Identifying and assessing the critical criteria affecting decision-making for green roof type selection

Accepted Manuscript Title: Identifying and Assessing the Critical Criteria Affecting Decision-Making for Green Roof Type Selection Authors: Amir Mahdiyar, Sanaz Tabatabaee, Arham Abdullah, Aminaton Marto PII: DOI: Reference:

S2210-6707(17)31723-7 https://doi.org/10.1016/j.scs.2018.03.007 SCS 1014

To appear in: Received date: Revised date: Accepted date:

18-12-2017 7-3-2018 7-3-2018

Please cite this article as: Mahdiyar, Amir., Tabatabaee, Sanaz., Abdullah, Arham., & Marto, Aminaton., Identifying and Assessing the Critical Criteria Affecting Decision-Making for Green Roof Type Selection.Sustainable Cities and Society https://doi.org/10.1016/j.scs.2018.03.007 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.

Identifying and Assessing the Critical Criteria Affecting Decision-Making for Green Roof Type Selection

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Amir Mahdiyar 1*, Sanaz Tabatabaee 2, Arham Abdullah2, Aminaton Marto1,3

Amir Mahdiyar [email protected], [email protected]

Sanaz Tabatabaee

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[email protected]

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Arham Abdullah

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[email protected]

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Aminato Marto [email protected]

Disaster Preparedness & Prevention Centre (DPPC), Malaysia-Japan International Institute of Technology

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Department of Structure and Materials, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor,

Malaysia.

Department of Environmental Engineering & Green Technology (EGT), Malaysia-Japan International Institute of

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(MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Malaysia.

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Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Malaysia.

*Corresponding author: [email protected], [email protected]

Highlights Four groups and 19 criteria are identified for decision making on green roof type selection.

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The causal relationships among all groups and criteria are analyzed using DEMATEL.

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Enhanced Fuzzy Delphi Method is developed for criteria identification in green roof type selection.

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The most important as well as the most influential criteria for decision making on green roof type selection

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are identified.

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Abstract

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Green roofs are categorized into different types based on their characteristics, and each type of green roofs offers different benefits and costs for both private and social sectors. Although there

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are studies conducted in many aspects of green roof installation, there is lack of study focusing

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on decision making (DM) for green roof type selection. The aim of this paper is to identify and assess the criteria affecting DM for green roof type selection in Kuala Lumpur, capital of

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Malaysia. Enhanced fuzzy Delphi method (EFDM) was developed for criteria identification.

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EFDM consists of two rounds; firstly, knowledge acquisition through a semi-structured interview, and secondly, criteria prioritization using a Likert scale questionnaire. As the results of EFDM, 19 criteria for green roof type selection were ranked, and categorized in four groups.

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Then, Decision Making Trial and Evaluation Laboratory was employed for the assessment of the causal relationships among the identified groups and criteria. It was concluded that the

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application of green roof is the most effective group for DM, while the cost of green roof is highly affected by other groups. Moreover, four highest influential criteria in DM for green roof

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type selection were discussed. Key word: decision-making; DEMATEL; enhanced fuzzy Delphi method; green roof

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Introduction

Green roof, as a sustainable alternative of the conventional roof, is defined as the use of vegetation covering the roof of a building (Refahi & Talkhabi, 2015). Installing green roof among private and public sectors is increasing due to the fact of having multiple benefits (Claus

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& Rousseau, 2012; Pianella, Clarke, Williams, Chen, & Aye, 2016). Green roofs are one of the

solutions to increase green area (Susca, Gaffin, & Dell’osso, 2011), especially in central business districts. The technique of green roof installation on flat roofs was described in 1867 (Jim,

2017), while from that time, different types of green roof construction details have been installed throughout the world (Kosareo & Ries, 2007). Generally, green roofs are categorized into two

major types, extensive green roof and intensive green roof (Peng & Jim, 2015; Williams, Rayner,

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& Raynor, 2010). However, several researchers stated that there is a third type of green roofs as a

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simple-intensive green roof which name is semi-intensive green roof (Berardi, GhaffarianHoseini, & GhaffarianHoseini, 2014; Bianchini & Hewage, 2012a; Luo, Huang, Liu,

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& Zhang, 2011; Mahdiyar, Abdullah, Tabatabaee, Mahdiyar, & Mohandes, 2015). It is notable

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that, there are significant differences in benefits, costs, maintenance periods and the plant types that can be planted in each type of green roofs (Peri, Traverso, Finkbeiner, & Rizzo, 2012).

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However, all types of green roofs are considered sustainable and environmentally-friendly

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(Gargari, Bibbiani, Fantozzi, & Campiotti, 2016). Intensive green roof has a thick layer of growing medium, wherein a variety of plants can

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be grown, especially where irrigation is available (Kosareo & Ries, 2007; Peng & Jim, 2015). It is worth mentioning that, additional structural support is needed due to the heavy weight of

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substrate; thus, this type of green roof is applied to the buildings considering additional structural support (Mahdiyar et al., 2016). On the other hand, extensive green roof has a thinner layer of substrate, which is a relatively lightweight and thus in some cases little or even no additional

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structural support is needed. It makes this type of roofs applicable to a larger range of buildings (Nardini, Andri, & Crasso, 2011). This advantage, together with minor need for irrigation and maintenance, has led to a larger application of extensive green roofs (Berardi, 2016). Moreover, extensive green roof provides a harsh environment for plant growth with wide temperature fluctuations, limited water availability, and high exposure to solar radiation and wind, which causes a highly stressed environment for growing plants (Nagase & Dunnett, 2010).

Furthermore, according to Allnut et al. (2014), semi-intensive green roof is a simple intensive green roof which needs lower additional structural support and maintenance in comparison with intensive green roof. Additionally, different types of plants can be used in semi-intensive green roof, while the thickness of soil is lower than intensive green roof. However, Yang, Yu, & Gong (2008) demonstrated that semi-intensive green roof is a green roof combination of both extensive

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and intensive green roof with at least 25% of extensive green roof.

In terms of building retrofitting, it has been proved that there are significant benefits

resulting from green roof retrofits (Berardi, 2016; Castleton, Stovin, Beck, & Davison, 2010; Williams et al., 2010). As reported by Castleton et al. (2010), all types of green roofs are

technically feasible for retrofitting; however, not all types are economically feasible. Moreover,

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Gargari et al. (2016) stated that the most important barrier of green roof installation for the

owners is their opinion regarding the maintenance costs of green roofs. Intensive and semi-

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intensive green roofs need a large amount of costs for maintenance; however, it is not true

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regarding the extensive green roof. According to Jim & Tsang (2011), extensive green roof is the

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suitable type of green roof for retrofitting due to its light weight and low required maintenance. It is noteworthy that the requirements for additional structural support for building retrofitting with

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any type of green roofs depend on the building’s roof structure (Berardi et al., 2014). As a result, initial costs, maintenance costs, structural capability, and the projects’ requirements should be

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considered during decision making (DM) on green roof type selection for retrofitting.

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Malaysia, as a developing country, has lack of greeneries in commercial areas in major cities. From the findings in a recent study conducted by Rahman, Ahmad, & Rosley (2013),

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most of the professionals in Malaysia agree that green roofs have potential marketability in this country. However, there is limited literature on every aspect of green roof installation in Malaysia. Furthermore, there are few green roof cases constructed before 2000 in Malaysia. In

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1992, an extensive green roof was installed at the fist level in Menara Mesiniaga in Subang Jaya, Selangor. In 1998, an extensive green roof was installed for Islamic art museum in tasik perdana, Kuala Lumpur, and public access was considered for the green roof. Another case is KLIA covered integrated parking, which an intensive green roof with public access installed in 1998. After 2000, it can be seen that green roof installation increased for residential buildings, especially, for condominiums and apartments. Currently, green roofs in Malaysia have become

increasingly popular not just due to its aesthetical value but also due to its positive impact on environmental issue. Several aspects of green roofs have been discussed for different types of green roofs, i.e. energy saving, water management, noise absorption, cost, etc. (Berardi et al., 2014). The key

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factors for the diffusion of green roof in Europe have been recently identified and assessed

(Brudermann & Sangkakool, 2017). Additionally, the decision process for selecting a green roof and a solar photovoltaic roof was recently clarified (Dimond & Webb, 2017). However, there is still lack of study conducted for the field of DM on green roof type selection, whether in a new

building or for retrofitting. Green roofs are complex technologies, and the performance of green roofs varies for different climates. Moreover, obtaining all benefits of green roof results in

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additional costs, while it may not be required. According to Tam, Wang, & Le (2016), there are some tangible and intangible benefits of green roofs that should be considered in

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recommendations for use of any type of green roofs. As a result, all effective criteria for green

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roof type selection should be identified and considered during the process of DM. It is worth

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mentioning that, cost of green roof installation is already discussed for different types of green roofs in many countries (Bianchini & Hewage, 2012b; Mahdiyar et al., 2016); however,

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considering the cost together with other criteria could be a new insight for DM on green roof

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type selection. The aim of this paper is:

to identify the criteria affecting green roof type selection;

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to analyze the importance of the identified criteria;

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to assess the influence and being-affected rank of the identified criteria for green

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roof type selection.

This study is conducted in Kuala Lumpur, capital of Malaysia. In this regard, EFDM was

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developed for criteria identification, and DEMATEL was employed for criteria assessment. The conclusions are based on the causal relationships among the decision criteria. 2

Criteria affecting green roof type selection

The benefits of green roof installation have been already reviewed by many researchers, e.g., Berardi et al. (2014) and Vijayaraghavan (2016). Different types of green roofs provide different

costs and benefits; as a result, the aim(s) of green roof installation should be clearly specified before green roof type selection. Additionally, financial criteria have been always the key criteria in DM in construction projects (Kibert, Monroe, & Peterson, 2011). It is proved that green roof installation needs much more capital than conventional one; however, it offers many financial benefits for both private and social sectors. According to Bianchini & Hewage (2012b), some of

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the financial benefits of green roof installation are related to the social sector; as a result, the costs and benefits for private sector should be fully understood. Moreover, Mahdiyar et al.

(2016) conducted a probabilistic cost-benefit analysis for extensive and intensive green roof

installation in Kuala Lumpur in order to calculate NPV and payback period for private sector.

Net present value (NPV) and payback period have been widely used for figuring out the future profit and payback of green roof installation (e.g. Acks (2006) and Carter & Keeler (2008)).

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Furthermore, maintenance of green roofs (i.e. irrigation, fertilization, and plant and pest

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management) is another critical criterion that needs to be considered at the time of DM on green roof type selection. Different types of green roof require different levels of maintenance

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(Mahdiyar et al., 2016), and the deeper the soil layer, the higher the level of maintenance.

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In terms of structural consideration, it is mentioned by many researchers that installing a green roof instead of a conventional roof may increase the dead load of the building (Berardi et

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al., 2014; Dinsdale, Pearen, & Wilson, 2006; Johnston & Newton, 2004; Kuhn & Peck, 2003; J.

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P. Zhang & Hu, 2011). Soil layer, media, vegetation and the furniture that might be utilized in the green roof design lead to the consideration of the additional weight at the time of structural

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design. Moreover, live load of the building may also increase if the public accessibility is considered to the green roof (Johnston & Newton, 2004; Kosareo & Ries, 2007; Magill, Midden,

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Groninger, & Therrell, 2011; Peck & Kuhn, 1999; Susca et al., 2011). Additional structural supports consist of the increase in the size of beam and column (Kuhn & Peck, 2003).

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Regarding the type of accessibility of green roofs, It is worth mentioning that intensive

green roof is suitable for public access (X. Zhang, Shen, Tam, & Lee, 2012), whereas extensive green roof is the best option for private access. Green roofs would be designed for different projects, and the type of accessibility to the green roof must be specified at design stage (Berardi et al., 2014; Dinsdale et al., 2006). If the designer decides to change the accessibility’s type of green roof after the design stage, it may results in change of design plans and structural design.

Furthermore, Kosareo & Ries (2007) mentioned that the results from their study regarding environmental life cycle assessment of green roofs may be confirmed or refuted by other cases with different climate. As a result, the performance of green roofs is climatic-sensitive, and it can be considered as a key criterion for green roof type selection. Moreover, roof slope and being exposure to shade or wind may affect the DM on green roof type selection. Roof slope is

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effective on the capability of green roofs in stormwater retention (Getter, Rowe, & Andresen, 2007). High wind speeds are potentially destructive to plants, especially trees (Jim & Peng,

2012), and also in shady areas of the roof, it is necessary to use leafy and shade-loving plants in these situations (Tilston, 2008). Finally, according to reviewing extensive literature, criteria

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might influence DM for green roof selection are shown in Table 1.

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In this paper, EFDM was developed for criteria identification, and DEMATEL was employed for

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criteria assessment in DM for green roof type selection in Kuala Lumpur. Following sections

3.1

EFDM Development Background of EFDM

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3.1.1

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discuss these methods in detail.

Delphi method is the core of EFDM. Wang & Lin (2008) stated that Delphi is an expert opinion

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survey method with three features: anonymous response, iteration and controlled feedback, and finally statistical group response. Its notable that Delphi method is used in the field of construction as the primary or secondary method (Hallowell & Gambatese, 2010). The detained information regarding Delphi method application can be found in the studies conducted to elaborated the use of Delphi method in knowledge acquisition, e.g., Rowe & Wright (1999), Hallowel, (2009) Kim, Jang, & Lee (2013) Alyami, Rezgui, & Kwan (2013). Furthermore, in

terms of the guideline for expert selection, Veltri (1985) and Rogrez & Lopez (2002) have defined different criteria, while Hallowell and Gambatase (2010) proposed the most flexible criteria for expert selection (see Table 2). Additionally, the number of panelists should be dictated by the characteristics of the study such as the number of available experts, the desired geographic representation, and the capability of the facilitator Hallowell & Gambatse (2010).

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Moreover, the number of experts in a Delphi panel can vary from 10 to 50 members (Alyami et al., 2013).

Over the years, researchers modified the conventional Delphi method to cope with the Delphi weaknesses (Ishikawa, Amagasa, & Shiga, 1993; Kuo & Chen, 2008). Murry, Leo, &

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Gigvh (1985) introduced the concept of integrating the conventional Delphi Method and the

fuzzy theory in order to remove the ambiguity and vagueness of the method as far as possible.

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Ishikawa (1993) applied the theory of fuzzy sets to Delphi method in order to overcome these

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problems using the maximum-minimum fuzzy type of Delphi method. This approach was

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applied to numerous studies and called fuzzy-Delphi method (FDM). FDM was employed by many researchers for extracting information from the experts (e.g. Wang & Lin (2008), Hsu & Yang (2000), and Hsu & Yang (2000)). After that, Yih (2010) mentioned some weaknesses of

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FDM and developed FwD. Table 3 indicates the weaknesses of Delphi method, FDM, and FwD.

It is worth mentioning that different approaches have been used by researchers for

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defuzzification of the values in FDM. For instance, Yu et al. (2010) used simple center of gravity as the threshold to defuzzify the fuzzy weight of each element to a definite value. In their study,

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if the definite value is more than the threshold, it is accepted; otherwise, it is rejected. In another study conducted by Kuo & Chen (2008), the threshold is considered regarding the 80/20 rule. It

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means that, if the geometric mean of the criterion is in the range of 80% and 100% of the using scale for ranking, then the criterion is accepted. Moreover, Yih (2010) used value of “3.5” as the threshold for the geometric mean in a Likert scale questionnaire ranging from 1 to 5. In EFDM development, it is tried to use the advantages of previous method and cope with their weaknesses. For instance, according to Delphi weakness, the more number of rounds exist, the fewer experts cooperate in the study. Consequently, there is a need to minimize the number

of rounds as far as it is sufficient for obtaining the aim of EFDM. Moreover, in FDM and FwD, the first round starts with a questionnaire consisting of the criteria related to the study; however, in this paper the effective criteria should be identified in the first round. On the other hand, the use of geometric mean can decrease the ambiguity produced by differences between the experts’ opinions and help to incorporate all given opinions in one investigation Hsu & Yang (2000). As

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a result, geometric mean was used for the analysis. EFDM details are provided in the following section. 3.1.2

Application of EFDM in criteria identification for green roof type selection

Similar to Delphi method, in EFDM, individuals are selected according to predefined guidelines and asked to participate in two rounds. The approach proposed by Hallowell & Gambatse (2010)

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was followed due to the higher capability of this approach in expert selection compared with the

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others. In this paper, 43 potential experts were contacted in order to data collection from both academic and industry points of views; however, 28 experts accepted to contribute in this

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research. The background of the experts is shown in Table 4. It is worth mentioning that expert

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sampling method was used in this paper, which is a non-random sampling method. The flowchart diagram of EFDM is illustrated in Fig.1 represents the sequence of the operations in two rounds

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of EFDM. Number of rounds in EFDM is considered based on the requirements of the study.

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First round must be conducted to discuss the criteria affecting DM, and the second round is to get

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their feedback regarding the importance of the discussed criteria in the first round.

3.1.2.1 First round There are two important objectives for conducting the first round of EFDM. Firstly, to discuss the potential criteria affecting DM on selecting the optimum type of green roof, and secondly, to categorize these criteria. For the former objective, some criteria that might influence DM on

selecting the type of green roof were gathered from reviewing the literature, and then, were discussed with the experts. Moreover, as it can be seen in Fig. 1, first round was conducted through a semi-structured interview, which includes some open-ended questions and discussions. As a result, the experts could delete any criterion or add the new one in this round. For the latter objective, card sorting is the method that has been used as explained by Abdullah (2003). In card

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sorting, the experts were given a number of cards each labeled with an object name. The experts have the task of repeatedly sorting the cards into piles such that the cards in each pile have something in common. 3.1.2.2 Second round

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(1) Design the questionnaire and send to the experts.

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Second round of EFDM consisted of three main parts:

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There is only one section in this questionnaire, “criteria affecting DM on green roof type

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selection." The questionnaire was designed based on the results of the first round. Likert scale was used to understand the importance of each criterion for DM. Likert scale is a widely-used

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instrument in measuring experts’ opinions (X. Zhang et al., 2012). This scale was selected since

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it allows the extreme of importance to be stated as 1, 2, 3, 4, and 5 indicating “very less important,” “less important,” “moderately important,” “important,” and “strongly important,”

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respectively.

(2) Organize experts’ opinions collected from the questionnaire into estimate, and create the

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Triangular Fuzzy Numbers (TFNs).

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In EFDM, the calculation process is adopted from the study conducted by Hsu & Yang (2000). The minimum and maximum values of experts’ opinions are calculated considering the TFNs. The geometric mean is considered as the membership degree of TFNs in order to develop the statistically-unbiased effect and, at that same time, avoid the effects of extreme values (Kuo & Chen, 2008). As shown in Fig. 2, in EFDM calculation, the geometric mean is the point of peak

(am) wherein the supported interval points a1, and a2 signify the minimum and maximum of experts’ values for each criterion. The geometric mean (MA) formula is: 𝑛

MA = √∏𝑛𝑖=1 𝑋𝐴𝑖

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(1)

Where 𝑋𝐴𝑖 indicates the appraisal value of the ith expert for criterion A, and i denotes the ith expert; i = 1, 2, …, n

In the present paper, the geometric mean was employed to indicate consensus of the

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expert group.

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(3) Selection of the criteria affecting DM.

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Since the experts’ values are considered as TFNs, geometric mean is also a TFN. As a result,

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defuzzification procedure needs to be carried out. In this paper, the highly important category is considered as the criteria/sub-criteria affecting green roof type selection as defined by Chong &

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Zin (2010) as follows.

if

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The criterion is “less important,"

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The criterion is “moderately important," if if

2.50 ≤ geometric mean < 3.50. 3.50 ≤ geometric mean ≤ 5.00.

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The criterion is “highly important,"

1.00 ≤ geometric mean < 2.5.

3.2

DEMATEL Method

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DEMATEL was introduced by Geneva Research Center of the Battelle Memorial Institute in 1970 (Chien, Wu, & Huang, 2014). This technique enables the decision maker to extract mutual relationships between all decision elements by visualizing the causal relationships among the decision elements in digraphs. Thus, the DEMATEL method can convert the relationship between the causes and effects of criteria into an organized structural model of the system. There are myriad studies employed DEMATEL as the method for criteria/factor assessment in different

fields, i.e. risk assessment (Govindan & Chaudhuri, 2016), supplier selection (Mirmousa & Dehnavi, 2016), green supply chain (Govindan, Khodaverdi, & Vafadarnikjoo, 2015), human resource (Pandey & Kumar, 2017), decision making (Lee, Huang, Chang, & Cheng, 2011). In order to figure out the causal relationships among the criteria/sub-criteria, based on the

1.

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procedure used by Chien et al. (2014) the steps below are performed:

Sets of pairwise comparisons according to the direction of influence of the relationship between the criteria/sub-criteria were generated. The comparison scale for pairwise comparison is 0, 1, 2, 3, and 4, which denotes no influence, low influence, medium influence, and high influence, respectively.

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The direct-relation matrix was generated, which is the average of pairwise comparison

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The direct-relation matrix was normalized and shown as matrix X. n

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k  max  ai , j i, j  1,2,..., n 1

𝑋 = .𝐴

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𝑘

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j 1

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(2)

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a1, 2  a1,n  0  a2,n       an , 2  0 

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 0 a 2 ,1 A    an,1

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the degree to which criterion i affects criterion j.

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matrixes that have been generated in step 1 by 28 experts. An n×n matrix A, in which Aij is

(3)

(4)

The total-relation matrix was calculated and shown as matrix T, where I denotes to identity matrix.

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T=X(I-X)-1 5.

(5)

The causal diagram was produced using Eqs. 6-8.

𝑇 = [𝑡𝑖,𝑗 ]𝑛×𝑛 𝑖, 𝑗 = 1,2, … 𝑛

(6)

𝐷𝑖 = ∑𝑛𝑗=1 𝑡𝑖,𝑗, 𝑖 = 1,2, … 𝑛

(7)

𝑅𝑗 = ∑𝑛𝑖=1 𝑡𝑖,𝑗, 𝑗 = 1,2, … 𝑛

(8)

Based on the amounts of Di+Rj and Di-Rj, four different zones are considered in terms of the criticality of the criteria. The causal diagram with four zones is illustrated in Fig. 3. The criteria fall into zone 1 are considered as the most critical criteria, while those criteria fall into

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zone four are the least critical. When Di−Rj is positive, the criterion is considered in the cause

group, and when Di − Rj is negative, the criterion is considered in the effect group. Plus, Di + Rj indicates the criticality of i to the whole system. According to Chien et al. (2014), four zones of the causal diagram in this paper can be defined as follows. 

Zone 1 (core group/criterion): the criteria/groups fall into this zone are the most

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influential criteria/groups on DM for green roof type selection. As a result, these criteria/groups must be considered with high priority for DM.

Zone 2 (driving group/criterion): all criteria/groups fall into this zone are considered as

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cause criterion/group with low influence, and should be the next target of the decision 

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maker in DM on green roof type selection.

Zone 3 (independent group/criterion): this zone is the third place that is needed to be

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considered for DM in green roof type selection. This zone consists of the criteria/groups which are affected by other criteria/groups. Moreover, the criteria/groups fall into this

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zone have low influence for DM on green roof type selection. In other words, these criteria/groups are not effective to or affected by other criteria/groups significantly. Zone 4 (by impact group/criterion): this zone includes the important criteria/groups

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which are affected by other zones and should not be focused directly. This zone is

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important but the last zone to be focused.

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3.3

Reliability and Validity tests

The content validity of the questionnaire in the second round of EFDM and DEMATEL were based on the literature review and experts’ opinions. As the questionnaires were approved by the pilot respondents, it was concluded that they have content validity. SPSS software was used to apply Cronbach’s Alpha test to understand whether the data provide a good support for internal

consistency reliability in the second round of EFDM. According to de Vet et al. (2017) , the value in Cronbach’s Alpha test should be more than 0.7, in order to achieve acceptable reliability. Additionally, MS-Excel TM was used in order to conduct all equations. The reliability and validity of the results are discussed in Section 4.1. Results and Discussion

4.1

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Criteria Identification

In the first round of EFDM, the effectiveness of each criterion in Table 1 was discussed with

experts. Moreover, the experts were asked regarding any missing criteria that might be effective in DM for green roof type selection. The outcomes of the discussion showed that two criteria

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namely “probable changes in the foundation” and “probable change in the lateral load resistance system” should be added to the list; however, “using the benefit of government incentive,” “Area

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of the roof,” and “height of the building” cannot be considered as criteria for green roof type

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selection. Furthermore, the experts were asked to categorize the criteria into appropriate groups.

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Consequently, four groups are considered for DM namely application, project features and environment, structural consideration, and costs. Tables 5 and 6 indicate the membership

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functions for all groups and criteria based on the experts’ responses to the questionnaire

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regarding the importance of each group and criterion in the second round of EFDM. According to the values in Tables 5 and 6, six out of 25 criteria, which are “exposure to

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wind,” “exposure to shade,” “creating another room,” “creating habitat for urban wild life,” “food production,” and “having a private garden” were rejected and the rate of rejection is

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around 24%. Fig. 4 indicates the number of sub-criteria added and deleted in both rounds of EFDM. Consequently, there are totally four groups consists of 19 criteria, which are effective in DM for green roof type selection. Furthermore, the rank order of each criterion and sub-criterion

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is calculated based on their geometric means. As shown in Tables 5 and 6, the most significant group for DM on green roof type selection is “application” followed by “cost.” In terms of criteria, the most important criteria is “energy saving” followed by “maintenance costs” and “payback period.” Additionally, according to the result of reliability analysis conducted in SPSS, the Cronbach’s Alpha is 0.926 among 19 criteria, and it indicates that the obtained results from the experts’ responses are consistent.

4.2

Criteria Assessment

The influence and being-affected rank of the effective groups and criteria for green roof type selection obtained from the DEMATEL total-relation matrix are shown in Tables 7 and 8. The

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calculated values of “D+R” and “D−R” were used to generate the causal diagram as shown in Figs. 5-9. The solid line and dash lines refer to bidirectional and unidirectional relationships between the groups/criteria, respectively. For instance, in Figs. 6 and 7, which show causal

diagram of all criteria together with G1 and G2 relationships respectively, C2 (from G1) and C11 (from G2) are connected with solid line. It indicates that there is a bidirectional relationship

between them and both criteria are being affected with each other. However, as it can be seen in

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Fig. 7, there is a unidirectional relationship between C10 (from G2) and C1(from G1). It shows

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that C10 affect C1, while C1 has no influence on C10. As a result, no relationship (for C1, and

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C10) was shown in Fig. 6, and a unidirectional relationship was shown in Fig. 7. The casual diagram of four influential groups in DM is illustrated in Fig. 5. As it can be

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seen, all these groups are connected with each other; however, G1 (Application), G2 (project features and environment), G3 (structural consideration), and G4 (cost) are located in zone 1,2,3

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and 4, respectively. It shows that G1 (Application) is the most influential group on the other

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groups and should be the first group to be considered in the process of DM for green roof type selection; however, G4 (cost) is the most affected criterion by the other criteria. In other words,

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any changes in G1 (Application), G2 (project features and environment), and G3 (structural

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consideration) result in changes in changes in G4 (cost) of green roof installation.

According to Figs. 6-9, the criteria located in zone 1 including C1 (storm water

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retention), C2 (recreational space provision), C10 (roof slope), and C11 (type of accessibility) were considered as the major criteria affecting all the criteria, and should be considered as the first criteria for DM on green roof type selection.

C2 (recreational space provision) is one of the most affecting criteria on the other criteria. Considering the green roof as a park-life roof (intensive green roof) for recreational spaces leads for requiring additional structural supports. The structural supports are needed for the significant additional dead load and live load of the building. Moreover, according to Bianchini & Hewage (2012b) and Mahdiyar et al. (2016), park-life roof which designed for recreational spaces

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(whether for public or private use) increases the property value of the buildings. In addition, maintenance costs are increased if the green roof designed for the provision of recreational

space. There is more possibility for damages also to the building and plants in case of installing intensive green roof (Brudermann & Sangkakool, 2017). Consequently, the positive and negative impacts of designing a green roof as a recreational space on the other criteria should be considered before DM on green roof selection.

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The criterion C10 (Roof slope) is also located in zone 1 and must be considered at the

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first step for DM. If the roof of the building has a considerable slope, it may not be suitable for

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installing intensive green roof. As a result, it affects many criteria such as C2 (recreational space provision), C11 (type of accessibility), C19 (probable damages). Moreover, roof slope is an

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influential factor in stormwater retention (Getter et al., 2007; VanWoert et al., 2005). It is worth mentioning that C1 (stormwater retention) and C11 (type of accessibility) are also located in

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zone 1. C1 (stormwater retention) has different level of influence on C3 (energy saving), C6

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(green building certificate award achievement) C7 (Noise absorption), C12 (probable increase in the size of beam), and C13 (probable increase in the size of column). In terms of C11 (type of

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accessibility), designing the green roof with public accessibility affects some criteria of DM for green roof selection, including C2 (recreational space provision), C12 (probable increase in the

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size of beam), and C13 (probable increase in the size of column), and C18 (maintenance cost).

5

Conclusion

Different types of green roofs offer different costs and benefits. Identifying and assessing the criteria affecting DM for green roof type selection requires an understanding of the cost, benefits, and the characteristics of each type of green roof. This paper contributed to identify and assess

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the important criteria in DM for green roof type selection. moreover, the weaknesses of the

methodologies for criteria identification such as Delphi method, FDM, and FwD were discussed. As a result, EFDM was developed in this paper for criteria selection. Based on EFDM, four

groups and 19 criteria affecting green roof type selection in Kuala Lumpur were identified. There are some interactions and relationships between the influential criteria for DM on green roof type selection. Consequently, DEMATEL method was employed to consider these relationships and

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assess the criteria. In this paper, DEMATEL questionnaire was distributed to the experts

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involved in green roof projects, and the data were used for identifying the major influential

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criteria in DM for green roof type selection.

The results showed that G1(application) is the most effective group in DM. moreover, in

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terms of criteria, C1 (stormwater retention), C2 (recreational space provision), C10 (roof slope), and C11 (type of accessibility) are the critical criteria for DM and significantly affect the other

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criteria. Consequently, these criteria should be considered for DM with high priority. It is

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noteworthy to mention that there are major differences between the rank orders of the criteria in Table 6 and their locations in DEMATEL causal diagram zones. For instance, G4 (cost) is one

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of the most important criterion for DM in green roof type selection; however, it is located in zone 4 (the least influential zone). It means that G4 (cost) is an important group for DM which is affected by other groups. In other words, it was concluded that in order to control the cost of

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green roof installation, the other groups which affect G4 (cost) must be controlled. For instance, C1 (stormwater retention) as a critical criteria can play a positive role in cost of green roof for

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the commercial developers and private home-builders (Brudermann & Sangkakool, 2017). Further research is required to develop a model for DM on green roof type selection. Moreover, as there are myriad criteria and sub-criteria, a decision support system can facilitate the process of DM.

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Engineering, 95, 1–9. https://doi.org/10.1016/j.ecoleng.2016.06.091 Williams, N. S. G., Rayner, J. P., & Raynor, K. J. (2010). Green roofs for a wide brown land:

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Opportunities and barriers for rooftop greening in Australia. Urban Forestry & Urban

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45(2), 411–420. https://doi.org/10.1016/j.buildenv.2009.06.017 Wong, Tay, S. F., Wong, R., Ong, C. L., & Sia, A. (2003). Life cycle cost analysis of rooftop gardens in Singapore. Building and Environment, 38(3), 499–509.

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https://doi.org/10.1016/S0360-1323(02)00131-2 Yang, H. S., Kang, J., & Choi, M. S. (2012). Acoustic effects of green roof systems on a lowprofiled structure at street level. Building and Environment, 50, 44–55. https://doi.org/10.1016/j.buildenv.2011.10.004

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management for conflicts and structural safety problems during construction: 1. Principles and methodologies. Automation in Construction, 20(2), 155–166. https://doi.org/10.1016/j.autcon.2010.09.013 Zhang, Q., Miao, L., Wang, X., Liu, D., Zhu, L., Zhou, B., … Liu, J. (2015). The capacity of

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16(1), 314–319. https://doi.org/10.1016/j.rser.2011.07.157

Figure captions

Fig. 1. EFDM flowchart

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Fig. 2. Triangular fuzzy number (Bojadziev and Bojadziev, 1997) Fig. 3. Causal diagram Fig. 4. Number of added and deleted sub-criteria Fig. 5. Causal diagram of the effective groups

Fig. 6. Causal diagram of the effective criteria together with G1 relationships

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Fig. 7. Causal diagram of the effective criteria together with G2 relationships

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Fig. 8. Causal diagram of the effective criteria together with G3 relationships

A

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EP

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M

A

Fig. 9. Causal diagram of the effective criteria together with G4 relationships

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A

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A

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A

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N

A

M

D

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EP

CC

A

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A

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D

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EP

CC

A

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N

A

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D

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A

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A

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Table 1 Criteria gathered from literature that might be effective for green roof type selection Sub-criterion

Reference (Karteris, Mallinis, & Tsiros, 2016; Mechelen, Dutoit, & Hermy,

Retaining the storm

2015; Stovin, Vesuviano, & Kasmin, 2012; Q. Zhang et al.,

water

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2015)

Providing recreational

(de Vries, van Dillen, Groenewegen, & Spreeuwenberg, 2013;

space

Mahdiyar et al., 2015; Saadatian et al., 2013)

(Ascione, Bianco, de’ Rossi, Turni, & Vanoli, 2013; Berardi, Energy saving

2016; Goussous, Siam, & Alzoubi, 2014; Jim & Peng, 2012; W. Yang, Wang, Cui, Zhu, & Zhao, 2015)

(Ondoño, Martínez-Sánchez, & Moreno, 2016; Peng & Jim,

adverse issues

2015; Susca et al., 2011; Wong, Tay, Wong, Ong, & Sia, 2003)

Creating another room

(McIntyre & Snodgrass, 2010)

N

U

Reducing environmental

A

(Alsup, Ebbs, & Retzlaff, 2009; Ascione et al., 2013; Nagase & Dunnett, 2010; Zahir, Raman, Mohamed, Jamiland, & Nopiah, 2014)

(Brenneisen, 2006; Molineux, Gange, Connop, & Newport,

EP

Using the benefit of

2015; Ondono, Martinez-Sanchez, & Moreno, 2016; William et

TE

Creating habitat for urban wild life

(Nyuk Hien, Puay Yok, & Yu, 2007)

D

Having a private garden

M

Aesthetic aspects

al., 2016) (Ascione et al., 2013; Claus & Rousseau, 2012; Perini & Rosasco, 2013; X. Zhang et al., 2012)

Obtaining green building

(Allnut et al., 2014; Fauzi, Malek, & Othman, 2013; Zahir et al.,

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government incentives

certificate

A

Noise absorption

2014) (Connelly & Hodgson, 2013; Seok, Kang, & Sung, 2012; N. H. Wong, Kwang Tan, Tan, Chiang, & Wong, 2010; H. S. Yang, Kang, & Choi, 2012)

Adding value to the

(Bianchini & Hewage, 2012b; Mahdiyar et al., 2016; Peck &

property

Kuhn, 1999; Rahman, Ahmad, Mohammad, & Rosley, 2015)

(Kuhn, Peck, & Kuhn, 2003; Lamnatou & Chemisana, 2015;

Food production

Nagase & Dunnett, 2010; Wong et al., 2003) (D’Orazio, Di Perna, & Di Giuseppe, 2012; Kokogiannakis,

Climate

Tietje, & Darkwa, 2011; Peng & Jim, 2015; Silva, Gomes, & Silva, 2016)

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(Jim & Peng, 2012; Ouldboukhitine & Belarbi, 2015; G. K. L.

Exposure to wind

Wong & Jim, 2015)

(Kuhn et al., 2003; Safikhani, Abdullah, Ossen, & Baharvand,

Exposure to shade

2014; Z.-H. Wang, 2014) (Townshend, 2007)

Height of the building

(Aly, 2013)

Roof slope

(Getter et al., 2007; Peng & Jim, 2015; Q. Zhang et al., 2015)

N

(Allnut et al., 2014; Berardi et al., 2014; Dinsdale et al., 2006; Pérez, Coma, Solé, Castell, & Cabeza, 2012; X. Zhang et al.,

A

Type of accessibility

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Area of the roof

Increase in the size of beam

M

2012)

(Berardi et al., 2014; Dinsdale et al., 2006; Johnston & Newton,

(Brudermann & Sangkakool, 2017)

Net present value

(Bianchini & Hewage, 2012b; Carter & Keeler, 2008; Mahdiyar

Payback period

et al., 2016; Peng & Jim, 2015)

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EP

Probable damages

2011; Peck & Kuhn, 1999; Susca et al., 2011)

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column

D

2004; Kosareo & Ries, 2007; Kuhn & Peck, 2003; Magill et al.,

Increase in the size of

(Kosareo & Ries, 2007; Morgan, Celik, & Retzlaff, 2013; Perini

A

Maintenance

& Rosasco, 2013)

Table 2 Requirements for expert recognition in a Delphi study (Hallowell & Gambatese, 2010)

Requirement

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At least one point in four different achievements and obtain 11 scores

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 Professional registration: 3 point  Year of professional experience: 1 point  Conference presentation: 0.5 point  Member of a committee: 1 point  Peer reviewed journal article: 2 point  Faculty member at an accredited university: 3 point  Writer/editor of a book: 4 point  Writer of a book chapter: 2 point  Advanced degrees:  BS:4 point  MS: 2 point  Ph.D.: 4 point

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Criteria

Table 3 Delphi, FDM, and FwD weaknesses

Delphi

Minimum two

1985 One

 Decline at the response rate, low convergence expert opinions, time and cost demanding, likelihood of filtering out specific opinions (Kuo & Chen, 2008).  Judgments made by experts cannot be appropriately reflected in quantitative terms, ambiguity may happen because of differences in meanings and interpretations of different experts opinions (M. Wang & Lin, 2008; Yu, Cheng, & Kreng, 2010).

 New information may be ignored as a single round of Delphi. (Yih, 2010)  It is not appropriate for the studies that involved criteria/factors have not been investigated in the literature.

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FDM

1950

Number of Weakness rounds

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Method year

Maximum two

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2010

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FwD

A

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 Another extra one more round may be needed, and possibly the round may not be significant for the research (Yih, 2010).  If in the second round any expert decides to change any answer or decides to add any new information, the first round should be repeated, and the process will be time consuming. Moreover, the experts may not be interested to re-do the first round.  It is not appropriate for the studies that involved criteria/factors have not been investigated in the literature.

Table 4 Background of the experts Number

Total points achieved according to the guideline proposed by Hallowell & Gambatse (2010)

Architect/landscape planner

13

Between 19 and 28

Contractor

6

Between 16 and 23

Academician

9

Between 23 and 29

Table 5 Membership functions of the groups Membership function

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Criterion

N

(minimum, geometric mean, maximum) (4, 4.80, 5)

Project Features

(3, 4.31, 5)

M

A

Application

Structural Consideration

TE EP CC

Rank order (1) (3)

(3, 4.20, 5)

(4)

(4, 4.70, 5)

(2)

D

Cost

A

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Background

Table 6 Membership functions of the criteria Membership function Group

Criterion

(minimum, geometric mean, maximum) (4, 4.43, 5)

order (5)

(3, 4.03, 5)

(11)

(4, 4.90, 5)

(1)

(4, 4.61, 5)

(4)

(2, 2.23, 3)

-

(4, 4.61, 5)

(4)

(2, 3.12, 4)

-

(3, 4.40, 5)

(7)

(2, 3.41, 4)

-

Noise absorption

D

(3, 4.20, 5)

(9)

Higher property value

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(4, 4.17, 5)

(10)

Food production

(1, 1.94, 3)

-

Climate

(4, 4.42, 5)

(6)

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Storm water retention

Rank

Recreational space provision

Project

Roof shape

(3, 4.29, 5)

(8)

Features and

Type of accessibility

(3, 3.67, 4)

(14)

Environment

Exposure to wind

(2, 2.76, 3)

-

Exposure to shade

(2, 2.43, 3)

-

Probable increase in the size of beam

(3, 4.01, 5)

(12)

Energy saving Environmental benefits

Aesthetic aspects Creating habitat for urban wild life

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achievement

A

Green building certificate award

N

Application

EP

Having a private garden

A

U

Creating another room

Probable increase in the size of column

(3, 4.20, 5)

(9)

Structural

Probable changes in the foundation

(2, 3.59, 5)

(16)

Consideration

Probable change in the lateral load (3, 3.75, 4)

(13)

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resistance system Net present value Payback period Cost Maintenance cost

(5)

(4, 4.70, 5)

(3)

(4, 4.83, 5)

(2)

(3, 3.65, 4)

(15)

N

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Probable damages

(4, 4.43, 5)

A

Table 7

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Green roof decision making group influence analysis

rank (D)

Being affected

Causal D+R

D-R

rank (R)

diagram zone

1.9847

1.9637

3.9484 0.0210

1

G2.Project features and environment

2.5744

0.3774

2.9518 2.1970

2

G3. Structural consideration

1.2260

1.9391

3.1651 -0.7132

3

CC

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Group

Influence

1.3512

2.8560

4.2072 -1.5049

4

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G1. Application

A

G4. Cost

Table 8 Green roof decision making criteria influence analysis

Criteria

rank (D)

Being affected

0.1499

C2. Recreational space provision

1.5038

0.5382

C3. Energy saving

0.7875

0.2997

C4. Environmental benefits

0.1141

0.3168

C5. Aesthetic aspects

0.6903

0.4868

D-R

diagram zone

1.3285 1.0286

1

2.0420 0.9656

1

1.0872 0.4878

2

0.4309 -0.2027

3

1.1771 0.2034

2 3

0.7493 -0.2099

0.4331

0.0785

0.5117 0.3546

2

0.3464

1.3930

1.7394 -1.0466

4

1.0751

0

1.0751 1.0751

2

1.2988

0

1.2988 1.2988

1

C11. Type of accessibility

0.8480

0.4616

1.3096 0.3864

1

0.6742

0.4749

1.1490 0.1993

0.5985

0.7849

1.3834 -0.1864

0.2434

0.5340

0.7774 -0.2906

A

0.4796

CC

N

U

1.1786

0.2697

D+R

rank (R)

C1. Storm water retention

C6. Green building certificate award

Causal

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Influence

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achievement

C9. Climate

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C10. Roof slope

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C8. Higher property value

D

C7. Noise absorption

C12. Probable increase in the size of

A

beam

C13. Probable increase in the size of column C14. Probable changes in the foundation

2

4

3

C15. Probable change in the lateral load

2

0.3622

0.7323 0.0080

C16. Net present value

0.2257

1.8410

2.0667 -1.6153

4

C17. Payback period

0.1490

1.9368

2.0858 -1.7877

4

C18. Maintenance cost

0.4080

0.8574

C19. Probable damages

0.2460

0.4649

A

CC

EP

TE

D

M

A

N

U

resistance system

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0.3702

1.2654 -0.4493

4

0.7109 -0.2190

3