Evidence-based ranking of green building design factors according to leading energy modelling tools

Sustainable Cities and Society 47 (2019) 101491

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Sustainable Cities and Society journal homepage: www.elsevier.com/locate/scs

Evidence-based ranking of green building design factors according to leading energy modelling tools

T

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Aladdin Alwisya, , Samer BuHamdana, Mustafa Gülb a b

Markin/CNRL Natural Resources Engineering Facility, Edmonton, Alberta, T6G 2W2, Canada Department of Civil and Environmental Engineering, University of Alberta, 7-257 Donadeo Innovation Centre for Engineering, Edmonton, Alberta, T6G 1H9, Canada

A R T I C LE I N FO

A B S T R A C T

Keywords: Green building Environmental impact Energy analysis Modeling tools

Green building has been acknowledged as a leading practice for mitigating the environmental impact throughout the life cycle of buildings. Recent studies have explored a wide range of systems, modeling tools, standards, and rating tools to facilitate the implementation of green building design concepts. However, these studies continue to utilize generic design parameters (factors) without considering their impact on the overall building environmental performance. This can lead to delays during the design process as a result of practitioners relying on their intuition to select and prioritize relevant factors. The proposed research establishes an evidence-based ranking system for green building design factors (GBDFs) in order to improve the green building design process based on an extensive systematic literature review. The identification phase of GBDFs is conducted through an extensive review of the literature on green buildings in reference to representative modeling tools. The evaluation phase is then carried out based on the frequency of relevant publications in accordance with leading modeling tools to generate the proposed evidence-based GBDF ranking. This ranking pioneers an innovative endeavor to prioritize GBDFs that not only assists designers, but also establishes baselines for further development of existing modeling tools.

1. Introduction According to the national inventory report from Environment and Climate Change Canada, buildings were responsible for 85.6 megatonnes of CO2 equivalent in Canada in 2015 (Government of Canada, 2017). The U.S. Energy Information Administration (EIA) annual report on energy consumption for 2016 states that existing residential and commercial buildings account for 39.7% of the total energy consumption in the United States. (Energy Information Administration, 2017). This sizable environmental impact of buildings has been the primary driver of a larger number of studies, in recent decades, with the aim of developing efficient and environmentally-friendly designs. As a result, researchers have been exploring several environmental design concepts, such as passive housing (Firląg & Zawada, 2013; Foster, Sharpe, Poston, Morgan, & Musau, 2016; Kovacic, Reisinger, & Honic, 2017), green buildings (Darko, Chan, Owusu-Manu, & Ameyaw, 2017; Olanipekun, Xia, & Nguyen, 2017; Wu, Mao, Wang, Wang, & Song, 2016), sustainable design (Choi, Oh, Park, & Park, 2016; Larson & York, 2017; Wong & Fan, 2013), and net zero energy building (Ferreira, Almeida, Rodrigues, & Silva, 2016; Marszal et al., 2011; Synnefa, Laskari, Gupta, Pisello, & Santamouris, 2017). Passive housing focuses

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on the passive heat exchange (gain/loss) throughout the building envelope, in addition to the passive heat gain from the internal heat loads generated by the occupants (Wang, Kuckelkorn, Zhao, Spliethoff, & Lang, 2016). Green building design and sustainable design, on the other hand, are holistic concepts that optimize all building elements in terms of energy and environmental performance (i.e., CO2 emission and the depletion of non-renewable energy resources). These two concepts have been used interchangeably, in addition to other terminologies such as green design, high performance building, and sustainable building (Li, Chen, Wang, Xu, & Chen, 2017; Zuo & Zhao, 2014). Finally, the net zero energy building concept has multiple definitions. It can be simply described as a building that achieves a balance between annual energy load and generations, where energy load represents the amount of energy necessary to satisfy a building’s requirements, and the generated energy comes from renewable sources such as solar thermal and photovoltaic, and geothermal heating (Sartori, Napolitano, & Voss, 2011). The implementation of these four design concepts in the construction of buildings—existing and new buildings—can significantly reduce the environment impacts throughout the life cycle of buildings. For instance, Canada Green Building Council (CGBC) predicts that green building investments between the years 2017 and 2030 in existing

Corresponding author. E-mail addresses: [email protected] (A. Alwisy), [email protected] (S. BuHamdan), [email protected] (M. Gül).

https://doi.org/10.1016/j.scs.2019.101491 Received 28 March 2018; Received in revised form 3 February 2019; Accepted 26 February 2019 Available online 21 March 2019 2210-6707/ © 2019 Elsevier Ltd. All rights reserved.

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Table 1 State-of-the-art in Green Building Rating Systems. Reference ID

Title

Year

Rating Systems

(He, Kvan, Liu, & Li, 2018)

How green building rating systems affect designing green

2018

(Shan & Hwang, 2018)

2018 2018 2017 2016

BREEAM, CASBEE, GS NZ, LEED BREEAM, ESGB, GBI, GG, LEED,

2015

BEAM Plus, BREEAM, CASBEE, LEED, ASGB-GBL

(Zuo & Zhao, 2014)

Green building rating systems: Global reviews of practices and research efforts Critical review and methodological approach to evaluate the differences among international green building rating tools A critical comparison of green building rating systems A comparative analysis of waste management requirements between five green building rating systems for new residential buildings A comprehensive review on passive design approaches in green building rating tools Green building research–current status and future agenda: A review

ASGB, BEAM PLUS, BREEAM, CASBEE, ISBT, GM, GS, LEED ASGB, BEAM Plus, BREEAM, CASBEE, CEPAS, EPRS, GBI, IGBC, ISBT, GG, GM, GS, GSAS, LEED BREEAM, CASBEE, Green Star, ITACA, LEED

2014

(Waidyasekara, De Silva, & Rameezdeen, 2013)

Comparative study of green building rating systems: In terms of water efficiency and conservation

BREEAM, EPRS, CASBEE, GBCA, GBI, HKBEAM, LEED BREEAM, EPRS, CASBEE, GS, GRIHA, GBI, HKBEAM, LEED,

(Mattoni et al., 2018) (Doan et al., 2017) (Wu, Shen, Ann, & Zhang, 2016) (Chen, Yang, & Lu, 2015)

2013

* ASGB: Assessment Standard for Green Buildings, BEAM Plus: Building Environmental Assessment Method Plus, BREEAM: BRE Environmental Assessment Method, CASBEE: Comprehensive Assessment System for Built Environment Efficiency, CEPAS: Comprehensive Environmental Performance Assessment Scheme, EPRS: Estidama Pearl Rating System, ESGB: Evaluation Standard for Green Building, GBI: Green Building Index, GBL: Green Building Labeling, GG: Green Global, GM: Green Mark, GRIHA: Green Rating for Integrated Habitat Assessment, GS: Green Star, GS NZ: Green Star New Zealand, GSAS: Global Sustainability Assessment System, HKBEAM: Hong Kong Building Environmental Assessment Method, IGBC: Indian Green Building Council, ISBT: International Initiative for a Sustainable Built Environment, LEED: Leadership in Energy and Environmental Design.

Fig. 1. Green building design factor methodology. BS: Building system; HVAC: Mechanical and electrical requirements; BD: Building design; WC: Weather conditions; RE: Renewable energy.

buildings, with a total floor area greater than 25,000 ft2, can reduce greenhouse gas emissions by 44% compared to the emissions in the baseline year of 2005. This would translate into $6.2 billion in energy cost savings and $32.2 billion growth in the national gross domestic product (Canada Green Building Council, 2017). This potential positive impact of the green building concept promotes research efforts to identify and evaluate key design factors that guide environmental improvement initiatives. An extensive investigation into the status of green building literature can be used to conduct the identification and evaluation of the influencing factors of green building design (Govindan, Rajendran, Sarkis, & Murugesan, 2015; Schott, Wenzel, & la Cour Jansen, 2016). According to the U.S. Green Building Council, “the term green building encompasses planning, design, construction, operations, and ultimately end-of-life recycling or renewal of structures. Green building pursues solutions that represent a healthy and dynamic balance between environmental, social, and economic benefits” (What is green building, 2017). Green building thus entails the use of holistic modeling tools to efficiently simulate the environmental behavior of buildings and to promote greener design practices. EnergyPlus, for instance, has been used in a sustainable design study to reduce the total energy demand of a 3-floor office building (total floor area of 5,173.32 m2, floor height of 3.80 m) through the optimization of defined building

envelope parameters. The authors of this study report 22.4% savings in heating, ventilation, and air conditioning (HVAC) requirements, a 19.7% reduction in CO2 emissions, and 81.8% savings for lighting requirements as a result of implementing their proposed sustainable design model (Kurian, Milhoutra, & George, 2016). That study and many others, to list few (Coma, Pérez, Solé, Castell, & Cabeza, 2016; MacNaughton et al., 2018; Sharma, Saxena, & Rao, 2019), showcase the positive results realized when green building principles are implemented in the construction industry. However, the increasing interest in the concept of green building, there is little evidence in the literature pertaining to the clear identification and ranking of the key design factors of green building. This lack of a systemic ranking hinders the widespread implementation of green building principles among construction practitioners. As such, the proposed research identifies the key design factors for green building and develops an evidence-based ranking system for these identified factors in accordance with leading energy modelling tools. 1.1. State-of-the-art in green building design Amid the increasing awareness of the negative environmental impact of buildings on the whole ecosystem in the early 1990’s, many rating systems were developed to promote greener practices in the 2

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Table 2 Green building design factors—building system. Reference ID

Modeling tool

Building system Windows

(Ali-Toudert & Weidhaus, 2017) (Palomba et al., 2016) (Maurer et al., 2013) (DeBlois, Bilec, & Schaefer, 2013) (Liang Wong, Eames, & Perera, 2012) (Wang & Holmberg, 2015) (Lou, Tsang, Li, Lee, & Lam, 2017) (Feliks Setiawan, Huang, Tzeng, & Lai, 2015) (Su, Wang, & Lin, 2013) (Aksamija, 2016) (Fuertes & Schiavon, 2014) (John et al., 2013) (Kung, Chen, & Robinson, 2011) (Abdallah, El-Rayes, & Clevenger, 2015) (Richman & Simpson, 2016) (Lai & Wang, 2011) (Tu, 2016) (Raviraj, Gupta, & Shet, 2016) (Stav & Lawson, 2012) (Saunders, Landis, Jones, Schaefer, & Bilec, 2012) (Liu, Huang, & Stouffs, 2015) (Zhang, 2012) (Sheweka & Magdy, 2014) (Méndez Echenagucia, Capozzoli, Cascone, & Sassone, 2015) (Ganguly, Hajdukiewicz, Keane, & Goggins, 2016) (Chen, Yang, & Zhang, 2017) (Chuah, Raghunathan, & Jha, 2013) (Bojic, 2019) (Mettanant & Katejanekarn, 2014) (Zhao, Lasternas, Lam, Yun, & Loftness, 2014) (Ascione et al., 2016) (Kerdan, Glvez, Raslan, & Ruyssevelt, 2015) (Elizondo, Lebassi, & Gonzalez-Cruz, 2008) (Hachem-Vermette, Cubi, & Bergerson, 2016) (Palmiere, Riascos, & Riascos, 2015) (Yao, Chow, & Chi, 2016) (Attia & Carlucci, 2015) (Sage-Lauck & Sailor, 2014) (Feng & Hewage, 2014) (Pan, Yin, & Huang, 2008) (Martinez Riascos & Palmiere, 2015) (Kang & Rhee, 2014) (Kim & Yu, 2016a) (Han & Shi, 2014) (Santos, Martins, Gervásio, & da Silva, 2014) (Tian, Love, & Tian, 2009) (Bucking, Athienitis, & Zmeureanu, 2013) (Kang, Park, & Rhee, 2009) (Yezioro, Dong, & Leite, 2008) (Wang, Esram, Martinez, & McCulley, 2009) (Cheung, Mui, & Wong, 2015) (Chen, Yang, & Sun, 2016) (Zainudin, Haron, Bachek, & Jusoh, 2016) (Olanipekun et al., 2017) (Kamal & Attia, 2013) (Issa, Olbina, & Reeves, 2012) (Mostafavi, Farzinmoghadam, & Hoque, 2015) (Kim & Yu, 2016b)

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Exterior walls

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principles in the design process in order to help design teams find the optimal design solution that reduce the negative environmental impact of buildings while satisfying cost considerations. In their work, they introduced multi-objective genetic algorithms that employs life cycle analysis methodology for the design alternative evaluation process (Wang, Zmeureanu, & Rivard, 2005). Thereafter, researchers have been introducing different frameworks and paradigms that aim to support the optimization of green building design using a variety of techniques including genetic algorithm, artificial neural network, and rule-based analysis (Ahmad, Thaheem, & Anwar, 2016; Alwisy, Barkokebas, Hamdan, Gül, & Al-Hussein, 2018; Magnier & Haghighat, 2010).

construction industry, e.g., ASGB, BEAM PLUS, LEED, etc. Thereupon, several researchers have been investigating the rating systems and their correspondent factors, criteria, practices and scoring mechanisms (see Table 1). The relatively large number of green building rating systems and organizations can be attributed to the special geographical and political considerations of the countries of origin (Retzlaff, 2008) Similarly, the significant influence of the design process on the overall construction performance has been driving researchers to further explore the relationship between sustainability principles and green building design. Wang et.al were among the first researchers to shed the light on the importance of incorporating green building 3

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Table 3 Green building design factors—mechanical and electrical requirements. Reference ID

Modeling tool

Mechanical and electrical requirements HVAC system

(Ali-Toudert & Weidhaus, 2017) (Palomba et al., 2016) (Maurer et al., 2013) (DeBlois et al., 2013) (Liang Wong et al., 2012) (Wang & Holmberg, 2015) (Lou et al., 2017) (Feliks Setiawan et al., 2015) (Su et al., 2013) (Aksamija, 2016) (Fuertes & Schiavon, 2014) (John et al., 2013) (Kung et al., 2011) (Abdallah et al., 2015) (Richman & Simpson, 2016) (Lai & Wang, 2011) (Tu, 2016) (Raviraj et al., 2016) (Stav & Lawson, 2012) (Saunders et al., 2012) (Liu et al., 2015) (Zhang, 2012) (Sheweka & Magdy, 2014) (Méndez Echenagucia et al., 2015) (Ganguly et al., 2016) (Chen et al., 2017) (Chuah et al., 2013) (Bojic, 2019) (Mettanant & Katejanekarn, 2014) (Zhao et al., 2014) (Ascione et al., 2016) (Kerdan et al., 2015) (Elizondo et al., 2008) (Hachem-Vermette et al., 2016) (Palmiere et al., 2015) (Attia & Carlucci, 2015) (Sage-Lauck & Sailor, 2014) (Feng & Hewage, 2014) (Pan et al., 2008) (Martinez Riascos & Palmiere, 2015) (Kang & Rhee, 2014) (Kim & Yu, 2016a) (Han & Shi, 2014) (Santos et al., 2014) (Tian et al., 2009) (Bucking et al., 2013) (Kang et al., 2009) (Yezioro et al., 2008) (Wang et al., 2009) (Cheung et al., 2015) (Chen et al., 2016) (Zainudin et al., 2016) (Olanipekun et al., 2017) (Issa et al., 2012) (Mostafavi et al., 2015) (Kim & Yu, 2016b) (Wang & Lin, 2012) (Nghiem & Pappas, 2011)

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Air changes

Ventilation rate

Heating set point

Cooling set point

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Lighting control

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of a building’s environmental factors may lead to redundant design activities as a result of over-emphasizing design factors with relatively insignificant impact on the overall building performance. Furthermore, the lack of systematic ranking of environmental design factors can prevent designers from realizing the full potential of energy and environmental optimization efforts due to insufficient investigations of significant factors. The present paper seeks to identify and rank green building design factors (GBDFs). The proposed evidence-based GBDF ranking system provides a set of guidelines for the selection and exploration of design factors during the environmental and energy optimization studies. The

Despite the richness of green building literature, there are still no clear guidelines for the selection and prioritization of the design variables (factors). To a large extent, these processes are heuristic and influenced predominantly by the expertise of the design or research team (Wu, Ng, & Skitmore, 2016). Practitioners select generic environmental factors, such as window type, window to wall ratio (WWR), wall type, energy source, glazing type, number of floors, or total floor area, (Kurian et al., 2016; Uğur & Leblebici, 2017), and then experiment on these factors individually according to specific green building methodologies or techniques for the purpose of improving the energy and environmental performance of a building. This arbitrary generalization 4

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Table 4 Green building design factors—building design. Reference ID

Modeling tool

Building design Floor area

(Ali-Toudert & Weidhaus, 2017) (Palomba et al., 2016) (Maurer et al., 2013) (DeBlois et al., 2013) (Liang Wong et al., 2012) (Lou et al., 2017) (Feliks Setiawan et al., 2015) (Su et al., 2013) (Aksamija, 2016) (John et al., 2013) (Kung et al., 2011) (Abdallah et al., 2015) (Richman & Simpson, 2016) (Lai & Wang, 2011) (Tu, 2016) (Raviraj et al., 2016) (Liu et al., 2015) (Zhang, 2012) (Sheweka & Magdy, 2014) (Méndez Echenagucia et al., 2015) (Ganguly et al., 2016) (Chen et al., 2017) (Chuah et al., 2013) (Bojic, 2019) (Mettanant & Katejanekarn, 2014) (Zhao et al., 2014) (Ascione et al., 2016) (Kerdan et al., 2015) (Hachem-Vermette et al., 2016) (Palmiere et al., 2015) (Yao et al., 2016) (Attia & Carlucci, 2015) (Sage-Lauck & Sailor, 2014) (Feng & Hewage, 2014) (Pan et al., 2008) (Martinez Riascos & Palmiere, 2015) (Kang & Rhee, 2014) (Kim & Yu, 2016a) (Han & Shi, 2014) (Santos et al., 2014) (Tian et al., 2009) (Bucking et al., 2013) (Kang et al., 2009) (Yezioro et al., 2008) (Wang et al., 2009) (Cheung et al., 2015) (Chen et al., 2016) (Zainudin et al., 2016) (Olanipekun et al., 2017) (Kamal & Attia, 2013) (Issa et al., 2012) (Mostafavi et al., 2015) (Kim & Yu, 2016b) (Nghiem & Pappas, 2011) (Ding, Shen, Wang, & Shi, 2015)

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Floor layout

Building volume

Floor height

Building orientation

Fenestration analysis

Occupancy analysis

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1.2. Research aim and scope

proposed ranking scores are generated based on an extensive review of green building literature since its early emergence as an innovative concept to mitigate the environmental impact of building (i.e., from 1990 to 2019). This review targets the energy modeling tools research area to identify, categorize, and evaluate GBDFs according to representative modeling tools. The evaluation of GBDFs is based on the frequency of published papers (i.e., publication exposure) in regard to modeling tools. The evaluation scores are categorized in order to generate the evidence-based GBDFs scores, as described in the methodology section.

The proposed research presents, to the best of the authors’ knowledge, the first attempt to identify and rank GBDFs according to leading energy/environmental modelling tools. The presented results are expected to benefit both the current design practices in the architectural community as well as the existing green building rating systems. The benefits to the design teams can be expressed through the establishment of factor-based prioritization guidelines based on the impact of the identified GBDFs on the energy consumption and CO2 emission (during the operation phase of buildings). Designers will be

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Table 5 Green building design factors—weather conditions. Reference ID

(Ali-Toudert & Weidhaus, 2017) (Palomba et al., 2016) (Maurer et al., 2013) (DeBlois et al., 2013) (Liang Wong et al., 2012) (Wang & Holmberg, 2015) (Lou et al., 2017) (Feliks Setiawan et al., 2015) (Su et al., 2013) (Aksamija, 2016) (Abdallah et al., 2015) (Richman & Simpson, 2016) (Lai & Wang, 2011) (Tu, 2016) (Raviraj et al., 2016) (Stav & Lawson, 2012) (Saunders et al., 2012) (Liu et al., 2015) (Sheweka & Magdy, 2014) (Méndez Echenagucia et al., 2015) (Ganguly et al., 2016) (Chen et al., 2017) (Chuah et al., 2013) (Bojic, 2019) (Mettanant & Katejanekarn, 2014) (Zhao et al., 2014) (Ascione et al., 2016) (Kerdan et al., 2015) (Elizondo et al., 2008) (Hachem-Vermette et al., 2016) (Palmiere et al., 2015) (Yao et al., 2016) (Attia & Carlucci, 2015) (Sage-Lauck & Sailor, 2014) (Feng & Hewage, 2014) (Pan et al., 2008) (Kang & Rhee, 2014) (Kim & Yu, 2016a) (Santos et al., 2014) (Tian et al., 2009) (Bucking et al., 2013) (Kang et al., 2009) (Yezioro et al., 2008) (Wang et al., 2009) (Cheung et al., 2015) (Chen et al., 2016) (Zainudin et al., 2016) (Olanipekun et al., 2017) (Kamal & Attia, 2013) (Issa et al., 2012) (Mostafavi et al., 2015) (Yao et al., 2016) (Ding et al., 2015) (Su, Riffat, & Pei, 2012)

Modeling tool

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Weather conditions Weather data

Solar analysis

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Insolation analysis

Sky model

Daylighting controls

Ground heat transfer ●

● ●

● ●

●

● ● ●

● ●

● ● ● ● ● ● ● ●

●

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●

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●

●

● ●

● ● ● ●

●

● ● ●

●

● ● ● ●

● ● ● ●

● ● ● ●

●

●

● ●

●

● ● ● ● ●

based scoring systems that assign more points to the more influencing factors. For instance, LEED v4, gives “Optimize Energy Performance Credit” one of the highest points ((1–20 points) due to its significant environmental and economic impact in buildings (US Green Building Council, 2014). Nevertheless, the existing scoring system, when using the whole-building energy simulation option, is based on the overall energy optimization results with no account to which construction systems, design parameters, or influential factors (i.e., GBDFs) to focus on in order to efficiently improve the energy performance of a building. Even when using a prescriptive compliance option (advanced energy design guide, or advanced buildings core performance guide), only 5 points are given towards effort made to improve key construction systems, such as building envelope systems, interior, and exterior lighting. In other words, whether the enhanced energy efficiency was achieved

able to make informed decisions with regard to which GBDFs to prioritize when attempting to meet different owner’s requirements in term of cost, aesthetic, and performance. As such, designer can reduce the time required to perform an effective environmental assessment (i.e., minimize time required and maximize desired results) by focusing on the more influential factors. For instance, if exterior walls have a higher-ranking score than heating, ventilation, and air conditioning (HVAC) systems (i.e., greater environmental impact), practitioners should spend less time and effort exploring HVAC systems and focus on an extensive investigation of exterior walls due to their significant impact on the overall building environmental performance. Similarly, the information reported in this paper benefits existing green building rating systems in refining their generic scores and credits, namely the energy use criterion, by utilizing specialized design6

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Table 6 Green building design factors—renewable energy system. Reference ID

(Ali-Toudert & Weidhaus, 2017) (Palomba et al., 2016) (Maurer et al., 2013) (DeBlois et al., 2013) (Su et al., 2013) (Abdallah et al., 2015) (Lai & Wang, 2011) (Stav & Lawson, 2012) (Saunders et al., 2012) (Sheweka & Magdy, 2014) (Chuah et al., 2013) (Bojic, 2019) (Ascione et al., 2016) (Elizondo et al., 2008) (Palmiere et al., 2015) (Attia & Carlucci, 2015) (Sage-Lauck & Sailor, 2014) (Feng & Hewage, 2014) (Kim & Yu, 2016a) (Tian et al., 2009) (Bucking et al., 2013) (Kang et al., 2009) (Wang et al., 2009) (Kamal & Attia, 2013) (Su et al., 2012)

Modeling tool

TRNSYS TRNSYS TRNSYS ESP-r eQuest eQuest eQuest EnergyPlus EnergyPlus EnergyPlus EnergyPlus EnergyPlus EnergyPlus EnergyPlus EnergyPlus EnergyPlus EnergyPlus EnergyPlus EnergyPlus EnergyPlus EnergyPlus EnergyPlus EnergyPlus DOE-2 EnergyPlus

Renewable energy system Solar thermal

Collector storage system

● ● ● ●

● ● ●

Green roof

Solar electric (Photovoltaic)

Wind energy

Geothermal energy

● ● ● ● ● ● ● ● ●

● ●

● ● ● ● ● ●

●

● ● ● ● ● ● ● ● ● ●

● ● ●

●

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Table 7 Green building design factors—summary. Category

Building system

Mechanical and electrical requirements

Building design

Weather conditions

Renewable energy system

Green building design factors

Windows Doors Shading systems Walls/ Exterior walls Interior/Internal walls Roofs Floors Ceiling

HVAC systems Air-changes Ventilation-rate Heating set-point Cooling set-point Lighting control

Floor area Floor layout Building volume Floor height Building orientation Fenestration Occupancy

Weather data Solar analysis Insolation analysis Sky- model Daylighting controls Ground heat transfer

Solar-thermal Collector storage system Green roof Photovoltaic Wind energy Geothermal energy

identification of GBDFs begins with the collection of environmental and energy variables by exploring available literature that utilizes representative energy modeling tools, such as EnergyPlus, TRNSY, and eQuest, in green building studies. Scopus® (elsevier, 2017)—the largest online database of peer-reviewed literature—is used to collect relevant studies for the purpose of identifying GBDFs by cross-referencing selected green building terminologies (green building, green design, sustainable building, sustainable design, and high performance building) with representative energy modeling tools. The collected environmental and energy design variables are categorized in accordance with their functionality to support further evaluation of GBDFs. For instance, windows, doors, and walls are design parameters related to the construction systems of a building, and thus they can be categorized under the building system category. Weather data, solar analysis, and sky model, on the other hand, are not derived from the building itself, and thus would fall under a different category, such as weather conditions. The developed categories are then compiled to form the complete GBDFs group as illustrated in Eq. (1).

using pricy heating and cooling systems or inexpensive innovative design solutions to building envelope elements, the overall score would be the same. Therefore, the proposed evidence-based ranking results can improve existing rating systems by distributing the points over GBDFs in accordance to their relative importance expressed by the current research trendiness. It should be noted that the research presented in this paper introduces an evidence-ranking of GBDFs according to literature survey of green building research utilizing leading energy/environmental modelling tools, which represents the first step towards a comprehensive overall ranking system to GBDFs. Therefore, the ranking system doesn’t consider any performance metrics beyond those generates using the selected modelling tools (i.e., energy consumption, CO2 emission, etc.). The consideration of other environmental performance metrics such as embodied energy, water use, land use, material use, and construction waste is out of the scope of this paper and recommended for future research.

n1

2. Methodology

{GBDFi }i = 1,2, … , n = ⋃ {GBDF (Gcj )} j=1

The proposed green building design factor (GBDF) review is an evidence-based review of current green building literature to support the two primary objectives of this research: (1) identification of the GBDFs, and (2) evaluation of the GBDFs. The research methodology is illustrated in Fig. 1, where the

(1)

where {GBDFi }i = 1,2, … , n is the complete list of energy factors generated by uniting {GBDF (Gcj )} , the environmental and energy design factors categorized under specific functionality. After identifying and categorizing GBDFs, the evaluation stage is 7

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Table 8 Modeling tool-based GBDF evaluation. GBDFs

Number of Publications

Building system

Mechanical and electrical requirements

Building design

Weather conditions

Renewable energy system

Windows Doors Shading systems Walls/ Exterior walls Interior/Internal walls Roofs Floors Ceiling HVAC systems Air-changes Ventilation-rate Heating set-point Cooling set-point Lighting control Floor area Floor layout Building volume Floor height Building orientation Fenestration Occupancy Weather data Solar analysis Insolation Sky-model Daylighting controls Ground heat transfer Solar-thermal Collector storage system Green roof Photovoltaic Wind energy Geothermal energy

EnergyPlus

TRNSYS

DOE-2

ESP-r

eQuest

Totals

276 17 71 260 56 138 144 49 252 12 27 45 41 93 49 3 12 7 121 37 106 167 189 4 8 51 18 13 4 29 86 48 31

126 18 53 222 33 125 142 54 156 15 26 41 34 17 33 1 4 1 67 6 40 166 63 26 15 6 41 320 32 6 29 9 16

40 2 9 58 16 34 11 5 34 3 4 1 1 18 3 0 0 2 22 20 10 36 43 5 6 15 3 1 0 1 5 7 2

42 1 20 30 12 11 10 5 9 5 1 5 5 5 1 0 0 0 12 4 15 14 30 1 4 3 0 2 1 3 22 4 1

38 2 21 26 2 18 9 3 31 0 0 2 1 10 5 0 0 1 17 4 10 13 25 0 1 7 0 0 1 4 7 3 2

522 40 174 596 119 326 316 116 482 35 58 94 82 143 91 4 16 11 239 71 181 396 350 36 34 82 62 336 38 43 149 71 52

five tools are selected as the representative modeling tools to be crossreferenced with a set of terms used in green building studies (green building, green design, sustainable building, sustainable design, and high-performance building) in order to search for literature utilizing different design factors to promote the green building concept. As illustrated in Tables 2–6, the identified GBDFs from the survey of available literature—using Scopus®—have been categorized as follows: building system, mechanical and electrical requirements, building design, weather conditions, and renewable energy. Windows, doors, shading systems, exterior walls, interior walls, roofs, floors, and ceiling fall into the first category of GBDFs. The mechanical and electrical requirements category comprises the systems and control measures responsible for distributing and consuming the required building energy to support occupants’ thermal, acoustic, and light comfort; this category includes HVAC systems, air changes, ventilation rate, heating set point, cooling set point, and lighting control. The building design category is related directly to the requirements set by the owner of the building (i.e., the client) that designers seek to satisfy by refining and modifying the floor area, floor layout, building volume, floor height, building orientation, fenestration analysis (i.e., exterior opening arrangement), and occupancy analysis. The fourth category—weather conditions—governs the energy and environmental simulation process by means of weather and climate data, solar analysis, insolation analysis, sky model, daylighting controls, and ground heat transfer. Finally, solar thermal, solar thermal collection storage systems, green roof, solar electric (i.e., photovoltaic (PV) panels), wind energy, and geothermal energy are the factors in the renewable energy systems category, which can influence the design of the building, especially the HVAC systems. Table 7 lists all the identified GBDFs under their respective categories. In the context of the present research, GBDFs are identified as the construction systems, architectural design parameters, and weather

initiated. The evaluation stage—modeling tool-based ranking—accounts for the frequency of publications addressing GBDFs along with the selected modeling tools (see Eq. (2)). n

Mt (GBDFi ) =

∑ Mt (GBDFi,j) j=1

(2)

where Mt (GBDFi ) is the ranking score of GBDFi calculated by adding the number of publications incorporating modeling tool j with GBDFi , and n is the number of representative modeling tools. 2.1. Identification of green building design factors Identification of GBDFs constitutes the first step toward the proposed evidence-based ranking system that seeks to improve the green building design process by guiding designers to invest more time and effort in factors with greater influence on the environmental performance of the building rather than in those with less influence. The set of prioritized GBDFs can then be explored during the simulation process (i.e., modeling) in order to reduce their impact on building energy and environmental performance. While the energy and environmental modeling process for a building can be conducted in a number of ways (engineered equations, statistical analysis, artificial intelligence, etc.), the holistic nature of green building studies requires holistic modeling tools. Nguyen, Reiter, and Rigo (2014) examine various simulation and modeling tools with the goal of enhancing a building’s energy efficiency by providing designers with the necessary tools to evaluate the environmental impact of their design. Through the analysis of 20 holistic simulation tools in accordance with the utilization rate in energy-related research studies, it is found that only 5 modeling tools—EnergyPlus, TRNSYS, DOE-2, ESP-r, and eQuest—are used in over 90% of the cases. Therefore, these 8

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Fig. 2. Modeling tool-based GBDFs ranking.

The focus on green building started in the early 1990s, therefor the search range covers the publications from 1990 till the present day, 2019. The first GBDFs evaluation—modeling tools-based ranking—is generated by cross-referencing each GBDF with each of the representative modeling tools individually (see Table 8). As illustrated in Fig. 2, HVAC systems, exterior walls, windows, weather data, and solar thermal are the top five factors in terms of influence. It is noted that the category-based (i.e., local) GBDF ranking is well aligned with of the global modeling-based ranking, as exterior walls are the top-ranked GBFD in the building system category (see Fig. 3a), as are HVAC systems, weather data, and solar thermal in their respective categories (see Fig. 3b, c, and e). However, building orientation, the top-ranked GBDF in building design, is not among the modeling-based top 5 GBDFs, given the relatively low number of publications using building design GBDFs. The categorized ranking provides an in-depth evaluation of GBDFs that can help designers concentrate on certain factors when simulating specific building performance.

elements that have been explored by researchers and design teams using the holistic modelling tools (i.e., EnergyPlus, TRNSYS, DOE-2, ESP-r, and eQuest) due to their significant influence on the environmental impact and energy performance of a building. Note that, in Tables 2–6, (●) indicates that the reference utilized the identified factor. 2.2. Evaluation of green building design factors—modeling tool-based The evaluation of GBDFs is based on the frequency of green building publications that incorporate the representative modeling tools in order to explore various techniques and technologies aimed to mitigate the environmental impact of green buildings. The number of publications in a research area reflects the interest of researchers to explore certain concepts, systems, or components and understand their respective impact. The higher the impact, the more research is conducted, which can be reflected through the number of publications. As such, using the frequency of publication as an indicator for importance has been a widely accepted metric in evidence-based research as could be seen in (Alwisy, 2018; BuHamdan, Alwisy, Bouferguene, & Al-Hussein, 2019; Chan & Owusu, 2017; Osei-Kyei & Chan, 2015; Zhang, Wang, Hu, & Ho, 2010)

3. Discussion and results The proposed evidence-based ranking scores give an overall indication of the influence of GBDFs on the environmental performance of 9

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Fig. 3. Categorized modeling tool-based GBDF evaluation.

buildings. To the best of authors’ knowledge, this study is first of its kind presenting these scores in a comprehensive manner. This can be used by practitioners to select and prioritize design factors, which in turn improves the design process by efficiently exploring the key factors. Likewise, researchers can utilize this ranking as a baseline to further advance existing modelling tools by upgrading the simulation capacity of GBDFs with high ranking scores, which in turn allows the end-users to efficiently and accurately estimate the impact of these factor on the building environmental performance. The overall ranking, as illustrated in Fig. 2, confirms the importance of HVAC and building envelope elements in the green building design process as the top three GBDFs—exterior walls, windows, and HVAC systems—belong to these two systems. It can also be noted that the overall climate conditions, represented by weather data as the top forth GBDF, have a great impact on the design process. For instance, buildings subjected to cold weather conditions necessitate more attention to the heat exchange implications than those located in warmer climates.

In addition to building envelope and HVAC system requirements, the effect of climate conditions can be mitigated through the consideration of building orientation, one of the top ten GBDFs. As one of the top 10 most influential factors, building occupancy is shown to be a key factor to be considered during the design process, which can be justified due to its significant impact on the design of HVAC system (type and capacity). The other GBDFs need also to be considered according to their variance in their influential impact on the overall building environmental performance. It is important to note that the low ranking to renewable GBDFs, except for solar thermal heating systems, does not undermine their importance to green building design, but rather it exposes a clear gap in existing environmental design studies. The categorized ranking results according to the five identified GBDFs categories (building systems, mechanical and electrical requirements, building design, weather conditions, and renewable energy) reveal a clear domination of the top ranked GBDFs in their designated categories. HVAC systems, building orientation, weather data, 10

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and solar thermal (as illustrated in Fig. 3b, c, d, and e, respectively), are the top ranked factors within their respective categories that have approximately 50% of the relative category-based ranking scores. The ranking scores of building systems category, on the other hand, show a more balanced relative importance among its factors, where exterior walls, window, roof, and floor (i.e. building envelope components) are the predominant top four factors (see Fig. 3a). In addition to the importance of the overall evidence-based ranking of GBDFs in guiding construction practitioners during the design process, the categorized ranking scores provide a more in-depth analysis to the design factors that can help the multi-disciplinary design teams—architectural, structural, mechanical, and electrical—prioritize their discipline-specific factors.

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4. Conclusion Rapid population growth and growing global awareness of the environmental impact of buildings have motivated researchers to seek innovative green solutions. In this context, green building design explores different methods and technologies to mitigate the environmental impact of buildings. Green building modeling tools consider major outcomes—energy use, water use, waste, etc.—under structured scoring systems in order to access building performance; however, the key influential energy parameters (i.e., factors) are still used as generic inputs with no structured framework. This gap in green building design could hinder designers’ efforts due to the lack of clear guidelines for the selection and prioritization of design inputs. Therefore, this paper presents a systematic framework for the evaluation and ranking of green building design factors (GBFDs). The literature review of green building publications under representative modeling tools results in 33 GBDFs organized under 5 categories. Consequently, the number of modeling tool publications addressing each GBDF leads to the proposed evidence-based ranking. This study presents global GBDF ranking scores, which may not address the special environmental considerations of different local design systems. Implementing the proposed ranking framework in existing energy and environmental modeling tools by fostering greener practices could fill the gaps in these tools in terms of renewable energy production and weather conditions. Acknowledgements This paper has been funded through the Natural Sciences and Engineering Research (NSERC). Project title: Collaborative Research and Development Grants - Project (CRDPJ) entitled "Framework for energy-based decision support system (DSS) for residential construction projects," File number: CRDPJ 505476 - 16 References Abdallah, M., El-Rayes, K., & Clevenger, C. (2015). Minimizing energy consumption and carbon emissions of aging buildings. Procedia Engineering, 118, 886–893. Ahmad, T., Thaheem, M. J., & Anwar, A. (2016). Developing a green-building design approach by selective use of systems and techniques. Architectural Engineering and Design Management, 12, 29–50. Aksamija, A. (2016). Regenerative design and adaptive reuse of existing commercial buildings for net-zero energy use. Sustainable Cities and Society, 27, 185–195. Ali-Toudert, F., & Weidhaus, J. (2017). Numerical assessment and optimization of a lowenergy residential building for Mediterranean and Saharan climates using a pilot project in Algeria. Renewable Energy, 101, 327–346. Alwisy, A. (2018). Criteria-based ranking of green building design factors according to leading rating systems. Energy and Buildings. Alwisy, A., Barkokebas, B., Hamdan, S. B., Gül, M., & Al-Hussein, M. (2018). Energybased target cost modelling for construction projects. Journal of Building Engineering. Ascione, F., Bianco, N., De Masi, R. F., De Stasio, C., Mauro, G. M., & Vanoli, G. P. (2016). Multi-objective optimization of the renewable energy mix for a building. Applied Thermal Engineering, 101, 612–621. Attia, S., & Carlucci, S. (2015). Impact of different thermal comfort models on zero energy residential buildings in hot climate. Energy and Buildings, 102, 117–128. Bojic, M. (2019). Photovoltaic electricity production in a residential house on Réunion. 24.

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