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How many environmental impact indicators are needed in the evaluation of product life cycles? Zoran J.N. Steinmann, Aafke M. Schipper, Mara Hauck, and Mark A.J. Huijbregts Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b05179 • Publication Date (Web): 10 Mar 2016 Downloaded from http://pubs.acs.org on March 21, 2016

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Environmental Science & Technology

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How many environmental impact indicators are needed in the

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evaluation of product life cycles?

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Zoran Joeri Nikolaj Steinmann, MSc a,*, Dr. Aafke Margaretha Schippera, Dr. Mara Hauckb, Prof. Dr.

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Mark Antonius Jan Huijbregtsa

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a) Department of Environmental Science, IWWR, Radboud University, 6525 AJ Nijmegen, The

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Netherlands

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b) Climate, Air and Sustainability, TNO, Princetonlaan 6, 3584 CB Utrecht, The Netherlands

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* Corresponding author: P.O. Box 9010 (box 89) NL-6500 GL Nijmegen, Visiting address:

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Heyendaalseweg 135 (room HG02.632), NL-6525 ED Nijmegen, The Netherlands, Tel: +31-(0)24-

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3652393, e-mail: [email protected]

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Keywords: PCA, life cycle impact assessment, methods, indicators, selection

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Abstract

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Numerous indicators are currently available for environmental impact assessments, especially in the

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field of Life Cycle Impact Assessment (LCIA). Because decision-making on the basis of hundreds of

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indicators simultaneously is unfeasible, a non-redundant key set of indicators representative of the

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overall environmental impact is needed. We aimed to find such a non-redundant set of indicators

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based on their mutual correlations. We have used Principal Component Analysis (PCA) in

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combination with an optimization algorithm to find an optimal set of indicators out of 135 impact

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indicators calculated for 976 products from the ecoinvent database. The first four principal

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components covered 92% of the variance in product rankings, showing the potential for indicator

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reduction. The same amount of variance (92%) could be covered by a minimal set of six indicators,

ACS Paragon Plus Environment


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related to climate change, ozone depletion, the combined effects of acidification & eutrophication,

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terrestrial ecotoxicity, marine ecotoxicity and land use. In comparison, four commonly used resource

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footprints (energy, water, land, materials) together accounted for 82% of the variance in product

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rankings. We conclude that the plethora of environmental indicators can be reduced to a small key

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set, representing the major part of the variation in environmental impacts between product life

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cycles.

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Introduction

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Over the course of the last two decades a wide variety of methods for quantifying environmental

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impacts have been introduced into the area of Life Cycle Impact Assessment (LCIA).1 There are clear

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differences in coverage and complexity of the various LCIA methods available. Damage-based

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indicators, also called endpoint indicators, provide insight in the impact of emissions and resource

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use through the full cause-and-effect chain, which ultimately leads to ecosystem and/or human

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health damage.2 Midpoint indicators provide a proxy for environmental damage by modeling only

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part of the cause-impact pathway. An example is the well-known global warming potential to

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quantify the contribution of various greenhouse gas emissions to climate change in terms of

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integrated radiative forcing.3 Finally, resource footprints estimate the resources required in a life

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cycle without further impact or damage modeling. Cumulative energy demand is an example of such

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an indicator.4-7 Additional variation in indicators results from the fact that there are several

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competing methods available that quantify the same type of effect, but with different models and


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data. For example, the TRACI method8 and the EDIP method9 both provide midpoint indicators for

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the impacts due to acidification.

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A key question that this wide variety of available methods raises is whether the outcome of a

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Life Cycle Assessment (LCA) will change among different indicators and methods.10-12 Furthermore, it

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is unpractical to base decisions for environmental product optimization or environmental policy on

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dozens of indicators simultaneously. Endpoint damage models solve the problem of having too many

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indicators, but this goes at the expense of increased uncertainty, as uncertainties involved in damage

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modelling can be very large.13-15

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Therefore, as an alternative to the endpoint damage models, there is a clear need to provide

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a limited set of indicators that is sufficiently small for efficient communication and decision-making,

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but at the same time representative of the overall environmental impact. Several attempts have

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been made to find a representative subset of relevant indicators. In a study commissioned by the

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Joint Research Centre of the European Commission (JRC), various midpoint and endpoint indicators

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and methods were qualitatively compared in terms of scientific and stakeholder acceptance.1

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However, with 14 different recommended midpoint indicators, the set is still quite large and some of

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the methods have since then undergone quite significant changes. Moreover, the JRC study did not

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consider any redundancy among the recommended indicators caused by similarity in the underlying

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processes that cause the emissions and environmental impacts. Others have suggested using a

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“resource-based indicator family”, consisting of a set of resource footprints covering the cumulative

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demand of land, water, carbon (or fossil energy) and, potentially, materials.16 However, while these

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resource footprints are easy to use and communicate17, they may not account for the full range of

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impacts employed in the damage-based methodologies. Finding a set of indicators that is optimal in

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both its size and coverage requires a more systematic and quantitative way of reducing the number

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of indicators. A few studies have attempted to achieve this via Principal Component Analysis (PCA).18-

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by PCA. For example, only two out of eleven indicators were needed to cover more than 95% of the

If correlations between indicators are high, substantial dimensionality reduction can be achieved



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total variance of the environmental impacts within a set of nine household electronic products.23 A

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more recent study, covering 17 impact indicators for two product categories (electricity and oil),

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employed a slightly different and novel technique to demonstrate that the number of impact

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indicators can be greatly reduced.24 However, while these studies provided valuable insights for one

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or a few method(s) and/or product group(s), their coverage has been too small to answer questions

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about which set of indicators is optimal in a more general sense. The search for an optimal set of

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indicators using PCA has, to the best of our knowledge, not yet been applied over a large range of

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products and impact assessment methods.

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Here, we aim to find an optimal set of environmental indicators to cover the variance in the

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rankings of a large number of products. We selected 976 products and 135 environmental indicators

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from the ecoinvent 3.1 database25 as input for our analysis. We combined PCA with multiple

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regression to arrive at the minimum set of indicators explaining the major part of the variance in the

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product rankings. Apart from this minimum set, we also evaluated the extent to which four

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commonly used resource footprints (fossil energy, water, land and materials) were representative of

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the variation in product rankings.

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Methods & Materials

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Products

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All products and corresponding environmental impacts were taken from the ecoinvent database

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(version 3.1). Environmental impacts of all products are expressed per kilogram of product. To avoid

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overlap among the products, we applied the following selection criteria:

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Aggregated product categories (e.g., ‘inorganic chemicals’) were removed from the dataset.

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In case of identical products, we preferred the global or ‘rest-of-the-world’ market mix over

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specific sub-types based on particular production methods or regions. For example, we

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selected global market rye grain and excluded organic rye grain and Swiss rye grain.


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In case of products with almost identical production chains (e.g., butanol and iso-butanol),

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we selected the products with the largest overall amount of emissions or resource

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extractions.

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After applying the first two criteria, there were 1002 products remaining. To apply the third criterion,

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we then searched for products with (nearly) identical product chains. This was done by dividing

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every emission/resource extraction of a product by the same emissions/resource extractions of all

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other products. This yielded 1001 sets of 1869 ratios for each product. If these ratios were (almost)

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equal for all emissions and resources considered (coefficient of variation 1) was retained. Our final selection contained

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976 products (Table S1), which we grouped into seven categories: Chemicals (435), Plastics (64),

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Ores, minerals & fuels (91), Building materials (72), Processed bio-based products (80), Agricultural &

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forestry products (106), Metal products & electronics (128).

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Indicators

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Ecoinvent25 reports a total of 692 impact indicators. Our method and the algorithm that we

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employed to find the optimal set of indicators is not suitable for use on sets of data with (almost)

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perfectly correlated data (i.e. a correlation coefficient of approximately 1) because two perfectly

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correlated indicators are indistinguishable.26 Therefore we excluded impact indicators that met at

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least one of the following criteria:

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Newer versions of the same impact indicator were assumed to fully replace the older ones.

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Therefore we only used the most recent version of each indicator. For example, the endpoint

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indicator calculated with the Ecoscarcity 201327 method was retained, while the same

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endpoint indicators from Ecoscarcity 2006 and 1997 were excluded.



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For most environmental impact indicators multiple versions are provided, either including or

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excluding long-term emissions. These long-term emissions can stem from landfill sites, long

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after the product is disposed. For most indicators there is no difference between the versions

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including and excluding the long-term emissions. For toxicity indicators however, excluding

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the emissions on a long-term leads to an underestimation of the damage. Therefore only the

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indicator versions including the long-term emissions were retained for our analysis.

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After these first two steps, the remaining set of 161 indicators included several indicators

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that are identical for different impact assessment methods. For example, the Ozone

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Depletion Potential (ODP) for an infinite time horizon is calculated in the same way in the

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CML200128, EDIP20039 and Impact200229 methods. Similarly, there are several indicators for

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global warming that are identical for different methods. In order to check whether there was

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complete correlation between the indicators we used the “trim.matrix” function on the rank

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correlation matrix of the 976 products in the R package “subselect”. 26 If two or more

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indicators were found to be indistinguishable, only the indicator in the first method (based

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on alphabetical order) was retained. This resulted in the removal of 26 indicators that all

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showed a correlation of 1 to one or more of the other indicators.

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After this procedure we retained a set of 135 environmental indicators for 976 products. The 135

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environmental indicators belong to 13 different methods (Table 1).

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Table 1. Impact assessment methods, numbers of indicators and corresponding main categories as included in

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the analysis.

Impact assessment method CML2001 28 EDIP 2003 9 Impact 2002 29 ReCiPe 2 TRACI 8 EcoIndicator99 30 Ecological scarcity2013 27 EPS2000 31, 32 Impact 2002 29

Number of indicators 49 21 14 31 9 3 1 1 3

Category

Midpoint indicators

Endpoint indicators


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ReCiPe 2 3 Fossil energy footprint * 1 Water footprint * 1 Resource footprints Material footprint * 1 Land footprint * 1 * Calculated from the inventory outcomes of the ecoinvent database

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Data analysis

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We employed PCA combined with regression analysis to find the minimum set of indicators

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necessary to adequately describe the variance in product rankings among the full set of impact

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indicators. In PCA the total variation in a dataset is rewritten as a set of non-correlated linear

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combinations of the original variables (in our case, the indicators), which are called principal

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components. The first principal component describes the maximum amount of variance and

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subsequent principal components explain decreasing amounts of variance. If there is a large

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correlation between variables, the first couple of principal components will cover the majority of the

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variance in the dataset and these components can be used as a highly parsimonious summary of the

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total dataset.33

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First, we performed a PCA on the rank correlation matrix, i.e. the correlation matrix of the

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dataset after ranking the products on each of the 135 indicators from 1 to 976 (see Table S2). This

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transformation to rank scores was done to give equal weight to each product in the dataset and to

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each impact indicator (i.e. each indicator now has the same mean and standard deviation). It is

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desirable to give equal weight to each product because the analysis of impact per kilogram of

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product is an arbitrary choice that does not reflect differences in production volumes or value to

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society. Without this transformation the correlation structure would be dominated by a few products

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that have very high impacts per kilogram (for example the precious metals gold and platina).

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Second, we determined the number of non-trivial components with the Avg-Pa (Average

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parallel analysis) stopping rule as described by Peres-Neto et al..34 This stopping rule is a criterion

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which tells whether a component describes more or less variance than one would expect if it was

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based on purely randomized data. Because we work with product rankings rather than impact scores,



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we adjusted the procedure to work with randomized product rankings rather than normally

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distributed random data. All integers from 1 to 976 were randomly sampled to create a dataset with

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the same number of rows (976) and columns (135) as the original data. This was repeated 1000

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times. A PCA was then performed on each of the 1,000 randomized datasets and the average

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variance explained by each component was calculated. If a component explained more variance in

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the original data than in the randomized data, the component was considered non-trivial.

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Third, to interpret the non-trivial components, the loading of each indicator as well as the

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principal component scores per product (and product group) were plotted. Biplots of the loadings

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show the association between the indicators and the principal components: the more extreme (high

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or low) the loading, the more representative the indicator is of that component. The principal

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component scores show the associations between the observations and the principal components,

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thus revealing the similarity of the indicators and products.

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Fourth, to find a subset of the original indicators most closely associated with the non-trivial

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principal components, we calculated the amount of variance in principal components scores that

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could be explained by subsets of the original indicators. Subsets of any number of indicators can be

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tested, whereby a set comprising all indicators would cover 100% of the variance and smaller subsets

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would cover less. The best set, in terms of explained variance, for each number of indicators was

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found by using the “improve” algorithm from the package “subselect” 26 in the statistical program R.35

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The improve algorithm uses multiple linear correlation to determine the amount of explained

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variance.

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Fifth, we defined the optimal size of the indicator set by using the explained variance of the

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so called non-trivial principal components as a benchmark. The indicator set with the lowest number

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of indicators and an explained variance equal to or higher than the explained variance of the non-

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trivial components was considered to be the preferred set of indicators.

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Finally, we tested the extent to which a set of four resource footprints (fossil energy, land, water and material use) covered variance in our dataset by using the multiple linear correlation


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analysis on all principal component scores, as mentioned above. Note that these four resource

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footprints were not part of the set of 135 indicators.

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Results

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Principal components and indicators

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The number of non-trivial components was four, which together covered 92.0% of the total variance

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in the dataset. The majority of the variance (83.3%) was covered by the first component, with 3.9%,

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3.1% and 1.9% covered by the three consecutive components (Figure 1). Since all correlations in the

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database were positive, the first component can be seen as an overall indicator of environmental

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impact. Indicators that are most similar to other indicators (i.e. have the highest mutual correlations)

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have the highest absolute loadings on this component. For the first component these were the

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indicators related to freshwater and marine ecotoxicity. However, most other indicators showed very

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similar loadings (Figure S1, see SI1 for a more extensive analysis of the principal components).

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Indicators with the lowest absolute loadings on the first component were related to land use, which

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means that indicators related to land use are least correlated to all other indicators. On the second

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principal component, high loadings were found for indicators related to land use and terrestrial

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ecotoxicity while indicators related to fossil energy use and global warming had low loadings. This

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means that after taking into account the fact that impact-intensive products are generally impact-

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intensive according to all impact indicators; products that use relatively large amounts of land are

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likely to use a relatively limited amount of fossil energy and vice versa. Indicators related to ionizing

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radiation (i.e. from the use of nuclear energy) and indicators of ozone depletion were further

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distinguished from the rest by components 3 and 4. See Figures S1 and S2 for the loadings of all

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indicators on the first four components.



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Figure 1. Variance explained per principal component based on our dataset (blue), per principal component

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based on random data (purple), and by the best set of indicators (orange). Solid lines with dots indicate the

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cumulative variance explained.

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Products

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The scores per principal component (Figure 2) indicate how the products are related to each of the

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components. Products with the highest and lowest overall ranks showed the most extreme scores on

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the first principal component. On a per kg basis, we found the highest impacts (lowest scores on the

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first principal component) for metal products and electronics (which include very high-priced and

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energy-intensive products such as gold), and the lowest impacts for ores, minerals & fuels and


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building materials. On the second component a clear distinction could be seen between the products

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on a biological base (agricultural & forestry products and processed bio-based products) and the rest

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of the products. This coincided with the high loadings for (agricultural) land use on the second

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principal component. A number of plastics showed the lowest scores on the third component. These

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plastics have relatively small emissions of ozone depleting substances as a result of their production

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process compared to the other emissions. The highest scores are found among the inorganic

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chemicals.

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Figure 2. Boxplots of principal component scores per product and product group: colored boxes cover the

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interquartile distance, black bars inside the boxes denote the medians, whiskers extend to the 5 and 95

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quantiles.

th


th


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Best set of indicators

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A set of six indicators was needed to cover slightly more variance than the first four principal

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components (i.e. 92.3% and 92.0% respectively; Figure 1). The best set of six indicators contained

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indicators of climate change, ozone depletion, terrestrial ecotoxicity, the combined ecosystem

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effects of acidification & eutrophication, marine ecotoxicity and land use (Table 2).

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The four resource footprints together accounted for an explained variance of 82.0% (Table

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2). The results suggest that the fossil energy indicator, with an explained variance of 72.9%, is a

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reasonable indicator of overall impact. The explained variance can be raised to 76.8% by adding

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material use and then to 80.1% by adding land use. The water footprint explained only 1.9% of

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additional variance; this is due to the fact that water consumption is related to both the energy-

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intensive process of electricity generation and the land-intensive process of crop production.

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Table 2. Best sets of impact indicators and the variance explained by sets up to six indicators, and the variance

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explained by the resource footprints.

#

Impact indicator

Method: Full name of indicator

1 2

Marine ecotoxicity Climate change Marine ecotoxicity Land use Human toxicity Climate change Climate change Ozone depletion Land use Marine ecotoxicity Climate change Ozone depletion Terrestrial ecotoxicity Acidification & eutrophication Marine ecotoxicity Climate change Land use Ozone depletion Acidification & eutrophication Marine ecotoxicity Terrestrial ecotoxicity

ReCiPe: METP100a (I) CML2001: upper limit of net GWP100a* ReCiPe: METPinf (H) Impact2002: land occupation ReCiPe: HTPinf (E) ReCiPe: GWP100a (H) CML2001: upper limit of net GWP100a CML2001: ODP40a Impact2002: land occupation ReCiPe: METPinf (E)

3

4

5

6

Explained variance 78.4% 84.0%

87.8%

90.1%

CML2001: GWP20a CML2001: ODP40a CML2001: TAETP20a Impact2002: terrestrial acidification & eutrofication ReCiPe: METPinf (E)

91.3%

CML2001: GWP20a CML2001: Land use: competition** CML2001: ODP40a Impact2002 ReCiPe Midpoint E: METPinf ReCiPe Midpoint I: TETP100a

92.3%


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Resource-based sets 1 Fossil energy Land Water Material 2 Fossil Energy, Material 3 Fossil Energy, Land, Material 4 Fossil Energy, Land, Material and Water

72.9% 37.9% 58.1% 54.3% 76.8% 80.1% 82.0%

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Abbreviations:

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METP: Marine EcoToxicity Potential

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GWP: Global Warming Potential

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HTP: Human Toxicity Potential

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ODP: Ozone Depletion Potential

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T(A)ETP: TerrestriAl EcoToxicity Potential

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inf/100a/40a/20a: Time Horizons of infinite duration and 100, 200 and 40 years respectively

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(I): Individualist perspective, (E): Egalitarian perspective, (H): Hierarchist perspective

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* Upper limit of GWP100a: a method where the upper values of several uncertain GHGs are used rather than

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the most likely value

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** Like the land footprint this consists of an unweighted summation of different land uses, however not all

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types are included

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Discussion

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Our analysis revealed a large amount of redundancy in a set of 135 impact indicators, since a

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single indicator already covered 78.4% of the variance in the ranking of 976 products. This result is in

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line with earlier research in which large correlations between a single indicator (energy demand) and

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different impact indicators were found.36, 37 However, even though one indicator might be able to

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cover a large proportion of the damage over a wide range of products, within certain product groups

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it may be less useful to distinguish products from each other.38 As the scores of the individual

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products demonstrate, several indicators are needed to distinguish products within specific groups.

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For example, bio-based products are mostly separated by the amount of land use, while some

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plastics have relatively high emissions of ozone depleting substances; therefore an indicator that



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takes this into account is necessary to distinguish products within this group. The requirement of a

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limited set of indicators is in line with the findings from Gutierrez et al. 23 who used a PCA-based

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approach for data reduction. These authors found that three indicators (respiratory inorganics, ozone

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depleting substances and minerals) were needed to cover the majority of the variance in

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environmental impacts of a small set of household electronics. Pascual-Gonzalez et al. 24 applied a

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combination of multiple regression and an approach called MILP (Mixed-Integer-Linear-

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Programming) to reduce the number of impact indicators for a large number of oil and electricity

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products. Fossil energy demand, respiratory inorganics and ozone depletion constituted the best set

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for oil, while for electricity the different types of primary energy carriers (water, wind, nuclear and

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fossil) and an indicator of climate change were needed. Our study, adds to these insights, by

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providing an application of a data reduction technique on a dataset that is both larger and more

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diverse than used up to now.

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According to our results, the best indicator set includes indicators of global warming (which is

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strongly correlated to all other indicators), land use and ozone depletion, supplemented by indicators

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for the combined effect of acidification and eutrophication (these two impacts are combined into

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one indicator in the IMPACT 2002 methodology), and terrestrial and marine ecotoxicity. While this

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set maximizes the amount of explained variance, these six indicators are not necessarily the most

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preferable according to additional criteria, such as the RACER (Relevant, Accepted, Credible, Easy and

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Robust) criteria.39 For example, the problem of ozone depletion is becoming less relevant due to

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successful emission reduction policies40 and the indicators of toxicity (including the statistically best

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indicator for marine ecotoxicity) are not always robust, due to the large uncertainties involved in

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their calculations.13-15 As the loadings of the indicators (figure S1) on the first component (which

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covers 83.3% of the total variance) show, there are several indicators with approximately the same

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explanatory power. This means that alternative sets of indicators can be defined which are only

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marginally worse in terms of explained variance compared to the set of six indicators derived from

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our analysis. The resource footprints are relatively easy and robust to calculate. Being indicators of


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resource use rather than indicators of environmental impact, it was not known whether they can be

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used as a proxy for environmental impacts. Our results indicate that they cover the variance in

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impact indicators quite well (82% explained variance with four indicators), which suggests that these

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indicators are relevant for use in the area of LCIA. Using simple resource footprints would reduce the

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need for the complicated mid- and endpoint damage models.

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In this study we have used product rankings calculated on a 1 kg basis, to give insight in the

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variation between very diverse products. It can be argued that other bases of comparison, for

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instance, based on products with the same functional unit, are more in line with one of the main

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applications of LCA: product optimization. Comparing products with the same functional unit was

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done by Sabio et al.21, who found four relevant environmental indicators for hydrogen supply chains,

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thereby also demonstrating that a small set of indicators was a sufficient base for the comparison of

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similar product alternatives. The products used in our study are mostly quite simple commodities,

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with only the electronics category covering more complex consumer products, such as LCD screens.

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Furthermore, the analysis was based on cradle-to-gate data, whereby the product’s use and disposal

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are not taken into account. The use phase of products can be energy-intensive, meaning that, if

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included, products with long use times would generally rank higher on indicators related to energy

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use, such as indicators of climate change. It is difficult to predict how the correlation structure, and

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therefore the indicator selection, would change when more complex products or use phase data are

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taken into account. The correlation between indicators could either increase or decrease, resulting in

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more or less explained variance for indicator sets of each size. However, in the case of taking into

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account the use phase, it is likely that there will be more explained variance with the same number

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of indicators, since all indicators related to energy use will increase simultaneously for products with

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long use times.

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Additionally, the coverage of the ecoinvent database may not be complete in terms of the

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inventory flows included (i.e. the extent to which relevant emissions and resource extractions are

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included for all products) and in terms of (novel) impact pathways covered. Limiting the inventory



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flows to a limited number of well-studied pollutants may result in an overestimation of the

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correlations between the impact indicators that are eventually calculated from these flows, for

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example not all of the toxic chemicals covered by the USEtox model13 are available in the ecoinvent

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database. Similarly, leaving out potentially relevant damage pathways (for example ecosystem

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damage caused by tropospheric ozone formation), may also cause an overestimation of the amount

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of variance that can be covered with a limited number of indicators. Despite these limitations, it

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should be noted that the ecoinvent database is one of the largest databases available for life cycle

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assessment, with clear guidelines on how to ensure completeness and consistency of the data as

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much as possible.41

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Finally, spatially explicit impact assessment methods, as recently developed for several

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impact categories, including water, land, acidification and eutrophication42-45, were not included in

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the assessment. This is due to the fact the coupling of spatially explicit inventory and impact

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information is still not common practice in LCA databases, including ecoinvent 3.1. Spatially explicit

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methods use a different amount of impact for the same environmental intervention, which can

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reorder the ranking of the impact indicator, depending on the location where the intervention takes

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place. Whether this results in more or less correlation between the different impact indicators

335

cannot be known a priori. Regionalization may increase the amount of variance to be explained by a

336

small set of indicators and therefore limit the coverage of our recommended set, however

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regionalization can potentially also increase the correlations between indicators. This would be the

338

case if regionalization results in several impact indicators that are consistently higher than average in

339

one country. Products that rely heavily on that country as a sourcing region will then have a higher

340

rank for all these indicators of impact, resulting in a higher correlation between the indicators than

341

one would observe without regionalization of the impacts. It is recommended to redo the PCA-

342

regression procedure after the introduction of spatially explicit impact assessment methods in

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common LCA practice.


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Summarizing, we introduced a procedure for systematically selecting a set of impact

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indicators that is optimal in covering the maximum amount of variance with a minimal number of

346

indicators. With this method we have demonstrated that reducing the number of indicators in our

347

dataset from 135 to six indicators is feasible without losing more than 8% of the total variance.

348

Although it is unlikely that any practitioner would have used 135 indicators simultaneously, there

349

was no reason to discard any of the indicators a priori other than for reasons of convenience. Our

350

results can be seen as comforting for LCA practitioners, since we showed a clear overlap between

351

and within methods. For the first time, we have demonstrated a large dimensionality reduction to be

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feasible over a wide range of indicators and products. This suggests that the plethora of indicators

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available for product assessments is largely superfluous and that a small set of key impact indicators

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is sufficient for product optimization and environmental policy. Alternatively, a set of 4 resource

355

footprints covering 82% of the total variance may be used.

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Author information

357

Corresponding author

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*Tel: +31-(0)24-3652393, e-mail: [email protected]

359

Notes

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The authors declare no conflict of interest

361

Acknowledgements

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This project was funded by the European Commission via the WP7 project Desire: number 308552.

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Associated content

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Supporting information



365

The supporting information contains additional information about the indicator loadings, including

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figure S1 and S2 which display the loadings of all indicators. A full list of all indicators is also included

367

in table S1, and the correlation matrix (table S2) is included as a separate Excel file. This information

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is available free of charge via the Internet at http://pubs.acs.org/.

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