Case Study

Calculation of Greenhouse Gas Emissions forHighway Construction Operations by Using aHybrid Life-Cycle Assessment Approach:Case Study for Pavement OperationsDarrell Cass, A.M.ASCE1; and Amlan Mukherjee, A.M.ASCE2

Abstract: Large quantities of greenhouse gases (GHG) are emitted in producing and acquiring materials for the construction, maintenance,and rehabilitation of highway infrastructure. The objective of this paper is to develop and illustrate a method that can be applied by stateagencies to quantify the life-cycle emissions associated with different pavement designs. It applies existing life-cycle assessment (LCA)methods that integrate process-level construction data. The research emphasizes the construction phase and contributes a method thatcan be used to develop and analyze construction phase life-cycle inventories. It describes on-site collection of material and equipment usagedata during construction and rehabilitation operations. Departing from traditional approaches that tend to use LCA as a way to comparealternative pavement materials or designs on the basis of estimated inventories, this paper proposes a shift to a context-sensitive process-basedapproach that uses actual observed construction data to calculate greenhouse gas emissions using a hybrid LCA. The goal is to supportstrategies that reduce long-term environmental impacts. A case study involving the rehabilitation of a concrete pavement was used to illustratethe proposed method. The key findings were as follows: total CO2 emissions are 787.19 and 1,383.28 MT per lane mile for Hybrid Models 1and 2, respectively; the production of the materials, equipment, and fuel used to construct the project account for 90% and 94% of the totalCO2 emissions throughout the construction phase for Hybrid Models 1 and 2, respectively; the equipment use and transportation impactstogether only represent 6–10% of the total emission through the construction phase. DOI: 10.1061/(ASCE)CO.1943-7862.0000349.© 2011American Society of Civil Engineers.

CE Database subject headings: Sustainable development; Emissions; Life cycles; Construction management; Highways and roads;Pavement management; Case studies.

Author keywords: Sustainability; Life-cycle assessment; Construction management; GHG; Operations.

Introduction

The challenge posed by global climate change is motivating stateand local transportation agencies to investigate strategies that re-duce the life-cycle greenhouse gas (GHG) emissions associatedwith construction and rehabilitation of highway infrastructure(Santero and Horvath 2009). This is forcing state agencies to con-sider ways of estimating and eventually reducing GHG associatedwith different pavement designs by using life-cycle approaches thataccount for material acquisition and manufacturing, construction,and usage phases. However, given the uncertain and nonprototyp-ical nature of pavement construction processes and site conditions,it is challenging to arrive at estimates for the construction phase thatcan be reliably used to discriminate between alternative pavementdesigns and/or materials. At the same time, there is an unmistakable

need for a method that can be used by decision makers to accuratelymeasure and monitor the GHG for different highway constructionprojects. A new method would help agencies improve constructionprocesses and consider policies that limit GHG and environmentalimpacts.

Therefore, the need is to collect and organize construction andhistorical performance data of pavements and analyze them by dif-ferent life-cycle stages, construction, and maintenance operations.The collection process must account for local and regional vari-ables that influence pavement construction processes, long-termperformance, and maintenance needs. The next challenge is toorganize the data so that accurate construction inventories (materi-als installed, equipment used in construction, and maintenanceoperations) can be established, and GHG can be calculated byusing existing life-cycle impact assessment metrics and methods(Hendrickson et al. 1998; Cicas et al. 2007; Zhang et al. 2006;Consultants PRé 2009; NREL 2009).

The objective of this paper is to describe a method to collect,organize, and analyze highway construction data, integrating themacross life-cycle stages for the calculation of GHG associated withthe materials acquisition, production, and construction life-cyclephases of a highway control section. The goal of this research isto develop general methods that will enable state and local agenciesto support decision making by using actual construction site datapertinent to local highway infrastructure. The method can be ap-plied to all or some representative control sections throughoutthe life cycle. Construction projects and highway maintenance

1Graduate Research Assistant, Dept. of Civil and Environmental Engi-neering, Michigan Technological Univ., 1400 Townsend Dr., Houghton, MI49931. E-mail: djcass@mtu.edu

2Assistant Professor, Dept. of Civil and Environmental Engineering,Michigan Technological Univ., 1400 Townsend Dr., Houghton, MI 49931(corresponding author). E-mail: amlan@mtu.edu

Note. This manuscript was submitted on August 5, 2010; approved onJanuary 6, 2011; published online on January 8, 2011. Discussion periodopen until April 1, 2012; separate discussions must be submitted for indi-vidual papers. This paper is part of the Journal of Construction Engineer-ing and Management, Vol. 137, No. 11, November 1, 2011. ©ASCE,ISSN 0733-9364/2011/11-1015–1025/$25.00.

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programs that apply the proposed method can monitor and controlGHG by identifying and addressing high-impact material and con-struction processes. This research is significant because it willenable decision makers to ask and answer questions that are criticalto identifying ways of improving construction operations, proc-esses, and design selection methods that reduce long-term emis-sions and environmental impacts. In the long run, this research willcontribute to improving methods that provide a basis for the imple-mentation of policies that monitor and mitigate GHG emissions.

Background

Significant advances have been made in life-cycle assessment(LCA) methodology that have extended its application to pave-ments. Application of these methods have allowed the quantifica-tion of GHG emissions, material wastage, and energy usage ofhighway construction materials and alternative pavement designs(Hendrickson et al. 1998; Cicas et al. 2007; Bilec et al. 2006; Zhanget al. 2006; Consultants PRé 2009; NREL 2009). These methodshave also been applied to infrastructure systems, such as bridges(Keoleian et al. 2005; Kendall et al. 2008). Construction equipmentemissions have also been partially addressed (Zammataro 2010;Kendall et al. 2008). However, these methods emphasize prescrip-tive approaches that present general conclusions regarding thecomparative impacts and performance of alternative pavementdesigns and pavement materials (Muga et al. 2009; Gambateseand Rajendran 2005; Zapata and Gambatese 2005; Horvath andHendrickson 1998) on the basis of estimated inventories and/orcase studies.

These problems have their roots in the application of traditionalLCA methodologies that are typically product driven. Pavements,on the other hand, can not be easily defined as products. Consider,for example, the application of LCA methods to differentiate be-tween a plastic cup and a paper cup. The products are comparableas they have similar usage, and are not significantly affected bythe context in which they are used. Most importantly, the identityof the product and the functional unit for comparison does notchange during the course of its lifetime.

In practice, the assumption of a pavement section being awell-defined product with a standard function unit is difficult toenforce. Unlike well-defined products, pavement control sectionsconstructed at the same time rarely receive the same treatmentsthrough the entire life cycle. Often, different lengths perform differ-ently as a result of regional usage and environmental conditions andare reconstructed at different times. Hence, a lane mile cannot beused as a standard functional unit. Neither can different designs beeasily compared without leading to inconsistent results. Indeed, itbecomes difficult, if not impossible, to treat classes of pavements asproducts that can be compared.

In response, pavement LCA studies have relied on explicit as-sumptions in the selection of design control sections and implicitassumptions regarding uniform climate conditions, usage patterns,and environmental contexts (such as availability of local water re-sources). Regional and local variations are rarely codified in suchstudies, and the emphasis is on using estimated material inventoriesand the assumption of uniform conditions. In addition, there is lim-ited consideration of construction process information, such as thetype of equipment used, and the impact of site location and layout,when considering the total life-cycle emissions. As a result, variousinconsistencies have arisen in the literature, as reported in a recentreview of pavement LCAs (Santero et al. 2010) as follows: incon-sistent functional units, improper system boundaries, imbalanced

data for asphalt and cement, use of limited inventory, impact assess-ment categories, and poor overall utility.

Efforts at developing decision-support frameworks, to informagency and stakeholder decisions, also remain fragmented.Prescriptive LCA frameworks have been developed to support de-cisionmaking between broad pavement classes (Guggemos andHorvath 2006; Horvath 2004). However, the assumptions underly-ing such frameworks often make them unsuitable for supportingpolicies that aim to reduce long-term GHG. They often lead toinaccurate generalizations that cannot be used to support context-sensitive policy. In addition, they leave limited room for monitor-ing, and/or rewarding continuous improvement in constructionplanning processes aimed at reducing GHG. Subjective point-basedsystems, such as Green Roads (Soderlund 2006) have been consid-ered for reducing construction emissions. While such systems areeasier to implement, they lack appropriate verification. These lim-itations are reflected in the Portland Concrete Association (PCA)report (Santero et al. 2010), which finds that the existing body ofwork exhibits methodological deficiencies and incompatibilitiesthat serve as barriers to the widespread utilization of LCA by pave-ment engineers and policymakers.

In view of the limitations of using LCA methods in comparingpavements as products, we propose a shift to a context-sensitive,process-based approach in calculating GHG of highway construc-tion processes. While LCA methods are unsuitable for comparingalternative designs as products, they can be legitimately used tocalculate GHG for particular projects. Therefore, we present a hy-brid LCA approach to calculate GHG of highway constructionprocesses and illustrate its implementation with a case study. Theapproach takes advantage of existing methods of calculating GHGwhile emphasizing the collection of data through the constructionphase of the pavement life cycle. It clearly outlines all the differentcategories of data that must be collected from the site to developmeaningful and accurate construction inventories. Future work willconsider the data collection and analysis process for the use phaseof pavements.

Hybrid Life Cycle Assessment

The prevailing approach to studying the environmental effectsof products or processes is to systematically account for the differ-ent stages through the life cycle, consisting of the materials extrac-tion phase, manufacturing/production stage, the use phase, andthe ultimate end-of-life/disposal phase. In doing so, a life-cycleenvironmental impact assessment must be carried out. This is doneby accounting for all inputs and outputs into a product or processand directly calculating the environmental impacts of each todetermine the total life-cycle environmental impacts of the productor process. Currently, this assessment is carried out in two ways,an input-output-based LCA, and a process-based LCA. Economicinput-output-based LCAs are based on economic transactions andresource interactions between an exhaustive set of economic sec-tors. The system-wide use of resources, as measured by economicinput and output across all related sectors, is used as an indicator ofemissions from industries in that sector. Input-output models iden-tify emissions that are immediately related to the product or processat hand and emissions from related economic activity across sec-tors. Process-based LCA practitioners, on the other hand, isolateprocesses using well-defined system boundaries and calculate di-rect emissions of all activities within the defined boundary. Thepractitioner itemizes the inputs (materials and energy) along withthe outputs (emissions) from each step in the product or process lifecycle, therefore controlling the inputs and outputs of the system.

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The advantages and disadvantages of these two methods are out-lined in a previous work (Hendrickson et al. 2006).

In our research effort, we use a hybrid LCA. Hybrid LCAs havebeen previously considered for application to construction proc-esses (Bilec et al. 2006). We use the structure of a process LCAto define the system boundaries of a construction process and toidentify and inventory the associated resource (materials and equip-ment) inputs and emission outputs. In addition, we include all traveldistances and transportation impacts to and from the site. In order toestimate the GHG for all the materials and equipment inputs, aninput-output and/or process LCA tool and an emission calculatorare used, respectively. In effect, we use two hybrid LCA modelsthat represent the environmental impacts of the construction proj-ects. In each model, the greenhouse gases are quantified as a func-tion of the construction operations and material/fuel usage. Eachhybrid model uses a combination of three analysis tools to estimatethe results. These tools include an input/output-based and process-based life cycle assessment model along with an emission calcu-lator. We discuss each of these tools in the following sections. Fig. 1conceptually represents the schema for this hybrid LCA.

SimaPro 7

SimaPro 7 is a process-based LCA software tool that assesses theenvironmental impact of certain products and/or processes. It canbe used to model the environmental impacts of all the life-cyclephases of a product. The results are determined using the relevantenvironmental/sustainability indicators. In this case, metric tons ofcarbon dioxide equivalents have been used.

For this study, SimaPro 7 was used to estimate• Material extraction/manufacturing impact and• Fuel production impact.

The materials were entered in terms of mass or volume, and theassociated emissions data were taken from ECO-Invent database.

Process LCAs such as SimaPro 7 can be very useful in accu-rately estimating emissions for specific processes, as long as allcomponents inside the system boundary can be identified and mea-sured. However, multiple interactions relating the chain of specificprocesses that comprise the material extraction and productionphase are difficult to account for. For example, the transportationimpacts from raw material extraction sites to the manufacturing/production facility are difficult to estimate using SimaPro 7.

Sector-wide input-output LCAs are better suited for estimatingaverage emissions associated with such systemwide interactions.

Economic Input-Output Life-Cycle Assessment

The Economic Input-Output Life-Cycle Assessment (EIO-LCA) isan analysis model that defines the scope and number of environ-mental effects quantified in an assessment. Developed at CarnegieMellon University (Hendrickson et al. 1998; Cicas et al. 2007), itestimates the economic contribution, resource requirements, andenvironmental emissions for a particular product, service, or activ-ity. For this study, EIO-LCAwas used to account for manufacturingof the materials used in each project, along with the manufacturingimpacts of the fuel and equipment to be used in the constructionproject. The usefulness of the EIO-LCA model is dependent on theaccuracy of the material and equipment inventories developed foreach pavement design and construction operation type. In addition,the outputs are reliant on the economic input of the identifiedmaterials and equipment in U.S. dollars and are based on the 1997U.S. economy. Average cost for each material or item varies byregion, and the costs reported in the contracts are agency costs (costto the DOT rather than cost of material production), which are inap-plicable to EIO-LCA studies. Therefore, material prices must beisolated from agency costs. It is important to use material prices(rather than estimated cost to the agency) that were reflected inthe project to obtain the most accurate results in EIO-LCA. Thiscan be used to investigate the impact of variability in pricing asa result of the availability of regional materials on life-cycle emis-sions. For this study, material prices were obtained through RSMeans data and then converted to 1997 dollars by using applicablecost indexes.

For this study EIO-LCA was used to estimate• Equipment manufacturing impact,• Material extraction/manufacturing impact, and• Fuel production impact.

e-CALC

To aid in quantifying the emissions of the construction equip-ment, an emission calculator tool such as e-CALC can be used(Sihabuddin and Ariaratnam 2009). The tool was developed in MSExcel using Visual Basic. The calculator uses the EnvironmentalProtection Agency (USEPA) approved formulas and test data toestimate emissions from each project. The input data requiredfor the calculation can be obtained from the equipment inventoryin the database. For this study, e-CALC was used to estimate• Equipment use impact and• To-site transportation impact.

The equipment inputs used in this tool need to be concise andcontain information such as make, model, year, rated hp, useful lifehours, cumulative hours used, and percentage power used. Haulingequipment information like gross vehicular weight (GVW) andmileage are needed. Whereas inspectors cooperated in collectingfield equipment usage information, they had limited motivation tomaintain all the equipment information needed for the study.Hence, for this study, the following assumptions were made to re-present the equipment, whenever the recorded information wasincomplete:• The power used was assumed to be 50%, considering average

and moderate working conditions as outlined in the CaterpillarPerformance Handbook (Caterpillar 1999).

• When useful life hours of equipment was not listed in theCaterpillar Performance Handbook, the useful life was as-sumed to be 10,000 h.

• The hours accumulated on the equipment used was consideredto be half the life of the equipment.

Fig. 1. Hybrid LCA schema

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• All construction equipment was considered to be fairly new,although this may not be the case; recent reports show that olderequipment emits significantly more emissions (Guggemos andHorvath 2006).

• For the hauling equipment, GVW was considered to be in therange of 33,001–60,000 lbs.

• The useful life associated with the diesel engines was consid-ered to be 1,000,000 miles, of which the current mileage washalf the useful life.

• In cases where equipment had no hourly usage assigned to it, adefault value was used.In future work, the sensitivity of the calculated emissions will be

investigated by relaxing the assumptions.Using these three systems, two hybrid LCA approaches were

developed. The first hybrid model (Hybrid Model 1) uses thefollowing tools to measure the associated components: (1) anEIO-LCA tool to estimate equipment manufacturing impact;(2) SimaPro 7, a process LCA tool, to measure material acquisitionand production and fuel production; and (3) e-Calc, to measure sta-tionary construction equipment use, on-site transportation impacts,and to-site transportation impacts.

The second hybrid model (Hybrid Model 2) uses the followingtools to measure the associated components: (1) EIO-LCA tool toestimate equipment manufacturing impact, material acquisition andproduction, and fuel production; (2) the emission calculator toolto estimate stationary construction equipment use, on-site transpor-tation impacts, and to-site transportation impacts.

Together, these models are used to represent the life-cycleimpacts through the construction phase of the pavement section.Fig. 2 illustrates how they translate to a data collection and analysisworkflow involving different stakeholders.

System Boundary

It is important, when investigating the impacts with two differentmodels, that the same system boundary should be considered ineach of the models. For this study, we have attempted to usethe same system boundary for the chosen projects in both LCAmodels, as illustrated in Fig. 3. Each LCA model will then reflectthe environmental impacts from the following specific activitiesthat are included within the system boundary:• Equipment manufacturing,• Material extraction/manufacturing,• Fuel production,• Stationary construction equipment use,• To-site transportation, and• On-site transportation.

Functional Unit

The functional unit used for the study is per lane mile of carbondioxide equivalent. The total emissions from each project willbe quantified with an LCA tool and an emission calculator and thendivided by each project’s total lane miles to represent emissions ona per-lane-mile basis. However, because of the context-sensitivenature of the proposed method, other functional units can be intro-duced. These can include, but are not limited to, emissions peroperation/activity, emissions per project type, emission per unitmaterial, and emissions per pay item. This is possible becausethe proposed method allows the measurement of emissions asso-ciated with each construction project, rather than estimating emis-sions for classes of pavement types. Impacts are observed andmeasured at operational and project administration levels, whichin turn affords the use of diverse functional units.

Fig. 2. Impact assessment process diagrams

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Case Study

The research presented in this report is within the context of apavement rehabilitation and reconstruction project in the state ofMichigan. A description of the project that was used in this studyis as follows:• A concrete pavement rehabilitation project, approximately

seven miles long, on a major interstate in southwest Michigan.The study focused primarily on concrete overlay rehabilitation,pavement removal, concrete pavement reconstruction, and pave-ment markings. At the time of data collection for this study, theproject was approximately 50% complete (the southbound sec-tion was complete, and rehabilitation had just begun on thenorthbound section);

• The pavement section itself originally consisted of jointed plainconcrete and is classified as a principal arterial interstate high-way with a four-lane surface where northbound and southboundsurfaces are 24 feet wide, the posted speed is 70 mph, and theaverage annual daily traffic (AADT) is 17,900.In this section, we focus on the method used to collect the con-

struction data directly from the site while the construction processwas ongoing. The long-term goal of this research is to provide stateagencies with recommendations on making this data collectionprocess compatible with existing reporting practices for all projects.This will allow continuous monitoring of material and fuel con-sumption for highway construction and maintenance projects.

Pavement Construction Data

Construction data collection led to the development of materialand equipment inventories used during the construction and reha-bilitation process. New construction, reconstruction, and differentmaintenance operations were considered. The primary challengein collecting this data was eliciting cooperation and collaborationfrom project engineers, contractors, and subcontractors on site.Given their existing responsibilities, there was a natural reluctanceon their behalf. Hence, it was imperative to take advantage ofexisting reporting methods without increasing the burden of report-ing. In addition, data was collected through direct observation byresearchers.

Data Collection Using FieldManager SoftwareConstruction management software is needed to develop theequipment and material inventories associated with each project.FieldManager was chosen for this research because the MichiganDepartment of Transportation (MDOT) already tracks and moni-tors all its construction and rehabilitation contracts status usingthis software. FieldManager, created by InfoTech, Inc. (2009), is aMicrosoft Windows–based interface designed for use by statetransportation agencies, local governments, engineering consul-tants, and large contractors. Inspectors (on behalf of MDOT) use

FieldManager to record, on a daily basis, information regardinggeneral site conditions, contractor personnel, equipment on site,and quantities of different material installed on site. This informa-tion is reported and archived in FieldManager through the inspec-tor’s daily report (IDR). Inspectors are already trained in usingFieldManager. Hence, this method takes advantage of current fieldexpertise and existing reporting practices to develop a more exhaus-tive data collection procedure. Fig. 4 illustrates the format of anIDR (note that the information is fictional). The IDRs were directlycollected from the FieldManager database. The following fields ofthe IDR were used to accurately account for the materials andequipment usage for each of the projects surveyed.

Contractor Personnel and EquipmentThe contractor personnel and equipment inputs of the IDR are criti-cal to quantifying project emissions. Recent studies have shownthat energy use and emissions of construction processes are pri-marily the result of construction equipment use, which can accountfor 50% of most types of emissions. Also, equipment larger than175 hp made prior to 1996 tends to have greater emissions thanmore recent models (Guggemos and Horvath 2006). Type and qual-ity of construction equipment used on site significantly affects aproject’s total emissions.

In taking full advantage of fields specified in the IDR, inspectorswere required to record equipment usage, identifying what equip-ment was working on site, how long the equipment worked, and theoperation the equipment was performing. The number of trips wasdetermined from the total amount of material placed on site andthe capacity of the hauling equipment. Environmental emissionsfor equipment could be calculated using its fuel combustion rates,characteristics, and length of usage period (further discussion in asubsequent section).

Information collected though FieldManager was supplementedwith information collected in collaboration with contractors. Thisincluded information regarding equipment specifics needed to cal-culate equipment emissions, such as the equipment model, year,make, type of fuel used (sulfur content), and engine type. In somecases, contractors were already tracking their equipment usage tomonitor efficiency and were willing to share the information. Insuch cases, information collected from the contractor was usedto validate the same information recorded in the FieldManagerIDR to check for human error or misreporting. This highlightsthe need for fostering collaboration between agencies and contrac-tors in exhaustive data collection through the implementation ofstandardized reporting procedures.

Inspectors also recorded the contractor personnel working onsite, including information describing the crews and how long theyworked on a particular day. Though this information was not usedfor calculating GHG, it is relevant to studying socioeconomicimpacts of a project on workforce and labor.

Material PostingMaterial used on the construction site was recorded with the IDR,by tracking progress made on each pay item as specified in the con-struction contract. The location, station information, and quantitiesof materials associated with each item installed were stored. Indoing so, an as-built record of procured and installed materials wasmaintained. Actual productivity and schedule of the constructionproject could also be monitored. Once the project is completedand the total amount of materials has been billed, the material was-tage or excess usage can be calculated by comparing the as-planneditems in the bid-tab to the actual installed materials.

Considering as-built quantities in the calculation of life-cycleimpacts and emissions is significantly more representative of proj-ect impacts compared to similar calculations done with estimated

Fig. 3. System boundary

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quantities. Data collected across similar and different construc-tion projects can be analyzed by classifying across pavementdesigns, construction operations, and site-specific conditions tohighlight sensitivity of impacts and emissions to local and regionalvariables.

On-Site ObservationsAs with any project, to accurately calculate the emissions, it wasnecessary for the researchers to perform actual site visits in order todetermine the following:• Location of plant site, material stockpiles, water source, and

disposal site to determine haul distances associated with trans-portation/hauling equipment (Fig. 5, shows the important pointsfor the case study project; the point of beginning and the pointof ending of the project are referred to as POB and POE,respectively);

• Fuel usage data from the contractor to compare with fuelconsumption rates of equipment; and

• Equipment checks to ensure that equipment posted in IDRmatches equipment actually being used on site.

All these items collected validate and support the data collectedthrough FieldManager and through direct reports from contractors.They also allowed researchers to subjectively evaluate the construc-tion site.

Data Analysis and Results

The next step in this study was to investigate the environmentalimpacts of the projects throughout the construction phase. In doingso, a partial LCA is conducted using LCA tools and an emissioncalculator. The impact assessment accounts for the environmentalimpacts associated with transportation and manufacturing of thematerials used in each project, along with the manufacturing im-pacts of the fuel and equipment used. This is done using a hybridLCA approach that accounts for the life cycle impacts; thus far, forall the materials and the equipment that define the constructionprocess being considered. Depending on the nature of the materialand/or equipment, a combination of life-cycle assessment tools can

Fig. 4. A sample inspector’s daily report

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be used. In the next section the hybrid LCA models previouslydiscussed are applied.

Material and Equipment Inventory Analysis

In creating an inventory of the total materials used for each project,a report was generated from the FieldManager database containingthe material description, the total sum of material placed to date,and the units associated with each material. For the purpose of thisstudy, only the driving materials placed were considered in theanalysis. These materials account for the principal materials defin-ing the project items. Table 1 shows the driving materials used inthe analysis of the case study project, classified by EIO-LCA sectorand unit price.

A challenge in developing the inventory used in SimaPro was toconvert the pay items to masses or volumes that can be used inthe LCA tool. DOT pay items are usually expressed in squareor linear feet. However, SimaPro requires these items to be in massor volumes. To convert these items, estimated densities were used(Reade Advanced Materials 2009). To calculate the amount of fuelused in the equipment, we analyzed a sample set of equipment us-age information and fuel consumption rates from an anonymouscontractor to obtain the estimated average fuel consumption rates.By using the average consumption rates and equipment usagetimes, we determined the total gallons of fuel used. The gallonsof fuel were then converted to megajoules to be entered intoSimaPro (Oak Ridge National Labs 2009).

A similar report was generated to determine the respectivematerial inventory to use in the EIO-LCA model. This report con-tained information regarding the description, the total sum ofmaterial placed to date, and the units associated with each material.

These materials were assessed a price using RS Means con-struction cost data; the prices were then converted to 1997 dollarsby using applicable cost indexes. (The prices were converted to1997 dollars, and the 1997 U.S. economy model was used becauseof the unavailability of the 2002 model at the time of the analysis.)The total material prices were allocated to economic sectors ofEIO-LCA as outlined in Table 1, and the total emissions causedby the material inventory could be determined.

In acquiring an inventory to represent the equipment being usedon the construction site, a report was generated containing thedescription of all equipment posted by the inspector in each day’sIDR along with the total quantity and hours associated with eachdescription. The construction equipment was generalized into 19different basic highway road construction equipment models.

After all equipment was categorized by type (i.e., dozer, exca-vator, etc.), engine tier, total quantity, and total number of hoursused were determined. The model year of the equipment used inthe analysis was assumed to be 2008. The chosen model yearaffects the tier number of the equipment, as represented in Table 2.Engine tier requirements greatly affect the equipment emissions.Tier requirements refer to emission standards mandated by theEPA for certain engine types. Beginning with Tier 1 standardsadopted in 1994, emission standards have become increasinglystringent. Therefore, equipment classified under higher tier num-bers meet stricter emissions standards. The models used and theirtotals are highlighted in Table 2.

On-Site Transportation Impacts

We calculated the travel distances and number of trips for thehauling equipment by using the site-specific location data that was

Fig. 5. Travel distances: (a) the material supply ring showing all locations from which materials are transported; (b) site location points (map datafrom Google Maps, © 2011, Google, Inc.)

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directly observed from the site. The number of trips was determinedfrom the total amount of material placed on site and the capacity ofthe hauling equipment. Hot mix asphalt (HMA) hauling truckswere assumed to have a hauling capacity of 28 tons of HMA (usedfor the HMA interlayer specific to this project), and concrete haul-ing trucks had a hauling capacity of 10 cubic yards of concrete. Forimpacts from hauling the concrete or HMA to the location of place-ment, the travel distances were calculated from the known batchplant locations to the points in the project where material was beingplaced. This is strictly a function of the site design and logistics.

In this project, the concrete batch plant was placed at the POB ofthe length to be paved, and trucks hauled the concrete back and

forth to the points at which it was placed. Assuming that therewas only one truck in the placement operation, the length of eachtruck trip was incremented by the distance that was paved by thevolume of concrete carried in the truck. The calculation formulatesto an arithmetic progression as follows:

D ¼ x × n × ðnþ 1Þ5280

ð1Þ

in which D = distance traveled on site in miles; x = distance pavedper truck trip in feet; and n = total number of truck trips. Theassumption of using a single truck to calculate the number of trucktrips is entirely reasonable, as we are not concerned about theduration of the operation and are only interested in the distancetraveled. For the project illustrated in this case study, x resulted in16.5 ft of concrete placed per truck (determined from truck capacityand pavement design), the number of truck trips was determined tobe 2,182 on the basis of the length of project and distance per truck.Therefore, by using the above equation, the total distance traveledfrom hauling trucks on site to and from the batch plant was deter-mined to be 14,885.3 miles. Considering that the project was 50%complete, the distance traveled was 7,442.67 miles. This distancewas represented in the emission calculator.

The sulfur content of the fuel also plays an important role inemissions from the equipment. As reported by the EPA, the sulfurcontent of the construction equipment was found to be 0.005%and the sulfur content of the on-highway equipment was found tobe 0.0015% (DieselNet 2009).

The emission calculator e-CALC was applied to the transporta-tion equipment (trucks) usage data along with the informationprovided in the IDRs to calculate the total emissions of the con-struction and hauling equipment being used on each project.

To-Site Transportation Impacts

The next step was to account for the distances traveled in transport-ing the raw materials to the concrete batch plant on site or to theHMA plant (which was off site in this case). These distances wereused to estimate the transportation emissions from the haulingvehicles supplying the site with materials. Also included are theemissions from hauling equipment traveling to and from materialstockpiles and pits to provide the materials that make up the con-crete and bases. Source locations for HMA aggregates wereunavailable, though the source of the binder was known. The im-pacts from transportation of HMA and concrete from the batchplant to the placement site are considered in the on-site equipmentimpacts of this study.

We determined the travel distances from the suppliers to site,from stockpiles to site, and from stockpiles/suppliers to batchplants. This was done using information available in the projecttesting orders, which were directly accessed from DOT offices.The testing orders provided addresses of material suppliers, alongwith limited descriptions of material stockpiles. Locations of thesestockpiles were also obtained through correspondence with thecontractors. As an example, Table 3 shows the materials consid-ered, their respective distances, and the number of trips from theproject site or batch plant. The number of trips was determinedfrom the total quantity of material posted in FieldManager, mixdesigns, testing orders, and assumed hauling capacity.

The concrete materials originated from the stockpile pits SweetPit and MDOT Pit. For the material suppliers, we assumed thatthere was one trip from the supplier to site. For example, onlyone trip was needed to deliver all the geotextiles from the geotextilesupplier. Once the distances were quantified, they were entered intothe emission calculator to assess the emissions from these impacts.

Table 1. Driving Materials

Material description Quantity Unit Unit price

EIO-LCA sector 331111 iron and steel mills and manufacturing

Lane ties epoxy coated 20586.0 Ea 2.96

Steel reinf. epoxy coated 7323.2 Lbs 0.58

Load transfer assy. 78794.9 Ft 5.39

Steel reinf. pavement mesh 386.0 Syd 1.15

Steel reinforcement 146.0 Lbs 0.45

Dowel bar epoxy coated 5398.7 Ea 2.15

Drainage structure cover 2460.0 Lbs 0.85

Fence woven wire 32704.0 Ft 2.87

EIO-LCA sector 212321 construction sand and gravel mining

Aggregate 23A 8200.7 Ton 19.55

Aggregate 22A 4310.9 Ton 19.55

Granular material Cl II 25645.8 Cyd 6.25

Granular material Cl III 6287.9 Cyd 6.25

Aggregate 4G 38429.7 Ton 21.00

Course agg. for concrete 17304.8 Cyd 21.00

Fine agg for concrete 10193.7 Cyd 19.25

EIO-LCA sector 313210 broadwoven fabric mills

Geotextile liner 594.7 Syd 2.17

Geotextile blanket 597.6 Syd 1.54

EIO-LCA sector 325211 plastics material and resin manufacturing

Joint sealer hot poured rubber 109383.8 LF 0.26

Joint filler fiber 72.4 Syd 15.84

EIO-LCA sector 32612 plastics pipe and pipe fitting manufacturing

Pipe nonperforated underdrain 9575.0 Ft 3.30

Pipe underdrain 118184.7 Ft 3.30

EIO-LCA sector 327310 cement manufacturing

Cement 71.2 Ton 226.60

Cement for concrete 5557.3 Cyd 377.43

EIO-LCA sector 324121 asphalt paving mixture and block manufacturing

HMA seperator layer 48213.2 Ton 57.64

EIO-LCA sector 325998 chemical product manufacturing

Curing compound 10461.7 Gal 18.30

EIO-LCA sector 325510 paint and coating manufacturing

Paint 165980.0 LF 0.25

EIO-LCA sector 327211 glass product manufacturing

Glass beads 165980.0 LF 0.01

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For example, from Table 3, we calculated that 2,900 truck tripswere needed to haul base material from the Warren Pit, andthe distance from the Warren Pit to the jobsite was 25.7 miles(information obtained from Google maps). Therefore, the total dis-tance represented in the emission calculator to find correspondingemissions was found to be 74,530 miles (¼ 2900 × 25:7). Theemissions of to-site transportation are shown in Table 4. Note thatthere is an order-of-magnitude difference between carbon emis-sions and all other gases. When converted to carbon equivalents,

the contribution from the other gases is minimal. The emissionscan be converted to CO2 equivalents using global warming poten-tial (GWP) values reported by the EPA (EPA 2010) When con-verting N2O to CO2, the GWP value (multiplier) is 310. Whenconverting CH4 to CO2, the GWP value is 21. Henceforth, allGHG are reported in carbon dioxide equivalents.

Equipment Manufacturing Impact

To finalize the greenhouse gas emission inventory, it was necessaryto consider the manufacturing impacts of construction equipmentitself. EIO-LCA was used to perform this analysis. In determiningthe equipment manufacturing impact, the equipment manufacturingsector of the 2002 U.S. economy was analyzed by using the EIO-LCA model. First, the purchasing price of generalized constructionequipment being used on the project was determined. Equipmentcosts were obtained from equipment vendors’ websites. Once theprice for the equipment representing the projects was determined;those prices were then converted to 2002 prices by using thefollowing formula:

EC2002 ¼ EC2009 ×ð1þ rÞnð1þ iÞn ð2Þ

in which EC = equipment cost; n ¼ 6 years; r = discount rate(assumed to be 5%); and i = inflation rate (assumed to be 3%).

The total impact for producing the machinery was then deter-mined. By using this information, the impacts were broken downfor each individual piece of machinery, and then broken down fur-ther by applying the portion of the machinery’s life that was

Table 2. Equipment Descriptions

Stationary equipment specifics

Name Make Model Year HP Tier Hours used

Utility tractor John Deere 6100D 2008 100 1 144

Skid steer CAT 299C 2008 90 3 627

Excavator CAT 345D L 2008 380 3 1230

Dozer CAT D6T 2008 200 3 1892

Front end loader CAT 980H 2008 349 3 1102

Grader CAT 140M 2008 183 3 494

Broom Broce Broom RCT350 2008 85 3 156

Rollera CAT CB-534D 2008 125 2 1562

Scraper CAT 623G 2008 330 3 258

Backhoe CAT 450E 2008 124 3 390

Cold planer CAT PM-201 2008 650 3 164

Road reclaimer CAT RM-300 2008 350 3 NA

Paver CAT AP-1000D 2008 224 3 552

Road widener Weiler W530 2008 114 3 NA

MTV Roadtec MTV-1500D 2008 300 3 NA

Trencher Vermeer T455 2008 125 1 120

Conc. paver G&Z S850 2008 275 3 400

Texture cure machine G&Z TC800 2008 85 3 400

Off-road hauling truck CAT 740 2008 453 3 10aCompares to PS-360C tire roller.

Table 3. Hauling Distances

Material description One-way dist. (miles) Trips

To concrete batch plant—On-site

Fly ash 338 1

Cement 46.5 1

Admixtures 141 1

Sweet pit (2NS) 14.2 2472

24 Pine Ave. (MDOT Pit) (6A) 26.3 3557

To HMA plant—Off-site (see Fig. 5)

Binder 165 1

To site from suppliers

Joint filler/sealer 183 1

Lane ties 303 1

Dowel bars/transfer assy. 94.4 1

Sewers/culverts 48.5 1

Drainage structures 42.9 1

Dr. str. covers 51 1

Geotextiles 50.9 1

Underdrains 54 1

Aggregate: Warren Pit (4G) 25.7 2900

Aggregate: Debest Pit (granular material) 10.2 7330

Table 4. To-Site Emissions

HC (lbs) CO (lbs) NOx (lbs) PM (lbs) CO2 (Tons) SOx (lbs)

31.4 tons of CO2 equiv./lane mile

744.96 4326.2 7698.59 18.3 960.9 17.7

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actually used in the project. This was done by amortizing theimpact of manufacturing the equipment over all the projects forwhich it is used. For example, if the expected life of a piece ofequipment is 10,000 h, and the number of usage hours on a par-ticular project is 1,200 h, then only 3/25 of the manufacturingimpact of that equipment is considered for the project.

The data collected from direct site observation of the case studywas applied to the each of the hybrid models. The estimated carbonequivalents per lane mile per phase is illustrated in Fig. 6. The keyfindings were as follows: (1) total CO2 emissions were 785.49 and1,381.58MT per lane mile for Hybrid Models 1 and 2, respectively;(2) the production of the materials, equipment, and fuel used toconstruct the project account for 90% and 94% of the total CO2emissions throughout the construction phase for Hybrid Models1 and 2, respectively; and (3) the equipment use and transportationimpacts together only represent 6–10% of the total emissionthrough the construction phase.

The main difference between the two hybrid models is thatemissions associated with material production and fuel productionphases were estimated using both the process LCA tool and theEIO-LCA tool. The EIO-LCA tool gives a higher estimate of emis-sions. This is expected, as the EIO-LCA tool is likely to be moreinclusive of sectorwide emissions than the process LCA tool. As aresult, the proposed hybrid models have different scopes. WhileHybrid Model 1 reflects sectorwide interactions on the basis ofnationally averaged economic input-output data, Hybrid Model2 accounts for emissions from isolated processes by using industrydata. Therefore, they are best used as supplementary information tosupport strategic and operational decisionmaking and improvepractice. In the long run, agencies can use such metrics to incen-tivize contractors to devise construction technologies and use inno-vative materials to reduce construction emissions. The emphasisand contribution of this research is the simplicity of the method,which, if widely used, could help further the identification ofleverage points and the influence of regional factors in reducingpavement GHG emissions.

Conclusion

In conclusion, this paper presents a hybrid LCA approach to cal-culating GHG for highway construction operations from directly

observed site-specific data. In this paper, we introduced a detaileddata collection and analysis method for estimating all resource in-puts to the construction process and all related emission outputs.The method is fundamentally very simple and does not rely onany technology that is not already available on the field. Mostimportantly, it directly involves contractors and field inspectorsin the data collection process without significantly increasing theirreporting burden. This will allow easy implementation by statehighway administrations.

The introduced method also emphasizes the measurement andconsideration of the influence of site-specific influences, a criticalcomponent of construction GHG. Contractors and state agenciescan use this method to monitor emissions for projects while theyare being constructed. This will enable and support policies thatwill incentivize contractors to innovate their processes to reduceemissions. In addition, it will help agencies compare the perfor-mance of different construction operations in the long run to drawstatistically significant conclusions, if any, regarding factors thatincrease or reduce construction emissions.

The current research effort is documenting emissions frommultiple projects in the state of Michigan. Future publications willaddress the sensitivity of construction emissions to site layout andthe role of construction schedules and operation design in reducingconstruction emissions.

Acknowledgments

The writers would like to acknowledge the Michigan Departmentof Transportation for its support in conducting this research. Thewriters would also like to thank the Michigan Tech TransportationInstitute and the Sustainable Futures Institute for their direction andsupport.

This publication is disseminated in the interest of informationexchange. MDOT expressly disclaims any liability, of any kind,or for any reason, that might otherwise arise out of any use of thispublication or the information or data provided in the publication.MDOT further disclaims any responsibility for typographical errorsor accuracy of the information provided or contained within thisinformation. MDOT makes no warranties or representations what-soever regarding the quality, content, completeness, suitability,adequacy, sequence, accuracy, or timeliness of the information

Fig. 6. Concrete overlay project phase impact diagram

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and data provided, or that the contents represent standards, speci-fications, or regulations.

References

Bilec, M., Ries, R., Matthews, H. S., and Sharrard, A. L. (2006). “Exampleof a hybrid life-cycle assessment of construction processes.”J. Infrastruct. Syst., 12(4), 207–215.

Caterpillar, Inc. (1999). Caterpillar performance handbook, 30th Ed.,Caterpillar, Peoria, IL.

Cicas, G., Hendrickson, C. T., Horvath, A., and Matthews, H. S. (2007).“A regional version of a U.S. economic input-output life-cycle assess-ment model.” Int. J. Life Cycle Assess., 12(6), 365.

Consultants PRé. (2009). “Simapro LCA software.” ⟨http://www.pre.nl/simapro/⟩ (May 1, 2009).

DieselNet. (2009). “Nonroad diesel engines.” ⟨www.dieselnet.com⟩(Oct. 5, 2009).

Gambatese, J., and Rajendran, S. (2005). “Sustainable roadway construc-tion: Energy consumption and material waste generation of roadways.”Proc., Construction Research Congress, ASCE, Reston, VA, 102–108.

Guggemos, A., and Horvath, A. (2006). “Decision-support tool for assess-ing the environmental effects of constructing commercial buildings.”J. Archit. Eng., 12(4), 187–195.

Hendrickson, C. T., Horvath, A., Joshi, S., and Lave, L. B. (1998).“Economic input-output models for environmental life-cycle assess-ment.” Environ. Sci. Technol., 32(7), 184–191.

Hendrickson, C. T., Lave, L. B., and Matthews, H. S. (2006). “Environmen-tal life cycle assessment of goods and services: An input-outputapproach.” Resources for the Future, Washington, DC.

Horvath, A. (2004). “Pavement life-cycle assessment tool for environmen-tal and economic effects (PaLATE).” ⟨http://www.ce.berkeley.edu/horvath/palate.html⟩ (Jun. 10, 2008).

Horvath, A., and Hendrickson, C. (1998). “Comparison of environmentalimplications of asphalt and steel-reinforced concrete pavements.”Transportation Research Record, NRC, No. 1626 (Environmentaland Social Effects of Transportation), 105–113.

Info Tech, Inc. (2009). “FieldManager.” [Computer Software].Kendall, A., Keoleian, G. A., and Helfand, G. (2008). “An integrated life

cycle assessment and life cycle cost analysis model for concrete bridgedeck applications.” J. Infrastruct. Syst., 14(3), 214–222.

Keoleian, G. A., et al. (2005). “Life cycle modeling of concrete bridgedesign: Comparison of engineered cementitious composite link slabsand conventional steel expansion joints.” J. Infrastruct. Syst., 11(1),51–60.

Muga, H., Mukherjee, A., Milhelcic, J. R., and Kueber, M. J. (2009).“An integrated assessment of continuously reinforced and jointed plainconcrete pavements.” J. Eng. Des. Technol., 7(1), 81–98.

National Renewable Energy Laboratory (NREL). (2009). “U.S. life-cycleinventory database.” ⟨http://www.nrel.gov/lci/⟩ (May 1, 2009).

Oak Ridge National Labs. (2009). “Bioenergy feedstock development pro-grams at ORNL: Quick-reference list of conversion factors.” ⟨bioenergy.ornl.gov⟩ (Nov. 21, 2009).

Reade Advanced Materials. (2009). “Weight per cubic foot and specificgravity.” ⟨http://www.reade.com/Particle_Briefings/spec_gra2.html⟩(Nov. 21, 2009).

Santero, N. J., and Horvath, A. (2009). “Global warming potential ofpavements.” Environ. Res. Lett., 4, 034011.

Santero, N., Masanet, E., and Horvath, A. (2010). “Life-cycle assessmentof pavements: A critical review of existing literature and research.”Rep. SN3119a, Portland Cement Association (PCA), Skokie, IL.

Sihabuddin, S., and Ariaratnam, S. T. (2009). “Quantification of carbonfootprint on underground utitility projects.” Proc., 2009 ConstructionResearch Congress, April 5–7, 2009, 618–627.

Soderlund, M. (2006). “Sustainable roadway design—Amodel for an envi-ronmental rating system.” M.S. thesis, Univ. of Washington, Seattle.

U.S. Environmental Protection Agency (EPA). (2010). “Inventory of U.S.greenhouse gas emissions and sinks: 1990–2002.” Report Annex 6, U.S.Environmental Protection Agency, Washington, DC.

Zammataro, S. (2010). “Monitoring and assessing greenhouse gas emis-sions from road construction activities: The IRF GHG calculator.”International Road Federation, Geneva.

Zapata, P., and Gambatese, J. (2005). “Energy consumption of asphaltand reinforced concrete pavement materials and construction.”J. Infrastruct. Syst., 11(1), 9–20.

Zhang, Z., Wu, X., Yang, X., and Zhu, Y. (2006). “Bepas—A life cyclebuilding and environmental performance assessment model.” Build.Environ., 41, 669–675.

JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT © ASCE / NOVEMBER 2011 / 1025

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yrig

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y; a

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