Cost data quality considerations for eco-efficiency measures

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a v a i l a b l e a t w w w. s c i e n c e d i r e c t . c o m

w w w. e l s e v i e r. c o m / l o c a t e / e c o l e c o n

Cost data quality considerations for eco-efficiency measures Andreas Ciroth⁎ GreenDeltaTC GmbH, Raumerstrasse 7, D-10437 Berlin, Germany

AR TIC LE D ATA

ABSTR ACT

Article history:

Cost data are a central aspect of eco-efficiency measures, either as means to assess value of

Received 29 March 2007

production, or, more directly, as one dimension of the efficiency ratio. Several aspects may

Received in revised form

affect the quality of cost data, among them definitions, time and space, and confidentiality

2 August 2008

issues. Somewhat surprisingly, cost data quality has received little attention in the field of

Accepted 5 August 2008

sustainability and eco-efficiency so far. Even worse, perhaps, is the lack of tools suitable for a

Available online 12 September 2008

cost data quality assessment and management. This paper discusses parameters that affect cost data quality, and will then propose a

Keywords:

pedigree matrix as a tool designed for managing cost data quality issues. The application of

Life cycle costs

the matrix is described, also in combination with a previously proposed, and broadly used,

Cost data quality

pedigree matrix for environmental data quality management.

Pedigree matrix

© 2008 Elsevier B.V. All rights reserved.

NUSAP scheme Eco-efficiency

1.

Introduction

1.1. Why cost data quality management is important also, and especially, in eco-efficiency applications Cost data are a central aspect of eco-efficiency measures, either as means to assess value of production, or, more directly, as one dimension of the efficiency ratio. Costs for a given good are often varying. Let us first look at some examples. In 2001, one dollar was the equivalent of 1.3 Euro; in 2004 it was 0.88 Euro, a change by 30% in less than three years, leading to diverging cost between the US and the Euro zone. Prices for NYMEX light sweet crude oil changed from about 25 US-$ per barrel in April 2004 to a peak of 72 US-Dollars in summer 2006 (GO-TECH Petroleum Web, 2007). In China, one Big Mac costs the equivalent of 1.3 US Dollars while in Switzerland it costs at the same time about 4 US Dollars, according to the Big Mac index published by the Economist (2007). These examples could be vastly extended; they indicate that market prices change over time and region. This is of course nothing new in economics, and also in everyday life. Prices relate to

⁎ Tel.: +49 30 48 496 031; fax: +49 30 48 496 991. E-mail address: [email protected].

costs.1 An analysis of factors influencing costs is of vital interest for any business, and it is therefore logical that cost analysis is a discipline of its own, sometimes as part of managerial economics (Hirschey et al., 1993, pp. 461), but also of evident interest on the macro-economic scale. Introductory books on economics abound with examples of cost concepts, cost functions, and of parameters influencing costs. For eco-efficiency applications, however, the situation is different.2 Costs are here often presented as “facts”, and barely 1 This simple sentence would deserve more elaboration which, however, clearly lies out of scope of this paper. To keep things simple, let's follow Baumol and colleagues (1991, pp. 28): “The relevant cost of any decision is its opportunity cost – the value of the next best alternative that is given up”. Prices reflect the “money costs” only. However, in a well-functioning market, “goods that have high opportunity costs will tend to have high money costs”, or even Samuelson and Nordhaus (1995, pp. 120): “In well-functioning markets price equals opportunity costs” (and money costs). 2 Understanding Eco-Efficiency similar to the 2nd conference on Eco-Efficiency in 2006, where this paper was first presented, as the ratio of production value or cost and environmental impact or improvement (E/E 2006). This definition is more specific than the rather descriptive one from BCSD, where eco-efficiency is defined as “achieved by the delivery of competitively priced goods and services that satisfy human needs and bring quality of life, while progressively reducing ecological impacts and resource intensity throughout the life-cycle to a level at least in line with the Earth's estimated carrying capacity” (Schmidheiny, 1992; BCSD, 1993).

0921-8009/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolecon.2008.08.005

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challenged. Quality discussion seems at a very early stage in applications (e.g. Rüdenauer et al., 2005; Saling et al., 2002; Hellweg et al., 2005; Suh et al., 2005). Quality is often not considered explicitly, and cost data sources are often not transparent. An exception are Hellweg and colleagues (2005). They present an ecoefficiency application for solid waste treatment options which may serve as an example on how pressing cost data quality management is. In order to assess “financial costs” (which they oppose to “environmental impacts”), the authors use several data sources released 1999 through 2003, from Swiss and German origin. Their cost data thus have a geographical, currency, and a time dimension. Many other eco-efficiency publications do not even display their cost data sources and references. Another example worth being mentioned here is Schmidt (2003). He looks into aspects affecting the data uncertainty. The first aspect may be paraphrased as time horizon of the study (retrospective studies are “pretty accurate” (Schmidt, 2003, p. 172), while prospective studies face higher uncertainties). The second aspect are “market dynamics”. Here, Schmidt gives several sub-aspects, including taxation, discounting rates, market access (number and kind of competitors), and local and temporal market price changes that are not technology driven. Third aspect is the life cycle stage where the study is based upon (from “product development kick-off” over “start production”, “1st market launch”, “full market penetration” to “first products to be disposed”). Most of these are product-series specific life cycle stages as commonly distinguished in microeconomics (product development kick-off; start production; first market launch, full market penetration, asf.) in contrast to those usually distinguished in life cycle assessment and also life cycle costing (e.g. UNEP, 2004; Hunkeler et al., 2008) that focus on the life of a single product. Finally, Schmidt distinguishes different types of “information about costs” (among them “product prices”, “taxation”, “residual value”, ”end-of-life costs/revenues”), and estimates LCC uncertainty per information type, per (economic) life cycle stage, and in a matrix for all combinations of them, deploying also the market and time related aspects. This approach might be criticised for several reasons. For instance, several of the “market dynamics” aspects overlap; tax changes will probably result in non-technology driven market price changes, as will appearance of new competitors on a market (“market access” aspect). Eco-efficiency and LCC are used to a product specific instead of a product series specific life cycle. Most important for this paper, the uncertainty evaluation matrix (of cost information over LC stages) is presented without supporting background data (one example with “assumed costs” aside), and therefore rather describes possible, plausible uncertainty relevance of different elements rather than presenting sources of uncertainty. Conclusions from the matrix, however, are straightforward and appealing. According to the matrix, uncertainty is lowest for the product price, and highest for end-of-life costs and revenues, and for making a decision about a product in a (product series specific) life cycle stage, uncertainty is highest at product development kick-off, and lowest when “first products are to be disposed”. These conclusions, however, are presented without supporting references. Guinée et al. (2004) present strategies to find prices of products for economic allocation in LCA, for 17 different cases, from “market price not known” to market not yet in existence”; the

solutions focus on finding a sound market price, but treat also different currencies (“no problem as long as the same currency is used in each process”), and locally diverging prices (“choose prices at relevant process locations or calculate averages”). No indicator of the quality of cost data is produced, but sensitivity analyses are recommended in order to investigate stability of the allocation. Therefore, one is tempted to state that there is, overall, a lack of awareness of cost data quality, and there is also a lack of tools and approaches that may be used for dealing with it. Even a discussion among scientists seems in a very early stage. Kuosmanen (2005), for example, finds that “assessment of economic impacts [in eco-efficiency applications] is often considered a relatively simple task. But a sound economic assessment is more than just a trivial accounting exercise”. He emphasises the fundamental economic idea that decisions are to be based on opportunity costs instead of accounting costs, while the latter are usually considered in eco-efficiency applications. Now evidently quality of cost data is a problem also for purely economic assessments. A life cycle costing study should address how stable the results are, and make statements about the quality of cost data used, and use tools to assess and improve it. This is often not done, as a recent survey of Life Cycle Costing (LCC) studies revealed (Ciroth et al., 2008). However, the survey showed that for eco-efficiency applications, it is even less common. This is not at all justified because eco-efficiency applications generally compare environmental impacts related to material flows with economic impacts related to costs. Due to this different nature of their assessment objects, the quotient of both is unlikely to ”level out” biases and volatilities inherent in both. Rather, one runs into comparing apples and oranges: The costs of gasoline a car needs is likely to change quicker, and in a different way, than the gasoline demand of the car and its environmental impacts; the quotient is thus less stable than each individual value.

1.2.

Cost allocation problems

Besides time and geography, there is another sort of “drivers” for differences in cost figures: The allocation of costs. This problem has relations to the allocation problem in LCA (for an early source mentioning this relation, see (Huppes, 19933), and also in economics. Economics deal with the allocation of goods and services, and allocation is therefore at the very core of economics. Cost allocation problems however relate in general to the allocation of costs incurred by the consumption of goods and services. Let's first look at examples. In 1992, Norsk Hydro made an annual surplus of 1,763 million Norwegian Crowns (m NOK), according to US-GAAP accounting standards. When accounted according to Norwegian law, however, the surplus was only 167 m NOK. More examples are given in Table 1, (Wagenhofer, 1999). These examples from financial accounting do not deal with costs directly. However these differences indicate differences in (internal) cost accounting used in companies, and therefore they may indicate differences in costs. This view is supported by literature statements of recent trends to align 3

Even, Huppes (1993) proposes allocation based on costs (“economic allocation”) for allocation problems in Life Cycle Assessments.

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Table 1 – Examples: different financial figures by different accounting systems

1.3.

Norsk Hydro, annual surplus Norwegian law 167 US-GAAP 1763 1992 (m NOK) SmithKline Beecham, equity UK-GAAP 7000 US-GAAP −600 capital 1989 (m USD) Hoechst AG, annual surplus HGB 2114 US-GAAP 1090 1996 (m DEM)

While this may already be a long list, managerial accounting systems have yet another issue especially important for life cycle approaches, namely the definition of cost types and their calculation. Basically, managerial accounting systems are built to the convenience of the management unit of the company. This may sound trivial, but in consequence the design of the system will differ from one company to the other, including cost structure and the definition of specific costs. For example, disposal costs may comprise only waste disposal fees, but they may rightly include also wages for employees who are responsible for waste handling in the company. Another important factor for differences in costs is the selected approach of cost assessment. A total cost accounting approach will allocate all costs to products, disregarding whether costs are fixed or variable. A marginal cost approach assesses costs per additional unit of product and leads often to lower costs. Activity based costing allocates, in principle, costs to activities instead of products. It often yields a lower share of overhead costs. In the long run, however, a company needs take into account all of its costs, completely independent from the allocation method that is used. Table 2 shows examples of the outcomes of total cost accounting and activity based costing. An EU 5th framework project “grEEEn” addressed this issue of differing cost management systems among different companies for the electronic industry in Europe, specifically in light of life cycle approaches and the WEEE directive. Unfortunately, project results are not available so far, besides presentations held in the course of the project (e.g. Lichtenvort et al., 2003). Fig. 1 underlines the two problems of cost definition and cost allocation, for labour costs.

financial and cost accounting (e.g. Wagenhofer, 1999, p. 10), and of surveys where facilitating (internal) controlling is mentioned as one important motivation for harmonising financial accounting in international companies (e.g. Horváth and Arnaout, 1997, pp. 262). Let's have a closer look at cost accounting and cost allocation. For a company, costs incur “from the outside” due to acquired goods and services, capital costs and personnel costs. In order to assess the price of a product that finally leaves the company one needs to allocate all these. For example, the electricity used for lighting a hall which contains several production lines with several products being produced within one day will need to be allocated to each product in order to get the electricity demand for each product. The electricity demand per product, in turn, can be multiplied with the specific costs of electricity (at time of production) in order to get the electricity costs for each product. Wages for personnel supervising the machines in the hall (let's assume a team of two supervises three production lines) need to be allocated as well. In this case, only costs and no material demand occur, and thus only costs need to be allocated. While wages and electricity demand will generally be found in the external accounting system of the company, other costs may not. Let's assume management of the company creates a retirement fund system, and starts paying more into this system than is foreseen by law and by the accounting standard the company is committed to. Management thus “creates” costs that exist only in the internal accounting system. Further let's assume the company sponsors a handball team, for creating brand value, and also because the owner of the company is a handball fan. Are these costs relevant for the products, in an eco-efficiency study of the company? In the end this seems rather a question of definition, and of setting goal and scope for the study. There are some examples where these imputed costs build a high share of the overall costs of a product. Wrapping up, there are several types of allocation tasks for costs to be distinguished: – material flows are available, allocated or non-allocated, and costs can be linked directly to these material flows (e.g., electricity demand); – material flows are not available but costs are reflected in the external accounting system; costs incur either close to the product (wages for production line managers) or rather remote (sponsoring funds for sports teams) – costs, as imputed costs, occur only in managerial accounting and are not reflected in the external accounting system (additional retirement plan)

1.4.

Cost definition and cost management systems

Confidentiality

While the previous two paragraphs addressed different reasons why costs might differ, and which possible drivers for cost differences for one and the same thing might exist, there is another, and I think final, point to consider. It is the confidentiality issue.

Table 2 – Different product costs by using two different cost methods Product cost according to TCA and ABC

TCA

ABC

Product A Overhead Direct cost Total

50.00 $ 20.00 $ 70.00 $

245.00 $ 20.00 $ 265.00 $

Product B Overhead Direct cost Total

100.00 $ 40.00 $ 140.00 $

79.47 $ 40.00 $ 119.47 $

TCA: Total Cost Accounting; ABC: Activity Based Costing (Roztocki, 1998).

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Fig. 1 – Definition of cost categories and determining the share of labour costs as two problems in LCC (IEC-60300-3-3, textboxes added). Profit of a company is, quite basically, the received revenues minus costs4: profit ¼ revenues  costs Now generating profit is one of the prime reasons for running a business. The revenue per product is its price, and as such well known to the customer. Telling the costs of a product thus might put a company in a weaker position in a pitch (if a client knows that a company will make about 50% profit from the deal he or she is negotiating, an offer with a lower bid is likely except in a monopoly situation). Especially in life cycle studies, cost data need to be gathered among competitors, and might, depending on the aggregation of the study, be visible in the report, and or be displayed to competitors. Life cycle approaches seem thus especially vulnerable to confidentiality issues. In summary, while the previous drivers for bad cost quality influenced the specific cost figure for any product, the confidentiality issue might prevent to openly report this figure.

1.5.

Data quality

Before presenting an approach for managing cost data quality in the following section, it is now time to briefly consider how data quality is understood in this paper. This is not trivial since there are various cost data quality concepts on offer. Several applications can be found in health economics. E.g., Swindle and colleagues (1996) implement a model for monitoring cost data quality in hospitals. LCC manuals tend to deal with data quality implicitly, by describing how the LCC model shall be put up, and what kind of analyses shall be used to 4 More specifically, business profit is the difference of revenues and accounted costs, while economic profit is business profit minus implicit costs of capital and any other inputs provided by the owner (Hirschey et al., 1993, p. 12).

investigate how stable and reliable the calculated cost figures are, e.g. Kawauchi and Rausand (1999). In ISO 14040 (2006), data quality is defined as: “characteristics of data that bear on their ability to satisfy stated requirements“. This definition and the standard are written for the life cycle perspective. A life cycle perspective considers the impacts of a product from resource extraction till disposal and thus seeks to integrate all relevant impacts of a product; it is often basis for eco-efficiency approaches (WBCSD, 2005). Thus the ISO 14040 definition fits nicely for our task of assessing quality for cost and environmental data in ecoefficiency approaches. By closer inspection, the ISO definition states that there is no fixed “quality criterion” that a datum “possesses”. To the contrary, quality must always be seen in relation to requirements that are posed upon the data. And these requirements, in turn, will depend on a specific application.

2.

The pedigree matrix concept

We are now prepared to present a tool for assessment and management of data quality, the pedigree matrix. Funtowicz and Ravetz (1990) introduced the pedigree matrix concept in uncertainty analyses, as a means to code qualitative expert judgement for a set of problem-specific “pedigree criteria” into a numerical scale, with criteria as columns of the table, the numerical codes as table lines, and linguistic descriptions for each value in each cell of the table. Basic aim is to come from qualitative description of relevant aspects of an object of study to quantitative figures assessing these aspects. The matrix thus is a tool for quantification of qualitative assessment descriptions (Fig. 2). Both rating scale and criteria shall be selected according to the needs of the object of study. There is no further formal requirement on the structure of the matrix. For example, van der Sluijs et al. (2003) present three different applications with indicator scores from 0 to 4 and 0 to 2, and with 4, 39, and 7 criteria.

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Fig. 2 – The pedigree matrix concept.

Weidema and Wesnæs (1996) transferred the pedigree matrix to Life Cycle Assessments; their matrix is square, with a rating scale from 1 to 5 and with 5 criteria. In 1998 Weidema published a slightly modified version (Weidema, 1998). It became widely acknowledged and was modified by some authors. One application example is the Swiss ecoinvent database (yet in a modified form, Frischknecht et al., 2005).

3. A pedigree matrix for cost data quality management Cost data quality will face the same challenges as the assessment of environmental data quality: Important aspects influencing data quality (definitions, accounting system, time, geography — see above) are often not tangible, only available in a qualitative way, and assessment of each should not take long time for there might be hundreds of datasets waiting for an assessment in one single study. And yet, despite rather difficult data basis and the need for a quick assessment, one might find a qualitative description of quality difficult to understand and to handle in the aggregation of datasets. A quantitative assessment result thus makes sense, especially if it is able to deal with qualitative information. A pedigree matrix does exactly this. The remainder of this section thus proposes a pedigree matrix based on the matrix by Weidema and Wesnæs, with modified nodes. Considering the Weidema/Wesnæs matrix makes sense for several reasons. The object of study will be a system of interlinked processes, in each case, and results from the environmental and the cost part can be combined easier later on. First task is to relate the cost data quality indicators, as elaborated in the previous sections (definitions, time, space,

Fig. 3 – The relation between cost data quality indicators relate and basic aspects of the pedigree matrix.

confidentiality) to the criteria foreseen by Weidema and Wesnæs. This is for some indicators straightforward (“time” affects the assessment criteria temporal differences, space affects geographical differences), others need more explanation. Definitions affect geographical differences (due to different currency) as well as further technological differences (different accounting systems). Confidentiality is related to both reliability and completeness of a data set (data that is modified or withheld due to confidentiality might become unreliable or will be missing at all) (Fig. 3). These new criteria used in the matrix can be assumed to be rather independent.5 Surprisingly enough, the Weidema matrix needs to be changed only in some aspects and can anyhow “absorb” all the criteria found necessary so far. The reliability criterion can be kept unchanged from the original matrix; it seems not important why cost data are not verified (for confidentiality or other reasons), the modes of the reliability fit also for costs. The same holds for completeness. For temporal differences, however, the time spans are shortened in order to consider that costs generally change faster than material flow relations in life cycle inventory (and most likely also for many impact assessment factors). For geographical differences, two parameters are added, namely currency and cost structure. A different currency is often linked with a change in geography; if the currency changes, a change in geography (e.g. from one country to another) has stronger influences on cost data changes. Even with the same currency, however, costs in Paris and Rostock (a town in Eastern Germany) will be very different for most goods, since the cost structure is very different in both cities. This motivates the new parameter costs structure. For technological differences, a different accounting system is added as subcriterion. The matrix certainly does not cover all factors influencing cost differences. Aim was to cover most of them, to avoid too many different indicators, to have a matrix that is applicable in parallel to an assessment of flows and environmental aspects, and finally, to arrive at indicators that one can reasonably assess. Therefore, parameters such as management differences or economies of scale are not directly considered although they might have an influence. Management differences (leading to aggressive pricing or moderate pricing) are to some extent covered by the region indicator (suggesting different management ‘style’ for different regions,

5 Still, geography will have influence on technology, thus both are dependent, but this seems impossible to resolve.

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Table 3 – Pedigree matrix for managing cost data quality issues in eco-efficiency Indicator score Reliability of source

1 Verified data based on measurements

Completeness Representative data from a sufficient sample of sites over an adequate period to even out normal fluctuations

2

3

4

5

Verified data partly based on assumptions or nonverified data based on measurements Representative data from a smaller number of sites but for adequate periods

Non-verified data partly based on assumptions.

Qualified estimate (e.g. by industrial expert)

Non-qualified estimate or unknown origin

Representative data from an adequate number of sites but from shorter periods

Representative data but from a smaller number of sites and shorter periods or incomplete data from an adequate number of sites and periods

Representativeness unknown or incomplete data from a smaller number of sites and/or from shorter periods Age of data unknown or more than 8 years of difference Data from unknown area or area with very different cost conditions

Temporal differences

Less than 0.5 years of difference to year of study

Less than 2 years difference

Less than 4 years difference

Less than 8 years difference

Geographical differences

Data from area under study, same currency

Average data from larger area in which the area under study is included, same currency

Data from area with slightly similar cost conditions, different currency

Further technological differences

Data from enterprises, Data from processes processes, and and materials under materials under study study from different enterprises, similar accounting systems

Data from area with slightly similar cost conditions, same currency, or with similar cost conditions, and similar currency Data from processes and materials under study but from different technology, and/or different accounting systems

worldwide); economies of scale do not fit to the linearity of an environmental assessment (if it is in the common form of an attributional assessment) and is for this reason omitted. These explanations aside, the matrix is presented here for the first time in a paper, comments and discussions are thus most welcome. Content that is changed compared to the updated (1998) pedigree matrix by Weidema is printed in bold (Table 3). Weidema 1998 stresses that reliability of source and completeness are independent of the study in which the data are applied, while the other indicators depend on the data quality goals for the study in which the data are applied. This holds also for cost data. The original term ‘correlation’ is changed to ‘difference’ following (Ciroth et al., 2002).

4.

Application

4.1.

Cost data quality for a single process

As an example, an evaluation result for a single process is presented, a vacuum cleaner production plant. The matrix assessment result is obtained by comparing the data set used in a study to the specific data set to be used in an ideal case. Let's assume that the data set used is a German production site from 1997. For a study about Danish vacuum cleaners with reference year 2000, the matrix result would be as presented in Fig. 4 (left) below. Every indicator scores within average. When applying the same data set in a study with the aim to assess vacuum cleaner production in China, for the year 2005, then

Data on related processes or Data on related materials but same processes or technology materials but different technology

the assessment gets much worse indicator scores. Temporal differences gets a 5 and is the worst value, geographical differences gets a 4 (different currency, region is known, cost structure is slightly different and not very different, Fig. 4, right). In a similar manner, every process in a product system can be evaluated. Result of this exercise is a matrix with all the processes in the system and their assessment result. For a practical application, two questions arise immediately. For one, the whole assessment procedure should best be automated in order to reduce application time. And second, it is not clear beforehand how the matrix should best be dealt with. An automatic assessment seems to some extent possible because the assessment criteria used in the matrix are applied in an automatic manner. Some criteria, as the cost conditions, however, are difficult to assess automatically at this stage.

Fig. 4 – Pedigree matrix assessment results for a vacuum cleaner production site in Denmark (left) and if using the same data set for China (right). Further explanation see text.

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5.

Fig. 5 – Aggregation at the level of aspects.

This is also a problem when assessing each process data set individually. In the long run, one could imagine lists of “similarities” of cost conditions, to be maintained and updated continuously, for a diversity of goods. As to the second point, the matrix can be applied in different ways. It serves to identify hot spots in data quality, which then can be further addressed depending on goal and scope of the study. Studies can specify in their goal and scope a certain required data quality with criteria results not higher than e.g. three. It may also serve to calculate aggregated quality indicators. This relates to a classic question in multidimensional decision making. The common answer is to select an aggregation that fits to goal and scope of the study, and this is indeed possible. Several options are at hand for the aggregated value. It can either be – – – –

the average (arithmetical mean), the geometrical mean the maximum value, and even the minimum value,

albeit the latter seems to make not much sense since it focuses on the good aspects of data quality alone. These options reappear in the next section (combination with environmental assessment).

4.2. Combination with environmental data quality management

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Conclusion and discussion

Cost data quality is a matter that deserves high attention in ecoefficiency studies; however, there is a current lack of tools for managing cost data quality. This paper is the first to present a pedigree matrix for assessing cost data quality. Pedigree, as combination of qualitative and quantitative assessment, or rather quantification of qualitative assessment results, seems highly suitable for dealing with cost quality issues. With a list of cost structure similarities, the assessment procedure can be performed in an automated manner which seems very helpful for practical applications, and which will render the application also attractive for databases. The matrix is developed based on a “classic” pedigree matrix proposed for data quality management in environmental studies by Weidema and Wesnæs in 1996. This has several advantages. The new matrix needs only small additions; the structure of the original matrix can be preserved. The resulting cost matrix thus will probably be easier recognised and understood by eco-efficiency practitioners. The identical structure of both matrices allows combining the assessment results from both, in different ways. The criteria proposed for the matrix will certainly benefit from a thorough discussion and from applications that are critically evaluated. More experience is needed for finding best ways for aggregation, both for the cost data as well as for the combination of cost and environmental (Weidema/Wesnæs) matrix, including also the appropriate level of aggregation. Already now it is very clear, however, that the application rules will depend on goal and scope of the specific study. For example, a more cautious aggregation shall be applied in highly sensitive case studies. Cautious aggregation modes might also be of use if compared product options differ only little in their cost data quality, in order to reveal existing differences by using a more sensitive assessment.

Since the pedigree matrix by Weidema and Wesnæs has the same basic aspects/criteria and the same indicator score ranges as the matrix proposed in this paper a combination of both is obvious. The combination can be done on several levels: – one can build pairs on the aspect level – one can aggregate per cost and environment, or – one can aggregate aggregates of the cost and the environmental assessment matrix. Building pairs on the aspect level is shown in Figs. 5, 6 shows an aggregation of cost and environmental results which in turn can, in principle, either be kept as two dimensions or aggregated. The different options for performing the aggregation exist here as well as for the environmental matrix alone. Taking always the maximum aspect value is the most cautious aggregation but means that one single lowest score leads to the lowest overall quality assessment result. A decision on the applied aggregation (over different cost results and also over cost and environmental results) will depend on the specific product and assessment results, on further experiences with the matrix, and not the least on preferences of addressees and practitioners conducting the study.

Fig. 6 – Aggregation of data quality as to cost and environment.

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Acknowledgment Jutta Hildenbrand, of University of Wuppertal, provided helpful comments on an earlier version.

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