DEALA — A novel economic life cycle impact assessment method for differentiated economic assessments in the context of life cycle sustainability assessments

Representativeness of environmental impact assessment methods regarding Life Cycle Inventories

The life cycle human health (HH) impacts related to aviation biofuels have been understood in a limited way. Life cycle impact assessment (LCIA) methods for assessing HH are often associated with a high level of uncertainty and a low level of consensus. As a result, it remains challenging to perform a robust assessment of HH impacts with a suitable LCIA method. This study aims to systematically compare six commonly used LCIA methods for quantifying HH impacts, in order to empirically understand the potential impacts of aviation biofuel production on HH and how the results are affected by the choice of methods. Three aviation biofuel production pathways based on different feedstocks (sugarcane, eucalyptus, and macauba) were analyzed and compared to fossil aviation biofuels, on the basis of a functional unit of 1 MJ aviation fuel. The majority of the LCIA methods suggest that, in respect to midpoint impacts, macauba-based biofuel is associated with the lowest impacts and eucalyptus-based biofuel the highest; whereas at endpoint level, the results are more scattered. The LCIA methods agree that biomass conversion into aviation biofuel, H2 production, and feedstock cultivation are major contributors to life cycle HH impacts. Additionally, we provide a guideline for determining an appropriate method for assessing HH impacts.

Environmental life cycle assessment of reverse osmosis desalination: The influence of different life cycle impact assessment methods on the characterization results

The inconsistency caused by different life cycle impact assessment (LCIA) methods is a long-term challenge for the life cycle assessment (LCA) community. It is necessary to systematically analyze the differences caused by LCIA methods and facilitate the fair comparison of LCA results. This study proposes an effective method of conversion factors (CFs) for converting the results of 8 LCIA methods for 14 impact categories and then demonstrates its application in the construction sector. Correlation analyses of the datasets of construction materials are conducted to develop CFs for the impact categories. A set of conversion cards are devised to present the CFs and the associated correlation information for the LCIA methods. It is revealed that the differences between LCIA methods are largely caused by the characterization methods, rather than due to the metrics. A comparison based only on the same metrics but ignoring the underlying LCIA mechanisms is misleading. High correlations are observed for the impact categories of climate change, acidification, eutrophication, and resource depletion. The developed CFs and conversion cards can greatly help LCA practitioners in the fair comparison of LCA results from different LCIA methods. Case studies are conducted, and verify that by applying the CFs the seemingly incomparable results from different LCIA methods become comparable. The CF method addresses the inconsistency problem of LCIA methods in a practical manner and helps improve the comparability and reliability of LCA studies in the construction sector. Suggestions are provided for the further development of LCIA conversion factors.

Life cycle impact assessment (LCIA) methods quantify the impact of life cycle inventory data within each impact category by means of classification and characterization. This paper evaluated whether the selected LCIA method influenced the life cycle assessment (LCA) scenario analysis for decision support in process development and its possible reasons. For this study, a scenario analysis was used from a biorefinery LCA case study, as this is a key practice in process development. The analysis was investigated using various LCIA methods for the three midpoint impact categories of global warming potential (GWP, 12 LCIA methods totaling 48 subcategories), eutrophication potential (EP, 9 LCIA methods totaling 18 subcategories), and water assessment (WA, 10 LCIA methods totaling 26 subcategories). The GWP category showed consistent interpretations for the scenario analysis from different LCIA methods. The subcategory of marine EP from the two LCIA methods disagreed on the best-case scenario. Another discrepancy was identified within the three general EP indicators, where the trend of the scenario analysis was inverted in one method because of the sensitivity of a single substance (ethanol). Within the subcategories of WA, the inclusion or exclusion of hydropower water impacts changed the scenario analysis in the blue water use and total freshwater use subcategories, and the general WA indicators also disagreed on the best-case scenario. It is important to understand these influences and the reasons behind the variations for decision support in process development.

Life-cycle assessment of a house with alternative exterior walls: Comparison of three impact assessment methods

In the OMNIITOX project 11 partners have the common objective to improve environmental management tools for the assessment of (eco)toxicological impacts. The detergent case study aims at: i) comparing three Procter &c Gamble laundry detergent forms (Regular Powder-RP, Compact Powder-CP and Compact Liquid-CL) regarding their potential impacts on aquatic ecotoxicity, ii) providing insights into the differences between various Life Cycle Impact Assessment (LCIA) methods with respect to data needs and results and iii) comparing the results from Life Cycle Assessment (LCA) with results from an Environmental Risk Assessment (ERA). The LCIA has been conducted with EDIP97 (chronic aquatic ecotoxicity) [1], USES-LCA (freshwater and marine water aquatic ecotoxicity, sometimes referred to as CML2001) [2, 3] and IMPACT 2002 (covering freshwater aquatic ecotoxicity) [4]. The comparative product ERA is based on the EU Ecolabel approach for detergents [5] and EUSES [6], which is based on the Technical Guidance Document (TGD) of the EU on Environmental Risk Assessment (ERA) of chemicals [7]. Apart from the Eco-label approach, all calculations are based on the same set of physico-chemical and toxicological effect data to enable a better comparison of the methodological differences. For the same reason, the system boundaries were kept the same in all cases, focusing on emissions into water at the disposal stage. Significant differences between the LCIA methods with respect to data needs and results were identified. Most LCIA methods for freshwater ecotoxicity and the ERA see the compact and regular powders as similar, followed by compact liquid. IMPACT 2002 (for freshwater) suggests the liquid is equally as good as the compact powder, while the regular powder comes out worse by a factor of 2. USES-LCA for marine water shows a very different picture seeing the compact liquid as the clear winner over the powders, with the regular powder the least favourable option. Even the LCIA methods which result in die same product ranking, e.g. EDIP97 chronic aquatic ecotoxicity and USES-LCA freshwater ecotoxicity, significantly differ in terms of most contributing substances. Whereas, according to IMPACT 2002 and USES-LCA marine water, results are entirely dominated by inorganic substances, the other LCIA methods and the ERA assign a key role to surfactants. Deviating results are mainly due to differences in the fate and exposure modelling and, to a lesser extent, to differences in the toxicological effect calculations. Only IMPACT 2002 calculates the effects based on a mean value approach, whereas all other LCIA methods and the ERA tend to prefer a PNEC-based approach. In a comparative context like LCA the OMNIITOX project has taken the decision for a combined mean and PNEC-based approach, as it better represents the ‘average’ toxicity while still taking into account more sensitive species. However, the main reason for deviating results remains in the calculation of the residence time of emissions in the water compartments. The situation that different LCIA methods result in different answers to the question concerning which detergent type is to be preferred regarding the impact category aquatic ecotoxicity is not satisfactory, unless explicit reasons for the differences are identifiable. This can hamper practical decision support, as LCA practitioners usually will not be in a position to choose the ’right’ LCIA method for their specific case. This puts a challenge to the entire OMNIITOX project to develop a method, which finds common ground regarding fate, exposure and effect modelling to overcome the current situa-tion of diverging results and to reflect most realistic conditions.

Many studies evaluate the results of applying different life cycle impact assessment (LCIA) methods to the same life cycle inventory (LCI) data and demonstrate that the assessment results would be different with different LICA methods used. Although the importance of uncertainty is recognized, most studies focus on individual stages of LCA, such as LCI and normalization and weighting stages of LCIA. However, an important question has not been answered in previous studies: Which part of the LCA processes will lead to the primary uncertainty? The understanding of the uncertainty contributions of each of the LCA components will facilitate the improvement of the credibility of LCA. A methodology is proposed to systematically analyze the uncertainties involved in the entire procedure of LCA. The Monte Carlo simulation is used to analyze the uncertainties associated with LCI, LCIA, and the normalization and weighting processes. Five LCIA methods are considered in this study, i.e., Eco-indicator 99, EDIP, EPS, IMPACT 2002+, and LIME. The uncertainty of the environmental performance for individual impact categories (e.g., global warming, ecotoxicity, acidification, eutrophication, photochemical smog, human health) is also calculated and compared. The LCA of municipal solid waste management strategies in Taiwan is used as a case study to illustrate the proposed methodology. The primary uncertainty source in the case study is the LCI stage under a given LCIA method. In comparison with various LCIA methods, EDIP has the highest uncertainty and Eco-indicator 99 the lowest uncertainty. Setting aside the uncertainty caused by LCI, the weighting step has higher uncertainty than the normalization step when Eco-indicator 99 is used. Comparing the uncertainty of various impact categories, the lowest is global warming, followed by eutrophication. Ecotoxicity, human health, and photochemical smog have higher uncertainty. In this case study of municipal waste management, it is confirmed that different LCIA methods would generate different assessment results. In other words, selection of LCIA methods is an important source of uncertainty. In this study, the impacts of human health, ecotoxicity, and photochemical smog can vary a lot when the uncertainties of LCI and LCIA procedures are considered. For the purpose of reducing the errors of impact estimation because of geographic differences, it is important to determine whether and which modifications of assessment of impact categories based on local conditions are necessary. This study develops a methodology of systematically evaluating the uncertainties involved in the entire LCA procedure to identify the contributions of different assessment stages to the overall uncertainty. Which modifications of the assessment of impact categories are needed can be determined based on the comparison of uncertainty of impact categories. Such an assessment of the system uncertainty of LCA will facilitate the improvement of LCA. If the main source of uncertainty is the LCI stage, the researchers should focus on the data quality of the LCI data. If the primary source of uncertainty is the LCIA stage, direct application of LCIA to non-LCIA software developing nations should be avoided.

PurposeAn adequate matching between the nomenclature of elementary flows in life cycle inventory (LCI) databases and life cycle impact assessment (LCIA) methods is key for ensuring the proper application of life cycle assessment (LCA). However, the nomenclature of elementary flows lacks harmonization among the LCA community. This paper aims at defining mapping rules and discussing main challenges related to the process of systematically mapping LCI nomenclatures to LCIA methods and models addressing biodiversity impacts.MethodsEight LCIA methods and models addressing biodiversity loss are analyzed: five comprehensive LCIA methods (i.e., LC-IMPACT, Impact World + , Ecological Scarcity 2013, ReCiPe 2016, and Stepwise), one land use intensity-specific LCIA model; and two approaches adapting the GLOBIO model to LCIA. These models and methods are mapped to two LCI nomenclatures (ecoinvent v3.6 as implemented in Simapro and Environmental Footprint (EF) 3.0). A mapping tool was developed to support the process of (a) mapping elementary flows by name, Chemical Service number or available synonyms; (b) implementing specific mapping rules regarding compartment/sub-compartment, and substance name; (c) mapping elementary flows to manually defined proxies (e.g., synonyms, spelling corrections and similar substances); and (d) assigning characterization factors (CFs). The process entails analyzing a case study to identify uncharacterized elementary flows.Results and discussionWe present a mapping of LCIA methods and models addressing impacts on biodiversity loss with specific LCI nomenclatures. Mapping rules are proposed for elementary flows regarding chemicals, carbon emissions, land use, water use, and particulate matter. Specific aspects to be considered in mapping elementary flows in LCIA and LCI nomenclatures are discussed. Main gaps in LCI nomenclatures are associated to toxicity and climate change impacts. The EF 3.0 was more aligned than ecoinvent 3.6 with the LCIA methods and models regarding elementary flows coverage and regionalization level. Analyzing uncharacterized flows revealed further coverage needs for “Chemical, organic” (between 19 and 20% uncharacterized flows), “Chemical, inorganic” (between 9 and 18% uncharacterized flows) and “Chemical, radioactive” (between 9 and 14% uncharacterized flows).ConclusionsThis paper contributes to the operationalization of LCIA methods and models addressing biodiversity impacts by proposing a systematic mapping process and rules for a better LCIA-LCI connection. Different development pathways of LCI (e.g., focused on substance name detail) and LCIA (e.g., towards improved regionalization level) have stretched the gap between both nomenclatures. Recommendations are provided identifying further efforts towards the harmonization of the nomenclature of elementary flows in the LCA community.

Uncertainty is present in many forms in life cycle assessment (LCA). However, little attention has been paid to analyze the variability that methodological choices have on LCA outcomes. To address this variability, common practice is to conduct a sensitivity analysis, which is sometimes treated only at a qualitative level. Hence, the purpose of this paper was to evaluate the uncertainty and the sensitivity in the LCA of swine production due to two methodological choices: the allocation approach and the life cycle impact assessment (LCIA) method. We used a comparative case study of swine production to address uncertainty due to methodological choices. First, scenario variation through a sensitivity analysis of the approaches used to address the multi-functionality problem was conducted for the main processes of the system product, followed by an impact assessment using five LCIA methods at the midpoint level. The results from the sensitivity analysis were used to generate 10,000 independent simulations using the Monte Carlo method and then compared using comparison indicators in histogram graphics. Regardless of the differences between the absolute values of the LCA obtained due to the allocation approach and LCIA methods used, the overall ranking of scenarios did not change. The use of the substitution method to address the multi-functional processes in swine production showed the highest values for almost all of the impact categories, except for freshwater ecotoxicity; therefore, this method introduced the greater variations into our analysis. Regarding the variation of the LCIA method, for acidification, eutrophication, and freshwater ecotoxicity, the results were very sensitive. The uncertainty analysis with the Monte Carlo simulations showed a wide range of results and an almost equal probability of all the scenarios be the preferable option to decrease the impacts on acidification, eutrophication, and freshwater ecotoxicity. Considering the aggregate result variation across allocation approaches and LCIA methods, the uncertainty is too high to identify a statistically significant alternative. The uncertainty analysis showed that performing only a sensitivity analysis could mislead the decision-maker with respect to LCA results; our analysis with the Monte Carlo simulation indicates no significant difference between the alternatives compared. Although the uncertainty in the LCA outcomes could not be decreased due to the wide range of possible results, to some extent, the uncertainty analysis can lead to a less uncertain decision-making by demonstrating the uncertainties between the compared alternatives.

In a life cycle assessment (LCA), the impacts on resources are evaluated at the area of protection (AoP) with the same name, through life cycle impact assessment (LCIA) methods. There are different LCIA methods available in literature that assesses abiotic resources, and the goal of this study was to propose recommendations for that impact category. We evaluated 19 different LCIA methods, through two criteria (scientific robustness and scope), divided into three assessment levels, i.e., resource accounting methods (RAM), midpoint, and endpoint. In order to support the assessment, we applied some LCIA methods to a case study of ethylene production. For RAM, the most suitable LCIA method was CEENE (Cumulative Exergy Extraction from the Natural Environment) (but SED (Solar Energy Demand) and ICEC (Industrial Cumulative Exergy Consumption)/ECEC (Ecological Cumulative Exergy Consumption) may also be recommended), while the midpoint level was ADP (Abiotic Depletion Potential), and the endpoint level was both the Recipe Endpoint and EPS2000 (Environmental Priority Strategies). We could notice that the assessment for the AoP Resources is not yet well established in the LCA community, since new LCIA methods (with different approaches) and assessment frameworks are showing up, and this trend may continue in the future.

Life cycle assessment (LCA) has been used to evaluate environmental impacts of products or processes including wastewater treatment. Uncertainty has not received adequate attention in LCA studies. Uncertainty inherited in LCA steps such as the life cycle inventory (LCI) or the life cycle impact assessment (LCIA) method use is unavoidable, but it affects LCA outcomes and associated decision-making. The objective of this paper was to show the impact of uncertainty from LCI and LCIA method on LCA outcomes by using a case study base approach on wastewater sludge treatment processes. A qualitative analysis included setting criteria about what data to be included in LCI, characterization of data, differentiating between major and minor contributors in LCI modeling, evaluation of data quality indicators, setting achievable alternative scenarios, and selecting proper LCIA method were used, in addition to quantitative analysis included assigning most appropriate values for data gaps and proper distribution, and conducting probabilistic analysis to evaluate overall uncertainty. This research used a full-scale wastewater treatment plant in Missouri, USA for case study in which multiple hearth incineration (MHI) is the existing process, while fluid bed incineration (FBI) and anaerobic digestion (AD) were proposed as the alternatives. Using ReCipe method, the study revealed that variation in LCA results of MHI is 63.4% for a single end-point score of 57.9 mPt. On the two alternative processes, it is 54.6% probable that FBI would have more environmental impact than AD. The case study showed that the proposed steps were able to address issues of data uncertainty. Due to differences in characterization, normalization, and weighting factors, different LCIA methods may point out different conclusions and need to be addressed in evaluation.

Sensitivity analysis of the use of Life Cycle Impact Assessment methods: a case study on building materials

In this chapter, a wide range of Life Cycle Assessment (LCA) methods, new initiative for reducing emissions and improving resource efficiency, and Product Environmental Footprint are examined, in order to introduce the research tendency in this filed and clarify the differences among these Life Cycle Impact Assessment (LCIA) methods. The LCIA methods are broadly categorized as resource based and emission based. Life Cycle Inventory (LCI) database are also investigated, and the features of the generic LCI database are presented. The data formats of the ecoinvent database are deeply examined, with the aim of clarifying the attributes, types of each data components to help users to understand the role of inventory database in the practices.

In life cycle assessment (LCA), the impact assessment on natural resources is still in the early stages of research, and the impacts of biotic resources are usually not evaluated. The human appropriation of net primary production (HANPP) is a well-known indicator of land use impacts, but it cannot be easily implemented in LCA. The objective of this paper was to create a life cycle impact assessment (LCIA) method on land use impacts on net primary production (NPP) based on the HANPP approach. To create an operational LCIA method, the midpoint characterization factors (CF) were calculated by comparing the NPP of plants occurring under current land uses with a baseline scenario, i.e., the NPP of potential natural vegetation. For the endpoint CF, we considered the backup technology concept and included in the calculation the marginal cost for additional biomass production through algae cultivation in the ocean. Site-generic and site-specific midpoint and endpoint characterization factors (CF) were created in a global scale for 162 countries and for four types of land uses (cropland, pasture, infrastructure, and wilderness). For cropland, we also created biomass-specific CF for ten particular crops in a global scale. The LCIA method was tested in particular case studies and seemed to produce comparable results, with the possibility of coupling it with other LCIA methodologies, as recipe endpoint. The LCIA method proposed in this paper provides an assessment of the decrease of biomass availability due to land use (affecting the AoP resources), which is an impact category poorly considered in LCA. Nevertheless, the method has some future challenges, for instance to take into account site-specific backup technologies for the endpoint CF.