life cycle attribute assessment : case study of quebec greenhouse tomatoes

A P P L I C AT I O N S A N D I M P L E M E N TAT I O N

Life Cycle AttributeAssessmentCase Study of Quebec GreenhouseTomatoes

Evan Andrews, Pascal Lesage, Catherine Benoıt, Julie Parent,Gregory Norris, and Jean-Pierre Reveret

Keywords:

agriculturecorporate social responsibility (CSR)industrial ecologylife cycle assessmentsocial life cycle assessmentsupply chain management

Address correspondence to:Evan AndrewsSylvatica7379 rue St-HubertMontreal, Quebec H2R [email protected]

c© 2009 by Yale UniversityDOI: 10.1111/j.1530-9290.2009.00142.x

Volume 13, Number 4

Summary

Practitioners of life cycle assessment (LCA) have recentlyturned their attention to social issues in the supply chain.The United Nations life cycle initiative’s social LCA task forcehas completed its guidelines for social life cycle assessmentof products, and awareness of managing upstream corporatesocial responsibility (CSR) issues has risen due to the growingpopularity of LCA.

This article explores one approach to assessing social issuesin the supply chain—life cycle attribute assessment (LCAA).The approach was originally proposed by Gregory Norris in2006, and we present here a case study. LCAA builds on thetheoretical structure of environmental LCA to construct asupply chain model. Instead of calculating quantitative impacts,however, it asks the question “What percentage of my supplychain has attribute X?” X may represent a certification froma CSR body or a self-defined attribute, such as “is locally pro-duced.” We believe LCAA may serve as an aid to discussionsof how current and popular CSR indicators may be integratedinto a supply chain model.

The case study demonstrates the structure of LCAA, whichis very similar to that of traditional environmental LCA. A laborhours data set was developed as a satellite matrix to determinenumber of worker hours in a greenhouse tomato supply. Datafrom the Quebec tomato producer were used to analyze howthe company performed on eight sample LCAA indicators,and conclusions were drawn about where the company shouldfocus CSR efforts.

www.blackwellpublishing.com/jie Journal of Industrial Ecology 565


Background, Goal, and Scope

Research into social aspects of product life cy-cles has expanded in the past few years. This is aseemingly natural extension, especially when lifecycle thinking is paired with the prevalent “triplebottom line” view of sustainability as economy,environment, and society (Elkington 1997). Itis a reasonable goal to be aware of social con-ditions in addition to the environmental con-ditions under which our goods and services areproduced.

A description of the state of social life cy-cle assessment (SLCA) can be found in theMarch 2008 issue of the International Journalof Life Cycle Assessment. Kloepffer (2008) isoptimistic that SLCA can deliver actionableanalysis, and Jørgensen and colleagues (2008)describe the numerous methodologies and is-sues that have emerged from SLCA literatureand working groups. Udo de Haes (2008) en-courages continued research on SLCA but alsoexpresses his reservations about life cycle assess-ment’s (LCA’s) ability to accommodate com-plex social indicators. Finally, in a reprintedabstract, Weidema (2006) proposes an impactassessment methodology that determines socialimpacts on the basis of human well-being inquality adjusted life years (QALYs). In late2006, Hunkeler published an article that in-ventoried labor hours and then related this tomidpoint indicators on housing, health, educa-tion, and necessities (Hunkeler 2006). There aremany parallels between the methodology of Hun-keler (2006) and the methodology of life cy-cle attribute assessment (LCAA) discussed inthis article. It is worth exploring how ideasin each of these approaches can inspire eachother.

In May 2006, the United Nations/Societyof Environmental Toxicology and Chemistry(UN/SETAC) Social LCA Taskforce released afeasibility study1 that concluded, “There are evi-dently no fundamental problems calling the fea-sibility of SLCA into question” (Griesshammeret al. 2006, 13). This optimistic statement wascouched in a recognition of the many hurdlesstill facing SLCA (particularly characterizationmodeling) and concluded with a call for nextsteps.

This article attempts to contribute to many ofthe next steps defined in the May 2006 feasibilitystudy.

• Conduct case studies—This article demon-strates a study of greenhouse tomatoes fromQuebec.

• Improve existing databases—To conductthe case study, we constructed a genericlabor-hours data set.

• Connect with indicators in the field of cor-porate social responsibility.

On the last bullet, the feasibility study states,“The connection with indicators in the field ofCSR . . . should be emphasized” (Griessham-mer et al. 2006, 13) This is an important con-sideration, especially as CSR reporting becomesmore commonplace and can provide LCAs withmuch needed data. A distinguishing factor be-tween CSR reporting and LCA is that LCAprobes deep into the supply chain and offers asolid basis for comparison of products. CSR dealswith specific issues at each stage in the supplychain and can help LCA practitioners under-stand important issues to measure. There is greatopportunity for LCA to team with CSR, and bothdisciplines will benefit if LCA can deliver an ap-propriate supply chain model that is compati-ble with well-established CSR indicators. Thesupply chain model must be structured to ac-commodate additive indicators and to encour-age additive forms of already established CSRindicators.

Norris (2006b) proposed a methodology thatsought to accommodate some of the unique char-acteristics of SLCA. LCAA aims to enable local,site-specific evaluation results (e.g., SA8000,2

ISO 14001,3 Fair Trade Certification4 ) to beintegrated into an LCA. Norris (2006a) furtherexpanded LCAA in a November 2006 presenta-tion in which he explored the LCAA-formulatedquestion “What share of the paper in McDon-ald’s Supply Chain comes from forests that areFSC [Forest Stewardship Council] certified (sus-tainable forestry)?” (Norris 2006a, slide 3).

The method of LCAA draws heavily on thetradition of environmental LCA. Accordingly, itprovides a potential link between the strengthof LCA (modeling a product system) and the

566 Journal of Industrial Ecology


indicators of the CSR world, which are not alwayswell adapted to LCA. LCAA has the potentialto piggyback off other initiatives (ISO 14001,GRI Sustainability Reporting,5 SA 8000, FSC,6

and the U.S. Green Building Council’s LEEDprogram7 ) that improve the world as economicactors implement them.

Within this context, this article explores thesocial dimension of LCAA, applying it to a casestudy developed by the SLCA research groupat the Interuniversity Research Centre for theLife Cycle of Products, Processes and Services(CIRAIG) and the University of Quebec at Mon-treal (UQAM). The case study’s goal is to demon-strate LCAA’s strengths and weaknesses.

This study examines as a functional unit $100(1997 USD) of tomatoes from a large greenhousein Quebec. The product system is analyzed oneight social indicators, although the underlyingdata sets and calculation of an “attribute assess-ment” are the main focus of the study.

LCAA Methodology Summary

The goal of an LCAA is to determine whatpercentage of a product’s supply chain has a par-ticular attribute. One might ask, “What share ofthe relevant supply chain is ISO 14001 certi-fied?” To arrive at a conclusion, one must takefour steps: modeling the product system,8 select-ing a relevant activity, measuring the attributes,and calculating the attributes’ share of the supplychain.

Step 1: Modeling the Product System(Technosphere Flows)

As in environmental LCA, the product systemcan be modeled in a variety of units: kilograms,Joules, parts, dollars, and so on. This LCAA studyrelies on an input−output (IO) data set to de-scribe product exchanges. As such, the unit oftechnosphere flows chosen for this study is thedollar.

Step 2: Selecting a Relevant Activity(Elementary Flows)

In environmental LCA, the inventory is theproduct of cumulative technosphere flows and

elementary flows. LCAA uses the same techno-sphere flows and a slight variation on elementaryflows. LCAA chooses an activity of interest inthe supply chain and assigns a “relevant activ-ity flow” to each unit process. For example, onegroup of researchers may be interested in know-ing how many worker hours are in the supplychain, whereas another team may need to knowthe number of acres under cultivation. These re-searchers would use hours and acres, respectively,as the relevant activity flows. This case study usesworker-hours, as did Hunkeler (2006).

Step 3: Measuring the Attributes(Characterization Factors)

This step in the LCAA process involves se-lecting indicators of interest. Each unit processis assigned a characterization factor (cf) per in-dicator. The cf must be 0, 1, a number in be-tween, or unknown. For example, if the indicatoris “SA 8000 certified,” the unit process’s cf couldbe 0 (not certified), 1 (certified), or unknown.For some indicators, it may be beneficial for thecharacterization factor to fall between 0 and 1,and the LCAA methodology presented in thisarticle can accommodate that type of indicatordefinition. Using semi-quantitative characteriza-tion factors for indicators (many will simply bebinary—yes/no) prepares the indicator data forthe next step.

Step 4: Calculating Attributes’ Share ofthe Supply Chain (Aggregation)

Each node in the supply chain associates its at-tributes with the inventory flow. Thus, if a facilityis determined to be ISO 14001 compliant, all itsworker hours become “yes” on the ISO 14001indicator. All hours associated with yes, no, andunknown are tabulated. They are then divided bythe total hours in the supply chain, which givesthe percentage of the supply chain that has theattribute, the percentage that does not have theattribute, and the percentage of the supply chainthat is unknown.

The LCAA methodology is demonstrated indetail in the case study below. Notations used inthe article are described in table 1.

Andrews et al., Life Cycle Attribute Assessment 567


Table 1 Notations used in the article

Dimension DimensionNotation terms names position nos. Description

Zbackground Zbackground(o,i) Direct requirements for background processesZforeground Zforeground(o+e,c), Rows o1. . .on Direct requirements for foreground processes

Rows e1. . .en

Z Z (o+e,i+c). Transactions matrix for all processesx Output vectory Demand VectorB Satellite matrix, relevant activity flowsI Identity matrix of Zg g (o+e) g(k,j) Supply chain inventoryQ Q (o+e,d) Q (k,j) Characterization factors matrixq q (o+e). q (k,j) cf vector (e.g.)R(yes,no,unknown) Indicator assessment matrixr(yes)

r(no)

r (unknown)

p Attribute assessment vectorP Attribute assessment matrix

The Supply Chain Model

This model begins with a traditional IO frame-work to outline a product system. It differs fromthe conventional environmental IO LCA mod-els in that it uses a different satellite matrix andthereby produces a different supply chain inven-tory. Whereas the environmental IO LCA in-ventories pollutants and resource consumptionin the supply chain, this particular LCAA modelinventories worker hours by unit process. Thefollowing sections describe the derivation of thedirect requirements matrix, how the model is aug-mented with company-specific (foreground) data,and how the labor hours satellite matrix is usedto inventory labor hours in the supply chain.

Direct Input Requirements

First, economy-wide purchasing data (directrequirements) are gathered for each sector code.The U.S. Bureau of Economic Analysis (BEA)publishes a large set of IO accounts in the UnitedStates. A direct requirements table, as publishedby the BEA (US Bureau of Economic Analysis2002), serves as a background data set into whichwe can connect our primary data sources to buildtheir supply chain models.

Augmenting the Direct RequirementsTable

In addition to generic sector purchasing data,this model includes purchasing data on fore-ground processes, such as the tomato companyand its direct suppliers. A direct requirementspurchasing table (Zforeground) is developed forthe foreground processes and is paired withZbackground to create a combined table, shown intable 2. The purchases of the tomato company’sfirst-tier suppliers, Zforeground, are approximatedthrough their BEA sector code. For example,the electricity supplier shares the same purchas-ing needs as generic sector 221100, electricitygeneration.

Z = Zbackground, Z foreground

The sum of each column in Z is the expendi-ture in dollars each sector or enterprise mustmake to produce $1 of output. The tomato com-pany, for example, spends almost $0.29 on di-rect inputs for every dollar of tomatoes that issold. This does not include “value-added” ac-tivities, such as labor expenditures, taxes, andprofit.



Tabl

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for

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9.64

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9.66

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2.78

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0.72

0.38

0.59



Inventorying Relevant Activities in theSupply Chain

Z represents the direct purchasing require-ments for both background sectors and for theknown enterprises in a particular supply chain.From this point, one can apply the Leontief for-mula to calculate the “output vector.”9

x = (I − Z)−1 y

The total output vector, x, is calculated for a givenfinal demand, represented by the demand vector(y). The output vector specifies the amount ofoutput from each enterprise or sector the modelneeds to analyze. In this model, y is the functionalunit, set to $100 of the tomato company. I is theidentity matrix of Z.

The output vector, x, is multiplied by asatellite matrix (B) to create a supply chaininventory, g.

g = Bx

From this point, the possibilities of LCAA be-gin to branch out, depending on the units of therelevant activity flows. In this study, we devel-oped a data set for B in worker hours per dollaroutput. By multiplying B (hours per dollar) by x($)/functional unit, we find a resulting g given inhours per functional unit. In this study, we foundapproximately 2.6 work hours in the cumulativeproduct system of $100 of tomatoes.

Although we have chosen hours as the inven-tory unit, researchers have numerous possibilitieswith which to inventory in a supply chain. Forexample, one may determine how many forestedacres are in a supply chain by developing a satel-lite matrix with the unit acres of forested landper dollar output. Analysts could then use thisinformation to determine the percentage of theforested acres in the supply chain that are FSCcertified (sustainable lumber). Different stake-holders have different goals, so researchers areencouraged to further develop other relevant ac-tivity flows (satellite data sets) with which toinventory the supply chain.

It is theoretically possible to omit the use of arelevant activity flow and to inventory the supplychain in only dollars (or some other technosphereflow). This approach is not recommended. Thesatellite matrix plays a central role in determining

Table 3 Partial example of background hoursdatabase

U.S. Bureau of Labor hoursEconomic Analysis per dollarsector code output

Copper, nickel, lead, and zincmining—212230

0.006908

Gold, silver, and other metal oremining—2122A0

0.009098

Stone mining andquarrying—212310

0.01181

Sand, gravel, and clay andrefractory mining—212320

0.01324

. . . . . .

what activity in the supply chain is relevant toan indicator. For example, suppose one soughtto determine the share of a supply chain that wasFSC-certified. Although money might have beenspent on law services somewhere in the supplychain, it does not make sense to expect a lawfirm to be FSC certified. To address this issue,one can assume that law offices have 0 acres offorest per dollar revenue and record this as thefirm’s relevant activity flow. This removes thelaw offices from the relevant supply chain.

Labor Hours Data Set

The relevant activity flow in this study isworker hours. In other words, we are asking, “Howmany labor hours are in the supply chain of theproduct?” To answer this question, we must haveaccess to a labor hours data set that provides hoursper dollar output by U.S. BEA sector. This mea-surement can also be understood as the inverseof the traditional measure of productivity (out-put per hour), which the U.S. Bureau of LaborStatistics (BLS) unofficially calls “unit labor re-quirements.” An example is provided in table 3.

The Unit Labor Requirements data set wasassembled from a number of sources, includingthe following:

• The 1997 U.S. Economic Census (U.S.Census Bureau 2004)

• Total Worker Hours and Output Index(Ahmed 2007)



Table 4 Data source contribution to data set

% hours % GDPData source data data

Census data 65.1 95.7BLS data 27.5 0.0StatsCan data 3.7 3.7BLS/census data 1.8 0.0BLS Table B-1 1.2 0.0Census—U.S. 0.6 0.6

economy average

Note: GDP = gross domestic product; BLS = U.S. Bureauof Labor Statistics; StatsCan = Statistics Canada.

• BLS Table B-1: Employees on Non-FarmPayrolls by Industry Sector (U.S. Bureau ofLabor Statistics 2007)

• NIPA Table 1.1.5 Gross Domestic Product(U.S. Bureau of Economic Analysis 2007)

• Table 383-0022—Multifactor Productivity(Statistics Canada 2007)

• Table 379-0023—Gross Domestic Product(Statistics Canada 2006).

The main sources of labor data are the 1997U.S. Economic Census and the BLS data set (to-tal worker hours). Table B-1 and NIPA Table1.1.5 were used to provide data for the govern-ment sectors. Due to scarcity of U.S. agriculturallabor information by NAICS Code, StatisticsCanada sources (Tables 383-0022 and 379-0023)were used to provide data in the agriculturalsector. It was assumed that the American andCanadian agricultural technology situations arereasonably comparable. Table 4 shows the per-centage each source contributes to the overalldata set. For example, 321 of the 491 sectors(65.4%) in the data set use U.S. Census data.

Indicators

Indicators for Life Cycle AttributeAssessment

The familiar supply chain model describedabove may be extended to describe how a partic-ular product’s supply chain fits with a consumer’svalues. For example, one may wish to promotelocal consumption with one’s purchases.

Table 5 Partial example of Q: q (o+e,d)

Sector/enterprise Local product

. . . . . .

336110 Unknown336120 Unknown336211 Unknown336212 Unknown336213 Unknown336214 UnknownAll other sectors UnknownTasty Tomatoes 1Greenhouse suppliers 1Cardboard packaging 1Electricity 1Seed beds 0Seeds 0Coco-peat 0Natural gas 1Municipal water 1Fertilizers 0Labeling equipment 0Plastic packaging 0Pesticides 0Bugs and bees 0

Note: 1 = local; 0 = not local.

Because the model has already determinedhow many hours are in the tomato’s supply chainby calculating the sum of the inventory vector,g, the final step of the LCAA is to determinehow many of those hours are local. The fact thatthe word local can take on many meanings showsthat defining the indicators in LCAA is an im-portant task that can greatly affect the outcomeof the analysis. This study defines local to meanfrom the Province of Quebec, but it could also bedefined as from Canada or within 500 miles. Assuch, a study’s indicator definitions should alwaysbe transparent.

While preparing the model’s data inputs onwhether a purchase is local, one may record alimited set of outcomes: The product is eitherlocal (1), not local (0), or unknown. In all casesfor the generic sectors, the indicator outcome is“unknown.” Some indicators may require a valuebetween 0 and 1, and this can be accommodatedby the model.

These data can be formalized into a matrix, Q(see table 5). Q must share a dimension withg of sectors (o) plus enterprises (e). Q is also



Table 6 Partial example of g

Labor hoursSector/enterprise per $100 tomatoes

. . . . . .

336110 4.81077E−06336120 2.85034E−07336211 1.32388E−05336212 2.74041E−07336213 4.31534E−09336214 5.37988E−07All other sectors 0.403406899Tasty Tomatoes 1.9570316Greenhouse suppliers 0.058627317Cardboard packaging 0.043660727Electricity 0.030776934Seed beds 0.026141125Seeds 0.01597915Coco-peat 0.013158227Natural gas 0.012898192Municipal water 0.011055329Fertilizers 0.009509072Labeling equipment 0.006619216Plastic packaging 0.005281519Pesticides 0.002007471Bugs and bees 0.000164824

dimensioned by selected LCAA indicators (d).In the current example, “locally produced” is onesuch LCAA indicator in Q (o+e,d).

Each characterization factor in Q (o+e,d) is thenused to weight the hours inventory, g, shown intable 6.

R(yes), R(no), and R(unknown) tell how manyhours of the supply chain are local, how manyare not local, and how many hours are not trans-parent (unknown). They result when one mul-tiplies the inventory, g, by the characterizationfactors, Q.

Finally, one can divide R(yes), R(no), andR(unknown) by the total number of hours in thesupply chain (

∑g) to arrive at the concluding

result, P. P states the percentage of supply chainworking hours that are local, the percentage thatare not local, and the percentage for which it isunknown whether they are local.

As shown in table 7, this study reveals that81% of the tomato company’s supply chain (mea-sured in worker hours) is local, 3% is not local,and we do not know whether 16% of the supplychain is local or not.

Primary Data Sources

Primary data were collected through two mainavenues. One important source was informa-tion from company interviews conducted byCatherine Benoit, Julie Parent, and Jean-PierreReveret of CIRAIG-UQAM’s SLCA researchgroup. Company reports and Websites were alsoused as supplementary information.

Other Indicators

In addition to the local indicator, we selectedseven others for demonstration. The potentialset of indicators was very large, and we madeno attempt to be comprehensive. One indicatortopic of special note is that of wage levels, whichmerits further research in relation to LCAA. Animportant question is whether wage levels areto SLCA what energy consumption is to envi-ronmental LCA: an important indicator that isclosely correlated with results across many impactcategories. Table 8 shows the indicators we chosefor this study, along with definitions.

These indicators can be seen as analogousto midpoint indicators in environmental LCA,in which the inventory results of the study arefocused onto a group of more meaningful cat-egories. Increasing the number of sustainabilityreports produced in the world is not necessar-ily an important end-goal in itself. Instead, thesemidpoint indicators are understood as a meansto an end. Environmental LCA uses scientificevidence to establish cause-and-effect pathwaysbetween midpoint and endpoint indicator sets.In the case of LCAA, we might choose to trackpublication of sustainability reports in our sup-ply chain because we think this kind of activityraises sustainability awareness or causes compa-nies to better manage their community impacts.In short, the goal is to encourage actions thatcontribute to human well-being.

Results

The final outcome is shown in figure 1. Thetomato company dominates the worker hours inthe supply chain, given that it is a labor-intensiveindustry. Therefore, when the tomato companyperformed well on an indicator, the cumulative



Table 7 Hours of local, not local, and unknown labor activity in the supply chain

Sector/enterprise Local Not local Unknown

. . . . . . . . . . . .

336110 0.000000 0.000000 4.81077E−06336120 0.000000 0.000000 2.85034E−07336211 0.000000 0.000000 1.32388E−05336212 0.000000 0.000000 2.74041E−07336213 0.000000 0.000000 4.31534E−09336214 0.000000 0.000000 5.37988E−07All other sectors 0.000000 0.000000 0.403406899Tasty Tomatoes 1.957032 0.000000 0.000000000Greenhouse suppliers 0.058627 0.000000 0.000000000Cardboard packaging 0.043661 0.000000 0.000000000Electricity 0.030777 0.000000 0.000000000Seed beds 0.000000 0.0261411 0.000000000Seeds 0.000000 0.0159791 0.000000000Coco-peat 0.000000 0.0131582 0.000000000Natural gas 0.012898 0.000000 0.000000000Municipal water 0.011055 0.000000 0.000000000Fertilizers 0.000000 0.0095091 0.000000000Labeling equipment 0.000000 0.0066192 0.000000000Plastic packaging 0.000000 0.0052815 0.000000000Pesticides 0.000000 0.0020075 0.000000000Bugs and bees 0.000000 0.0001648 0.000000000Total (%) 81 3 16

indicator result was very positive. In general, wecan conclude that the tomato company is bestserved to look inward, toward its own operations,when trying to maximize its positive impact onthe world’s workers.

The tomato company’s behavior dominatesthe results in figure 1 because it is 75% of thehours of its own supply chain, or 62% if the sup-ply chain is measured in dollars. Accordingly, the

tomato company should focus on implementingCSR systems at its own facilities. Nevertheless,the company may also choose to put some focuson its purchasing habits. Figure 2 shows the con-tribution of hours to the supply chain of the top15 contributors to the tomato company’s supplychain.

The tomato company’s first-tier suppliers aredenoted with an (f) in figure 2. Some of the largest

Table 8 Demonstration indicators and definitions for Quebec tomato case study

Indicator Definition

Locally produced The supplier is located in QuebecSME The supplier has fewer than 100 employeesWorkplace insurance All employees are covered by workplace injury insuranceMedical insurance All employees are provided basic medical insurance by either state or employerSD report The company has published a sustainability report within the past 2 yearsWage above poverty Poverty wage is set at the minimum wageWage above living Living wage is set at two times the minimum wageDisclose H&S rate The company publishes its health and safety incidence rate each year

Note: SME = small and medium-sized enterprise; H&S = health and safety.



Figure 1 Percentage of supply chain by indicator for $100 of Quebec greenhouse tomatoes. SME = smallor medium-sized enterprise (with fewer than 100 employees); SD Report = company that has published asustainability report within the past 2 years; Disclose H&S Rate = company that publishes its health andsafety incidence rate each year.

contributors to the company’s supply chain aregeneric sectors. It is difficult for the tomato com-pany to influence generic sectors because it doesnot purchase directly from them. Nine compa-nies within the top 15 contributors, however,could be influenced by the tomato company. The9 companies represent 8.5% of the tomato supplychain. By managing itself and these 9 suppliers,the tomato company could engage in CSR with83% of the worker hours in its supply chain.

Analysis of the supply chain without thetomato company exposes what is happening inthe upstream supply chain. Figure 3 shows theattribute assessment for the remaining 25% ofthe product system, not including the tomatocompany. Figure 3 enables the tomato com-pany to examine its suppliers’ performance onattributes it cares about. If the tomato companybelieves deeply in transparency of health andsafety (H&S) rates, it has some work to do in

Figure 2 Top 15 supply chain contributors. Mgmt = management; Splrs. = suppliers; (f) = first-tier supplier.



Figure 3 Attribute assessment for cradle to in-gate (with tomato company excluded from supply chain).SME = small and medium-size enterprises (with fewer than 100 employees); SD Report = company that haspublished a sustainability report within the past 2 years; Disclose H&S Rate = company publishes its healthand safety incidence rate each year.

managing that attribute in its supply chain. Thecompany does a relatively good job of local pro-curement, and an encouraging one out of threecompanies in its known supply chain producesa sustainability report. Nonetheless, few data areavailable about the appropriateness of how thetomato company’s suppliers pay their employ-ees. This may be a taboo topic for discussionin business relationships, but it is certainly sig-nificant to a company practicing life cycle CSRmanagement.

As noted already, the tomato company com-prises almost 75% of its own supply chain whenmeasured in worker hours. This is a predictableoutcome, given that the majority of its operatingexpenditures go toward labor. Depending on itssector, a company can generally predict whetherit should spend its CSR resources managing itsown operations, looking outward with purchas-ing policies, or both. The tomato company isin line with its sector (greenhouse and nurseryproduction—BEA code 111400), which consti-tutes 71% of the dollars in its own supply chain.

Discussion of Limitations

General Observations

The quality of the assessment is always an im-portant consideration when the analysis guides

decision making. There remains much to do inassessing the strength of the results obtainedthrough an LCAA methodology. Nevertheless,some interesting observations can be made.

This case study illustrates an LCAA approachthat is IO based, but, of course, an LCAA could beimplemented with a process-LCA data set. In thatcase, quality concerns inherent to process-basedLCA would become more relevant. It thereforeappears that the reliability of the life cycle at-tribute data is heavily dependent on the LCAdata set used to derive the inventory results. Asmore uncertainty information becomes availablein process and IO LCA, it can be used to quantifythe uncertainty in LCAA results calculated withthose data sets.

Limitations of the Case Study

Data ResolutionThis study uses an IO framework and closely

mirrors the calculation routine of an environ-mental IO model through the life cycle inven-tory stage. This means that we combined totalsupply chain activity and sectoral activity fac-tors to derive the inventory results. Both sources,the economic flows table and the satellite matrix(labor hours, in the case of this study), have un-certainties. One such example is the diminished



accuracy of the data sets due to a lack of sectordetail (480 sectors in this data set, compared tomany thousands in process LCA or many mil-lions of actual products in the real world). Thisis a well-known drawback of IO LCA relativeto process LCA, and in trade for reduced accu-racy, the assessment results include a more com-plete assessment of all the activity in the supplychain. One can increase the resolution by gather-ing primary data, and we did this for the tomatocompany and its first-tier suppliers. These dataenable the tomato company to pinpoint whichcompanies have the most room for improvementin their supply chains. Additional data collectionwork must be done to increase the resolution be-yond one tier of suppliers, however, to determinewhere those improvements might be made.

Economic and Labor Hours DataThe data presented in this study are geograph-

ically limited, as is characteristic of many studiesdone with IO models. The model used in this casestudy is based on the U.S. IO accounts from theBEA. If a large part of a product’s supply chainis produced overseas, then the accuracy of thestudy will be affected. Although this case studydoes not include extensive analysis of these ef-fects, one can assume that total worker hours inthe supply chain will be underestimated. This re-sult is due to generally lower productivity (dollarsper hour) in other countries compared with theUnited States. The study assumes a high level ofU.S. productivity, whereas in reality it would takemore hours to make the same product overseas.Researchers can address the issue of significantforeign influence on the model by making foreignsuppliers as explicit as possible in the model—inother words, including them as foreground pro-cesses so their true expenditures and productivitydata are accounted for in the model.

This particular case study was conducted witha Quebec company that uses many Canadian sup-pliers. Although the data used are specific to theUnited States, it was assumed that these datawere also reflective of the Canadian economy interms of production technology and productivity.Furthermore, it was also assumed that the directinputs to produce various goods and services wereroughly similar between the two countries.

The current model uses 1997 economic andworker hours data. The structure of the U.S.economy has changed significantly since 1997,especially in sectors that make heavy use of infor-mation technologies. One may also consider thatproductivity has changed in the United States(and in the world) since 1997. This is undoubt-edly true, and this fact highlights the need for(1) regular updates to the data sets and (2) ana-lysts to gather as much company-specific data aspossible.

A large and important limitation of the modelis the productivity data in the agricultural andconstruction sectors. The study uses highly ag-gregated data due to a scarcity of labor hoursdata for these sectors. The difficulty in obtain-ing data on these sectors should not be surpris-ing, given the seasonality and informal workerarrangements that characterize them. Neverthe-less, they are very important both to the economyand to the world of work.

Impact MethodThis study’s LCAA is similar to a traditional

LCA through the inventory phase, but the appli-cation of the impact methods is quite different.LCA models often have large uncertainties dueto impact factors, such as carcinogenic impactper kilogram of product. The LCAA methodol-ogy demonstrated here avoids that problem byusing only primary data for indicators and explic-itly assigning an indicator value of “unknown”to processes in the supply chain for which im-pact data were not collected. One can imagine,however, that future approaches to LCAA will infact draw on background databases that identifysectors and locations in the supply chain that aremost likely to be hot spots.

After consideration of the limitations, thisLCAA model is still fairly robust. It can be auseful tool with which to explore where com-panies are encouraging activity in their supplychains and how they might manage CSR withinand outside of their organizational boundaries.

Conclusion andRecommendations

The tomato company scored well on the set ofgiven indicators for this case study. At least 75%



of the product system had the attribute of intereston five of eight indicator categories. The resultswere primarily driven by two special character-istics of the particular product system. First, thetomato company is a large part of its own supplychain, so its management practices have an inor-dinate influence on cumulative supply chain per-formance. Second, the tomato company operatesin a strong social context—the Province of Que-bec. This second point has many implications forthe product system’s performance on the chosenindicators, such as wages, worker insurance, andprovided health care. These findings underscorethe need to develop strong communities in whichcompanies operate.

Because the tomato company is the largestcontributor to its supply chain, it should focus onmanaging CSR activities within the company. Ifthe tomato company wants to institute a purchas-ing policy, it should be sure to engage the suppli-ers that contribute the most working hours toits supply chain: greenhouse supplies, cardboard,electricity, and seed beds.

This case study speaks to the usefulness anddrawbacks of LCAA. On the whole, the methodof LCAA has potential. Further research isneeded on indicator definitions that draw fromexisting and forthcoming CSR indicator frame-works. One important indicator on which re-search should focus is wage levels. Additionalresearch is needed to develop additional satel-lite data sets (B). This will enable stakeholdergroups to inventory the supply chain in units thatare meaningful to them. Attention may also bepaid to converting existing environmental satel-lite matrices so that environmental LCA andLCAA can simultaneously be applied to the sameproduct system. Finally, as with any LCA model,the results are only as good as the underlyingdata. LCAA practitioners should be aware of thedata quality and reliability issues specific to thedata set at the foundation of their model, whetherthose data sets are based on IO, process, or hybridmethods.

Acknowledgements

Funding for the tomato case study comes fromthe Social Sciences and Humanities ResearchCouncil of Canada (SSHRC) to promote in-

ternational research collaboration. Jean-PierreReveret and Catherine Benoıt at the Universityof Quebec at Montreal (UQAM) were particu-larly instrumental in establishing a healthy re-search environment.

Notes

1. More recently, the Social LCA task force releasedguidelines for SLCA of products (UNEP 2009).

2. SA8000 is the Social Accountability 8000 Standard(www.sa-intl.org).

3. ISO 14001 is the International Organization forStandardization’s 14001 Standard (www.iso.org/iso/iso_14000_essentials).

4. There are many fair trade initiatives, many of whichoperate through Fairtrade Labelling OrganizationsInternational (FLO; www.fairtrade.net).

5. GRI is the Global Reporting Initiative (www.globalreporting.org).

6. FSC is the Forest Stewardship Council (www.fsc.org).

7. For the U.S. Green Building Council, see www.usgbc.org.

8. Analytica modeling software was used in this study.The Analytica model that was used is available fromthe authors.

9. Many notations used in this article follow conven-tions described by Heijungs and Suh (2002).

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About the Authors

Evan Andrews was a visiting researcher atthe University of Quebec at Montreal at thetime this article was written and is now an an-alyst at Sylvatica, a life cycle assessment firmbased in Montreal, Canada. Pascal Lesage is asenior analyst at Sylvatica. Catherine Benoıt andJulie Parent are PhD candidates at the Univer-sity of Quebec at Montreal. Gregory Norris isfounder and director of Sylvatica and an ad-junct lecturer at the Harvard School of PublicHealth in Boston, Massachusetts, USA. Jean-Pierre Reveret is a professor at the Universityof Quebec at Montreal.


life cycle attribute assessment : case study of quebec greenhouse tomatoes

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