Eco-Efficiency of Construction Materials:Data Envelopment Analysis

Omer Tatari1 and Murat Kucukvar2

Abstract: Sustainability assessment tools are critical in the process of achieving sustainable development. Eco-efficiency has emerged as apractical concept that combines environmental and economic performance indicators to measure the sustainability performance of differentproduct alternatives. In this paper, an analytical tool that can be used to assess the eco-efficiency of construction materials is developed. Thistool evaluates the eco-efficiency of construction materials by using data envelopment analysis, a linear programming-based mathematicalapproach. Life-cycle assessment (LCA) and life-cycle cost (LCC) are utilized to derive the eco-efficiency ratios, and data envelopmentanalysis (DEA) is used to rank material alternatives. Developed mathematical models are assessed by selecting the most eco-efficient exteriorwall finish for a building. Percent improvement analysis was carried out to investigate target environmental effect categories that need morereduction to reach 100% eco-efficiency. Through this study, the goal is to show that DEA-based eco-efficiency assessment model could beused to evaluate alternative construction materials and offer vital guidance for decision makers during material selection. DOI: 10.1061/(ASCE)CO.1943-7862.0000484. © 2012 American Society of Civil Engineers.

CE Database subject headings: Construction materials; Sustainable development; Data analysis; Computer programming.

Author keywords: Eco-efficiency; Sustainable development; Data envelopment analysis; Linear programming.

Introduction

The construction industry is one of the major contributors to envi-ronmental problems, such as global warming, ozone depletion,acidification, natural resources depletion, solid waste generation,and indoor air quality. Buildings account for 38.9% of U.S. primaryenergy use, which involves 72% of U.S. electricity consumption.Buildings are also one of the biggest contributors to natural resour-ces depletion and greenhouse gas emissions, accounting for 38% ofall CO2 emissions [Energy Information Administration (EIA)2008]. The total amount of building-related construction and dem-olition debris was 170 million tin the U.S. in 2003 (EPA 2009).Because the resource and energy consumption, emissions, wastegeneration from construction, and annual spending are growingsteadily, concerns related to sustainability are increasing. Sustain-able development primarily consists of the utilization of natural andphysical resources that enable individuals and communities to pro-vide for their immediate social, economic, and cultural wellbeingwithout compromising the ability of future generations to meet theirneeds (Lacasse 1999). The construction industry must inevitablyemploy certain environmental assessment tools in the process ofachieving sustainable development because it consumes a substan-tial amount of natural and physical resources and has significantenvironmental burdens during its life cycle. To measure theprogress, several metrics need to be devised.

The vital need for sustainability metrics has been acknowledgedby the nation’s leading scientific and industrial organizations. Forinstance, the need for a scientific evaluation framework for evalu-ating and integrating the life cycle environmental and economicperformance of the nation’s infrastructure was emphasized as acritical research agenda by the National Science and TechnologyCouncil (NSTC) (2008). The criticality of integrated performancemetrics as a catalyst for increased investment in sustainable tech-nology investments was highlighted. As a result, the ability toevaluate different investments by an assessment framework wouldlead “to the success of national efforts to minimize environmentaleffects, reduce overall energy use, and reduce greenhouse gas emis-sions” (NSTC 2008). Moreover, the National Research Council(NRC) workshop, titled “Toward sustainable infrastructuresystems,” emphasized the need for science-based metrics that inte-grate the physical, economic, social, and environmental dimensions(NRC 2009). Yet, there are many challenges related to quantifyingthe abstract concept of sustainability in the construction industry.The ASCE acknowledged this challenge of quantifying environ-mental effects and arriving at an objective measure for evaluationsof different alternatives (2004). The primary difficulty lies inobjectively evaluating environmental, social, and economic dimen-sions and the subcategories within each dimension.

There has been an emphasis on life cycle assessment (LCA) andbuilding eco-labels in recent literature (Haselbach 2008; Horvathand Hendrickson 1998; Keoleian et al. 2005; Stokes and Horvath2006). In accordance with the need of sustainability assessment,LCA-based decision support tools were developed for assessingthe environmental effect of the different buildings and building ma-terials in a various parts of the world. For example, ENVEST (anenvironmental impact estimating design software) was developedin UK to estimate the life-cycle environmental effects of a buildingfrom the early design stage, and it includes the environmentaleffects of materials used during construction and maintenance(Envest). ATHENA, which was developed by Athena SustainabilityInstitute in 2000, is an LCA-based environmental decision support

1LEED AP, Assistant Professor, Dept. of Civil, Environmental, andConstruction Engineering, Univ. of Central Florida, Orlando, FL 32816(corresponding author). E-mail: tatari@ucf.edu

2Graduate Research Assistant, Dept. of Civil, Environmental, andConstruction Engineering, Univ. of Central Florida, Orlando, FL 32816.

Note. This manuscript was submitted on February 15, 2010; approvedon September 1, 2011; published online on September 3, 2011. Discussionperiod open until November 1, 2012; separate discussions must be sub-mitted for individual papers. This paper is part of the Journal of Construc-tion Engineering and Management, Vol. 138, No. 6, June 1, 2012.©ASCE, ISSN 0733-9364/2012/6-733–741/$25.00.

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tool for building materials and buildings (Seo and Hwang 2001).The Building Environment Assessment Tool (BEAT), developed bythe Danish Building and Urban Research, is an LCA-based inven-tory tool and database for the environmental assessment of buildingproducts, building elements, and buildings based on the DanishLCA method environmental design of industrial products (EDIP).Also, the U.S. National Institute of Standards and Technology(NIST) Green Buildings Program has developed Buildings forEnvironmental and Economic Software (BEES) to develop and im-plement a systematic methodology for selecting environmentallyand economically balanced building materials. Currently, BEESversion 4.0 software is publicly available, and it aids in selectingbuilding products for use in commercial office and housing projectsin a way that balances environmental and economic performancesimultaneously (Lippiatt 2007).

Another area that drew much attention was derivation of a prac-tical eco-labeling method for measuring sustainability of buildings.For example, the U.S. EPA has initiated voluntary energy effi-ciency program of Energy Star that aims to reduce user costs whileprotecting the environment through energy efficient products andpractices (Augenbroe et al. 2000). Additionally, leadership inenergy and environmental design(LEED) has received wide accep-tance and has been adopted by several federal and state agencies forevaluating their buildings (Abbaszadeh et al. 2006). The LEEDbuilding rating system employs a simplified checklist that isprimarily used in the design process. To obtain a rating, a buildingmust satisfy eight prerequisites and then obtain points for creditsrelated to sustainable sites, water efficiency, energy and atmos-phere, materials and resources, indoor environmental quality, anddesign process.

On the other hand, although not adopted widely in the construc-tion industry, eco-efficiency has emerged as an alternative tool thatcombines environmental and economic performance indicators tomeasure the sustainability performance of different design alterna-tives. The objective of this paper is to develop an analytical tool thatcan be used to assess the eco-efficiency of construction materials.This tool is used to evaluate the projects using data envelopmentanalysis (DEA), a linear programming-based mathematical ap-proach. The LCA and life-cycle cost (LCC) are used to derivethe eco-efficiency ratios, and DEA is utilized to rank alternativeswithout a need to subjectively weigh life-cycle effect dimensionsand LCC. Developed mathematics will be assessed by selecting themost eco-efficient exterior wall finish for a building. The rest of thepaper is organized as follows. First, the need for eco-efficiencyassessment is discussed. Next, basic aspects of DEA are explained.Then, the data collection and model development are described.Next, analysis results and discussion are presented. Finally, thefindings are summarized and future work is pointed out.

Eco-Efficiency Assessment

Eco-efficiency emerged as a management philosophy by the WorldBusiness Council for Sustainable Development (WBCSD) in 1993,following the 1992 Rio Summit (Saling et al. 2002). In the Summit,eco-efficiency was defined as the delivery of the competitivelypriced goods and services that satisfy human needs and enhancethe quality of life while progressively reducing ecological effectsand resources intensity throughout product life cycles to a level ap-propriate with the estimated capacity of the Earth (Kibert 2008).Similarly, the Organization for Economic Cooperation and Devel-opment (OECD) defines eco-efficiency as the efficiency with whichecological resources are used to meet human needs (OECD 1998).The European Environment Agency (EEA) uses eco-efficiency

indicators to quantify progress toward sustainability on the macro-level and defines eco-efficiency as more welfare from less nature. Itcontends that eco-efficiency is achieved by decoupling resource useand pollutant release from economic development (EEA 1999).Realizing the goal-oriented and practical nature of eco-efficiency,the President’s Council on Sustainable Development (PCSD)embraced it as a necessary tool to link environmental protectionand economic growth (PCSD 1999).

Eco-efficiency is a concept that can provide a useful frameworkthat includes the main principles of sustainable development toaid in decision making for infrastructure projects. Eco-efficiencyanalysis was used successfully as a valuable assessment toolto measure sustainability [Kicherer et al. 2007; Korhonen andLuptacik 2004; Kuosmanen and Kortelainen 2005; National RoundTable on the Environment and Economy (NRTEE) 2001; Syrrakouet al. 2006]. As an important sustainability assessment tool thatsearches for environmental improvements yielding paralleleconomic benefits, eco-efficiency is concerned with three broad ob-jectives related to resources consumption, effect on nature andproduct value (WBCSD 2000):• Reducing the consumption of resources: This involves reducing

the use of energy, materials, water, and land, improving recycl-ability and product durability.

• Reducing the effect on nature: This includes minimizing airemissions, water discharges, waste disposal, the dispersion oftoxic substances, and economic variable measuringd encoura-ging the sustainable use of renewable resources.

• Increasing the product value: This indicates providing morebenefits to customers through product functionality, flexibility,and durability.Consistent with the aforementioned WBCSD definition, the

eco-efficiency ratio consists of two independent variables: an eco-nomic variable measuring the value of products or services added,and an environmental variable measuring their added environmen-tal effects. The eco-efficiency ratio expresses how efficient theeconomic activity is with regard to nature’s goods and services.According to the definition, eco-efficiency is measured as the ratiobetween the added value of what has been produced (e.g., income,high quality goods and services, jobs, and gross domestic product(GDP)) and the added environmental effects of the product orservice (Zhang et al. 2008). Eco-efficiency improvement can beaccomplished by reducing the environmental effect added whileincreasing the economic value added for products or servicesduring their life cycle.

Eco-efficiency analysis has been used successfully as a valuableassessment tool to assess sustainability in various domains(Kicherer et al. 2007; Korhonen and Luptacik 2004; Kuosmanenand Kortelainen 2005; NRTEE 2001; Syrrakou et al. 2006).Although the importance of eco-efficiency as a sustainabilityassessment tool has significantly increased worldwide, there arefew studies that adopted this concept in the construction domain.Li et el. (2010) developed a methodology for eco-efficiency evalu-ation for residential development at the city level, in which theylinked the economic value and environmental effects together. Theyproposed a methodology that selects the ecological footprint as anaggregate environmental indicator to represent all resourcesconsumed and all wastes produced by residential development.On the other hand, Saling et al. (2002) developed a specialized formof an eco-efficiency analysis tool that focuses on quantifying theenvironmental effects of a product on the basis of six categories,such as raw materials and energy consumption, land use, air, waterand solid emissions, potential toxicity, and potential risks.Economic data was also gathered, including all costs undertakenin manufacturing or the use phase of the product. The aim of their

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tool was to compare similar products and processes to simultane-ously improve environmental and economic performance. Theyderived the overall eco-efficiency score on the basis of normalizingrespective categories and applying an overall weighting scheme. Inanother study, utilizing BASF method, Takamura et al. (2002) com-pared the eco-efficiency of three preventive maintenance technol-ogies of existing roadways: traditional hot-mix overlay, polymermodified hot-mix overlay, and cold mix microsurfacing. In thisstudy, LCA and LCC were utilized as the denominator and numer-ator for an eco-efficiency ratio

Eco-efficiency ratio ¼ LCCLCA

ð1Þ

The approach of utilizing LCC to represent the economic valueadded was adopted in several research studies (de Haes et al. 2004;Michelsen et al. 2006; Saling et al. 2002). The primary advantage inutilizing LCC is to be able to account for all costs associated withthe life cycle environmental effects. As a result, this would properlyassess the economic value for the whole life cycle. The LCCmethod consists primarily of costs, such as purchase, installation,maintenance, repair, and replacement, which include the initial andfuture cost, respectively (Lippiatt and Boyles 2001). For the de-nominator, LCA’s environmental effect categories, based onTRACI (Bare et al. 2002) are used. These effect categories includeacidification (ACD), ecological toxicity (TOX), eutrophication(EUT), global warming (GWM), fossil fuel depletion (FFD), smog(SMG), water intake (WTI), human health (HHL), ozone depletion(OZD), and habitat alteration (HAB). By utilizing the effect catego-ries, weights could be assigned to each category in the denominator.The eco-efficiency ratio is then calculated by using DEA. The DEAmethodology is adopted in this study because of the complex natureof multiple categorie that need to be assessed. Merging economicLCCs with several environmental effect categories into a singlescore involves some challenges. Instead of assigning weights toeach category, DEA compares alternatives without any a priorisubjective intervention and weight assignment. The weighting ofeach effect category is assigned by DEA through rigorous math-ematical programming. Additionally, DEA is flexible enough toincorporate different levels of subjectivity as needed, as will beseen in the next sections.

Eco-Efficiency with Data Envelopment Analysis

Several researchers have proposed the use of DEA to evaluate theeco-efficiency (Barba-Gutiérrez et al. 2009; Hua et al. 2007;Korhonen and Luptacik 2004; Kuosmanen and Kortelainen2005). The DEA was first introduced by Farrell (1957), and theterm DEA was later coined by Charnes et al. (1978). It is a well-established nonparametric mathematical programming approachthat is used to compare the relative efficiency of similar units withrespect to multiple inputs and outputs.The DEAwas originally de-veloped and was mostly used for comparing productivity efficien-cies of schools, hospitals, universities, and bank branches. Later,DEA was applied to performance assessment of products (Doyleand Green 1991; Papahristodoulou 1997) and environmental ef-fects in production (Fare et al. 1989; Zhou et al. 2008). It was usedas an effective tool to measure efficiency of decision-making units(DMUs) in a given context and was utilized in over 4,000 scientificjournal articles or book chapters (Emrouznejad et al. 2008).The DEA has also been utilized in few studies in the constructionresearch domain (El-Mashaleh 2010; El-Mashaleh et al. 2007;Juan 2009; McCabe et al. 2005; Ozbek et al. 2009; Pilateris andMcCabe 2003; Xue et al. 2008). Most studies have concentrated

on evaluating the productivity and technical performance of thestudies, phenomena.

The basic premise of DEA is to assess the efficiency of oneDMU relative to other DMUs in consideration. To achieve this,a linear program is constructed for each DMU. The basic math-ematical program equation, coined by Charnes et al. (termed asCCRfor Charnes, Cooper, and Rhodes), is as follows (1978):

max z ¼Xs

r¼1

μryro∕Xm

i¼1

vixio ð2Þ

subject to

Xs

r¼1

μryrj∕Xm

i¼1

vixij ≤ 1; j ¼ 1;…; n ð3Þ

μr; vi ≥ 0 ð4Þ

where μr = output multiplier; vi = input multiplier; o = DMU that isevaluated; s = number of outputs; m = number of inputs; j = numberof DMUs; yrj = amount of output r produced by DMU j; and xij =amount of input i used by DMU j. The objective function z is theweighted sum of outputs for the DMU under evaluation. The DEAconsists of multiple inputs and outputs and seeks to minimize theinputs to produce the desired output. If the output cannot be pro-duced by the combination of the input of all the other DMUs, thenthe DMU in consideration is on the efficient frontier. In the caseswhere the inputs of other DMUs produce the output of DMU inconsideration, that DMU is considered not efficient because theinputs of other DMUs were able to produce more output for theDMU in question.

The basic DEA model may take different forms by manipulatingthe objective function and adding different restrictions. It is criticalto choose the suitable DEA model for the purpose of the study. Thecomplexity that lies within DEA is to accurately select the rightstrategy. This strategy depends on whether the studied phenomenacan be modeled as a constant return to scale or variable return toscale. Another important decision is related to whether DEA ismodeled to minimize the output or maximize the input of thestudied DMUs. Also, several weight restriction methodologiescan be employed to enhance DEA models with respect to differentconstruction material-related decision-making needs.

The DEA was also used to measure eco-efficiency. Eco-efficiency ratio was modeled as input-output model in whichenvironmental effects represent the inputs to the system and theeconomic value added as the output of the system (Kuosmanen andKortelainen 2005). As a result, the environmental effects are forcedto be minimized to achieve the same level of economic value.Alternatives that impose higher environmental effects to producethe same level of economic value were deemed as inefficient. TheDEA can be adapted to mitigate the subjective judgment aboutthe weights of the environmental and economic performanceindicators because DEA does not require a priori weight assign-ments (Kuosmanen 2005).

Model Development

Fig. 1 presents the general DEA framework in modeling eco-efficiencies of construction materials. According to DEA notationin Fig. 1, the inputs constitute LCA, and the output constitutesLCC. Utilizing this framework, two DEA models were developed:CCR-based ECODEA-1 model, and weight restricted ECODEA-2model.

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CCR-Based ECODEA-1 Model

The first model, ECODEA-1, utilized the CCR model depictedabove. Kuosmanen and Kortelainen (2005) first introduced theviability of this model for eco-efficiency ratio calculation. TheCCR model depicted above transforms to the following equations:

max z ¼ Y0∕Xm

i¼1

vixij ð5Þ

subject to

Yj∕Xm

i¼1

vixij ≤ 1; j ¼ 1;…; n ð6Þ

vr ≥ 0 ð7Þwhere Yo = LCC of DMU 0. Because LCC is the only output,output multipliers are not needed for the model. The DMU isregarded as eco-efficient when z ¼ 1. This model does not forceany weight restrictions on environmental effects. Thus, the flexiblychosen weights for environmental effects are enabled to maximizethe relative eco-efficiency of the DMU with respect to othercompared DMUs (Kortelainen 2008). To solve this model as alinear program, it is linearized by taking the inverse of theeco-efficiency ratio as follows:

min z�1 ¼ 1Y0

×Xm

i¼1

vixij ð8Þ

subject to

1Yj

×Xm

i¼1

vixij ≥ 1; j ¼ 1;…; n ð9Þ

vr ≥ 0 ð10ÞThis mathematical model is solved through linear programming,and the eco-efficiency ratio is derived by taking the inverse of z.

Weight Restricted ECODEA-2 Model

To incorporate expert opinion suggestions on the weights of envi-ronmental effects, a soft restriction was included on weights duringlinear programming minimization. This resulted in a new DEAmodel, ECODEA-2. ECODEA-2 utilizes the Cone-Ratio DEAmethodology (Talluri and Yoon 2000). The primary reason in uti-lizing the second model is to increase the discriminatory power ofDEA and to compare the results with the unrestricted model. Also,through imposing boundaries to weights, certain environmentaleffects are forced into the weight calculations. ECODEA-2 modelis depicted in the following equations:

min z�1 ¼ 1Y0

×Xm

i¼1

vixij ð11Þ

subject to

1Yj

×Xm

i¼1

vixij ≥ 1; j ¼ 1;…; n ð12Þ

αjv1 � vj ≥ 0; j ¼ 2; 3;…; s ð13Þ

βjv1 � vj ≤ 0; j ¼ 2; 3;…; s ð14Þ

vr ≥ 0 ð15Þwhere αj and βj = nonnegative scalars. Weights based on BEESstakeholder panel judgments (Gloria et al. 2007) were used toconstruct the following restrictions:

VGlobalWarming ≥ VFossil Fuel Depletion ≥ VCriteria Air Pollutants

≥ VWater Intake ≥ VHumanHealth ≥ VEcological Toxicity

≥ VEutrophication ≥ VHabitat Alteration ≥ VSmog

≥ V Indoor Air Quality ≥ VAcidification ≥ VOzoneDepletion

ð16ÞThrough these weight restrictions, it was aimed to control the

eco-efficiency ratios and compare them with ECODEA-1. Also,because expert judgments can be critical in prioritizing environ-mental effects, ECODEA-2 could be used where expert opinionis sought.

Application of ECODEA on Exterior Wall Finishes

Possible exterior wall finishes for a building were utilized to illus-trate the use of the ECODEA models for material selection. Thissection presents the data collection and results.

Data Collection

The LCA and LCC data regarding exterior wall finishes of a build-ing were extracted from BEES 4.0 software (BEES Version 4.0).The BEES software was utilized because of the availability of con-struction material data and built-in capability of presenting the re-sults on the basis of life cycle environmental effect categories. Thefunctional unit selected for the exterior wall finishing materials was0:092 m2 (1 ft2) of exterior surface over 50 years. During this time,heating and cooling energy use difference on the basis of products’relative thermal performance was taken into account. The buildingis assumed to be using electricity for heating and cooling needs andis located in the city of Atlanta. Material properties of each wallfinish are well-documented in the BEES software.

Phases of LCA, including raw materials extraction, transporta-tion of raw materials to manufacturing, manufacturing, transporta-tion to site, installation at site, use, and end of life, were estimatedby BEES software. Transportation distance from manufacture touse was calculated, as shown in Table 1, on the basis of the distanceof factory locations from Atlanta. Dryvit EIFS Cladding Outsula-tion is manufactured in West Warwick, RI. Headwaters wall fin-ishes are manufactured in San Antonio. Trespa Meteon Panelsare manufactured in the Netherlands and shipped to Charleston,SC. Sea transportation have been included in BEES software,and only the destination from Charleston, SC to Atlanta was added.All other materials were assumed to be produced within a 80.47 km(50 mile) distance.

The resultant environmental effect was calculated by using theenvironmental effect categories based on TRACI (Bare et al. 2002),as shown in Table 2. These effect categories that were assessed areACD, TOX, EUT, GWM, FFD, SMG,WTI, HHL, OZD, and HAB.

Fig. 1. Inputs and outputs of construction materials eco-efficiencyframework

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Both OZD and HAB had zero values for all wall finishes and werenot shown in the table. The BEES software was also utilized forestimating the overall cost of wall finishes over a 50-year studyperiod. In the BEES model, categories of cost involve costs for pur-chase, installation, operation, maintenance, repair, replacement,and the negative cost item of residual value, which is the productvalue remaining at the end of the study period (Lippiatt 2007). Ad-ditionally, a 3.0% discount rate was used for adjusting the futurecosts of each exterior wall finishing alternative to their presentvalue. Because there was an imbalance in the data magnitude,the data were mean normalized (see Table 3), a procedure thatis followed to prepare the data for DEA (Sarkis 2007). Mean nor-malization was conducted by calculating the mean for each inputand output and dividing each input or output by its respective mean.

Results and Discussion

The ECODEA-1 model without weight restrictions was solved, andthe results were ranked on the basis of the eco-efficiency ratios (seeTable 2). The ECODEA-1 LP models were solved 11 times: one foreach construction material. To illustrate an example of these iter-ations, ECODEA-1 formulation for DMU 1, ABR1, is as follows:

min z�1 ¼ 11:01

× ð:94v1 þ :95v2 þ :97v3 þ :98v4:þ 1:28v5:

þ :94v6:þ :88v7:þ 1:19v8 þ :94v9Þ ð17Þ

subject to

11:01

× ð:93v1 þ :86v2 þ :92v3 þ :95v4:þ 1:27v5:þ :91v6:

þ :85v7:þ :07v8 þ :91v9Þ ≥ 1ð18Þ

11:01

× ð:95v1 þ :97v2 þ :95v3 þ :99v4:þ 1:28v5:þ :95v6:

þ :93v7:þ 2:12v8 þ :95v9Þ ≥ 1ð19Þ

11:01

× ð1:12v1 þ 1:04v2 þ 1:19v3 þ 1:10v4:þ :90v5:

þ 1:08v6:þ 1:78v7:þ :05v8 þ 1:13v9Þ ≥ 1ð20Þ

11:01

× ð:06v1 þ :14v2 þ :12v3 þ :09v4:þ :30v5:þ :13v6:

þ 3:33v7:þ 1:14v8 þ :07v9Þ ≥ 1ð21Þ

11:01

× ð1:14v1 þ 1:18v2 þ 1:14v3 þ 1:14v4:þ :90v5:

þ 1:17v6:þ :25v7:þ 1:25v8 þ 1:15v9Þ ≥ 1ð22Þ

11:01

× ð1:15v1 þ 1:21v2 þ 1:15v3 þ 1:15v4:þ :90v5:

þ 1:18v6:þ :31v7:þ 1:60v8 þ 1:16v9Þ ≥ 1ð23Þ

11:01

× ð1:15v1 þ 1:21v2 þ 1:15v3 þ 1:15v4:þ :90v5:

þ 1:18v6:þ :31v7:þ 1:52v8 þ 1:15v9Þ ≥ 1ð24Þ

Table 2. Environmental Effect and LCC of Exterior Wall Finishes

Exterior wallfinishes

Environmental effect categories

LCCACD TOX EUT GWP FFD SMG WTR HHL CAP

ABR1 7,614.69 41.71 2.41 18,163.95 15.11 61.71 3.58 93.80 2.1 7.29

ABR2 7,493.35 37.7 2.29 17,702.75 14.98 60.11 3.48 5.21 2.03 7.3

GBRM 7,665.19 42.47 2.38 18,368.88 15.17 62.5 3.78 167.80 2.13 11.66

CDRS 9,024.32 45.66 2.98 20,429.89 10.6 70.89 7.27 3.78 2.53 6.74

DECO 511.61 5.96 0.3 1,755.27 3.53 8.48 13.61 89.94 0.16 7

HFRS 9,237.98 51.59 2.84 21,274.07 10.63 77.09 1.03 99.11 2.57 2.37

HSST 9,271.30 53.02 2.88 21,421.30 10.7 77.57 1.27 126.64 2.59 3.22

HMCT 9,266.27 52.85 2.87 21,401.64 10.7 77.49 1.25 120.50 2.58 3.22

TRMP 9,903.21 48.66 3.16 22,515.46 16.25 83.61 8.16 4.58 2.72 23.47

GSTC 9,110.94 52.18 2.69 20,936.93 9.84 73.11 1.3 138.34 2.57 3.22

GVNL 9,666.52 49.09 2.66 20,852.89 12.75 71.17 0.22 19.96 2.68 3.59

Note: Units of measurement: ACD (mg H+ equivalents/unit), TOX (g 2, 4-dichlorophenoxy-acetic acid equivalents/unit), EUT (g nitrogen equivalents/unit),GWP (g CO2 equivalents/unit), FFD (MJ/unit), SMG (g NOx equivalents/unit), WTR (L/unit), HHL (g benzene equivalents/unit), CAP (micro disability-adjusted life years/unit), LCC (present value $/unit).

Table 1. Transportation Distance from Manufacture to Use

Exterior wall finish Acronym

Distance from manufactureto use (miles)

Atlanta Chicago

Anonymous brick

and mortar Product 1

ABR1 50 50

Anonymous brick

and mortar Product 2

ABR2 50 50

Generic brick and

mortar

GBRM 50 50

Generic cedar siding CDRS 50 50

Dryvit EIFS cladding

outsulation

DECO 1,020 971

Headwaters fiber-

reinforced stucco

(FRS)

HFRS 880 1,200

Headwaters scratch

and brown stucco

Type S

HSST 880 1,200

Headwaters masonry

cement Type S

HMCT 880 1,200

Trespa meteon

panels

TRMP 320 908

Generic stucco GSTC 50 50

Generic vinyl siding GVNL 50 50

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11:01

× ð1:23v1 þ 1:11v2 þ 1:27v3 þ 1:21v4:þ 1:37v5:

þ 1:27v6:þ 2:00v7:þ :06v8 þ 1:21v9Þ ≥ 1ð25Þ

11:01

× ð1:13v1 þ 1:19v2 þ 1:08v3 þ 1:12v4:þ :83v5:

þ 1:11v6:þ :32v7:þ 1:75v8 þ 1:15v9Þ ≥ 1ð26Þ

11:01

× ð1:20v1 þ 1:12v2 þ 1:07v3 þ 1:12v4:þ 1:08v5:

þ 1:08v6:þ :05v7:þ :25v8 þ 1:20v9Þ ≥ 1ð27Þ

vr ≥ 0 ð28Þ

Similar to DMU 1, LP models were solved for each DMU, andthe optimal weights resulting from each run were recorded, asshown in Table 4. The calculated optimal weights (vr) show whichinputs were utilized for each DMU for their calculation. Forinstance, for DMU 1, the weights show that eco-efficiency was cal-culated by using only ACD and WTR, whereas other effect catego-ries were all 0. The ECODEA-1 results indicate that eco-efficiencyratios range from 44 to 1. Among wall finishes, DECO, TRMP, andGVNL were found to be 100% eco-efficient. The CDRS was foundto be the least eco-efficient (.44) when compared with the otherexterior wall finishes in the study.

Additionally, ECODEA-2 LP models were solved eleven times:one for each construction material. The ECODEA-2 results werealso ranked on the basis of the eco-efficiency, as shown in Table 5.Weights have changed because of the weight restrictions that havebeen introduced. This restriction has affected the eco-efficiency ra-tios greatly. For instance, the flexibility of weight in ECODEA-1allowed ABR1 to assign unrestricted weights to any input to maxi-mize the eco-efficiency ratio, whereas ABR1 weights are restrictedin ECODEA-2. On the basis of this restriction, only DECO andTRMP were found to be 100% eco-efficient in ECODEA-2. Bothwall finishes were 100% eco-efficient in ECODEA-1, as well. TheGVNL, which was found to be 100% eco-efficient usingECODEA-1, dropped to seventh in ranking with a ratio of 0.28.The HFRS was found to be the least eco-efficient (.13). The resultsindicate that ratios decreased when compared with ECODEA-1 be-cause of additional weight restrictions, Mean eco-efficiency ratiosdropped from 0.72 to 0.234. Looking closely at the results, TRMPand DECO were 100% eco-efficient in both models. AlthoughTRMP have the highest LCC ($23.47), their environmental effectis minimal when compared with other materials. The DECO on theother hand has much lower LCC value ($7), and it has minimalenvironmental effects compared with other materials. Other mate-rials that had lower LCC, such as HFRS, were not as eco-efficientbecause of the higher environmental effects per dollar.

The ECODEA-1 and ECODEA-2 results were expected to bedifferent because of the weight restrictions that were imposed to

Table 3. Normalized Data Set

Exterior wallfinishes

Environmental effect categories

LCCACD TOX EUT GWP FFD SMG WTR HHL CAP

ABR1 0.94 0.95 0.97 0.98 1.28 0.94 0.88 1.19 0.94 1.01

ABR2 0.93 0.86 0.92 0.95 1.27 0.91 0.85 0.07 0.91 1.02

GBRM 0.95 0.97 0.95 0.99 1.28 0.95 0.93 2.12 0.95 1.62

CDRS 1.12 1.04 1.19 1.10 0.90 1.08 1.78 0.05 1.13 0.94

DECO 0.06 0.14 0.12 0.09 0.30 0.13 3.33 1.14 0.07 0.97

HFRS 1.14 1.18 1.14 1.14 0.90 1.17 0.25 1.25 1.15 0.33

HSST 1.15 1.21 1.15 1.15 0.90 1.18 0.31 1.60 1.16 0.45

HMCT 1.15 1.21 1.15 1.15 0.90 1.18 0.31 1.52 1.15 0.45

TRMP 1.23 1.11 1.27 1.21 1.37 1.27 2.00 0.06 1.21 3.26

GSTC 1.13 1.19 1.08 1.12 0.83 1.11 0.32 1.75 1.15 0.45

GVNL 1.20 1.12 1.07 1.12 1.08 1.08 0.05 0.25 1.20 0.50

Table 4. ECODEA-1 Results and Corresponding Weights

Wallfinish DMU Ratio Rank

Weights (vr) for

ACD TOX EUT GWP FFD SMG WTR HHL CAP

ABR1 1 0.64 6 0 0 0 0 0 0 1.42 0 0.35

ABR2 2 0.70 5 0 0 0 0 0 0 1.59 1.64 0

GBRM 3 0.98 4 0.35 0 0 0 0 0 1.42 0 0

CDRS 4 0.44 11 0 0 0 0 2.37 0 0 0.24 0

DECO 5 1.00 3 0 0 0 0 2.25 0 0.09 0 0

HFRS 6 0.47 10 0 0 0 0 0.40 0 1.36 0 0

HSST 7 0.57 9 0 0 0 0 0.40 0 1.36 0 0

HMCT 8 0.58 8 0 0 0 0 0.40 0 1.36 0 0

TRMP 9 1.00 1 0.35 0 0 0 0 0 1.42 0 0

GSTC 10 0.59 7 0 0 0 0 0.40 0 1.36 0 0

GVNL 11 1.00 1 0 0.38 0 0 0 0 1.43 0 0

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the latter. An important insight from this difference is the strongeffect of weights on eco-efficiency ratios. Decision makers whoare interested to use expert judgment in deriving the weightingof environmental effects should use ECODEA-2. On the otherhand, ECODEA-1 could be used where no a priori subjectivejudgment is needed. However, ECODEA-1 might ignore certainenvironmental effects that may have significant effect on theenvironment.

The DEA also offers insights about percent improvements thatcould be made to reduce the environmental effect, whereas LCC isheld constant to reach 100% eco-efficiency (see Table 6). Althoughit is not always possible to reduce the environmental effects of ma-terials, nevertheless, percent improvement analysis gives importantinformation regarding ecological inefficiencies. This informationcould be used to achieve dematerialization or aid in selecting moreeco-efficient submaterials during production of exterior wall

Table 5. ECODEA-2 Eco-Efficiency Results and Corresponding Weights

Wallfinish DMU Ratio Rank

Weights (vr) for

ACD TOX EUT GWP FFD SMG WTR HHL CAP

ABR1 1 0.44 5 0 0 0 0.56 0.56 0 0.56 0 0.56

ABR2 2 0.45 4 0 0 0 0.56 0.56 0 0.56 0 0.56

GBRM 3 0.69 3 0 0 0 0.56 0.56 0 0.56 0 0.56

CDRS 4 0.35 6 0 0 0 0.85 0.85 0 0 0.51 0.85

DECO 5 1.00 1 0 0 0 0.76 0.76 0 0.19 0 0.76

HFRS 6 0.17 11 0 0 0 0.56 0.56 0 0.56 0 0.56

HSST 7 0.23 10 0 0 0 0.56 0.56 0 0.56 0 0.56

HMCT 8 0.23 9 0 0 0 0.56 0.56 0 0.56 0 0.56

TRMP 9 1.00 1 0 0 0 0.76 0.76 0 0.19 0 0.76

GSTC 10 0.23 8 0 0 0 0.56 0.56 0 0.56 0 0.56

GVNL 11 0.26 7 0 0 0 0.56 0.56 0 0.56 0 0.56

Table 6. ECODEA-1 Based Percent Improvements of Exterior Wall Finishes

Wall finish DMU

Percent Improvements (%) for

ACD TOX EUT GWP FFD SMG WTR HHL CAP

ABR1 1 �36 �41 �39 �40 �51 �37 �36 �94 �36

ABR2 2 �47 �47 �46 �49 �58 �46 �31 �30 �46

GBRM 3 �2 �11 �5 �09 �25 �3 �2 �95 �2

CDRS 4 �69 �69 �70 �68 �56 �66 �67 �56 �69

DECO 5 0 0 0 0 0 0 0 0 0

HFRS 6 �60 �64 �64 �62 �53 �64 �53 �93 �60

HSST 7 �52 �58 �56 �55 �43 �57 �43 �94 �52

HMCT 8 �51 �57 �56 �54 �42 �56 �42 �93 �52

TRMP 9 0 0 0 0 0 0 0 0 0

GSTC 10 �54 �60 �56 �56 �41 �57 �41 �95 �55

GVNL 11 0 0 0 0 0 0 0 0 0

Table 7. ECODEA-1 Based Percent Improvements of Exterior Wall Finishes

Wall finish DMU

ECODEA-1 ECODEA-2

Chicago Atlanta Chicago Atlanta

Ratio Rank Ratio Rank Ratio Rank Ratio Rank

ABR1 1 0.67 6 0.64 6 0.50 5 0.44 5

ABR2 2 0.69 5 0.70 5 0.51 4 0.45 4

GBRM 3 1.00 4 0.98 4 0.76 3 0.69 3

CDRS 4 0.41 11 0.44 11 0.36 6 0.35 6

DECO 5 1.00 1 1.00 1 1.00 1 1.00 1

HFRS 6 0.53 10 0.47 10 0.21 11 0.17 11

HSST 7 0.61 9 0.57 9 0.26 10 0.23 10

HMCT 8 0.62 8 0.58 8 0.26 9 0.23 9

TRMP 9 1.00 1 1.00 1 1.00 1 1.00 1

GSTC 10 0.63 7 0.59 7 0.27 8 0.23 8

GVNL 11 1.00 1 1.00 1 0.30 7 0.26 7

JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT © ASCE / JUNE 2012 / 739

finishes. For instance, on the basis of ECODEA-1, for ABR1 tobecome 100% eco-efficient, it needs to reduce ACD by 36%, TOXby 41%, EUT by 39%, GWP by 40%, FFD by 51%, SMG by 37%,WTR by 36%, HHL by 94%, and CAP by 36%. The DECO,TRMP, and GVNL do not need any improvement in reducingtheir environmental effects because they are 100% eco-efficient.The same analysis could be done using ECODEA-2, as well.

To evaluate possible changes of the city location, ECODEAmodels were run for the city of Chicago, as shown in Table 7.Although some of eco-efficiency ratios have changed slightly,the rankings for both ECODEA-1 and ECODEA-2 models haveremained unchanged. A possible explanation would be the smallamount of difference in both models in manufacturing site distanceto the building.

The results showed that DEA is an effective tool to evaluateconstruction material alternatives and offer a critical insight tothe decision maker that can lead to buildings that use much moreeco-efficient materials. With the inclusion of soft weight restric-tions by using BEES’ recommendations, analysis regarding theeffect of weight restrictions was conducted. Percent improvementanalysis provided valuable information to the decision makersregarding what environmental effects need more improvements.Although BEES model was used to calculate both LCA andLCC, other LCA software tools, such as SimaPro and Athena,could be utilized as well. Because the mentioned LCA softwaretools utilized process-based LCA methodology, the results areexpected to be similar to the study here. Yet, it should be notedthat SimaPro and Athena do not utilize TRACI environmentaleffect categories, and their raw data would have to be used tocalculate these categories on a separate platform.

Conclusions

In this paper, a DEA-based eco-efficiency assessment framework ispresented as an effective and practical way to evaluate constructionmaterials. The developed framework utilized LCC and LCA as thenumerator and denominator for calculating the eco-efficiency ratioand solved LP models to calculate eco-efficiency ratios for exteriorwall finishes. Two DEA models were developed for this study. TheECODEA-1 calculated the ratios without enforcing any weightrestrictions, whereas ECODEA-2 used BEES’ recommendedweighting guidelines as an input to the model. Both modelspredicted DECO and TRMP to be 100% eco-efficient. Percentimprovement analysis was carried out to investigate environmentaleffect categories that need to be reduced to reach 100% eco-efficiency. Eco-efficiency ratios were analyzed for two cities tocompare the results and gain more insight.

This paper makes several contributions to construction research,including developing a mathematical model that does not requiresubjective weighting to assess the sustainability of constructionmaterials and presenting a practical way to apply eco-efficiencyto construction materials. The analysis of DEA results could bevery helpful to decision makers to compare relative eco-efficiencyof building materials. However, DEA compares eco-efficiency byanalyzing other sections in the data set. This is a major drawback ofDEA because the eco-efficiency ratios are relative to the eco-efficiency of other materials in the data set. Also, accuracy ofthe results depends on the accuracy of the data extracted. Takingthese limitations into consideration, the developed DEA-basedeco-efficient assessment models could provide immediate assess-ment of building material eco-efficiency and offer vital guidancefor decision makers during material selection. In future work,the scope of the study could be expanded to address more complex

decision-making situations in construction projects. Furthermore,different DEA formulations could be developed and assessed fordifferent decision-making settings.

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