Costs, corresponds to the expected NPV, measured by a seven-level scale (20 to 380 €/m2 investment) (Cype, 2016); − Thermal insulation is measured by a four-level scale (decrease of 0 to 30%) (Ascione et al., 2013; Silva et al., 2016);
25
− Sound insulation is defined by a four-level scale (4 to 10 dB) (Connelly and
530
Hodgson, 2013; Grant et al., 2003; Lagström, 2004).
531 532
Both added value criteria (aesthetics and recreation) were scored through direct
533
comparison. Fire security was the only criterion defined through a qualitative
534
performance scale using the indirect comparison method. This process is defined in
535
section 3.2.
536
Finally, the decision-maker created good and neutral reference levels for each criterion.
537
In this context the analysts asked the following questions regarding the fire security
538
criterion: “Considering the typical energy needs of residential buildings, identify the
539
percentage of consumption reduction that would be neither attractive nor unattractive”
540
and “please identify the percentage of satisfactory reduction that would be considered
541
as an attractive building service improvement”. A similar process was applied to the
542
remaining five criteria. Figure 4 presents the performance scale results for each
543
criterion, with the neutral and good performance highlighted in blue and green,
544
respectively. Figure 4 also shows green roof options ordered with good and neutral
545
reference levels in terms of their performance in the added value in terms of aesthetics
546
and recreation. This sorting contributed to the definition of the DP of both criteria.
547
Figure 4. Performance profile of evaluation criteria
548 549 550
The design of the building included strict requirements concerning the thermal
551
insulation and fire security. Since no benefits arise from improving those parameters,
552
the decision-maker assumed the good performance level on the highest possible level of
553
the performance scale. The neutral performance level was associated with the second-
554
highest level in thermal insulation criterion, i.e., 20% energy consumption reduction. A
26
555
neutral requirement of fire penetration in the building of 30 minutes was attributed to
556
fire security, i.e., a D/Eroof classification.
557
Future functional problems of sound insulation might arise due to less demanding
558
specifications of the project. Any additional benefit concerning this issue would be
559
valuable. For this reason, the decision-maker defined neutral and good performances in
560
the lowest possible performance levels. This formulation allowed small contributions of
561
sound insulation to be more valued than the ones regarding thermal insulation and fire
562
security.
563
The investment cost was not the main concern for the company. However, during the
564
meeting, the decision-maker revealed that the company would only invest between 140
565
and 200 €/m2 in a 10-year period. This statement established good and neutral reference
566
levels to the costs.
567
From the decision-maker point of view, the aesthetics of any green roof ranked between
568
good and neutral reference performance levels. These systems had a clear distinction
569
concerning recreation aspects. Accessible green roofs ranked above the neutral level
570
and inaccessible green roofs ranked below the neutral level, since they do not provide
571
additional recreational areas.
572 573
4.3.
Construction of the Model
574
The decision-maker gave a consistent set of judgments to build the MCDA model and
575
allow the determination of value functions for each criterion and weight of those
576
criteria. Tables 1 to 5 of Supplemental Information (SI) present the related judgments
577
introduced in M-MACBETH software. The questioning procedure during this stage
578
followed the costs example. The analysts asked the decision-maker to define which was
579
“the difference between a green roof that costs 20 €/m2 and a green roof that costs
27
580
380 €/m2, over a period of 10 years”. The analysts asked similar questions to judge the
581
difference in attractiveness between each two pairs of impact levels for the remaining
582
pairs of DP of the cost criterion (please see SI - Table 1).
583
SI Table 2 (b) presents the matrix of MACBETH judgments for sound insulation.
584
Thermal insulation shows similar judgments in the MACBETH matrix (SI Table 2 a)
585
and, therefore, they should show similar value functions. To fill in the tables, the
586
analysts asked questions like “which is the difference in overall value between a green
587
roof that has no influence on the thermal comfort of the building’s users and a green
588
roof that is able to reduce the energy needs in terms of thermal comfort by 30%?”.
589
About the aesthetic and recreation criteria, the decision-maker compared each pair of
590
green roof solutions instead of the performance levels (SI Tables 4 and 5). As expected,
591
there was no difference in the aesthetic value between the same type of systems
592
depending on whether they are accessible or not, i.e., the difference of attractiveness is
593
“null”.
594
The weighting criteria followed a similar procedure. Table 7 presents the criteria
595
ordered from the one with most to less impact as follows: aesthetics, sound insulation,
596
costs, thermal insulation, fire security, and recreational value. Table 7 also shows the
597
judgments on the weights comparing each pair of swings. To build the judgement
598
matrix, the decision-maker answered questions such as “which is the difference in
599
overall value between a green roof that can decrease the noise in building’s interior by
600
10 dB and has the minimal performance in the remaining criteria, and a green roof that
601
can decrease the energy consumption of the building by 30% and has the minimal
602
performance in the remaining criteria?”. No judgmental inconsistencies were found by
603
the software. In the end, the decision-maker revised the inputs and validated the
604
judgmental statements.
28
605
Table 7. Judgments matrix of weighting coefficients
606
Aesthetics Aesthetics
Sound Very Weak
Sound Costs
Costs
Thermal
Fire
Recreation
Lower
Very Weak
Weak
Moderate
Strong
Very Strong
Very Weak
Weak
Moderate
Strong
Very Strong
Weak
Moderate
Strong
Very Strong
Weak
Moderate
Strong
Weak
Moderate
Thermal Fire Recreation
Weak
Lower
607 608
5.
Results and Discussion
609
This section presents and discusses the results of the model application to the case
610
study, following the specifications of section 3.4. Section 5.1. discusses the MACBETH
611
numerical value functions resulting from the qualitative matrices of judgments. Section
612
5.2. presents the performance scores of the green roof options for each criterion and
613
debates on the best global value trade-offs. Sections 5.3. and 5.4. address the sensitivity
614
and robustness analysis. Section 5.5. provides an overview of the main findings,
615
limitations of the study and future developments.
616 617
5.1.
Performance Scales and Weighting Criteria
618
The set of judgments introduced in the MACBETH software (section 4.3) allowed
619
determining the value scales for each criterion. Value scales represent the decision-
620
maker judgments as measures of attractiveness in a numerical scale interval. Figures 5
621
to 10, display the MACBETH scale axis for each criterion that traduces the proportion
622
of the differences in attractiveness allowing to compare the value intervals of the DP.
623
The value scale for the cost showed an extreme difference of attractiveness on values
624
above 140 €/m2. These give rise to the value function presented in Figure 5, suggesting
625
that the maximum fair investment for the decision-maker is between 140 and 200 €/m2. 29
626
Validation of value function by the decision-maker confirms an approximate maximum
627
value of 180 €/m2.
628
Figure 5. Scale of value function of the descriptor Cost
629 630 631
The value scale of aesthetic criterion in Figure 6 shows that there is no difference
632
between systems with different accessibilities, as the scores are similar. This means that
633
for the decision-maker an accessible green roof has the same aesthetic value as an
634
inaccessible green roof. Intensive and semi-intensive solutions present more similar
635
scores when compared to the extensive ones, proving that the latter has substantially
636
lower visual impact.
637
Figure 6. Scale of value function of the descriptor Aesthetics
638 639 640
The same happens to the recreation criterion (Figure 7). According to the
641
decision-maker judgments all inaccessible solutions (extensive, semi-intensive and
642
intensive green roofs) have the same attractiveness and have an extreme difference of
643
attractiveness for the remaining options (accessible green roofs). This suggests that such
644
solutions add no recreational value to the building, being scored with much lower unit
645
values than the neutral performance level. The added value of accessible intensive green
646
roofs is significantly high, yet lower than the satisfactory performance level. The added
647
value of extensive and semi-intensive green roofs is low.
648
Figure 7. Scale of value function of the descriptor Recreation
649 650
30
651
Figure 8 presents the linear scale of the fire security value function. It reveals equal
652
differences of attractiveness between the levels of the DP, which can be related to the
653
significant requirements considered in the building’s design phase, e.g., sprinklers, fire
654
protection closures, fire extinguishers, and others.
655 656
Figure 8. Scale of value function of the descriptor Fire security
657 658
Figure 9 shows an almost linear value function of the thermal insulation criterion,
659
revealing a lower difference of attractiveness between the desirable scenarios of 20%
660
and 30%energy consumption decrease , in comparison to a much higher difference
661
between the levels above the neutral performance level. This relates to the significant
662
thermal requirements already provided by the project.
663 664
Figure 9. Scale of value function of the descriptor Thermal insulation
665 666
The optimal solution of the MACBETH linear problem might not be unique. For sound
667
insulation, for example, other possible value scales would also verify constraints 4–7. In
668
such cases, MACBETH software allows the selection of some elements and the
669
readjustment of their position within an interval verifying those constraints. Figure 10
670
illustrates adjustments made by the decision-maker in the proportions of the scale
671
intervals of sound insulation. Those result in a final linear scale with equal differences
672
of attractiveness between the DP.
673 674
Figure 10. Scale of value function of the descriptor Sound insulation
675
31
676
After determining the value scales for each criterion, the judgments introduced in the
677
MACBETH software also allow determining the importance of each criterion. Figure 11
678
presents the weights of the criteria as percentages. Aesthetics, sound, and costs have
679
similar importance, which is significantly higher than the remaining. Since the aesthetic
680
value is the primary purpose for the installation of a green roof in the residential project,
681
it has the most difference in attractiveness. Sound insulation is the second selected
682
criterion due to (i) the building’s proximity to a public access area, (ii) the large glazed
683
areas and (iii) the noise from the 7th floor equipment. The investment costs are likely to
684
have some weight in the final decision (3rd selected criterion).
685
The project was already strict on thermal insulation and fire security, so any additional
686
benefit would have low impact. The recreational value is considered as having the
687
lowest contribution for the evaluation model, as the final use for the green roof is left to
688
the future property owners.
689
Figure 11. Bar chart of weighting coefficients
690 691 692
5.2.
693
Each green roof option is evaluated for each criterion, determining their partial values
694
on those criteria. For that, their performance level is scored with a numerical value
695
according to the corresponding criterion scale.
696
Extensive green roofs have lower aesthetic impact than intensive ones as their substrate
697
thickness limits the vegetation variety. Their installation cost is significantly higher in
698
contrast to traditional roof solutions, yet lower when compared to semi-intensive or
699
intensive green roofs. Extensive systems require minimal irrigation and maintenance.
700
Therefore, the vegetation may be left to its natural growing and degradation process
701
which can also have a negative impact on the system aesthetics. Intensive green roofs 32
Global Attractiveness of each Option
702
are typically accessible and may include decorative elements, such as benches and
703
vessels, and, for this reason, are often compared to public gardens. Their greater
704
thickness supports a wider variety of vegetation which allows to improve the roof visual
705
impact. However intensive solutions usually have higher maintenance and installation
706
costs than extensive ones. Semi-intensive green roofs are intermediate accessible
707
solutions (FLL, 2008). The above mentioned green roof systems have different
708
characteristics and present different performances in terms of the evaluation criteria. To
709
allow the evaluation of the recreational criteria, each system is distinctly separated in
710
terms of accessibility/use. Figure 12 distinguishes the six green roof solutions
711
considered in this decision-making problem.
712 713 714
Figure 12. Green roof options
715
Table 8 defines the performance profiles of each green roof option, assigning a DP level
716
to each option. Green roofs costs and benefits are evaluated according to existing
717
studies/data on their performance, as explained below.
718
Extensive green roofs present the lowest 10-year NPV, while the intensive ones present
719
the highest. Those values increase slightly if the roof is accessible (CYPE, 2016). There
720
is evidence that when properly maintained, particularly with regular irrigation, a green
721
roof with a minimal substrate depth of 5 cm and a maximum limit of organic materials
722
delays the fire propagation by at least 30 minutes, which is equivalent to a Broof
723
classification. Otherwise, the existence of dry vegetation may increase the fire hazard
724
(Zinco, 2016). Taking this into account, intensive and semi-intensive green roofs can be
725
classified as Broofs. The same does not apply to extensive green roofs which are
726
considered as Droofs or Eroofs, since no maintenance is required for these systems.
727 33
Table 8. Performance of each green roof option
728
Costs (€/m2) Fire Thermal (%) Sound (dB)
29.95 D/Eroof 5 4
179.2 Broof 17 6
303.88 Broof 27 10
34.95 D/Eroof 5 4
258.93 Broof 17 6
378.67 Broof 27 10
729 730
The literature also provides some contributions to the thermal and acoustic effects of
731
green roofs.
732
factors influence their performance (e.g., climate). Studies suggest that extensive, semi-
733
intensive and intensive green roofs decrease in average the annual building energy
734
consumption up to 5, 17 and 27%, respectively, in the Mediterranean climate (Ascione
735
et al, 2013; Silva et al., 2016) and decrease the sound transmission by 4, 6 and 10 dB,
736
respectively (Connelly and Hodgson, 2013; Grant et al., 2003; Lagström, 2004).
737
Accessibility does not influence these benefits, so there is no distinction between the
738
performance of accessible and inaccessible roofs.
739
With regards to the aesthetic and recreation criteria, the options performance profiles
740
are defined through comparison and ranking. Figure 4 presents this comparison.
741
Intensive green roofs have greater diversity and denser vegetation. Therefore in the
742
aesthetic criterion, intensive green roofs are closer to the best performance reference
743
level, and extensive green roofs are closer to the worse performance reference level.
744
The same happens to the recreation criterion, with accessible and inaccessible green
745
roofs being closer to the best and worst performance reference levels, respectively. This
746
classification results from the additional usable space provided by green roofs.
747
Table 9 presents partial values of each green roof option on each criterion and the
748
calculation of global attractiveness of each option. The global attractiveness is
34
However, there are still many uncertainties, considering that several
749
calculated through Equation 1 of the additive aggregation using the weighting
750
coefficient presented in Figure 11.
751
Figure 13 displays the overall scores. The intensive green roof has the best trade-off,
752
followed by the intensive accessible one. Extensive accessible and extensive have
753
negative scores, with -1.10 and -6.36, respectively. The lower and upper options
754
represent the overall lowest and highest performance on all criteria, respectively.
755
Table 9. Scores for each green roof options on each criterion
756
Global
Costs
Aesthetics
Recreation
Fire
Thermal
Sound
Upper
100.00
100.00
100.00
100.00
100.00
100.00
100.00
Intensive
69.43
-173.13
83.33
-87.50
100.00
70.00
300.00
Intensive Accessible
52.40
-297.78
83.33
87.50
100.00
70.00
300.00
Semi-int
42.93
34.67
66.67
-87.50
100.00
-60.00
100.00
Semi-int Accessible
22.20
-98.22
66.67
50.00
100.00
-60.00
100.00
Lower
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Extensive Accessible
-1.10
165.66
25.00
25.00
0.00
-275.00
0.00
Extensive
-6.36
168.78
25.00
-87.50
0.00
-275.00
0.00
Weights
0.2105
0.2456
0.0526
0.1053
0.1579
0.2281
757 758
Figure 13. Overall scores of green roof options
759 760 761
Figure 14 presents the profiles for all options, organized by decreasing order of
762
preference, allowing to visualize the contribution of each criterion. From the analysis of
763
these results it can be assumed that:
764
-
Sound insulation and fire security have a positive contribution in most options.
765
Globally, their value decreases as the options are less attractive. Both criteria
766
have no relevance in case of accessible/inaccessible extensive green roofs
767
(neutral situation).
768 769
-
Aesthetics contributes positively to all option since it improves the impact on the building’s visual appearance when compared with the status quo. The 35
770
contribution is independent of the roof accessibility. The same is not true to the
771
recreation which only applies if the roof is accessible. In these situations a
772
negative contribution is shown. -
773
Thermal insulation contributes positively to intensive accessible/inaccessible
774
green roofs and negatively to the remaining, due to the increase of systems
775
thermal conductivity. -
776
Cost contributes negatively to most options (except accessible/inaccessible
777
extensive green roofs) since the NPV of installation and maintenance costs are
778
higher than the defined upper reference.
779 780 781 782 783
Figure 14. Profiles for all green roof options ordered by decreased preference: (a) intensive; (b) intensive accessible; (c) semi-intensive; (d) semi-intensive accessible; (e) extensive accessible; (f) extensive
784 785
5.3.
786
This study performs a sensitivity analysis to incorporate the uncertain and imprecise
787
data often involved in the decision-making process. This is especially important in the
788
present case study since some of the information used in the model structure is collected
789
and adapted from previous studies (e.g., quantification of thermal and sound insulation
790
benefits).
791
This study also aims to evaluate the uncertainty on the decision-maker judgments,
792
concerning the weighting criteria where several ‘what-if’ questions may arise from a
793
challenging trade-off judgmental process. This allows assessing the robustness of the
794
alternatives global ranking to the model’s parameters variation. For example, it is
795
possible to assess if the best green roof option changes if small variations on the weight
796
of any criterion are observed. Figures 15 - 17 shows the sensitivity analysis performed 36
Sensitivity Analysis
797
in the MACBETH software. These figures vertical axis indicates global score changes
798
of each option, when a variation of the horizontal axis is considered, i.e., weight
799
variations of a specific criterion. The vertical red line corresponds to the criteria weight
800
based on the decision-maker' qualitative judgments and, therefore, corresponding to the
801
base scenario.
802
The main objective is to identify if the best green roof option changes when the weight
803
of one criterion is modified, while maintaining the proportion between the remaining
804
criteria Figure 18 shows that the model’s results are not sensitive to variations on the
805
weight of the aesthetic and fire security criteria, as the relative global scores of the
806
green roof alternatives remain unmodified. This is relevant when considering that the
807
aesthetic has the highest influence in the evaluation of the case study decision problem,
808
i.e., weigh in the model.
809
To better understand the graphs, see the following example. Figure 15 (a) presents the
810
sensitivity analysis performed to the aesthetics criterion weight. If this criterion has a
811
total contribution ( i.e., the maximum weight possible out of 100 in the horizontal axis)
812
then the remaining criteria have no contribution to the decision problem (weight
813
coefficient = 0). As seen in section 5.1 the same green roof typologies are equally
814
scored in aesthetics, showing no dependence on the systems' accessibility. This is
815
observed when the graph lines converge, proving that if aesthetics is the sole evaluating
816
criterion, there is no difference in attractiveness between accessible and inaccessible
817
green roofs. Nevertheless, the ranking of intensive, semi-intensive and extensive green
818
roofs remains the same, according to their decreasing aesthetic value. The same happens
819
to the fire security sensitivity analysis (Figure 15 b). The convergence lines show that
820
semi-intensive and intensive green roofs are equally preferable to extensive green roofs
37
821
if fire security is the only criterion. This is also in accordance with extensive green roofs
822
worse performance to fire security criterion, as shown in Table 8 performance profile.
823
Figure 15. Weights sensitivity analysis for a) aesthetics and b) fire security
824 825 826
Figure 16 shows that the weight variation of sound insulation and costs affects the
827
global attractiveness of these solutions. This is relevant since these two criteria are
828
ranked in second and third among the other weighting criteria. Nevertheless, intensive
829
green roofs remain the best option when a 10% variation is applied, which is significant.
830
To be more precise, semi-intensive green roof solutions become the best choice by
831
decreasing the weight of the sound insulation from 22,01 to 11. If its weight increases,
832
no modifications are expected. Also, changes in the best trade-off are observed with
833
small changes in costs. Semi-intensive and intensive accessible green roofs become the
834
best options when an 8% increase or a 12% decrease in costs applies respectively.
835
Figure 16. Weights sensitivity analysis for (a) sound insulation and (b) costs
836 837 838
For the remaining criteria, the conclusions are similar. Variations on the thermal
839
criterion’s weight introduce few variabilities on the results (Figure 17 a). Lastly, an 8%
840
increase on the recreation weight (Figure 17 b) makes the intensive accessible green
841
roof the best choice. Given the fact that this option outperforms the intensive green roof
842
in this particular aspect and has the same performance in the remaining criteria.
843
Figure 17. Weights sensitivity analysis for a) thermal insulation and b) recreation
844 845
38
846
Sensitivity analysis is especially important for investors since they are concerned with
847
the results robustness, especially when costs are involved. In general, the intensive
848
green roof is identified as the best solution for this decision problem and extensive
849
green roof is the worst, even when considerable variations of the weighting criteria are
850
admitted. This is because intensive green roofs outperform extensive and intensive ones
851
in all the benefits. However, their high installation and maintenance costs are critical
852
barriers to the implementation of these systems. In fact, costs variations show the
853
highest impact on the results of sensitivity analysis. Considering an extreme situation,
854
where investors are very concerned with investment values, i.e., increasing the weight
855
of costs, most solutions rank above the reference level with negative global scores of
856
attractiveness (up to value scores of -280). Extensive (accessible and inaccessible) and
857
semi-intensive (only inaccessible) green roofs are the only ones with positive outcomes
858
since they have lower life-cycle costs. In this case, decision-makers should equate
859
different options that also meet the problem objectives or reformulate the problem.
860
Just like costs, sound insulation has a great influence on results (second most important
861
evaluating criterion). In general, the sound insulation provided by intensive green roofs
862
is significantly higher than other solutions, making the results less sensitive to this
863
parameter variation. Differences between 0 and 300 in the intensive green roofs global
864
score values are observed for the entire variation interval of the weighting scale, without
865
any drop in the ranking position.
866 867
5.4.
Robustness Analysis
868
Robustness analysis aims to study which robust conclusions can be obtained for
869
different levels of information scarcity, imprecision, and uncertainty (Bana e Costa and
870
Beinat, 2005). MACBETH software organizes the information into the model in three
39
871
types: ordinal, MACBETH and cardinal. Ordinal information refers only to order,
872
excluding any attractiveness difference information (preference intensity). MACBETH
873
information includes the introspective semantic judgments in the model but ignores any
874
scoring or weighting scale compatible with those judgments. Cardinal information
875
denotes a specific scale and validation by the evaluator. The MACBETH information is
876
also organized into two sections: local and global. Local information refers to specific
877
information in a given criterion, such as the qualitative judgments required to build the
878
criteria value scales. Global information relates to information weighing the criteria,
879
affecting both the scores given to each criterion and the options partial values . This
880
means that local information affects isolated parts of the MACBETH model and global
881
information has an impact on the entire model (Bana e Costa and Beinat, 2005).
882
Results of robustness analysis can be verified through the two symbols displayed in the
883
software: Dominance: an option dominates another if it is at least as attractive as the other in all criteria and it is more attractive than others in at least one criterion. Additive dominance: an option additively dominates another if it is always found to be more attractive than the other using an additive model under a set of information constraints.
884
Several scenarios focusing on different types of information are exhaustively assessed
885
in the robustness analysis. Table 10 describes five scenarios selected to illustrate the
886
global results.
887
Table 10. Scenarios and type of information considered in the robustness analysis
888
Scenario 1 Local section
40
Ordinal
Scenario 2 Ordinal MACBETH
Scenario 3 Ordinal MACBETH
Scenario 4 Ordinal MACBETH
Scenario 5 Ordinal MACBETH
Cardinal* Global section
Ordinal
Ordinal
Ordinal MACBETH
Cardinal* Ordinal MACBETH Cardinal*
Cardinal** Ordinal MACBETH Cardinal**
889 890 891 892
* 0% degree of imprecision ** 10% degree of imprecision
893
Figure 18 shows the Scenario 1 . In this scenario is shown that by selecting the
894
robustness analysis command in the MACBETH software, the ordinal information is
895
selected by default at the local and global sections. At this level, there is not enough
896
information to determine what options are the most attractive. The same conclusion is
897
observed in Figure 19, which presents the Scenario 2, considering the MACBETH
898
information in the local section. However, in this scenario the options are reordered.
899 900 901
Figure 18. Robustness analysis – Scenario 1
902 903 904
Figure 19. Robustness analysis – Scenario 2
905 906
Figure 20 shows the Scenario 3, which also considers the MACBETH global
907
information and cardinal local information. In this analysis, a new reordering of the
908
options occurs and there are additive dominance situations. For example, it is observed
909
that the intensive option is globally more attractive than the semi-intensive accessible
910
and extensive accessible options, as well as the semi-intensive option is more attractive
911
than the extensive accessible option (situation of additive dominance).
912 913 914
Figure 20. Robustness analysis – Scenario 3
915 41
916
Figure 21 shows the Scenario 4, in which the robustness analysis considers the three
917
types of information in local and global sections. A new reordering of the options
918
occurs, which follows the order previously obtained with the application of the additive
919
model (Figure 13). In this case, the intensive green roof option dominates the
920
remaining.
921
In Figure 22 the Scenario 5 explores the variation in the degrees of imprecision of the
922
cardinal information. For a degree of imprecision of 10% in the local and global
923
information sections, a new order of the options occurs. In this case it becomes more
924
difficult to determine what options are the most attractive.
925 926
Figure 21. Robustness analysis – Scenario 4
927 928 929
Figure 22. Robustness analysis – Scenario 5
930 931 932
5.5.
933
This paper agrees with some of the main findings of other studies (see section 2). As
934
Brudermann and Sangkakool (2017), this study presents building aesthetics as the main
935
driver behind the implementation of green roofs. However, this opposes to the studies of
936
Rosasco and Perini (2019) and Sangkakool et al. (2018), ranking aesthetics as one of the
937
least relevant characteristics of green roofs. Overall, MCDA has a strong dependence on
938
the case studies specifications and, consequently, on the inputs of the analysis. For
939
example, Mahdiyar et al. (2019) examined three case studies of residential buildings and
940
identified a different green roof option for each building. Thus, it becomes clear that
941
project priorities and specific requirements have a significant impact on the decision-
42
Discussion
942
making process. This study is no exception, so it is important to consider those
943
specifications while discussing the results. This study faced some constraints and
944
singularities. The project in concern was ongoing which prevented changes in the
945
approved plans. Considering that the decision process was taken in a later project phase
946
this inhibited the addition of other benefits. As an example, the PVs performance placed
947
on the top floor could be improved if combined also with a green roof. Therefore, it is
948
recommended that the decision-making process is taken on an early project stage in
949
order to maximize its advantages.
950
Four companies with ongoing largescale projects were contacted, given the limited
951
green roof market in Portugal. From these, only one participated in this study, selecting
952
one decision-maker responsible to take into consideration the interests of all parties.
953
Future research should encourage the engagement of several stakeholders to promote
954
interaction, collective commitment, and shared understanding of the decision problem.
955
Including a large panel of stakeholders with different backgrounds allows taking into
956
account different opinions. However, it is important to stress that group decision biases
957
(such as group thinking) can make it more difficult to reach consensus between all
958
actors. This is the case of the study of Rosasco and Perini (2019) in which academics
959
and professionals assigned different weights to economic aspects of green roofs. The
960
same happens in the study of Naing et al. (2017).
961
The economic aspects seem to be a topic that raises some controversy. The Portuguese
962
property market is experiencing increasing levels of demand and rising selling prices.
963
Therefore, real estate companies operating in central Lisbon, targeting the premium
964
market, are aware of the quick return on property investment. That said, they consider
965
that are less concerned about potential investment costs and more focused on distinction
966
factors in the market, such as aesthetics and sustainability. In the present study, the
43
967
decision-maker followed this opinion, minimizing the importance of costs. However,
968
the MCDA results showed important cost contribution to green roof selection.
969
Thus, if this study is replicated for other stakeholders (e.g. potential buyers of these
970
apartments) the costs are expected to have more impact. This proves the need for
971
multicriteria analyses to support decision-making. Since people’s perception is not
972
consistent and their decisions aren´t always aligned with their stated preferences.
973
In view of the above, all studies have a common challenge of reporting comprehensive
974
case study applications. Like any other, this paper has specific particularities limiting
975
global conclusions regarding the installation of green roofs. For example, it concerns a
976
new project while most of the cases nowadays involve building's retrofitting. The
977
second scenario means different viewpoints and priorities such as the loading capacity
978
of the building.
979
Additionally, this study has only addressed private costs and benefits. This might not
980
happen for public or largescale investments, in which benefits for the society and
981
environment need to be assessed as well.
982
Also, if the building is used for other purposes, benefits can show different impacts. As
983
an example, an office building may have high energy demand due to heating and
984
cooling. Therefore, the use of green roofs could have a different impact on the building.
985
All these aspects are critical and challenging. Future research should take the context
986
(e.g. building, use, location, stakeholders) into consideration and reproduce the
987
methodology for different scenarios in order to provide more information and promote a
988
comprehensive discussion.
989
Regardless of the studies limitations and contrasting results, all authors have
990
demonstrated the advantages of installing green roofs in buildings or cities. Guzmán-
991
Sánchez et al. (2018) and Rosasco and Perini, (2019) showed that green roofs are
44
992
preferable to traditional solutions and, when economic aspects related with their
993
installation and maintenance are a major barrier to their implementation, there are
994
strategies to encourage investors to develop green areas in cities. A few examples of
995
common practices are green policies and financial incentives, which have a great
996
contribution to these decisions as suggested by Brudermann and Sangkakool (2017) and
997
Sangkakool et al. (2018).
998 999
6.
Conclusions
1000
Overall, this paper helps to structure and support decision-problems and contributes to
1001
understand investors preferences when installing green roofs in buildings. The present
1002
study adopts a multicriteria decision analysis methodology in order to calculate the
1003
attractiveness of different green roof systems by assigning numerical scores.
1004
The application of the proposed methodology to a case study of a real estate investor
1005
proves the idea that MCDA outputs strongly depend on the decision-maker preferences.
1006
This study selected the MACBETH method, as it focuses more on the stakeholders’
1007
values and less in the model outputs. Also, it motivates the involvement in the
1008
consultation process and the understanding of the decision-problem.
1009
The use of the MACBETH method in the context of green roofs installation is a step
1010
forward in literature. Other authors selected different methods such as AHP and
1011
TOPSIS. However, MACBETH proved to be an effective approach, as the decision-
1012
maker is asked to give qualitative judgments to construct the model instead of numerical
1013
values, which can lead to common mistakes. Either way, it has also some limitations.
1014
The M-MACBETH software relies on a measurement theory foundation that can raise
1015
practical issues. The linear programming of the software requires consistent judgments
1016
from the decision-maker. Individuals are naturally inconsistent due to many reasons
45
1017
such as lack of information or biased thinking and, therefore, the model outputs can be
1018
somewhat different from the original concept/preferences of the decision-maker. This
1019
whole process can be confusing. Also, the linear programming method allows the
1020
selection of multiple optimal solutions of green roofs. This is why the presence of an
1021
analyst is recommended, to help discussing the best option.
1022
This paper promotes the installation of green roofs in residential buildings comparing to
1023
a do-nothing scenario. However, these solutions may not be easily accepted by other
1024
investors due to high installation and maintenance costs. To progressively promote the
1025
integration of green roofs in urban planning it is crucial to spread the word about their
1026
benefits, raise education campaigns and implement political strategies, such as financial
1027
incentives.
1028 1029
Acknowledgements
1030
This work was supported by the FCT (Portuguese Foundation for Science and
1031
Technology) through scholarships PD/BD/135172/2017 and SFRH/BD/117717/2016
1032
and the research project GENESIS (PTDC/GESURB/29444/2017). The authors would
1033
like to thank the decision-maker for the contribution and cooperation during this
1034
research work.
1035
The authors would like to thank Maria Manso for her input in the English revision.
1036 1037
References
1038 1039 1040 1041
Alves, A., Gersonius, B., Sanchez, A., Vojinovic, Z., Kapelan, Z., 2018. Multi-criteria Approach for Selection of Green and Grey Infrastructure to Reduce Flood Risk and Increase CO-benefits. Water Resour. Manag. 32, 2505–2522. https://doi.org/10.1007/s11269-018-1943-3
1042 1043 1044
Ascione, F., Bianco, N., de’ Rossi, F., Turni, G., Vanoli, G.P. Green roofs in European climates. Are effective solutions for the energy savings in air-conditioning?. Applied Energy. 104 (2013) 845–859. doi:10.1016/j.apenergy.2012.11.068. 46
1045 1046 1047 1048
Bana e Costa, C., Chagas, M.P. A career choice problem: An example of how to use MACBETH to build a quantitative value model based on qualitative value judgments. European Journal of Operational Research. 153 (2004) 323–331. doi:10.1016/S03772217(03)00155-3.
1049 1050
Bana e Costa, C., Beinat, E. Model-structuring in public decision-aiding. London School of Economics and Political Science. London. 2005. doi:ISBN 0 7530 1701 6.
1051 1052 1053 1054
Bana e Costa, C., Fernandes, T.G., Correia, P.V.D. Prioritisation of public investments in social infrastructures using multicriteria value analysis and decision conferencing: A case study. International Transactions in Operational Research. 13 (2006) 279–297. doi:10.1111/j.1475-3995.2006.00549.x.
1055 1056 1057
Bana e Costa, C., Lourenço, J.C., Chagas, M.P., Bana e Costa, J.C. Development of Reusable Bid Evaluation Models for the Portuguese Electric Transmission Company. Decision Analysis. 5 (2008) 22–42. doi:10.1287/deca.1080.0104.
1058 1059 1060
Bana e Costa, C., De Corte, J.M., Vansnick, J.C. Macbeth. International Journal of Information Technology & Decision Making. 11 (2012) 359–387. doi:10.1142/S0219622012400068.
1061 1062
Bana e Costa, C., Lourenço, J., Oliveira, M. Model structuring & Descriptors of performance. Decision Analysis Models and Applications (Class Slides). (2018) 1–15.
1063 1064
Belton, V., Stewart, T.J. Multiple criteria decision analysis: an integrated approach. Springer. 2001.
1065 1066 1067
Berardi, U., GhaffarianHoseini, A., GhaffarianHoseini, A. State-of-the-art analysis of the environmental benefits of green roofs. Applied Energy. 115 (2014) 411–428. doi:10.1016/j.apenergy.2013.10.047.
1068 1069 1070
Bianchini, F., Hewage, K. Probabilistic social cost-benefit analysis for green roofs: A lifecycle approach. Building and Environment. 58 (2012) 152–162. doi:10.1016/j.buildenv.2012.07.005.
1071 1072 1073
Brudermann, T., Sangkakool, T., 2017. Green roofs in temperate climate cities in Europe – An analysis of key decision factors. Urban For. Urban Green. 21, 224–234. https://doi.org/10.1016/j.ufug.2016.12.008
1074 1075
BS476 : Part 3. Fire tests on building materials and structures. Classification and method of test for external fire exposure to roofs (2004).
1076 1077 1078
Chow, J.F., Savić, D., Fortune, D., Kapelan, Z., Mebrate, N., 2014. Using a systematic, multi-criteria decision support framework to evaluate sustainable drainage designs. Procedia Eng. 70, 343–352. https://doi.org/10.1016/j.proeng.2014.02.039
1079 1080
CYPE. Gerador de preços para construção civil. Portugal. CYPE Ingenieros, S.A., (2016). http://www.geradordeprecos.info/ (accessed July 12, 2016).
1081 1082 1083
Connelly, M., Hodgson, M. Experimental investigation of the sound transmission of vegetated roofs. Applied Acoustics. 74 (2013) 1136–1143. doi:10.1016/j.apacoust.2013.04.003. 47
1084 1085 1086
Cruz, C.O., Silva, C.M., Dias, P.V., Teotónio, I. Economic impact of changing thermal regulation - An application to the city of Lisbon. Energy and Buildings. 149 (2017) 354–367. doi:10.1016/j.enbuild.2017.05.030.
1087 1088 1089
Di Matteo, M., Dandy, G.C., Maier, H.R., 2017. Multiobjective Optimization of Distributed Stormwater Harvesting Systems. J. Water Resour. Plan. Manag. 143, 04017010. https://doi.org/10.1061/(asce)wr.1943-5452.0000756
1090 1091 1092
European Commission, 2014. Guide to Cost-benefit Analysis of Investment Projects: Economic appraisal tool for Cohesion Policy 2014-2020, Publications Office of the European Union. https://doi.org/10.2776/97516
1093 1094 1095
European Commission, 2019a. European Green Capital. http://ec.europa.eu/environment/europeangreencapital/lisbon-is-the-2020-europeangreen-capital-award-winner/ (accessed 6.11.19).
1096 1097 1098
European Commission, 2019b. 2020 – Lisbon. http://ec.europa.eu/environment/europeangreencapital/winning-cities/2020-lisbon/ (accessed 6.11.19).
1099 1100
FLL. Guidelines for the Planning. Construction and Maintenance of Green Roofing Green Roofing Guideline. 2008.
1101 1102 1103
Franco, L.A., Montibeller, G. Problem Structuring for Multicriteria Decision Analysis Interventions. In: J.J. Cochran (Ed.), Wiley Encyclopedia of Operations Research and Management Science, Wiley. (2010).
1104 1105
Grant, G., Engleback, L., Nicholson, B. Green Roofs: their existing status and potential for conserving biodiversity in urban areas. 2003.
1106 1107 1108
Guarini, M.R., Battisti, F., Chiovitti, A., 2018. A methodology for the selection of multi-criteria decision analysis methods in real estate and land management processes. Sustain. 10. https://doi.org/10.3390/su10020507
1109 1110 1111 1112
Guzmán-Sánchez, S., Jato-Espino, D., Lombillo, I., Diaz-Sarachaga, J.M., 2018. Assessment of the contributions of different flat roof types to achieving sustainable development. Build. Environ. 141, 182–192. https://doi.org/10.1016/j.buildenv.2018.05.063
1113 1114 1115
Kohler, N. From the design of green buildings to resilience management of building stocks. Building Research and Information. 46 (2018) 578–593. doi:10.1080/09613218.2017.1356122.
1116 1117
Lagström, J. Do Extensive Green Roofs Reduce Noise?. International Green Roof Institute. 10 (2004) 40.
1118 1119 1120
Liquete, C., Udias, A., Conte, G., Grizzetti, B., Masi, F., 2016. Integrated valuation of a nature-based solution for water pollution control. Highlighting hidden benefits. Ecosyst. Serv. 22, 392–401. https://doi.org/10.1016/j.ecoser.2016.09.011
1121 1122
Mahdiyar, A., Tabatabaee, S., Abdullah, A., Marto, A., 2018. Identifying and assessing the critical criteria affecting decision-making for green roof type selection. Sustain. 48
1123
Cities Soc. 39, 772–783. https://doi.org/10.1016/j.scs.2018.03.007
1124 1125 1126 1127
Mahdiyar, A., Tabatabaee, S., Durdyev, S., Ismail, S., Abdullah, A., Wan Mohd Rani, W.N.M., 2019. A prototype decision support system for green roof type selection: A cybernetic fuzzy ANP method. Sustain. Cities Soc. 48, 101532. https://doi.org/10.1016/j.scs.2019.101532
1128 1129 1130
Naing, Y.M., Nitivattananon, V., Shipin, O. V, 2017. Green Roof Retrofitting : Assessment of the Potential for Academic Campus. Eng. J. 21. https://doi.org/10.4186/ej.2017.21.7.57
1131 1132 1133
Nurmi,V., Votsis, A., Perrels, A., Lehvävirta, S. Green Roof Cost-Benefit Analysis: Special Emphasis on Scenic Benefits. Journal of Benefit-Cost Analysis. 7 (2016) 488– 522. doi:10.1017/bca.2016.18.
1134 1135 1136
Mateus, R.J.G., Bana e Costa, J.C., Matos, P.V., 2017. Supporting Multicriteria Group Decisions with MACBETH Tools: Selection of Sustainable Brownfield Redevelopment Actions. Gr. Decis. Negot. 26, 495–521. https://doi.org/10.1007/s10726-016-9501-y
1137
Parnell, G. Decision Analysis in One Chart. Decision Line. (2009) 20–24.
1138
Phillips, L. Decision Analysis in 2005. 2005.
1139 1140 1141
Phillips, L., Bana e Costa, C. Transparent prioritisation, budgeting and resource allocation with multi-criteria decision analysis and decision conferencing. Annals of Operations Research. 154 (2007) 51–68. doi:10.1007/s10479-007-0183-3.
1142 1143 1144
Radinja, M., Comas, J., Corominas, L., Atanasova, N., 2019. Assessing stormwater control measures using modelling and a multi-criteria approach. J. Environ. Manage. 243, 257–268. https://doi.org/10.1016/j.jenvman.2019.04.102
1145 1146 1147
Rosasco, P., Perini, K., 2019. Selection of (Green) Roof Systems: A SustainabilityBased Multi-Criteria Analysis. Buildings 9, 17. https://doi.org/10.3390/buildings9050134
1148 1149 1150
Sangkakool, T., Techato, K., Zaman, R., Brudermann, T., 2018. Prospects of green roofs in urban Thailand – A multi-criteria decision analysis. J. Clean. Prod. 196, 400– 410. https://doi.org/10.1016/j.jclepro.2018.06.060
1151 1152 1153
Silva, C.M., Gomes, M.G., Silva, M. Green Roofs Energy Performance in Mediterranean Climate. Energy and Buildings. 116 (2016) 318–320. doi:10.1016/j.enbuild.2016.01.012.
1154 1155 1156
Snep, R., Van Ierland, E., Opdam, P., 2009. Enhancing biodiversity at business sites: What are the options, and which of these do stakeholders prefer? Landsc. Urban Plan. 91, 26–35. https://doi.org/10.1016/j.landurbplan.2008.11.007
1157 1158 1159
Teotónio, I., Silva, C.M., Cruz, C.O. Eco-solutions for urban environments regeneration: The economic value of green roofs. Journal of Cleaner Production. (2018). doi:10.1016/j.jclepro.2018.07.084.
1160
Tomalty, R., Komorowski, B., Doiron, D. The Monetary Value of the Soft Benefits of 49
1161 1162 1163
Green Roofs, (2010) 114. http://www.greenroofs.org/resources/Monetary_Value_of_Soft_Benefits_of_Green_Ro ofs.pdf.
1164 1165
Velasquez, M., Hester, P.T., 2013. An Analysis of Multi-Criteria Decision Making Methods. Int. J. Oper. Res. 10, 56–66.
1166 1167
Zinco. ZinCo Green Roof Systems. (2016). http://www.zinco.pt/ (accessed April 20, 2016).
50
1168
Supplemental Information
1169
Table 1. Judgments matrix of the descriptor Cost
1170 €/m2
20
20
80
140
200
260
320
380
Moderate
Strong
Extreme
Extreme
Extreme
Extreme
Moderate
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
Extreme
80 140 200 260
Extreme
320 380
1171
Table 2. Judgments matrix of the descriptor (a) Thermal insulation (b) Sound insulation
1172 1173
(a) % 30 20
(b) 30
20
10
0
dB
Weak
Moderate
Extreme
10
Moderate
Strong
8
Weak
6
10 0
10
8
6
4
Weak
Moderate
Extreme
Moderate
Strong Weak
4
1174 1175
Table 3. Judgments matrix of the descriptor Fire security Broof Broof Croof D/Eroof
Croof
D/Eroof
Froof
Weak
Moderate
Strong
Weak
Moderate Weak
Froof
1176
51
Table 4. Judgments matrix of the descriptor Aesthetics
1177
Upper
Intensive Accessible Weak
Upper Intensive Accessible
Intensive
Semi-int Accessible
Semi-int
Extensive Accessible
Extensive
Lower
Weak
Moderate
Moderate
Very strong
Very strong
Extreme
Null
Weak
Weak
Very strong
Very strong
Extreme
Weak
Weak
Very strong
Very strong
Extreme
Null
Strong
Strong
Strong
Strong
Intensive Semi-int Accessible Semi-int Extensive
Null
Accessible
Very strong Very strong Weak Weak
Extensive Lower
1178
Table 5. Judgments matrix of the descriptor Recreation
1179
Upper
Upper Intensive Accessible Semi-int Accessible Extensive Accessible Lower Intensive Semi-int Extensive
1180
52
Intensive
Semi-int
Extensive
Accessible
Accessible
Accessible
Very Weak
Moderate
Strong
Moderate
Strong
Weak
Lower
Intensive
Semi-int
Extensive
Extreme
Extreme
Extreme
Strong
Extreme
Extreme
Extreme
Moderate
Extreme
Extreme
Extreme
Weak
Extreme
Extreme
Extreme
Strong
Strong
Strong
Null
Null
Very Strong
Null
Highlights
•
Multicriteria decision model for the installation of green roofs/walls
•
Determination of the best trade-off between the underlying costs and benefits
•
Incorporation of the preferences of decision-makers in economic evaluations
•
Support for the selection of the optimal solution
•
Validation of the methodology through a case study (residential building)
Author Contribution Section
Inês Teotónio: Conceptualization; Methodology; Formal analysis; Investigation; Writing – Original Draft; Writing – Review and Editing; Visualization. Marta Cabral: Conceptualization; Methodology; Formal analysis; Investigation; Writing – Original Draft; Writing – Review and Editing; Visualization. Carlos Oliveira Cruz: Conceptualization; Validation; Writing – Review and Editing; Supervision; Funding acquisition. Cristina Matos Silva: Conceptualization; Validation; Writing – Review and Editing; Supervision; Funding acquisition
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: