However, graphical representation of uncertainty also bears the risk of being suggestive, easily misinterpreted, or too complex. One of the most common ways to represent uncertainty is to plot the probability distributions of the output variables, as presented in Fig. 11.13 and discussed in Sect. 11.4.2. Alternatively to PDFs in Fig. 11.13, the Cumulative Distribution Functions (CDFs) of the outputs could be derived in order to characterise uncertainty. Another tool to represent uncertainty are the so-called probability boxes, based on a probability bounds approach (Karanki et al. 2009). The book “Environmental Decisions in the Face of Uncertainty” from the Institute of Medicine (IOM 2013) contains a useful overview and more in-depth discussion on graphical and other representations of uncertainty. For example, a frequent mistake when representing uncertainty in LCA is the use of error bars


For example, a frequent mistake when representing uncertainty in LCA is the use of error bars. Figure 11.16 illustrates this with error bars that we added to the original graph from the Quantis study discussed above, so that the resulting graph below represents uncertainty in an alternative way to Fig. 11.13. This representation of uncertainty can be seen in numerous LCA publications and presentations. The error bars here represent the absolute uncertainty of each compared option, but they do not consider interdependence of uncertainties between scenarios. However, by presenting them next to each other, Fig. 11.16 suggests that the error bars can be Fig. 11.15 Box or whisker plot of freshwater ecotoxicity characterisation factors for 2499 organic chemicals and 4 emission compartments from USEtox 2.02 R.K. Rosenbaum et al.

11 Uncertainty Management and Sensitivity Analysis

Hauschild et al., LCA: Theory and Practice


directly compared among each other in order to determine if the uncertainty allows to visually distinguish these options. As discussed above, only the uncertainty of the difference (or ratio) for each paired comparison among these options (which will be smaller while only considering the uncertainty of the difference (or ratio) between two options) will truly allow to determine whether both options are distinguishable or essentially equal. The useful way of using error bars in this example would therefore be to present one for each pairing of these compared options, parallel to Fig. 11.14. When using graphs, the scale of an axis should always reflect the underlying uncertainty. This is particularly important in LCA, where many impact scores may have an uncertainty spanning from one to several orders of magnitude, in which case it would be misleading to present them on a linear scale


In such cases, the results should preferably be shown using a log-scale, which will only emphasise larger differences between impact scores. Contrary to a frequent perception, this has nothing to do with data manipulation, since scores can still be identified by their exact value. It simply avoids over-exaggeration of very small differences that may look very large on a linear scale while (almost) disappear on a log-scale. A similar effect of over-exaggeration is achieved when zooming into a certain range of an axis, e.g. only showing the highest values from 80 to 100%, which will show differences between two points as much larger compared to the full range of the axis. As indicated above, these approaches are complementary and should be used as such. Sometimes a repetition of the same (important) information via two different ways and at two different places in a report may be preferable over a concise, non-repetitive communication. Fig


Sometimes a repetition of the same (important) information via two different ways and at two different places in a report may be preferable over a concise, non-repetitive communication. Fig. 11.16 Uncertainty bars for the climate change impact scores of four compared alternatives to dry hands (figure adapted from Quantis (2009) by adding error bars for illustrative purposes) Uncertainty Management and Sensitivity Analysis


11.6 Management of Uncertainty The strategy of how to consider and manage uncertainties in an LCA study depends on a number of factors that will determine what is feasible. The most important limitation is likely the availability of resources (time and/or budget) to collect additional information in order to quantify, represent and reduce uncertainty. Accessibility and level of operationalisation of the technical aspects of uncertainty assessment (e.g. databases providing default uncertainties for background LCI data and LCIA characterisation factors, LCA software providing ways to efficiently propagate uncertainties) is also frequently named as a potential barrier. In any case, there is always a minimum of uncertainty management that will be feasible without requiring important resources


In any case, there is always a minimum of uncertainty management that will be feasible without requiring important resources. In many scientific fields, uncertainty is managed using a tiered approach with each tier (or level of detail) progressively increasing the requirements and sophistication of uncertainty assessment and management. A particular advantage of such an approach is that it allows an iterative improvement and refinement of uncertainty management from a first qualitative listing of uncertainty sources, to a first quantitative estimation and screening, up to a sophisticated full uncertainty assessment as a study advances. This type of approach caters nicely to the iterative nature of LCA (see Sect


This type of approach caters nicely to the iterative nature of LCA (see Sect. 6.3) and allows the LCA practitioner to adapt the extent of uncertainty management in a study to the available resources, instead of suggesting that uncertainty management always has to be done using the most complex approaches or not at all if resources are too limited to allow for a quantitative approach. An example for such a tiered approach is the Guidance on Characterizing and Communicating Uncertainty in Exposure Assessment from the World Health Organisation (WHO 2008)


An example for such a tiered approach is the Guidance on Characterizing and Communicating Uncertainty in Exposure Assessment from the World Health Organisation (WHO 2008). It proposes four progressive tiers with increasing complexity from tier 0 (the absolute minimum) to tier 3 (the most sophisticated level): Tier 0: Screening uncertainty analysis Tier 1: Qualitative uncertainty analysis Tier 2: Deterministic uncertainty analysis Tier 3: Probabilistic uncertainty analysis While the details of this framework are adapted to chemical exposure assessment, its underlying principle of iteratively increasing sophistication and complexity is a useful inspiration for LCA. Figure 11.17 shows the different levels of detail for each tier, from no uncertainty analysis (point estimate) at the bottom to probabilistic uncertainty analysis at the top


Figure 11.17 shows the different levels of detail for each tier, from no uncertainty analysis (point estimate) at the bottom to probabilistic uncertainty analysis at the top. An expert working group of the UNEP-SETAC Life Cycle Initiative on uncertainty management in LCA drafted a similar framework for LCA during a series of workshops between 2009 and 2012, which is a useful starting point R.K. Rosenbaum et al.


towards integration of uncertainty management into LCA practice


They proposed five tiers: Tier 0: Minimum transparency with a clear definition of what is considered a notable difference between scenarios for each impact category; Tier 1: Screening level focusing on identification of important sources of parameter uncertainty providing information on importance and sensitivity of parameters, choices, assumptions, etc.; Tier 2: Qualitative and semi-quantitative uncertainty assessment of important sources of uncertainty with systematic identification and description of uncertainties for all parameters, choices and assumptions including parameter and scenario uncertainty; Tier 3: Quantitative uncertainty assessment of all sources of uncertainty with systematic quantification of uncertainties and variability for all parameters, choices and assumptions accounting for all quantifiable uncertainties; Tier 4: Fully probabilistic LCA representing all relevant sources of influence by fully characterised uncertainty and variability separately


In essence, different levels of sophistication are possible when establishing a strategy to integrate uncertainty management into a study and it is not always the most sophisticated level that is required. Compared to completely ignoring uncertainty, even a basic (e.g. qualitative) consideration is already better than nothing and a good and essential first step to pinpoint sources of uncertainty in the results of any LCA study. This helps to be conscious about potential pitfalls and misinterpretation when making a decision based on the conclusions of a study. It should also be noted that a tiered uncertainty assessment framework essentially serves as an orientation providing coherence for different levels of sophistication of uncertainty assessment. Fig. 11.17 Levels of detail for tiered uncertainty management strategies [taken from Paparella et al. (2013)] Uncertainty Management and Sensitivity Analysis


It groups those elements of an uncertainty assessment that can be combined meaningfully on each level. 11.7 Perspectives Uncertainty and variability are inherent properties of LCA, all its models, data, assumptions, and choices that are required when performing an LCA. Uncertainty is not the enemy, but it is unavoidable and its assessment can be helpful when put to good use for improving and interpreting LCA results. Uncertainty and its reduction is the very reason for the iterative nature of LCA and should hence be used as a guiding principle for the changes applied during each iteration of an LCA. Uncertainty and variability have many sources, some of which are quantifiable, while others are not, but all need to be considered when interpreting and discussing results and the robustness of a conclusion. In order to be successfully applied in LCA, uncertainty assessment requires some knowledge of the underlying principles and methods as well as a set of tools supporting: 1


In order to be successfully applied in LCA, uncertainty assessment requires some knowledge of the underlying principles and methods as well as a set of tools supporting: 1. Quantification and storage of uncertainty, variability, and correlation or interdependence of inputs, models, assumptions, etc. 2. Propagation of input uncertainties to model output uncertainty 3. Tools for sensitivity, uncertainty, uncertainty contribution analysis and scenario comparison 4. Skilled interpretation and communication of relevant uncertainty information Even though uncertainty assessment is an additional procedure to handle and provide resources for when conducting an LCA, it has multiple uses that will help ensuring that resources spent on the iterative improvement of the study actually contribute to a tangible improvement in uncertainty of the results and their enhanced interpretation in order to provide robust conclusions


Uncertainty assessment can notably be used to: – Identify sources of uncertainty that dominantly contribute to the uncertainty of results – Effectively target the iterative improvement of data, models and assumptions towards those elements that dominate the result(s) and their uncertainty – Identify processes and elementary flows where archetypical or spatially explicit LCI and LCIA data will significantly reduce the uncertainty of the results due to the integration of spatial (or temporal) variability into the LCA – Enhance the interpretation of results, e.g. which alternatives are truly different and which are not – Quantify the confidence in the robustness of a conclusion or the probability of being wrong R.K. Rosenbaum et al.


Not only does the assessment and management of uncertainty in LCA provide a lot of opportunities and advantages, but ignoring it actually bears potentially important risks. For example, resources spent to improve the study may be inefficiently used when improving data and models with limited contribution to result uncertainty (e.g. when results are not sensitive to changes in inputs). Conclusions drawn from deterministic results may not only lack robustness but actually be misleading (e.g. when differences between results are not significant, i.e. falling within the uncertainty ranges of results). From today’s perspective, a lot can be done already to consider uncertainty, with many LCI databases and the first LCIA methods providing uncertainty estimates for their data, and most LCA software providing functionality to propagate those into the results


When exploring those options, it is important to be aware of the limitations that most if not all LCA software (while writing this book in 2016) does not provide the possibility to consider LCIA uncertainties, which may not always be obvious to the user. Running the uncertainty analysis will thus essentially propagate the uncertainties from the LCI database and result in a very incomplete quantification of uncertainty that may be missing many important sources on the LCIA side. Using this kind of uncertainty information to establish whether or not two alternatives have significantly different impact scores may still provide misleading conclusions and a false impression on their robustness due to its bias towards LCI uncertainty. To overcome this limitation, updates of LCIA methods will (increasingly) provide uncertainty estimates for characterisation factors (Bulle et al., in review)


To overcome this limitation, updates of LCIA methods will (increasingly) provide uncertainty estimates for characterisation factors (Bulle et al., in review). With high uncertainties being a frequent, critical argument towards LCA, it is worth asking if LCA results are actually more uncertain than those from other assessment tools. No doubt that the precision of LCA results will be inferior to that of many other environmental assessment tools, especially the local and site-specific ones. This has a lot to do with scale, since LCA typically models entire supply chains that will usually be global, involve many processes about which little information is available, covering a broad range of environmental indicators and impact categories, and often spanning considerable time periods (defined in the duration of the functional unit) to be represented


The combination of large spatiotemporal scales and the complexity due to broad inventory flow and impact coverage, which is unique to LCA among environmental assessment tools, is the source of a lot of variability and uncertainty due to e.g. aggregating over larger spatial or temporal space and is thus simply a function of the space considered and data available. However, as discussed in this chapter, contrary to most environmental assessment tools, LCA does not attempt to predict absolute impacts, but rather focuses on the relative difference in potential impacts between alternatives, although exceptions exist, such as Environmental Product Declarations (EPD) which are “stand-alone” environmental profiles. Any systematic error and source of variability or uncertainty will usually have little influence on the uncertainty of the difference between alternatives. Therefore, the focus in LCA is accuracy and not necessarily precision. Uncertainty Management and Sensitivity Analysis


While this chapter provides an overview of a range of aspects around uncertainty management in LCA, we recommend the cited literature for those readers looking for more in-depth insights into specific aspects. For further reading beyond literature cited above we recommend the following: Deeper insights on uncertainty representation in the context of LCA were published by Heijungs and Frischknecht (2005). For log-normally distributed parameters, Strom and Stansbury (2000) discuss the determination of distribution information from minimal literature information and provide a comprehensive overview on log-normal distributions. Heijungs and Kleijn (2001) further discuss contribution analysis, perturbation analysis, uncertainty analysis, comparative analysis, and discernibility analysis. De Schryver et al. (2011) explore how value choices in LCIA influence the uncertainty of (human health) characterisation factors. Clavreul et al


De Schryver et al. (2011) explore how value choices in LCIA influence the uncertainty of (human health) characterisation factors. Clavreul et al. (2013) combine probability and possibility theories to represent stochastic and epistemic uncertainties in a consistent manner in LCA. Even though it does not discuss life cycle assessments and has a more risk-assessment based focus, a useful read regarding environmental decision making under uncertainty including aspects of communication and management of uncertainty is the book “Environmental Decisions in the Face of Uncertainty” from the Institute of Medicine (IOM 2013) which is freely available via The National Academies Press (NAP) website. When discussing LCA indicators and results, we should be at least as critical, if not even more critical when presented with no or small uncertainties as we are when presented with large, but properly quantified uncertainties


Or to say it more eloquently with the words of physicist and Nobel laureate Richard P. Feynman: “What is not surrounded by uncertainty cannot be the truth”. Acknowledgements The authors gratefully acknowledge the support from Jon Dettling (Quantis) for allowing us to use their study as an example. Björklund, A.: Survey of approaches to improve reliability in LCA. Int. J. Life Cycle Assess. 7, 64–72 (2002). doi:10.1007/BF02978849 Bulle, C., Margni, M., Kashef-Haghighi, S., Boulay, A.-M., Bourgault, G., De Bruille, V., Cao, V., Fantke, P., Hauschild, M.Z., Henderson, A., Humbert, S., Kounina, A., Laurent, A., Levasseur, A., Liard, G., Patouillard, L., Rosenbaum, R.K., Roy, P.-O., Shaked, S., Jolliet, O.: IMPACT World+: A Globally Regionalized Life Cycle Impact Assessment Method (in review) Ciroth, A.: Uncertainties in life cycle assessment. Int. J. Life Cycle Assess. 9, 141–142 (2004)


Int. J. Life Cycle Assess. 9, 141–142 (2004). doi:10.1007/BF02994186 Ciroth, A., Fleischer, G., Steinbach, J.: Uncertainty calculation in life cycle assessments—A combined model of simulation and approximation. Int. J. Life Cycle Assess. 9, 216–226 (2004). doi:10.1007/BF02978597 Ciroth, A., Muller, S., Weidema, B., Lesage, P.: Empirically based uncertainty factors for the pedigree matrix in ecoinvent. Int. J. Life Cycle Assess. 21, 1338–1348 (2016). doi:10.1007/ s11367-013-0670-5 R.K. Rosenbaum et al.


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Environ. Sci. Technol. 46, 12864–12872 (2012). doi:10.1021/es301509u Funtowicz, S.O., Ravetz, J.R.: Uncertainty and Quality in Science for Policy. Kluwer Academic Publishers, Dordrecht (1990) Greenland, S., Senn, S.J., Rothman, K.J., Carlin, J.B., Poole, C., Goodman, S.N., Altman, D.G.: Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur. J. Epidemiol. 31, 337–350 (2016). doi:10.1007/s10654-016-0149-3 Groen, E.A., Heijungs, R.: Ignoring correlation in uncertainty and sensitivity analysis in life cycle assessment: what is the risk? Environ. Impact Assess. Rev. 62, 98–109 (2017). doi:10.1016/j. eiar.2016.10.006 Groen, E.A., Heijungs, R., Bokkers, E.A.M., de Boer, I.J.M.: Methods for uncertainty propagation in life cycle assessment. Environ. Model Softw. 62, 316–325 (2014). doi:10.1016/j.envsoft. 2014.10.006 Hauschild, M.Z., Potting, J.: Spatial Differentiation in Life Cycle Impact Assessment: The EDIP2003 Methodology


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EcoBalance—Pract. tools thoughtful Princ. Sustain., 6–8 Nov 2002, Tsukuba, Japan (2002) Heijungs, R.: Sensitivity coefficients for matrix-based LCA. Int. J. Life Cycle Assess. 15, 511–520 (2010). doi:10.1007/s11367-010-0158-5 Heijungs, R., Frischknecht, R.: Representing statistical distributions for uncertain parameters in LCA: relationships between mathematical forms, their representation in EcoSpold, and their representation in CMLCA. Int. J. Life Cycle Assess. 10, 248–254 (2005). doi:10.1065/lca2004. 09.177 Heijungs, R., Huijbregts, M.A.J.: A review of approaches to treat uncertainty in LCA. Trans. 2nd Bienn. In: Meet. Int. Environ. Model. Softw. Soc. iEMSs, Osnabrück, Ger, pp 332–339 (2004) Heijungs, R., Kleijn, R.: Numerical approaches towards life cycle interpretation five examples. Int. J. Life Cycle Assess. 6, 141–148 (2001). doi:10.1007/BF02978732 Heijungs, R., Lenzen, M.: Error propagation methods for LCA—a comparison. Int. J. Life Cycle Assess. 19, 1445–1461 (2014)


Int. J. Life Cycle Assess. 6, 141–148 (2001). doi:10.1007/BF02978732 Heijungs, R., Lenzen, M.: Error propagation methods for LCA—a comparison. Int. J. Life Cycle Assess. 19, 1445–1461 (2014). doi:10.1007/s11367-014-0751-0 Heijungs, R., Suh, S., Kleijn, R.: Numerical approaches to life cycle interpretation—The case of the Ecoinvent’96 database. Int. J. Life Cycle Assess. 10, 103–112 (2005). doi:10.1065/ lca2004.06.161 Hoekstra, R., Morey, R.D., Rouder, J.N., Wagenmakers, E.-J.: Robust misinterpretation of confidence intervals. Psychon. Bull. Rev. 21, 1157–1164 (2014). doi:10.3758/s13423-0130572-3 Uncertainty Management and Sensitivity Analysis


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Weidema, B.P., Frees, N., Petersen, E., Øllgaard, H.: Reducing uncertainty in LCI, Danish Environmental Protection Agency, Copenhagen, Denmark (2003) WHO (2008) Guidance Document on Characterizing and Communicating Uncertainty in Exposure Assessment. International Programme on Chemical Safety (IPCS), World Health Organization (WHO), Geneva Author Biographies Ralph K. Rosenbaum LCA expert and environmental modeller focusing on LCIA development since early 2000s. Contributed to several UNEP/SETAC working groups towards global harmonisation of LCA methodology. Interested in LCIA modelling of emissions and water/soil resource use, operationalisation of uncertainty management and spatial differentiation. Stylianos Georgiadis Background in Applied Mathematics, focusing on stochastic and statistical modelling with application in urban water systems, food risk assessment, reliability and queueing models


Interested in uncertainty quantification in risk management, life cycle assessment and decision analysis. Peter Fantke Develops methods for LCIA, health impact assessment and chemical alternatives assessment since 2006. Has contributed to UNEP/SETAC LCIA working groups and is USEtox Manager. Interested in quantifying and characterising chemical emissions, uncertainty analysis, consumer exposure, chemical substitution and model parameterisation. Uncertainty Management and Sensitivity Analysis


Chapter 12 Life Cycle Interpretation Michael Z. Hauschild, Alexandra Bonou and Stig Irving Olsen Abstract The interpretation is the final phase of an LCA where the results of the other phases are considered together and analysed in the light of the uncertainties of the applied data and the assumptions that have been made and documented throughout the study. This chapter teaches how to perform an interpretation. The process of interpretation starts with identification of potentially significant issues in the previous stages of goal and scope definition, inventory analysis and impact assessment, and examples of potential significant issues are given for each phase. The significance is then determined by checking completeness, sensitivity and consistency for each of these identified issues. The outcome is used to inform previous phases on the needs for strengthening the data basis of the study, and where this is not possible to reconsider the goal and scope definition of the study


The outcome is used to inform previous phases on the needs for strengthening the data basis of the study, and where this is not possible to reconsider the goal and scope definition of the study. Finally, guidance is given on how to draw conclusions based on the previous steps of the interpretation, qualify the conclusions in terms of their robustness, and develop recommendations based on the results of the study. Learning objectives After studying this chapter, the reader should be able to: • Explain the purpose of interpretation and its relationships to the other phases of the LCA. • Explain what is meant by “significant issues” and give examples of potential significant issues from each of the methodological phases. • Describe procedures to identify significant issues


• Explain what is meant by “significant issues” and give examples of potential significant issues from each of the methodological phases. • Describe procedures to identify significant issues. • Explain how sensitivity analysis and uncertainty information is used in combination to focus the data collection in previous phases of the LCA and to qualify the conclusions that are drawn from the results of the study. M.Z. Hauschild (&)  A. Bonou  S.I. Olsen Division for Quantitative Sustainability Assessment, Department of Management Engineering, Technical University of Denmark, Kongens Lyngby, Denmark e-mail: mzha@dtu.dk © Springer International Publishing AG 2018 M.Z. Hauschild et al. (eds.), Life Cycle Assessment, DOI 10.1007/978-3-319-56475-3_12


12.1 Interpretation is the phase of the LCA where the results of the other phases are considered together and analysed in the light of the uncertainties of the applied data and the assumptions that have been made and documented throughout the study. The outcome of the interpretation should be conclusions or recommendations that (1) respect the intentions of the goal definition and the restrictions that this imposes on the study through the scope definition and (2) take into account the appropriateness of the functional unit and system boundaries. The interpretation should present the conclusions of the LCA in an understandable way and help the users of the study appraise their robustness and potential weaknesses in light of any identified studylimitations


Central elements of the interpretation phase such as sensitivity analysis and uncertainty analysis are also applied throughout the LCA process together with impact assessment tools as part of the iterative loops which are used in the drawing of boundaries and the collection of inventory and impact assessment data (see Chaps. 8–10). A more detailed presentation of these elements is given in Chap. 11. The interpretation proceeds through three steps as illustrated in Fig. 12.1. 1. The significant issues (key processes and assumptions, most important elementary flows) from the other phases of the LCA are identified (see Sect. 12.2). Direct applications Scope definition Inventory analysis Impact assessment Goal definition Conclusions, limitations and recommendations Identification of significant issues Evaluation by completeness, sensitivity and consistency checks Interpretation Fig


12.1 The elements of the interpretation phase and their relations to each other and to the other phases of the LCA (revised from ISO 2006a, b) M.Z. Hauschild et al.


2. These issues are evaluated with regard to their influence on overall results of the LCA and the completeness and consistency with which they have been handled in the study (see Sect. 12.3). 3. The results of the evaluation are used in the formulation of conclusions and recommendations from the study (Sect. 12.4). In cases where the study involves comparison of two or more systems, there are additional considerations to be included in the interpretation (Sect. 12.5). 12.2 Identification of Significant Issues The purpose of the first element of the life cycle interpretation is to analyse the results of earlier phases of the LCA in order to determine the most environmentally important issues, i.e. those issues that have the potential to change the final results of the LCA. The significant issues can be methodological choices and assumptions, inventory data for important life cycle processes, and/or characterisation, normalisation or weighting factors used in the impact assessment


The practitioner is encouraged to prepare a list of such choices during the practical execution of the LCA, the definition of goal and scope, the modelling of the product system and the impact assessment, to help with their identification (see for example reporting recommendations for life cycle inventory phase in Sect. 9.7). Table 12.1 provides examples of such influential issues. As discussed in Chap. 11, sensitivity analysis can be performed as a contribution analysis where the contribution from each process or stage to the total results for an impact category is quantified and expressed. It can also be done as a dominance analysis, where the processes or stages are ranked according to their relative share in the total impact


It can also be done as a dominance analysis, where the processes or stages are ranked according to their relative share in the total impact. The identification of significant issues draws on the sensitivity analysis activities in the evaluation element of the interpretation phase in combination with information about potential key assumptions and uncertainty ranges for potential key numbers in inventory analysis and impact assessment. At the same time, the evaluation element takes the identified significant issues as an important input. The two elements are thus performed in iteration. In the illustrative case on window frames in Chap. 39, life cycle impacts are dominated by the use stage in all impact categories for all four window frame designs


In the illustrative case on window frames in Chap. 39, life cycle impacts are dominated by the use stage in all impact categories for all four window frame designs. Parameters related to the use stage, such as the modelled heat loss, the assumed mix of heating sources, the LCI processes used to represent each heat conversion technology in the heat mix, and the relevant characterisation factors and normalisation references involved in the impact assessment were thus identified as significant issues. Life Cycle Interpretation


12.3 Evaluation The evaluation element establishes the basis for the conclusions and recommendations that can be formulated in the final element of the interpretation (see Sect. 12.4). It is performed in an iterative interaction with the identification of key issues in order to determine the reliability and stability of the results from the identification element


Like the identification of key issues, the evaluation covers the results from the earlier phases of the LCA, the inventory analysis and the impact assessment, in Table 12.1 Examples of significant issues What to look for How to identify significant issues Goal and Scope definition—methodological choices and assumptions Functional unit Choice of functional unit, system expansion (assumption of alternative/replaced technologies), allocation model and setting of system boundaries are discrete choices that can be checked by running the different possibilities as scenarios and comparing the results to determine their influence on the final outcome and conclusions Handling of multifunctional processes – System expansion – Allocation criteria Cut-off decisions and boundary settings Inventory analysis—data for product system processes Data for activities occurring in many parts of the product system, e.g


transportation or energy transformation processes Sensitivity analysis is performed by varying the single issue, or in case of interdependency by joint variation of the concerned issues, and analysing their influence on the outcome of the study The range of variation applied for a given issue should reflect the uncertainty by which it is accompanied Data for key processes: processes that contribute substantially to the environmental impact of the product system in one or more impact categories Data for key elementary flows: processes that contribute substantially to the overall results for an impact category Impact categories that dominate the total impacts from the product system Impact assessment factors Characterisation or normalisation factors used in the impact assessment Sensitivity analysis is performed by varying the single issue, or in case of interdependency by joint variation of the concerned issues, and analysing their influence on the outcome of the study The range of


analysis is performed by varying the single issue, or in case of interdependency by joint variation of the concerned issues, and analysing their influence on the outcome of the study The range of variation applied for a given issue should reflect the uncertainty by which it is accompanied Choice of impact assessment method and selection of impact categories Other impact assessment methods and potentially omitted impact categories may be tested to see if they give different outcomes of the study M.Z


Hauschild et al.


accordance with the goal and scope of the study, with focus on the significant issues identified among methodological choices and data. The outcome of the evaluation is crucial to determine the strength of the conclusions and recommendations from the study, and it must therefore be presented in a way that gives the commissioner and user of the study a clear understanding of the outcome. The evaluation involves: • Completeness check. • Sensitivity analysis in combination with uncertainty analysis. • Consistency check. 12.3.1 Completeness Check Completeness checks are performed for the inventory and the impact assessment in order to determine the degree to which the available data is complete for the processes and impacts, which were identified as significant issues


If relevant information is found to be missing or incomplete for some of the key processes or the most important elementary flows or impact categories, the necessity of such information for satisfying the goal and scope of the LCA must be investigated. If deemed necessary, the inventory and impact assessment phases must be revisited in order to fill the identified gaps. Alternatively, the goal and scope definition may have to be adjusted to accommodate the lack of completeness. If an important data deficiency cannot be remediated, this should be considered when formulating the limitations in the conclusions from the study (see Sect. 12.4). If the missing information is found to be of little importance, this should be documented in the reporting of the completeness check. Taking the completeness check of the illustrative case on window frames (see Chap. 39) as an example, several gaps were identified


Taking the completeness check of the illustrative case on window frames (see Chap. 39) as an example, several gaps were identified. In relation to LCI, the applied heat mix was thus only representative for district heating (and hence not appropriate for situations with local heating sources), and the LCI unit processes used to model the energy technologies applied in the heat mix were geographically representative for Norway and Switzerland and hence not fully representative for Denmark. With regard to LCIA, the use of site-generic characterisation factors for some impact categories may not be fully representative for the specific impact pathways of environmental flows released in or close to Denmark. Once identified, those gaps therefore underwent the procedure described in Fig. 12.2 to be addressed in the study. Life Cycle Interpretation


12.3.2 Sensitivity Check Sensitivity check has the purpose of identifying the key processes and most important elementary flows as those elements that contribute most to the overall impacts from the product system. Sensitivity analysis can be performed and presented as a contribution analysis (which activities contribute to which environmental impact scores, by how much and through which elementary flows?) or a dominance analysis (which activities contribute most to which impacts or flows?). See Chap. 11 for a more detailed discussion of sensitivity analysis and how it is performed. In the illustrative case on window frames, not all significant issues were covered in the sensitivity analysis due to lack of sufficient data and knowledge to construct sensitivity scenarios in some of the cases


A sensitivity scenario reflecting the EU27 heat mix was established and results showed that impacts for a few impact Yes No Fill gaps Yes No Adjust goal and scope Can important data gaps be filled? Can the goal and scope be adjusted? Scope definition Inventory Goal definition Formulate limitations in the conclusions Fig. 12.2 Iterative interaction between completeness check and the earlier phases of the LCA M.Z. Hauschild et al.


categories (mainly related to toxicity) were lower than in the baseline scenario, while most impacts were higher due to a larger share of oil and natural gas in the EU27 heat mix. In support of the iterative approach applied in LCA, sensitivity analysis is also used as a steering activity in the iteration loops that are performed throughout the LCA in support of boundary setting for the product system, inventory data collection and impact assessment. The findings from these earlier sensitivity analyses are brought into the sensitivity check of the interpretation phase. In the interpretation phase, sensitivity analysis is used together with information about the uncertainties of significant issues among inventory data, impact assessment data and methodological assumptions and choices to assess the reliability of the final results and the conclusions and recommendations which are based on them (Sect. 12.3) (Table 12.2)


12.3) (Table 12.2). Table 12.2 Tools for sensitivity analysis Factors checked for sensitivity Tools for sensitivity analysis Data uncertainty The influence of data uncertainty for key issues can be checked by allowing the data to vary within the limits given by the uncertainty estimates while modelling the product system and checking the results. If the information about the (stochastic) uncertainties of the individual elementary flows and characterisation factors allows it, it is also possible to calculate the uncertainty of the final results in terms of inventory and environmental impacts (e.g. simulating it using Monte Carlo techniques). See Chap. 11 for a more detailed discussion of uncertainty analysis and how it is performed Methodological uncertainty The influence of methodological (systematic) uncertainties can be checked by analysing different possible choices (e.g. of applied allocation principle) as scenarios and reporting the influence on the final results


of applied allocation principle) as scenarios and reporting the influence on the final results. Methodological choices which may be relevant to include in a sensitivity analysis include: handling of multifunctional processes (system expansion assumptions or allocation rules), cut-off criteria, boundary setting and system definition, and judgements and assumptions concerning data in the inventory; and for the impact assessment: selection of impact categories, assignment of inventory results (classification), calculation of category indicator results (characterisation), and normalisation and weighting of impact scores Life Cycle Interpretation


The combination of sensitivity analysis and uncertainty analysis helps identify focus points for improved inventory data collection or impact assessment. As illustrated in Fig. 12.3, data with a high uncertainty need not be a focus point for improvement if the sensitivity to this data is very low. In the same way, data which has a strong influence on the final results of the study may also not require further data collection effort if the representativeness of the data is high and its uncertainty negligible. The focus point for improvement of data quality should be data with a strong influence on the overall results and a high uncertainty or questionable. If such data cannot be improved, the result is a low precision which must be reported. If the precision is insufficient to meet the requirements from the intended application of the results, it may be necessary to revise the goal of the study. Figure 12.4 provides a decision tree for handling the sensitivity check


Figure 12.4 provides a decision tree for handling the sensitivity check. Sensitivity Uncertainty Maybe here? Not here! Here! Maybe here? Fig. 12.3 Focusing collection of improved data by combining sensitivity and uncertainty information M.Z. Hauschild et al.


12.3.3 Consistency Check The consistency check is performed to investigate whether the assumptions, methods, and data, which have been applied in the study, are consistent with the goal and scope. Are differences in the quality of inventory data along a product life cycle and between different product systems consistent with the significance of the processes, which the data represent and with the goal and scope of the study? Inventory data quality concerns both the time-related, the geographical, and the technological Yes Yes Yes Formulate limitations in the conclusions Is the data influential? Is the uncertainty high? Can accuracy or representativeness be improved? Inventory Impact assessment No Collect/refine data further Precision meets the requirements? No Yes No No Revise the goal No extra focus No extra focus Scope definition Goal definition Fig. 12.4 Combination of sensitivity analysis and uncertainty information to focus improvement of the LCA data Life Cycle Interpretation


representativeness of the data, the appropriateness of the chosen unit process to represent the process of the product system, and the uncertainty of the data. In case of comparison between different product systems, the consistency check also investigates whether allocation rules and system boundary setting as well as impact assessment have been consistently applied to all compared product systems. When inconsistencies are identified, their influence on the results of the study is evaluated and considered to draw conclusions from the results. Taking the window frame case as example again, the main identified inconsistency is between the goal and scope and the interpretation of the results which does not give due consideration to changes that may occur in particular in the background system within the time frame of the study (at least 20 years)


Important changes are the Danish heat mix (for which the share of fossil fuels is expected to decrease) and the technological development in the heat supply technologies (see Chap. 39 for further details). 12.4 Conclusions, Limitations and Recommendations Building on the outcome of the other elements of the interpretation, and drawing on the main findings from the earlier phases of the LCA, the final element of the interpretation has to draw conclusions and identify limitations of the study, and develop recommendations to the intended audience in accordance with the goal definition and the intended applications of the results. The conclusions should be drawn in an iterative way: based on the identification of significant issues (Sect. 12.2) and the evaluation of these for completeness, sensitivity and consistency (Sect. 12.3), preliminary conclusions can be drawn


12.2) and the evaluation of these for completeness, sensitivity and consistency (Sect. 12.3), preliminary conclusions can be drawn. It is then checked whether these preliminary conclusions are in accordance with the requirements of the scope definition of the study (in particular data quality requirements, predefined assumptions and values, and limitations in methodology and study). If the conclusions are aligned with the requirements, they can be reported as final conclusions, otherwise they must be re-formulated and checked again. Recommendations based on the final conclusions of the study should be logical and reasonable consequences of the conclusions. They should only be based on significant findings and relate to the intended application of the study as defined in the goal definition. In the illustrative case on window frames (Chap


They should only be based on significant findings and relate to the intended application of the study as defined in the goal definition. In the illustrative case on window frames (Chap. 39) it was concluded for example that the wood composite (W/C) window has the lowest impact among the four compared windows in all impact categories, and that impacts occurring in the use stage are generally dominating the total impacts and are caused by the demand for heat to compensate the heat losses that occur through the window. Albeit not visible in the results, due to the disregard of technological changes related to heat supply over the time frame of the study (likely going towards lower impacts), the dominance of the use stage impacts is likely to decrease with time, depending on M.Z. Hauschild et al.


what technological improvements are introduced in the other stages of the window life cycle, but it is still expected to remain significant in a foreseeable future. A follow-up study is recommended to further address these dynamics. 12.5 Interpretation for Comparative Studies In studies that involve a comparison of product systems, the interpretation has to consider a number of additional points to ensure fair and relevant conclusions from the study. • Significant issues must be determined for each of the systems, and special attention should be given to issues that differ between the systems and which have the potential to change the balance of the comparison. • The completeness check must have specific focus on differences in the completeness of the treatment of some of the significant issues between the product systems


• The completeness check must have specific focus on differences in the completeness of the treatment of some of the significant issues between the product systems. If there are differences that could influence the comparison results, these should be eliminated if possible and otherwise kept in mind in the formulation of conclusions. • If an uncertainty analysis is performed to investigate whether the difference between two systems is statistically significant, the analysis should be performed on the difference between the systems (one system minus the other), which should be checked for a statistically significant difference from zero taking into account potential co-variation between processes of the two systems (e.g. processes which are the same). See the discussion of this point in Chap. 11


processes which are the same). See the discussion of this point in Chap. 11. • When an LCA is intended to be used in comparative assertions intended to be disclosed to the public, the ISO 14044 standard requires that the evaluation element include interpretative statements based on detailed sensitivity analyses. It is emphasised in the standard, that the inability of a statistical analysis to find significant differences between different studied alternatives does not automatically lead to the conclusion that such differences do not exist, rather that the study is not able to show them in a significant way. • A consistency check must be performed of the treatment of the key assumptions and methodological choices in the different systems to avoid a bias and ensure a fair comparison


• A consistency check must be performed of the treatment of the key assumptions and methodological choices in the different systems to avoid a bias and ensure a fair comparison. – Are differences in the quality of inventory data between the compared product systems acceptable, considering the relative importance of the processes in the product systems, and are the differences consistent with the goal and scope of the study? For example, if one study is based on specific and recent data with a high degree of representativeness for all the key processes while the other uses extrapolation from literature data, there is a bias in the inventory data that can make a comparison invalid. – Have allocation rules and system boundary setting been consistently applied to all product systems? Life Cycle Interpretation


– Has the impact assessment been performed consistently for the systems, have the relevant impact categories been included for all systems, and have the impacts been calculated in the same way and with the same coverage of elementary flows for all the systems? The influence of any identified inconsistencies on the outcome of the comparison should be evaluated, and taken into consideration when conclusions are drawn from the results. This chapter is to a large extent based on the ILCD handbook and the ISO standards 14040 and 14044. Due to the scope of this chapter, some details have been omitted, and some procedures have been rephrased to make the text more relevant to students. For more details, the reader may refer to these texts: EC-JRC: European Commission—Joint Research Centre—Institute for Environment and Sustainability: International Reference Life Cycle Data System (ILCD) Handbook—General guide for Life Cycle Assessment—Detailed guidance, 1st edn. March 2010


March 2010. EUR 24708 EN, Luxembourg, Publications Office of the European Union (2010) ISO: Environmental Management—Life Cycle Assessment—Principles and Framework (ISO 14040). ISO, the International Organization for Standardization, Geneva (2006a) ISO: Environmental Management—Life Cycle Assessment—Requirements and Guidelines (ISO 14044). ISO, the International Organization for Standardization, Geneva (2006b) Author Biographies Michael Z. Hauschild Involved in development of LCIA methodology since the early 1990s. Has led several SETAC and UNEP/SETAC working groups and participated in the development of the ISO standards and the ILCD methodological guidelines. Main LCA interests are chemical impacts, spatial differentiation and science-based boundaries in LCIA. Alexandra Bonou LCA expert and modeller focussing on ecodesign since 2009. Has worked on integrating life cycle thinking and environmental target setting in organisations of the private and public sector


Has worked on integrating life cycle thinking and environmental target setting in organisations of the private and public sector. Interested in life cycle management, product development, social life cycle assessment. Stig Irving Olsen LCA expert both as researcher and as consultant. Involved in the development of LCA methodologies since mid 1990s. Contributed to UNEP/SETAC working groups on LCIA methodology. Main LCA interest is human toxicity impacts, emerging technologies, and decision making. M.Z. Hauschild et al.


Chapter 13 Critical Review Ralph K. Rosenbaum and Stig Irving Olsen Abstract Manipulation and mistakes in LCA studies are as old as the tool itself, and so is its critical review. Besides preventing misuse and unsupported claims, critical review may also help identifying mistakes and more justifiable assumptions as well as generally improve the quality of a study. It thus supports the robustness of an LCA and increases trust in its results and conclusions. The focus of this chapter is on understanding what a critical review is, how the international standards define it, what its main elements are, and what reviewer qualifications are required. It is not the objective of this chapter to learn how to conduct a critical review, neither from a reviewer nor from a practitioner perspective. The foundation of this chapter and the basis for any critical review of LCA studies are the International Standards ISO 14040:2006, ISO 14044:2006 and ISO TS 14071:2014


The foundation of this chapter and the basis for any critical review of LCA studies are the International Standards ISO 14040:2006, ISO 14044:2006 and ISO TS 14071:2014. Learning Objectives After studying this chapter, the reader should be able to: • Explain when a critical review is needed and what is its purpose. • Provide perspectives on the difference between critical review, scientific review and validation. • Explain the principles, procedure, requirements, content, deliverables and options when conducting critical review and which international standards describe it. R.K. Rosenbaum (&) IRSTEA, UMR ITAP, ELSA Research Group and ELSA-PACT—Industrial Chair for Environmental and Social Sustainability Assessment, 361 rue Jean-François Breton, BP 5095, 34196 Montpellier, France e-mail: ralph.rosenbaum@irstea.fr S.I. Olsen Division for Quantitative Sustainability Assessment, Department for Management Engineering, Technical University of Denmark, Bygningstorvet, Building 115, 2800 Kgs


Olsen Division for Quantitative Sustainability Assessment, Department for Management Engineering, Technical University of Denmark, Bygningstorvet, Building 115, 2800 Kgs. Lyngby, Denmark © Springer International Publishing AG 2018 M.Z. Hauschild et al. (eds.), Life Cycle Assessment, DOI 10.1007/978-3-319-56475-3_13


• Discuss the necessary qualifications of a reviewer and how they are selected and by whom. • Describe the possible roles, obligations, tasks, and deliverables of a reviewer. The focus of this chapter is on understanding what a critical review is and what its main elements are. It is important to note that it is NOT the objective of this chapter to learn how to conduct a critical review, neither from a reviewer nor from a practitioner perspective. 13.1 Numerous LCA studies have been published and many of them based on the highest standards of quality and robustness, but there are also an alarming number of studies that contain either important mistakes or plain manipulations in order to obtain an intended result that would support a specific, pre-defined claim


These mistakes and manipulations may be subtle and difficult to detect but can also be immediately identifiable to the trained eye and a number of studies based on surprisingly blunt and evident manipulations have been published over the years. Especially some earlier studies have become classic and illustrative examples in LCA teaching of how not to do LCA (or comparative environmental claims in general) and they also nicely illustrate the purpose and need for critical review of published LCA studies. Two entertaining examples are: (1) SUV versus hybrid car: A famous example is a study from the automotive marketing company CNW Marketing Research, Inc. from 2007 called “Dust to Dust: The Energy Cost of New Vehicles From Concept to Disposal”


from 2007 called “Dust to Dust: The Energy Cost of New Vehicles From Concept to Disposal”. This study compared the life cycle energy costs of a number of automobiles from 2005 and had no hesitation to conclude (and widely communicate) that many large sport utility vehicles (SUVs) including GM’s massive Hummer models H2 and H3 use less energy per mile driven than many smaller vehicles including the Toyota Prius hybrid car. Gleick (2007) analysed the information and commented “that the report’s conclusions rely on faulty methods of analysis, untenable assumptions, selective use and presentation of data, and a complete lack of peer review. Even the most cursory look reveals serious biases and flaws: the average Hummer H1 is assumed to travel 379,000 miles and last for 35 years, while the average Prius is assumed to last only 109,000 miles over less than 12 years”


(2) Fast-food versus classic restaurant: A study from the 1990s comparing a fast-food restaurant with a normal restaurant that surprisingly concludes the environmental superiority of the fast-food option. When the study was redone by independent practitioners, they demonstrated that the system boundaries were chosen in a way that comparability of both options was not supported since important processes from the fast-food restaurant were excluded. Correcting these manipulations then yielded a different picture (Lang et al. 1994). The whole story can be found in Jolliet et al. (2015). R.K. Rosenbaum and S.I. Olsen


Product systems can be very complex, involving a high number of processes and locations. Their modelling in LCA builds on multiple data sources from measurements to unit process databases and involves influential assumptions, drawing on a diversity of expertise from process engineering to environmental and sometimes also social science. Results are often communicated to stakeholders and decision makers that cannot control the quality of the studies, and manipulation and mistakes in LCA studies are as old as the tool itself. The understanding of a need for an independent critical review of LCA studies thus came very early in the history of the methodology. The SETAC LCA “Code of practice” proposed it first in 1993 (Consoli et al. 1993) with more detailed procedural guidelines published later by Klöpffer (1997) and Weidema (1997), which still stand until today as essential references on how to conduct a critical review


1993) with more detailed procedural guidelines published later by Klöpffer (1997) and Weidema (1997), which still stand until today as essential references on how to conduct a critical review. As its superseded predecessor from the late 1990s, the revised international standard ISO 14044 (2006a) defines review procedures (although in much less detail than Klöpffer and Weidema, respectively) to ensure that an LCA study is conform to ISO requirements. As a further development from there, ISO published the technical standard ISO TS 14071 (2014) that aims to specify detailed ISO requirements for critical reviews. In consequence, this should ensure that all claims of a critically reviewed LCA study are well justified and supported by assumptions, methods and data used. Besides preventing misuse and unsupported claims, critical review may also help identifying mistakes and more justifiable assumptions as well as generally improve the quality of a study


Besides preventing misuse and unsupported claims, critical review may also help identifying mistakes and more justifiable assumptions as well as generally improve the quality of a study. It thus supports the robustness of an LCA and increases trust in its results and conclusions. In general, there are different kinds of review processes associated with scientific and technical developments and they all fulfil different objectives and vary in their approach and process. Two different types of review processes are mainly relevant in the context of an LCA study: (1) scientific peer-review and (2) critical review according to ISO 14044 (2006a). While this chapter is focusing on the latter, there is much confusion between both and it is essential to clearly distinguish them and understand their differences. Table 13.1 provides a simplified overview of general tendencies for similarities and differences between both types of review


Table 13.1 provides a simplified overview of general tendencies for similarities and differences between both types of review. Besides several similarities, the essential differences between these two review types are thus linked to their duration, depth, cost, transparency, confidentiality, content and objectives. As discussed by Curran and Young (2014), there is also an important and frequently ignored difference between the terms “critical review” and “verification” with their essential difference being that critical review relies on expert judgement whereas verification is based on comparison against objective evidence. The focus of this chapter and the basis for any critical review of LCA studies are the International Standards ISO 14040 (2006b), ISO 14044 (2006a) and ISO TS 14071 (2014). However, it is worth noting that other review schemes exist that may be specified in more detail than in ISO 14044 and ISO TS 14071, while still being fundamentally based on them


However, it is worth noting that other review schemes exist that may be specified in more detail than in ISO 14044 and ISO TS 14071, while still being fundamentally based on them. These review schemes often have a very specific context of application and in most cases also a geographically limited relevance. One example is the International Reference Life Cycle Data System (ILCD) of the Critical Review


European Commission with its series of ILCD handbooks, including one specifically dedicated to review schemes (EC-JRC 2010a) and another to reviewer qualifications for LCI datasets (EC-JRC 2010b) linked to the European reference Life Cycle Database ELCD. While these are valuable sources of information for the interested reader and we recommend them for further study, they will not be discussed in detail in this chapter. Table 13.1 Similarities and differences between scientific peer-review and critical review according to ISO 14044 (note that this table represents a general tendency for each criteria, not an absolute truth as there will likely be cases of review processes that may differ on either side of the table) R.K. Rosenbaum and S.I. Olsen


13.2 Critical Review Process As presented, critical review is a procedure intrinsically linked to ISO 14044 (2006a) which defines it as a “process intended to ensure consistency between a life cycle assessment and the principles and requirements of the International Standards on life cycle assessment” (Clause 3.45). However, critical review may also be performed just in order to improve the quality of the study and thus the trust in it. The following will detail why, how, and when critical reviews are performed. 13.2.1 Purpose Critical review of an LCA study is useful in all cases where quality, robustness, and trust in results are wanted. Whether or not a review is required depends on the goal definition, i.e. the intended application and decision context, the reasons for carrying out the study, and the intended audience


Whether or not a review is required depends on the goal definition, i.e. the intended application and decision context, the reasons for carrying out the study, and the intended audience. ISO 14044 recommends the use of critical reviews in general and makes it mandatory for “LCA studies where the results are intended to be used to support a comparative assertion intended to be disclosed to the public” (ISO 2006a). These mandatory critical reviews have to (“shall” in ISO terminology, which indicates an obligation) be performed by a panel of interested parties including at least three experts. A comparative assertion is defined by ISO as (ISO 2006a): an “environmental claim regarding the superiority or equivalence of one product versus a competing product that performs the same function”. However, this definition may not be broad enough. In the European context the Product Environmental Footprint (PEF) is an example of an LCA that will typically be subject to such review requirements


However, this definition may not be broad enough. In the European context the Product Environmental Footprint (PEF) is an example of an LCA that will typically be subject to such review requirements. Even though a comparative assertion is not explicitly stated in the report, Environmental Product Declarations (EPD) and PEF aim to give data and information to be used in comparisons and they could therefore be regarded as a basis for comparative assertions. In fact, critical review by at least one independent and qualified external reviewer (or review team) is mandatory in the PEF methodology (European Commission 2013). 13.2.2 Chronology A critical review can basically be performed in two alternative ways. The first is to review the LCA after the study is completed (a posteriori review). The second approach is an integrated/interactive review where the reviewer(s) follows the study from the definition of goal and scope, through data collection to the conclusion (concurrent review)


The second approach is an integrated/interactive review where the reviewer(s) follows the study from the definition of goal and scope, through data collection to the conclusion (concurrent review). In the a posteriori approach at least one iteration of review comments and associated modifications of the study are performed and the critical review report should reflect the entire review process. In the concurrent review Critical Review


scheme the reviewer(s) can be involved in several of the different steps throughout the conduction of the study, i.e. (ISO 2014): (a) “the goal and scope definition; (b) inventory analysis including data collection and modelling; (c) impact assessment; (d) life cycle interpretation; (e) draft LCA report” with the critical review statement being issued for the final version of the LCA report. ISO 14044/ISO TS 14071 do not specify any requirements or preferences to one or the other approach (neither does PEF), and they can hence always be freely chosen. Most literature recommends the concurrent review (Weidema 1997; Klöpffer 2005, 2012; Hamilton and Ayer 2013; Schulz and Mersiowsky 2013). An a posteriori critical review involves a risk of delays in the final phase. The reviewer(s) has to comment on the draft final reports usually within a few weeks and there is a risk that serious flaws in methodology or data quality, or new aspects appear


The reviewer(s) has to comment on the draft final reports usually within a few weeks and there is a risk that serious flaws in methodology or data quality, or new aspects appear. Doing the necessary corrections may be hindered by budget and timing. Also, the review process requires communication between the practitioner and the reviewer(s) and in some cases the practitioner may not be available after the completion of the study (Klöpffer 2005). The concurrent review approach has the benefits that potential problems can be corrected at an early stage of the study. There may be some extra time needed at the beginning of the study to guide it onto the right track, but this will likely be less time consuming than delays caused by new aspects surfacing at the end of the study or by the need to figure out how assumptions and calculations influence the results


This obviously also influences the timing of the study in itself since the practitioner has to wait for review comments at different milestones throughout the study. Typically, one month additional time should be expected (Schulz and Mersiowsky 2013). A minor concern raised by Curran and Young (2014) is the risk that reviewers may become vested in the study and thus lose their independence. 13.2.3 Requirements According to ISO 14044 (2006a), “the critical review process shall ensure that: • the methods used to carry out the LCA are consistent with the international standard; • the methods used to carry out the LCA are scientifically and technically valid; • the data used are appropriate and reasonable in relation to the goal of the study; • the interpretations reflect the limitations identified and the goal of the study; and • the study report is transparent and consistent”. R.K. Rosenbaum and S.I. Olsen


The European PEF guide (European Commission 2013) outlines the same requirements although it additionally mentions that the data quality should meet requirements and that the study report shall be accurate. This list gives guidance for the reviewer(s) and may also serve as the structure for review reports (Klöpffer 2012). In most instances the reviewer is expected not only to do an “administrative ISO check”, but to also be a discussion partner accompanying the LCA project (Curran and Young 2014). Thus, as stated by ISO TS 14071 (2014) “The critical review should cover all aspects of an LCA, including data appropriateness and reasonability, calculation procedures, life cycle inventory, impact assessment methodologies, characterisation factors, calculated LCI and LCIA results, and interpretation”. Regarding two aspects ISO TS 14071 leaves it optional whether or not the critical review includes them: 1. Assessment of the life cycle inventory (LCI) model, 2


Regarding two aspects ISO TS 14071 leaves it optional whether or not the critical review includes them: 1. Assessment of the life cycle inventory (LCI) model, 2. Assessment of individual data sets. Curran and Young (2014) note that in contrast to the usually comprehensive review of methods and assumptions, there is often a limited examination of data and quantitative results. This may be due to a combination of limitations of time (budget), weak transparency and/or poor accessibility of data sets. In order to perform the critical review it is important that reviewers are granted access to the data and inventory model by the commissioner and practitioner. In LCA it is difficult to establish objective quality criteria, and specific criteria for whether or not a study is correct cannot be defined


In LCA it is difficult to establish objective quality criteria, and specific criteria for whether or not a study is correct cannot be defined. Therefore, much of the critical review has to rely on professional judgement regarding the consistency between goal and scope, data and models used, interpretations applied and the robustness of the conclusions drawn. The previous chapters in this part of the book specify in detail the requirements for conducting an LCA and thus also the aspects that reviewers should be aware of when performing a critical review. Several authors discuss in further detail the specific considerations and questions to ask during the review process, and the interested reader is referred to those (Consoli et al. 1993; Klöpffer 1997; Weidema 1997)


1993; Klöpffer 1997; Weidema 1997). 13.2.4 Deliverables The deliverables of the critical review are (ISO 2006a, 2014): • Comments to specific intermediate steps of the LCA (goal and scope definition, LCI, LCIA, interpretation) for a concurrent review, • Comments to the final draft LCA report, • Review report, • Review statement. Critical Review


The review report documents how the critical review was conducted including all reviewer comments and recommendations given plus a response (to each comment/recommendation) from the practitioner that may indicate consequent changes applied to the study and/or the report or a justification of the respective issue in the study or the report in respect to the comment. Annex A of ISO TS 14071 (2014) contains an informative template for a critical review report. Hamilton and Ayer (2013) suggest that “In general, you should ensure that the following steps in the process are documented: • Review panel comments to the study team • Study team responses to the review panel • [...] • Correspondence between the panel and the study team”. The critical review statement is a short text that clearly states whether or not the study is conform to the requirements of ISO 14040 and 14044


The critical review statement is a short text that clearly states whether or not the study is conform to the requirements of ISO 14040 and 14044. It should also discuss “any particular strengths, limitations and remaining improvement potentials of the LCA study or the critical review process” (ISO 2014)


ISO TS 14071 clearly states what has to be included in the critical review statement (ISO 2014): • “Title of the study; • The commissioner of the LCA study; • The practitioner of the LCA study; • The exact version of the report to which the critical review statement belongs; • The reviewer(s) or, in the case of a panel review, the panel members, including the identification of the panel chairperson; • A description of the review process, including information on: – Whether the review was performed based on ISO 14044:2006, 6.2 or 6.3; – Whether the review was performed in parallel or at the end of the study; – Whether the review included or excluded an assessment of the LCl model; – Whether the review included an analysis of individual data sets; • A description of how comments were provided, discussed and implemented; • A statement of the result of the critical review, i.e. whether the study was found to be in conformance with ISO 14040 and ISO 14044 or not”


whether the study was found to be in conformance with ISO 14040 and ISO 14044 or not”. Hamilton and Ayer (2013) also recommend that “It is important that the final critical review statement includes: • The date of issuance [...] of the study • [...] • Documentation of any outstanding issues that were not resolved during the review • A summary of the comments/responses from the review process”. The final LCA report has to mandatorily include the review statement and review report, as well as all comments and recommendations of the reviewer(s) and R.K. Rosenbaum and S.I. Olsen


any responses by the practitioner to them. It is a requirement of the ISO 14044 standard that the review statement and review report must be included in the LCA report (typically as an appendix to the report). The critical review statement has to be signed by the chairperson and should also be signed by the other reviewers. This signature is strictly individual and personal and cannot be representing an institution nor be replaced by an institutional stamp or label. This means that the reviewer (s) publically (if the report is published) state the conformance or non-conformance of the study to ISO 14040 and 14044 with their names and signatures, which ensures that especially intentional manipulations (but also larger mistakes) that would affect the LCA’s conformance to ISO should have been identified and corrected. This can be seen as a sort of quality insurance, making the reviewer(s) personally responsible for the review process and content


This can be seen as a sort of quality insurance, making the reviewer(s) personally responsible for the review process and content. 13.3 Reviewer Qualifications, Tasks and Selection Since the purpose of a critical review is to perform a critical expert judgement as to whether the ISO 14044 criteria are fulfilled, the expert(s) should of course be independent of the LCA, but not necessarily external to the company. In fact, the foremost requirement for any reviewer, internal or external, in the context of a critical review is complete independence from the study (but not necessarily its commissioner or practitioner), i.e. not involved in the commissioner’s or the practitioner’s project team, nor otherwise implicated in the definition of the scope or the conducting of the LCA


not involved in the commissioner’s or the practitioner’s project team, nor otherwise implicated in the definition of the scope or the conducting of the LCA. In the case of an internal expert, this person may, however, be full-time or part-time employee of either the commissioner or the practitioner of the study, or otherwise be related to either or both of them, while still being independent of the study. An external expert has no financial dependency on either the commissioner or the practitioner, nor any political or other interest in the study results. The necessary qualifications for reviewers performing a critical review depend on a number of factors, such as the type of review scheme (a posteriori or concurrent) and the goal and scope of the LCA: 1. Critical review practice is essential for at least one reviewer who has to be well experienced with the process of a critical review according to ISO 14044 (2006a) and ISO TS 14071 (2014)


Critical review practice is essential for at least one reviewer who has to be well experienced with the process of a critical review according to ISO 14044 (2006a) and ISO TS 14071 (2014). For a panel-based critical review this will usually be the chairperson of the review panel. 2. LCA expertise: As a general rule, there has to be at least one expert on LCA methodology and practice as well as the ISO 14040/14044 requirements. For a panel-based critical review this will usually be the chairperson of the review panel. 3. Technical expertise mostly concerns the LCI phase and is required in order to ensure that the underlying product system model and data are representative and adequately modelled according to goal and scope of the LCA. Technological Critical Review


experts do not necessarily have to be familiar with the LCA methodology. This may comprise specific expertise, such as on the • Product, service or organisation, • Process(es) and technology, • Relevant practice(s) including national or regional specificities if needed. 4. Scientific expertise may be required to ensure adequate consideration of environmental and/or social issues and phenomena of relevance for a given goal and scope definition. This applies particularly to the LCIA phase, but may also be relevant for aspects of the other LCA phases, notably the interpretation. 5. Other expertise may in some cases be necessary depending on the goal and scope, e.g. legal issues, stakeholder concerns, NGOs, etc. Proficiency in the language of the study is of course required from all reviewers. The number of reviewers or review panel members is then a function of the expertise required and the expertise each reviewer brings into the process


The number of reviewers or review panel members is then a function of the expertise required and the expertise each reviewer brings into the process. So far, there is no official accreditation or certification required (or available) and the expertise of reviewers will usually be evaluated via their curriculum vitae including a list of relevant references. ISO TS 14071 (2014) also proposes an example of a self-declaration statement that can be used. To the authors’ knowledge, several organisations intend to establish critical reviewer databases, but until the finalisation of this book no database has reached formal recognition in the global LCA community. The selection of reviewer(s) will typically be done by the practitioner and/or the commissioner of an LCA. In the case of a panel review, they appoint an external independent expert as chairperson, who then selects other independent experts for the review panel. All experts are contracted by the commissioner or practitioner


All experts are contracted by the commissioner or practitioner. This contract normally involves adequate remuneration, a commitment to tasks and timing and a non-disclosure agreement to ensure confidentiality of information and data that need to be accessed by the experts in order to complete the critical review. The reviewers’ contract cannot contain conditions that influence the result of the critical review process. ISO TS 14071 also explicitly states that reviewer tasks cannot be subcontracted or delegated and thus have to be performed by the contracted reviewers personally. ISO TS 14071 lists the respective tasks of the two principal roles in a critical review process, the chairperson and the reviewer


ISO TS 14071 lists the respective tasks of the two principal roles in a critical review process, the chairperson and the reviewer. The reviewer’s role essentially involves: • Commenting on the LCA report (or parts of it during a concurrent critical review); • Contributing to the critical review report; • Expressing agreement or disagreement concerning the critical review statement including a justification in case of disagreement. R.K. Rosenbaum and S.I. Olsen


The chairperson has the same role as a reviewer but with the additional tasks of running the critical review process via: • Setting up of the review panel; • Distribution of tasks relative to each panel member’s competencies; • Coordination of the review process including ensuring a common understanding of the required tasks among all reviewers and their relation to ISO 14040 and 14044; • Recording and sharing each reviewer’s comments within the panel and with the practitioner/commissioner; • Resolve potential conflicting positions between reviewers, aiming at a consensual critical review statement or if that is not possible including a minority position in the statement; • Enable and support a smooth communication among all panel members and with the practitioner and commissioner; • Ensure the generation and panel approval of review report and statement


Consequently, the workload and required experience level regarding the critical review process will be higher for the chairperson which should be reflected in the contractual conditions. Furthermore, this also means that besides technical qualifications, the chairperson should be particularly skilled in communication and project management. How to become a critical reviewer is a frequently asked question and there are several ways once an interested candidate has acquired the necessary competencies and experience. The opportunity to participate in such a process may come via different channels, typically via colleagues who may have been asked first and refer to you, via mailing lists, or via a direct contact with an offer. In any case, it is advisable to first participate as expert in a reviewer panel a few times in order to get acquainted with the process and usual practice


In any case, it is advisable to first participate as expert in a reviewer panel a few times in order to get acquainted with the process and usual practice. Having participated in a few critical reviews, you could propose to take on the additional responsibility of acting as chairperson. 13.4 Conclusions Critical review is an important element of an LCA study that helps ensuring conformance to the relevant LCA standards ISO 14040 and 14044 and thus building credibility and trust in its methodology, data, results, the robustness of its conclusions, and ultimately increasing its acceptance among stakeholders. In the authors’ experience, critical review can trigger a tremendous improvement of an LCA’s rigour, transparency, technical quality and robustness, especially if conducted concurrently to the study


In the authors’ experience, critical review can trigger a tremendous improvement of an LCA’s rigour, transparency, technical quality and robustness, especially if conducted concurrently to the study. It also helps bringing in external and independent views and experiences, which typically enriches the methodological aspects, such as modelling, data, and the interpretation of results in a study. It is, however, not a guarantee that the study is perfect or even as perfect as possible since there will Critical Review


always be aspects that could not be considered, that were overlooked, or that could not be addressed. Also, a critical review does not verify or validate the goals of an LCA or how its results will be used (ISO 2006b), which means that if the objective itself is problematic the LCA study may be stated as conform to ISO, while still supporting misleading conclusions or recommendations beyond the LCA report. Critical review is also not a validation process against objective evidence, such as measurements or other observations. This chapter provides a broad overview of several complementary aspects related to the critical review, without discussing them all in detail


This chapter provides a broad overview of several complementary aspects related to the critical review, without discussing them all in detail. The authors recommend the cited references for further reading, particularly the ISO standards of course, along with the publications by Klöpffer (1997, 2005, 2012), Weidema (1997), Hamilton and Ayer (2013), and Curran and Young (2014), which will provide further depth and details, practical aspects, and experiences for the interested reader. They are certainly essential reads for aspiring critical reviewers and chairpersons, but also for practitioners of an LCA that will be exposed to a critical review. Consoli, F., Allen, D., Boustead, I., Fava, J., Franklin, W., Jensen, A., de Oude, N., Parrish, R., Perriman, R., Postlethwaite, D., Quay, B., Séguin, J., Vigon, B.: Guidelines for Life Cycle Assessment: A “Code of Practice”. SETAC, Pensacola, FL (1993) Curran, M.A., Young, S.B.: Critical review: a summary of the current state-of-practice. Int


SETAC, Pensacola, FL (1993) Curran, M.A., Young, S.B.: Critical review: a summary of the current state-of-practice. Int. J. Life Cycle Assess. 19, 1667–1673 (2014). doi:10.1007/s11367-014-0778-2 EC-JRC: International Reference Life Cycle Data System (ILCD) Handbook: Review schemes for Life Cycle Assessment (LCA). EUR 24710 EN (2010a). doi:10.2788/39791 EC-JRC: International Reference Life Cycle Data System (ILCD) Handbook: Reviewer qualifications for Life Cycle Inventory data sets. European Commission, Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy (2010b). doi:10.2788/95543 European Commission: Recommendation 2013/179/EU on the use of common methods to measure and communicate the life cycle environmental performance of products and organisations. Off J Eur Union 210 (2013). doi:10.3000/19770677.L_2013.124.eng Gleick, P.H.: Hummer versus Prius “Dust to Dust” Report Misleads the Media and Public with Bad Science


Off J Eur Union 210 (2013). doi:10.3000/19770677.L_2013.124.eng Gleick, P.H.: Hummer versus Prius “Dust to Dust” Report Misleads the Media and Public with Bad Science. Pacific Institute, Oakland, CA (2007) Hamilton, M., Ayer, N.: Navigating the LCA Critical Review Process - EarthShift White Paper (2013) ISO: Environmental Management—Life Cycle Assessment—Requirements and Guidelines (ISO 14044). ISO, the International Organization for Standardization, Geneva (2006a) ISO: Environmental Management—Life Cycle Assessment—Principles and Framework (ISO 14040). ISO, the International Organization for Standardization, Geneva (2006b) ISO: Environmental Management—Life Cycle Assessment—Critical Review Processes and Reviewer Competencies: Additional Requirements and Guidelines to ISO 14044:2006 (ISO 14071). ISO, the International Organization for Standardization, Geneva (2014) Jolliet, O., Saade-Sbeih, M., Shaked, S., Jolliet, A., Crettaz, P.: Environmental Life Cycle Assessment


ISO, the International Organization for Standardization, Geneva (2014) Jolliet, O., Saade-Sbeih, M., Shaked, S., Jolliet, A., Crettaz, P.: Environmental Life Cycle Assessment. CRC Press Taylor & Francis, Boca Raton, FL (2015) Klöpffer, W.: Peer (Expert) review in LCA according to SETAC and ISO 14040. Int. J. Life Cycle Assess. 2, 183–184 (1997). doi:10.1007/BF02978411 Klöpffer, W.: The critical review process according to ISO 14040-43. Int. J. Life Cycle Assess. 10, 98–102 (2005). doi:10.1065/lca2004.12.190 R.K. Rosenbaum and S.I. Olsen


Klöpffer, W.: The critical review of life cycle assessment studies according to ISO 14040 and 14044. Int. J. Life Cycle Assess. 17, 1087–1093 (2012). doi:10.1007/s11367-012-0426-7 Lang, B., Lupi, C., Omlin, M., Reinhardt, I.: Wo speist man ökologischer? Möglichkeiten und Grenzen von Ökobilanzen im Restaurant-Vergleich. GAIA—Ecol Perspect Sci Soc 3, 108–115 (1994) Schulz, M., Mersiowsky, I.: Critical review of LCA—essential for quality and understanding. In: 6th International Conference on Life Cycle Management. LCM 2013. Gothenburg, Sweden, p. 4 (2013) Weidema, B.P.: Guidelines for critical review of product LCA. Belgium, Brussels (1997) Author Biographies Ralph K. Rosenbaum LCA expert and environmental modeller focusing on LCIA development since early 2000s. Contributed to several UNEP/SETAC working groups towards global harmonisation of LCA methodology


Rosenbaum LCA expert and environmental modeller focusing on LCIA development since early 2000s. Contributed to several UNEP/SETAC working groups towards global harmonisation of LCA methodology. Interested in LCIA modelling of emissions and water/soil resource use, operationalisation of uncertainty management and spatial differentiation. Stig Irving Olsen LCA expert both as researcher and as consultant. Involved in the development of LCA methodologies since mid 1990s. Contributed to UNEP/SETAC working groups on LCIA methodology. Main LCA interest is human toxicity impacts, emerging technologies, and decision making. Critical Review


Chapter 14 Use of Input–Output Analysis in LCA Tuomas J. Mattila Abstract Input–output analysis can be used as a tool for complementing the traditionally process-based life cycle assessment (LCA) with macroeconomic data from the background systems. Properly used, it can result in faster and more accurate LCA. It also provides opportunities for streamlining the LCA inventory collection and focusing resources. This chapter reviews the main uses of input– output analysis (IO) to ensure consistent system boundaries, to evaluate the completeness of an LCA study and to form a basis for in-depth inventory collection. The use of IO as a data source for social and economic sustainability metrics is also discussed, as are the limitations of the approach. All aspects are demonstrated through examples and references both to recent scientific literature and publicly available datasets are provided. The aim of the chapter is to present the basic tools for applying IO in practical LCA studies


The aim of the chapter is to present the basic tools for applying IO in practical LCA studies. Learning Objectives After studying this chapter, the reader should be able to: • Understand the historical background of input–output analysis and how it relates to LCA. • Understand the basic equations of input–output analysis. • Use input–output datasets to find background information on product systems and processes. • Use hybrid input–output analysis to identify hotspots and the effect of cut-off in process-LCA. • Use input–output analysis to improve process-LCA dataset. T.J. Mattila (&) Finnish Environment Institute SYKE & Luonnonkoneisto Engineering Co-operative, Helsinki, Finland e-mail: tuomas.mattila@ymparisto.fi © Springer International Publishing AG 2018 M.Z. Hauschild et al. (eds.), Life Cycle Assessment, DOI 10.1007/978-3-319-56475-3_14


• Use input–output analysis as a basis for collecting more detailed process-LCA data. • Find social and economic data to supplement environmental LCA. • Understand the strengths and the limitations of using input–output analysis as supplement to process-LCA. 14.1 This chapter introduces how to use input–output analysis (IO) in life cycle assessment (LCA). IO was initially developed for macroeconomic systems analysis and planning, but it shares many approaches and methods with process-based LCA. After decades of separate methodological development, the recent trend is to combine the tools into environmentally extended input–output analysis (EEIO), hybrid IO-LCA and comprehensive sustainability assessment. The application of IO together with LCA is assisted by the fact, that it shares the same structure as attributional LCA, linking environmental impacts to economic demand through a product system


The application of IO together with LCA is assisted by the fact, that it shares the same structure as attributional LCA, linking environmental impacts to economic demand through a product system. An important problem in conventional process-based LCA is cut-off, or the omission of certain parts of the product system (see Chap. 9). LCA attempts to model every environmental, social and economic impact caused by a product throughout its life cycle from “cradle to grave”, integrated over time and space. In practice, this is impossible, and certain simplification for the system boundaries have to be introduced. Everything outside those system boundaries is considered to be “cut-off” from the analysis. If this cut-off is allowed to be subjective, it ruins the idea of comparable and repeatable results. Therefore detailed cut-off criteria, product category rules, standards and handbooks have been developed for standardising and harmonising system boundary setting (EC-JRC 2010)


Therefore detailed cut-off criteria, product category rules, standards and handbooks have been developed for standardising and harmonising system boundary setting (EC-JRC 2010). The product system of an LCA can be thought of as a branching tree. It starts from the functional unit and branches out to the first tier of inputs needed to supply the functionality. Each of these first tier inputs then branches out into second tier inputs and so forth (EC-JRC 2010). This branching out is repeated until all the identified inputs and outputs are either resources extracted or emissions emitted to the environment (i.e. “elementary flows”). In practice, only a part of this branching out is done in an individual study. In a typical study, primary data is collected for the foreground processes, which are closest to the final user (see more about foreground process in Sect. 8.2.3). The remaining inputs are connected to LCA databases, which include product systems from previous studies


8.2.3). The remaining inputs are connected to LCA databases, which include product systems from previous studies. This forms the background system. The result is a branching process diagram, which proceeds from an individual product towards more general background processes. In addition, there are processes and flows for which no data can be found, and they are considered cut-off. This dataset is then used to estimate, how much environmental T.J. Mattila