Impact Assessment of Abiotic Resources in LCA - ACS Publications

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Impact Assessment of Abiotic Resources in LCA: Quantitative Comparison of Selected Characterization Models Jakob T. Rørbech,† Carl Vadenbo,‡ Stefanie Hellweg,‡ and Thomas F. Astrup*,† †

Department of Environmental Engineering, Technical University of Denmark, Miljovej, Building 113, DK-2800 Kongens Lyngby, Denmark ‡ Institute of Environmental Engineering, ETH Zurich, Switzerland S Supporting Information *

ABSTRACT: Resources have received significant attention in recent years resulting in development of a wide range of resource depletion indicators within life cycle assessment (LCA). Understanding the differences in assessment principles used to derive these indicators and the effects on the impact assessment results is critical for indicator selection and interpretation of the results. Eleven resource depletion methods were evaluated quantitatively with respect to resource coverage, characterization factors (CF), impact contributions from individual resources, and total impact scores. We included 2247 individual market inventory data sets covering a wide range of societal activities (ecoinvent database v3.0). Log− linear regression analysis was carried out for all pairwise combinations of the 11 methods for identification of correlations in CFs (resources) and total impacts (inventory data sets) between methods. Significant differences in resource coverage were observed (9−73 resources) revealing a trade-off between resource coverage and model complexity. High correlation in CFs between methods did not necessarily manifest in high correlation in total impacts. This indicates that also resource coverage may be critical for impact assessment results. Although no consistent correlations between methods applying similar assessment models could be observed, all methods showed relatively high correlation regarding the assessment of energy resources. Finally, we classify the existing methods into three groups, according to method focus and modeling approach, to aid method selection within LCA.

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INTRODUCTION Concerns for resource scarcity and declining availability of natural resources have brought increased attention to the need for systematic quantification of impacts related to resource consumption as well as resource recovery. Life cycle assessment (LCA) is one of many environmental assessment tools that within the recent decades have responded to this concern with development of new resource depletion assessment methods.1,2 The main advantages of LCA are the elaborate framework for data collection and quantification of environmental exchanges between the system of interest and the environment, application of scientifically based impact models assessing specific environmental concerns, for example, global warming, ozone depletion, human and eco-toxicity, etc., and finally room for more subjective application of normalization and weighting principles supporting the interpretation of the results.3,4 Within LCA, the area of protection (AoP) related to natural resources in general, and abiotic resources in particular, has been considered controversial in several respects.5,6 Predominantly from the early 1990’ies and onward, a wide range of abiotic resource assessment methods have been developed: Ranging from methods, such as CML-IA 2002,7,8 EDIP 19979 and Schneider et al.10 relating resource consumption to the © 2014 American Chemical Society

resource base, to methods quantifying surplus energy needs for future resource extraction11 and methods focusing on exergy12,13 or solar energy demand14 (SED). While numerous and somewhat diverse impact models regarding the environmental mechanism of natural resource depletion have been suggested in literature,15 general consensus of the appropriateness of indicator types and interpretations of the environmental concern regarding natural resources have not yet been reached.16 Outside the LCA field, resource criticality, for example, as defined by Graedel et al.,17 has been widely applied for decision making. The application field of criticality differs from that of LCA; the latter attempts to assess the consequences of resource extraction on the environment, while the former addresses the risk for a specific user that a resource is not available in the future. Due to the difference in application scope, criticality is not further discussed in this paper. Received: Revised: Accepted: Published: 11072

May 15, 2014 August 30, 2014 September 10, 2014 September 10, 2014 dx.doi.org/10.1021/es5023976 | Environ. Sci. Technol. 2014, 48, 11072−11081

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depletion and how methodology principles affect state-of-theart LCA is available. Improved information on quantitative differences between individual methods is needed for researchers and practitioners alike to fully appreciate the consequences of assessment method selection and the associated resource model choices for the final LCA results. The aim of this paper is to provide a systematic evaluation of existing assessment methods to enable an informed selection of resource depletion indicators within LCA. For a set of 11 resource depletion characterization models, covering a wide range of resource modeling approaches, the specific objectives were to evaluate (i) resource coverage for metals, minerals and nonrenewable energy resources between methods and differences in characterization factors, (ii) comparability of total impact scores based on a large inventory data set representing 2747 market activities in the global economy (ecoinvent, database v3.037), (iii) importance of contributions to the total impact scores from key resource categories (metals, energy, and minerals) within the same inventory data set, and finally on this basis to (iv) qualitatively assess critical differences in AoP and impact assessment methods, and subsequently suggest alternative classification of the methods to provide an improved basis for informed selection.

LCIA methods encompassing resource depletion have been broadly classified by four types:18−20 (i) methods aggregating resource consumption based on mass or energy, (ii) methods relating resource consumption to geological reserves, (iii) methods relating current resource consumption to environmental interventions caused by potential future extraction of resources, and (iv) methods quantifying thermodynamic losses such as exergy and solar energy. Understanding the assessment principles of these methods and the effects on the impact assessment results is fundamental for selection and interpretation of resource depletion indicators. Type 1 indicators are simply aggregating the material extracted from the natural environment or the energy consumption based on physical quantities (e.g., ecological rucksack as material input per service-unit21 or cumulative energy demand22). These methods have been criticized for not addressing differences in declining availability and are generally not considered sufficient as impact indicators for resource depletion within LCA,19,20 but for example, cumulative energy demand has been shown useful as screening indicator for specific environmental performance parameters.23 Type 1 methods are not discussed further in this study. Type 2 indicators based on use-to-availability ratios7−9,24 (e.g., CML-IA 2002 and EDIP 2003 (EDIP)9,25) have been criticized for their arbitrary choice of geological reserve and reserve base.19,20 Overall, these methods assume a relationship between reserve exploitation and decline in availability. The choice of geological reserve basis (ultimate reserve, reserve base, economic reserve, etc.)8,26 introduces uncertainty due technological developments, short investment horizons, and differences in availability not directly related to the size of the reserve.19,20 Type 3 indicators represent a diverse group of methods assessing future decline in resource availability and the associated increase in extraction costs, caused by present resource consumption through future scenario modeling (e.g., Eco-indicator 99 (EI99),27 EPS 2000 (EPS),28 ReCiPe 2008 (ReCiPe),29 newer methods such as Swart & Dewulf (ORI),30 Vieira et al.,31 and Ponsioen et al.32). Specific challenges for these model concepts include (i) uncertainty of predicting future effects of present extraction, (ii) prediction of future technological developments, and (iii) “translating” the indicators into specific environmental concerns similar to the endpoint categories of human health or the natural environment as defined by ISO standard on LCA.3,19 Type 4 indicators include methods addressing consumption of a single universal limited resource (e.g., exergy,33 cumulative exergy extraction from the natural environment (CEENE)12 and cumulative exergy demand (CExD)13). These methods do not consider scarcity of natural resources but rather the more abstract consumption of selected limited flow resources, for example, exergy destruction, as the long-term availability problem. So far, no comprehensive comparison of these resource depletion methods has been provided to demonstrate the effects of the individual resource models for the impact assessment results. Existing studies in the literature involving quantitative comparisons of resource assessment methods provide limited information for several reasons: (i) only a few methods are included,12,13,23,34,35 (ii) evaluations are restricted to single activities or inventory data sets,30,34 or (iii) only characterization factors (CF) are compared without addressing the effects on actual life cycle inventories (LCI).29,36 Consequently, only limited systematic information on how existing resource assessment methods address resource

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MATERIALS AND METHODS Life Cycle Impact Assessment (LCIA) Method Selection. Eleven methods were selected to represent distinctly different resource depletion models, preferably with wide application in existing literature.2 Focus was limited only to abiotic resources to ensure sufficient comparability between the individual resource depletion models. The selection of methods was based on the following principles: (i) methods with distinct natural abiotic resource depletion models, (ii) methods applying impact modeling beyond simple aggregation of mass or energy, and (iii) methods with documented use in a substantial number of case studies or methods recently being developed.2 Focus was placed on abiotic nonrenewable resources to facilitate direct comparison of total nonweighted impact scores between methods. Two use-to-availability methods (Type 2), EDIP and CMLIA 2002, were included. EDIP applies the economic available reserve, whereas CML-IA 2002 provides two different models applying: (i) the ultimate reserve model (default in the CML method) (CML)24,38 and (ii) the ILCD-recommended reserve base model (ILCD).16 Here ultimate reserves include all resources in the upper crust of the Earth,8 while reserve bases represent the identified resources available by todays extraction techniques and economic reserves represent the resources that are also currently economically profitable to extract.26 Four methods assessing decline in ore grade quality through future scenario modeling (Type 3) were included: EPS, EI99, IMPACT 2002+ (I2002+)39 and ReCiPe. These methods apply economic or energetic measures of decreased future availability of resources as a function of present extraction activities. Additionally, the recently developed Ore Requirement Indicator (ORI)30 was included. This method assesses annual changes in ore requirements as a function of declining ore grades due to present mining activities. This represents a slightly different approach from the previous four methods as the model assumes constant marginal decline, rather than using nonlinear models and applying scenarios of reserves and future mining activity. Noteworthy, Vieira et al.31 presented a similar approach assessing marginal decrease in metal ore grade with 11073

dx.doi.org/10.1021/es5023976 | Environ. Sci. Technol. 2014, 48, 11072−11081


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of large differences in functional units of the individual data sets (e.g., one airport compared with one kWh electricity), the total impact scores covered many orders of magnitude. To facilitate a comparison of the total impact scores across all data sets and to ensure robustness of the approach with respect to the “size” of functional units, the calculated impact scores for all pairwise combinations of the 11 methods were successively rescaled and evaluated according to the following procedure: (i) the 2747 data sets was divided into ten groups from top to bottom as a function of the geometric mean per data set, (ii) the R2-value for the log−linear correlation was obtained, and finally (iii) the highest group of data sets was rescaled by the ratio between the averages of the geometric mean in this group and of the lowest group of data sets (see Figure S1 SI for an illustration of the procedure). Steps (ii) and (iii) were repeated nine times, thereby successively rescaling all the top nine groups relative to the bottom group. Regression analysis was carried out before any rescaling as well as after each rescaling, obtaining ten individual R2-values for each pairwise combination of the 11 methods. Average R2-values and associated standard deviations (SD) were determined to identify pairs of resource methods with (i) good correlation between total impact scores and with (ii) high robustness toward rescaling and thereby the “size” of functional unit. To supplement the above regression analysis and avoid potential effects from rescaling the data sets, ratio distribution analysis was carried out for ratios between total impact scores of all pairwise combinations of the 11 methods (e.g., EDIP/ CML, ILCD/CML, ILCD/EDIP, etc.). To facilitate comparison between methods, these ratios were normalized by the median ratio within each pairwise combination. Boxplots showing the uniformity of the normalized ratios over the entire range of data sets were produced. The importance of key resource types for the overall impact scores was evaluated by dividing all resources into three overall categories: metal, energy (including fossil and nuclear) and mineral resources (for details see SI Table S1). Average contribution profiles representing the share of the total impact scores associated with the three individual resource categories were determined for each resource model covering all inventory data sets as an entity. For the eight economic sections, these data were further disaggregated identifying resources contributing with more than 15% to the overall impact score.

the increase in marginal additional tonnage needed for extraction of a metal based on a geological distribution model. Unfortunately, only the CF for copper has been published at the time of this study, thus the method could not be included in the quantitative analysis. Finally, three “universal limited resource consumption” methods (Type 4) were included: CEENE, CExD, and SED. Table 1 provides an overview of the selected methods and their main characteristics. For overview of the included CFs for the individual methods please refer to Table S1 in Supporting Information (SI). For more comprehensive description of the individual LCIA methods and underlying resource depletion models, we refer to the individual sources,9,12−14,24,27−30,38−41 the ILCD handbook,16 and two articles of Carvalho et al.2 and Klinglmair et al.36 Inventory Database Used for the Evaluation. To facilitate a comprehensive comparison of the methods based on actual impact assessments, a large set of inventory data sets for products and services was required. The ecoinvent database v3.037 was used for this purpose containing a large number of transforming activities covering an extensive variety of societal products and services. Furthermore, the ecoinvent database offers multiple system models based on different sets of “rules” linking the individual transformation activities via market activity data sets (market data sets) to provide aggregated LCIs, e.g. based on average supply mixes or an unconstrained supplier (for further details refer to the ecoinvent database v3.0 documentation).37 For this study, all nonempty market data sets (2747 data sets) in the system model “allocation, ecoinvent default” were included. The data sets were analyzed with respect to extraction of abiotic resources, that is, environmental exchanges categorized as “Raw, in ground”. Application of the ecoinvent database ensured consistency with respect to system boundaries and use of background processes, thereby providing a large database with a wide range of environmental interventions. For clarity reasons, the inventory data sets were subdivided according to sections of economic activity as defined by the International Standard Industrial Classification of All Economic Activities.43 Seven individual sections were identified containing more than 30 inventory data sets (in total 2704 data sets), while six smaller sections were combined into a single group of “Others” (in total 43 data sets). For complete overview of the included economic sections, subdivisions and number of data sets, please refer to Table S2 in the SI. Quantitative Comparison and Statistical Analysis. Log−linear regression analysis of characterization factors (CFs) for the individual resources within each method was performed to quantify correlations between the included resource depletion models, that is, to evaluate how similar the individual methods assess resources. The regression analysis was performed pairwise for all combinations of the 11 methods (e.g., CML vs EDIP, CML vs ILCD, etc.), including only CFs for resources common for both methods (see SI Table S1 for a complete list of included resources and CFs). Log−linear regression was selected over normal regression analysis to avoid correlation results disproportionally influenced by numerically high values in the data sets. The approach followed the principles of two previous studies by Huijbregts et al.22,23 In addition to CFs, the regression analysis was performed on the total impact scores as well as for each resource category (metals, energy, and minerals) separately from each resource model over all market data sets as an entity. As a consequence

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RESULTS AND DISCUSSION Resource Coverage and Characterization Factors. As a first level of comparison, characterization factors for individual resources were compared between resource models. Figure 1 shows the number of resources pairwise shared between models and the R2-value for log-linear regression of CFs between models (color-scale indicates degree of correlation between CFs). For a detailed overview of CFs for all individual resources and all assessment methods, see SI Table S1. Overall, the groups of (i) EI99, I2002+, and CExD and (ii) EDIP, ILCD, EPS, ReCiPe, and ORI correlated relatively well with correlation values above 0.68 and 0.61, respectively. In contrast, CML, CEENE, and SED generally showed relatively poor correlation with most other methods. Good correlation between CFs in individual methods indicates that the impact assessment of the resource models “value” the compared resources similarly. The relatively good correlation between ORI and all other methods but CEENE, for example, suggests that the 4−9 shared resources are similarly evaluated within the 11074


CML-IA 2002: alternative to CML, use-to-availability ratios based on present production and the estimated reserve base. Recommended by ILCD

Type 4: Universal Limited Resource Cumulative Exergy Extraction from the Natural Environment: total extraction of exergy from nature embedded in target resources, as the exergy CEENE12 difference between a resource and a defined reference state in the natural environment, to society 13 CExD as implemented in Cumulative Exergy Demand: total removal of exergy from nature embedded in processed material (including slags and tailing), as exergy difference 42 ecoinvent v.2.2 between the material and a defined reference state in the natural environment, to society SED14 Solar Energy Demand: total direct and indirect solar energy requirements needed to provide a product or service

64 68

MJse-eq

9

kg/year

MJex-eq

19

$

53

12

MJ

MJex-eq

13 64

42

29

48

metals & minerals

MJ ELU

Person Reserves kg Sb-eq

EDIP 2003: Economic availability based on economic reserves per person 16

kg Sb-eq

unit

CML-IA 2002 (default-model): Use-to-availability ratios based on present production and ultimate reserves in the upper crust of the earth

model concept

Type 3: Ore Grade Quality EI9927 (H,A) Ecoindicator 99: Surplus energy representing additional energy requirements for extraction and processing of low grade deposits in the future EPS28,41 fossils: as implemented EPS 2000: future extraction costs related to mining of average earth crust composition using renewable energy sources only in ecoinvent v.2.242 I2002+39 v.Q2.21 IMPACT 2002+: surplus energy representing additional energy requirements in future, similar to EI99, with the addition of including the extractable energy content resources used destructively (fossils and uranium) to the surplus energy of these ReCiPe29 v.1.08 (H,A) ReCiPe 2008: marginal increase in future extraction costs relative to current extraction costs, as future ore concentrations decline when cumulative production increases ORI30 ore requirement indicator: present annual change in ore requirements per kg of metal content

ILCD fossils: v.3.5 minerals: v.4.1

16,24,38

Type 2: Use-to-availability CML24,38 fossils: v.3.5 minerals: v.4.1 EDIP9,25

method

11075

1

1

1

0

1

1

0 1

1

1

1

nuclear

4

5

4

0

5

4

3 4

4

4

4

fossils

no. of included resources

Table 1. Summary of Basic Concepts and Coverage of Abiotic Resources for the Selected LCIA Methods (see Table S1 in SI for a Complete List of Characterization Factors)

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Figure 1. Pairwise comparison of characterization factors (CF) in resource depletion methods: The number of resources in common and log-linear regression correlation (R2-values in bold) within the shared resource CFs. Coloring illustrates degree of correlation: from white (high correlation) to red (low correlation).

coverage and numerical values of individual CFs have substantial influence on the total score (see Figure S4 in SI). Figure 3 shows all average R2-values and associated standard deviations (SD) over the ten-step rescaling of impact scores. White-to-red coloring indicates high to low correlation and white-to-blue coloring indicates low to high standard deviation, respectively. Low SDs indicate small changes in the 10 R2values and thereby high robustness toward scaling of the functional units. Generally, little consistency between correlations of CFs in Figure 1 and of total impact scores in Figure 3 could be observed. This illustrates the importance of resource coverage, and the combined effects of differences in CFs and magnitude of resource consumption in individual data sets. Overall, the methods could be divided in two groups with relatively high internal correlation: (i) CML, EI99, I2002+, ReCiPe, CEENE and CExD (R2: >0.95; SD: 0.00−0.02) and (ii) EDIP, ILCD, EPS and ORI (R2: 0.82−0.95; SD: 0.03− 0.09). All other combinations of methods had R2-values within 0.59−0.85 (SD: 0.07−0.19). SED and ORI showed relatively poor correlation across the methods agreeing with their different approach regarding energy resources compared with the other methods: ORI excludes energy resources, and SED accounts solar energy needed to generate natural resources. This reduces the focus from energy-rich and geological scarce resources toward bulk resources (e.g., gravel and sodium chloride), albeit often used in much larger quantities. For several of the methods, energy resources represented the largest contribution to the overall impact scores, mainly because energy resources are dominant counted by mass extracted. This was for example relevant for CML that had very low correlations with most other methods regarding CFs (see Figure 1), but high correlations with respect to the overall impacts (see Figure 3) for a range of methods. No consistent pattern of high correlation between methods applying similar model concepts could be identified as even small changes to the model concept appeared to have major impacts on the results, e.g. for CML versus EDIP and ILCD. This clearly illustrates that the general model features themselves (see Table 1) are not sufficient to ensure comparability between LCA studies. Further disaggregation of

methods (except for CEENE). Interestingly, however, also poor correlation could be observed between a range of methods applying similar assessment approaches: CML with EDIP (R2 = 0.12) and ILCD (R2 = 0.11), ReCiPe with EI99 (R2 = 0.34) and I2002+ (R2 = 0.21), CEENE with CExD (R2 = 0.45), and SED with CEENE (R2 = 0.07) and CExD (R2 = 0.18), respectively. For example, ILCD and CML apply the same model approach except for the use of estimated reserve base rather than the ultimate reserve, and CEENE and CExD have an important difference in the boundary conditions as tailings are included as an environmental extraction only in CExD. This indicates that despite applying similar model concepts (see Table 1), the impact assessment models can deviate significantly producing very different CFs. The number of resources covered by the individual methods varied significantly (9−73 resources, see Table 1). This affected the regression analysis, because of the varying number of common resources for which CFs could be analyzed. Applying the impact assessment methods to LCI data will reveal the consequences from resource coverage, the effects from variations in CFs, and the influence of typical resources consumption levels in real-world processes. Correlation between Total Impact Scores. As previously mentioned, total impact scores from the 11 methods cover a wide range of numerical values as a consequence of the differences in functional units. Figure 2 shows examples of correlations between selected methods with data organized in economic sections. The log-scale in the figure should be noted: for example, in Figure 2A, data sets with identical impact scores by CML may correspond to variations of up to 4 orders of magnitude for the ILCD method. While Figures 2A−C illustrate examples of methods with low-to-medium correlations (ILCD-CML, ORI-I2002+, and SED-ReCiPe), Figure 2D illustrates good correlation between CExD and CEENE. Especially the economic sections C (Manufacturing) and B (Mining and quarrying) indicated different behavior in the four examples. This may be explained by the type of these data sets with focus on specific resources (mainly related to mining and product manufacturing activities); differences in resource 11076



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Figure 2. Total impact scores for selected pairs of resource depletion methods over the entire inventory database of 2747 market datasets organized by economic section (for details see SI Table S2).

Figure 3. Pairwise comparison of resource depletion methods: Through a ten-step iterative rescaling of the functional units of the inventory data, means of log-linear regression R2-values and standard deviations obtained by the step-wise rescaling are shown. Coloring illustrates i) degree of correlation (mean R2-value) from white (high correlation) to red (low correlation), and ii) magnitude of standard deviation between samples of R2values from white (low standard deviation) to blue (high standard deviation).

impact scores into groups of metal, energy and mineral resources (see SI Figure S2 for details) indicated consistently high correlations for impacts from energy resources for all methods (R2 > 0.91), whereas correlations for metal and mineral resources generally were lower and less systematic (R2

> 0.61). This suggests that energy resources are evaluated more alike across the individual methods than the two other groups of resources. Boxplots of impact score ratio distributions, eliminating influence from the functional units, supported the findings in 11077



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(in ILCD), iron (SED), molybdenum (in ORI), nickel and tantalum (in EDIP) contributed most within metal resources. Gravel, sodium chloride and to a smaller extend calcite were the most important mineral resources. All other resources contributed less than 15% to the total impact score, suggesting that other resources were of lower importance for the results, regardless of the selected method. Three aspects were found to be important: (i) large variations in CFs and consumption of individual resources resulted in a few resources significantly affecting the total impact scores, (ii) some methods only included few resources (e.g., EI99, I2002+, ReCiPe, and ORI) thereby automatically increasing their relative importance, and (iii) some methods included many resources with relatively small contributions (e.g., EDIP, ILCD, EPS, and SED) thereby leaving little room for individual resources to become critical. Despite the low number of energy resources (3−5 resources), these resources appeared critical for the correlation between impact results, both due to high CFs in half of the methods and high consumption volumes in the inventory data sets (see Figure 4 and Figure S2, S3, and S4 in the SI) Importance of Energy Resource Assessment. To assess the importance of energy resources in comparison to metal resources, we compared the relative magnitude of CFs within each method. Energy-related CFs of CML, I2002+, CEENE, and CExD (hard coal, oil, gas, and uranium) were among the highest 50% of CFs within these methods, whereas for EDIP, ILCD, EPS, ReCiPe, and SED, CFs for hard coal, oil, and gas were within the lowest 40% of CFs. EI99 did not show a similar consistent pattern, and ORI did not include energy resources at all. The “future scenario modeling” methods EI99, I2002+, and ReCiPe only included 16, 17, and 25 resources with 3, 5, and 6 energy resources, respectively (see SI Table S1 for resource classification). Thereby, energy resources constituted 19−29% of all included resources in these methods, compared with the remaining methods where energy resources constituted 7−14% of the resources. This further explains the large contribution from energy resources to the total impact scores of EI99, I2002+ and ReCiPe and illustrates the dominant role of energy resources in some methods (see also Figure 4 and SI Figure Figure S2 and Table S3). AoP Interpretation in the Methods. Based on the data analysis, three “interpretations” of the AoP for natural resources have been identified among the assessed methods: (i) increased effort in the future resulting from present resource extraction, (ii) loss of physical resource availability, and (iii) consumption of universally limited resources. The “future scenario modeling” methods (EI99, I2002+, EPS, and ReCiPe) targeted the first type of concern by focusing

Figure 4. Average contributions from three main resource types (energy, metal, and mineral resources) to the total impact score for the entire inventory database of 2747 market datasets for each resource method (see SI Table S1 and Figure S4 for classification of resources and for the individual resource contributions per economic section). Mean and standard deviation values are provided in SI Table S3. The "Inventory" is based on the mass of consumed resources.

Figure 3 with high levels of correlation within two groups of methods: (i) CML, EI99, I2002+, ReCiPe, CEENE and CExD, and (ii) EDIP, ILCD, EPS and ORI (for details see SI Figure S3). For example, low variability in ratios (high level of correlation) could be observed between CML and EI99, I2002+, ReCiPe, CEENE, and CExD (SI Figure S3:CML), and between EDIP and ILCD, EPS and ORI (SI Figure S3:EDIP). Contributions from Individual Resource Types to Total Impact Scores. Figure 4 illustrates the average contribution from metal, energy, and mineral resources to the total impact score of all 2747 inventory data sets as well as the average resource consumption per weight (“Inventory” in Figure 4) (see SI Table S3 for details). Figure 4 demonstrates significant differences between the individual methods. Metal resources appeared to strongly influence the total impacts for EDIP, ILCD, EPS, and ORI, whereas CML, EI99, I2002+, ReCiPe, CEENE, and CExD were dominated by contributions from energy resources. SED was somewhat different from the other methods as mineral resources contributed with almost 50% of the total impact score. Some resources contributed more than others (resource contribution data for the eight economic sections are shown in SI Figure S4, see SI Table S1 for overview of resources). Oil, gas, hard coal, and uranium were the most important energy resources. Cadmium (in EDIP and EPS), copper, gold, indium

Table 2. Grouping of Methods According to the AoP Addressed and Proposed Classification into Mid- and End-Point Levelsa areas of protection

midpoint models

concern

human health & natural environment

increased impacts to human health and natural environment in future as a function of increased energy and resource demands per unit caused by present extraction activities

loss of natural resource availability

permanent loss of natural resource availability as a function of present extraction activities

universal limited resource

consumption of an ultimately limiting resources

EI99; I2002+; ReCiPe; EPS EDIP; ILCD; CML

endpoint models

ORI Vieira et al.b CEENE CExD SED

a

Note that this classification deviates from earlier classifications. For explanations see text. bThe method by Vieira et al.31 was not included in the comparative analysis as only data for a single resource (Cu) were published at the time of the study. 11078



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ness. Awareness of the model assessment approach, resource coverage, and comparability with other resource depletion models is fundamental when including resource impacts in LCA studies. This represents a critical, although often neglected, step in the selection and classification phases as well as in the result interpretation of an LCA. It is therefore recommended to prioritize methods with wide coverage of resources, to ensure comparability between studies and avoid burden-shifting. Furthermore, it is suggested that interpretation of the AoP for natural resources should be clearly linked to the environmental concerns and structure presented in Table 2, both in relation to dissemination of results and future developments of resource depletion indicators within LCA.

on increases in energy and economic costs associated with reduced physical resource availability in the future (expressed as declining ore grade). The relationship between these units and environmental impacts is not apparent, unless they are further translated into the additional environmental impacts arising from increased energy consumption for resource extraction in the future. Therefore, it may be considered that resources themselves are not the safeguard subject, but rather the associated effects on the environment and human health.20,44 By quantifying the impacts as surplus energy or surplus cost, these methods are not reaching the endpoint damages to human health and the natural environmental, but may serve as midpoint indicators for these concerns (Table 2). The included assumptions regarding future technological and societal developments are likely to induce large uncertainties in the results from these methods. Furthermore, methods of this type generally face considerable challenges in providing comprehensive sets of CFs for abiotic resources: only 16−25 resources were included (Table 1), mainly due to limited information on geological ore grade distributions and inadequate mining data.30 The second type of concern (physical loss of natural resource availability), as an endpoint with the intrinsic value of preserving present physical availability of resources, is similar to the AoP of natural environment modeling diversity losses as “potentially disappeared fractions of species”.45 With this interpretation of AoP for natural resource the approach by ORI appeared consistent as endpoint indicator; however with the problem of inadequate mining data. CML, EDIP, and ILCD assumed declining availability as a function of decreasing reserves, thereby reducing issues related to limited data availability. CML, however, applied the ultimate reserve as deposit reference which does not decline as a function of extraction. Only with the assumption that ultimate reserves are proportional to ultimate extractable reserves, this method can be applied as indicator of physical decline in availability. The third type of concern (consumption of resources assumed to be the overall limiting factor for the societal metabolism, for example, solar energy, entropy generation or exergy consumption) involves two main assumptions: (i) society may mobilize any needed resource through (additional) exergy consumption; therefore resources are considered substitutable in terms of depletion,19 and (ii) the main resource problem is competition rather than depletion as the universal limiting resource is directly or indirectly limited by the solar energy supply. These assumptions may be questionable, however, as substitution between resources may not always be possible or realistic. Applicability of Resource Impact Assessment Methods. Based on the evaluation of resource depletion models, characterization factors and associated impact scores for the wide range of inventory data sets, it should be evident that considerably different answers may be obtained from the individual methods. The selection of a method in specific LCAs may significantly affect the final results regarding resource consumption and affect comparability with other studies. Ideally, the choice of impact assessment methods should reflect the environmental concerns of the target audience. In case of the resource methods, the resource coverage should encompass the resources included in the LCIs of the product system in question. Therefore, the appropriateness and complexity of a resource model should be balanced against the resource coverage to avoid burden-shifting due to model incomplete-

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ASSOCIATED CONTENT

* Supporting Information S

Supporting Information includes details regarding the resource depletion models, resource coverage, resource types, characterization factors, grouping of economic sections, and the contribution and correlation analysis. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: +45 4525 1600; fax: +45 4593 2850; e-mail: thas@ env.dtu.dk. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We acknowledge financial support of the IRMAR project funded by the Danish Council for Strategic Research. The authors thank Alexis Laurent and Mark Huijbregts for helpful comments to the work.

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REFERENCES

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Impact Assessment of Abiotic Resources in LCA - ACS Publications

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