Uncertainty In Life Cycle Assessment Research Articles

Influence of BIM's level of detail on the environmental impact of buildings: Danish context

Integrating a BIM-based life-cycle assessment (LCA) in the early design stages of building projects can promote a reduction of environmental impacts. However, the reliability of the analysis depends on the quality of the model. BIM modelling of timber-frame construction is often simplified, potentially hindering the LCA outcome due to quantity inaccuracy and a failure to consider elements regarded as negligible. This study addresses the methodological problem that influences the calculation of the environmental impact by examining the various environmental impacts caused by inventory data obtained from BIM models with a level of detail of (LOD)350–400 vs LOD200. The environmental impacts obtained from both models of a timber building calculated through an attributional LCA method are compared. Material inventory sourced from the LOD200 model results in a 14.7% lower value of embodied impact. The discrepancies resulted from aluminium, mineral wool and bitumen, which are identified as critical materials requiring quantity adjustment. Relying on simplified BIM models for LCA may lead to the GWP being under-estimated with incorrect identification of hotspots in the early design stages. The results aksi question the cut-off criterion of the current EN15978 norm that serves as a foundation for developing environmental policies within the industry. It recommends exclusion of materials constituting less than 1% of the overall mass from the system boundary. This study underscores the potential significance of materials falling below this threshold, challenging the validity of the criterion, and suggesting that such materials should be carefully evaluated and included in the LCA to ensure a comprehensive assessment of the environmental impacts. The overall objective of the study is to emphasize the importance of employing accurate BIM models in LCAs to make informed decisions that are aligned with the sustainability goals, encouraging practitioners to consider the impact of critical materials, even those with seemingly minimal contributions. With this knowledge, the practitioners are able to take meaningful actions that compensate for the LCA uncertainty, mitigating the environmental burden of the most impactful areas. Most importantly, the findings aim to identify the error in design LCA versus as-built studies, helping to develop design LCA tools that predict the as-built impact more precisely and earlier in the design process. The findings can also improve the expected building model definition in carbon policies.

Lifespan prediction of existing building typologies

Life-cycle assessment (LCA) is currently one of the construction industry's most widely used methods of environmental assessment. Because LCA calculations is based on life-cycle thinking are the lifespan of the building a sensitive parameter in relation to the calculated overall environmental impact of the building. More accurate assessments of the lifespans of buildings are therefore a prerequisite for reducing errors and uncertainties in LCA and Life Cycle Costing (LCC) calculations. This study evaluates a generalized logistic lifespan prediction model for existing building typologies. The model is tested as part of a Danish case study based on building data collected from 124,096 cases of demolition. The objective is to investigate whether the typical lifespan used in LCA or LCC calculations accurately describes the remaining service life of existing buildings based on different building typologies and construction periods. The study show that office buildings generally have much shorter expected lifespans than previously assumed and that multi-family housing has nearly twice the lifespan of single-family housing. The results show a tendency for a declining lifespan based on the construction period, in which newer buildings, i.e. those that are no more than thirty years old, have a lifespan that is much shorter than the average lifespan for all construction periods. These results are highly relevant for both LCA and LCC calculations of buildings since they indicate that newer buildings in northern Europe have a lower lifespan than previous studies have shown.

The environmental assessment of tertiary treatment technologies for wastewater reuse by considering LCA uncertainty

Tertiary treatment methods can result in severe environmental impacts that should be taken into consideration when deciding about the best system for wastewater reuse applications beside technical and economic issues. Life cycle assessment (LCA) commonly is used for evaluating the environmental impacts of these methods. Given the importance of uncertainty analysis, it is still an uncommon practice in LCA researches in recent years, especially those focused on tertiary wastewater treatment technologies (TWWTTs). Therefore, the main purpose of this study is to investigate the parameter and input data uncertainty in LCA of TWWTTs. Considering huge amount of data collected for LCA from different sources with various precision, there is high uncertainty in obtained LCA results that could affect the ranking of different methods based on their environmental impacts. The Monte-Carlo based method is used for uncertainty analysis of LCA and it is investigated how it can affect the final ranking of tertiary treatment methods based on their environmental impacts. In present study, LCA is used to study the environmental impacts of 22 tertiary treatment systems used for further treatment of effluent of South of Tehran wastewater treatment plant (WWTP) located at the south of Tehran, Iran. The treated wastewater of this WWTP is planned to be used for different purposes including landscape irrigation and groundwater recharge as well as industrial and non-potable household (toilet flushing) usages. Among the 22 tertiary treatment systems, the best system for each application was chosen, based on the LCA results and the uncertainty of LCA.

A Tier-Wise Method for Evaluating Uncertainty in Life Cycle Assessment

As a decision support tool, life cycle assessment (LCA) is prone to multiple uncertainties associated with the data, model structures, and options offered to practitioners. Therefore, to make the results reliable, consideration of these uncertainties is imperative. Among the various classifications, parameter, scenario, and model uncertainty are widely reported and well-acknowledged uncertainty types in LCA. There are several techniques available to deal with these uncertainties; however, each strategy has its own pros and cons. Furthermore, just a few of the methods have been included in LCA software, which restricts their potential for wider application in LCA research. This paper offers a comprehensive framework that concurrently considers parameter, scenario, and model uncertainty. Moreover, practitioners may select multiple alternatives depending on their needs and available resources. Based on the availability of time, resources, and technical expertise three levels—basic, intermediate, and advanced—are suggested for uncertainty treatment. A qualitative method, including local sensitivity analysis, is part of the basic approach. Monte Carlo sampling and local sensitivity analysis, both of which are accessible in LCA software, are suggested at the intermediate level. Advanced sampling methods (such as Latin hypercube or Quasi-Monte Carlo sampling) with global sensitivity analysis are proposed for the advanced level.

Life Cycle Assessment under Uncertainty: A Scoping Review

Today, life cycle assessment (LCA) is the most widely used approach to model and calculate the environmental impacts of products and processes. The results of LCAs are often said to be deterministic, even though the real-life applications are uncertain and vague. The uncertainty, which may be simply ignored, is one of the key factors influencing the reliability of LCA outcomes. Numerous sources of uncertainty in LCA are classified in various ways, such as parameter and model uncertainty, choices, spatial variability, temporal variability, variability between sources and objects, etc. Through a scoping review, the present study aims to identify and assess the frequency with which LCA studies reflect the uncertainty and what are the tools to cope with the uncertainty to map the knowledge gaps in the field to reveal the challenges and opportunities to have a robust LCA model. It is also investigated which database, methodology, software, etc., have been used in the life cycle assessment process. The results indicate that the most significant sources of uncertainty were in the model and process parameters, data variability, and the use of different methodologies and databases. The probabilistic approach or stochastic modeling, using numerical methods such as Monte Carlo simulation, was the dominating tool to cope with the uncertainty. There were four dominant LCA methodologies: CML, ReCiPe, IMPACT 2002+, and TRACI. The most commonly used LCA software and databases were SimaPro® and Ecoinvent®, respectively.

Comparison and analysis of product stage and service life uncertainties in life cycle assessment of building elements

Abstract Life cycle assessment (LCA) has the potential to inform building decisions from the planning process to conceptual design. As such, there is intrinsic uncertainty that needs to be explored further to allow for proper decisions to be made. These uncertainties may be related to parameter definition, such as life cycle inventory or model as service life definition. This paper aims to analyze the influence of two recognized sources of uncertainties in LCA of buildings: product stage uncertainties and uncertainties from SL during the use stage. The Monte Carlo simulation method is applied to conduct uncertainty analysis of the LCA results of four building elements, namely, external cement plaster, external clay brick wall, external painting and internal painting. The functional unit is 1 m2 of each building element. Three different building reference study periods are considered: 50, 120 and 500 years. A global warming potential impact category is chosen since it is one of the most significant indicators for climate change mitigation strategies. Results indicate that SL uncertainties are greater than product stage uncertainties for the four building elements analyzed. Furthermore, based on the findings from this study, distribution choice influences the uncertainty analysis results in Monte Carlo simulation. Standardizing modeling of SL in the LCA of buildings could guide building LCA practitioners and researchers and lead to more comparable results.

Multilevel environmental assessment of regional farming activities with Life Cycle Assessment: Tackling data scarcity and farm diversity with Life Cycle Inventories based on Agrarian System Diagnosis

CONTEXTPolicymakers still lack methods to assess the environmental impacts of agriculture at regional scales. Life Cycle Assessment (LCA) is renowned in assessing the environmental footprint of economic activities; however, it has to be adapted to be of use for this purpose. OBJECTIVEOur objective is to develop a methodology to carry out relevant LCA of agricultural productions at the regional scale, and to address, in particular, two major challenges which are sources of uncertainties in LCA, i.e., data scarcity and farming system diversity. METHODSWe introduce an innovative method for building Life Cycle Inventories (LCI) of agricultural regions, capable of capturing farming system diversity in the context of data scarcity. It combines LCA with Agrarian System Diagnosis (ASD), which has been adapted to meet the heavy data requirements of the LCI step. This method, which we named “ASD-based LCI”, was applied to the semi-arid irrigated plain of Kairouan in Tunisia.After ASD is carried out, a typology of farming systems is built using different data sources: literature review and ASD-based data (e.g., historical and landscape analysis, interviews). This paves the way for a stratified sampling of farms, after which each selected farm is studied in-depth, through field visits and extensive interviews, to collect activity data, i.e., data related to energy and material input and output flows (e.g., manure, seeds, electricity) at the crop / livestock level. The data set quality is improved by filling remaining data gaps using various approaches, e.g., analogy, crop modelling, or expert knowledge. The effect of stratified sampling and data gap filling on uncertainty reduction is evaluated using the pedigree matrix approach and the “uncertainty factor” (UF) which determines the uncertainty interval around the mean of any LCI data. RESULTS AND CONCLUSIONSNine farming systems - including three “corporate agriculture”, five “family agriculture” and one “landless farmer” archetypes - and seventy cropping and livestock systems were characterized. The pedigree matrix approach showed that - with regards to statistics-based data- the uncertainty interval could be reduced twofold, and by a multiple of four with ASD-based LCI, without or with extrapolation, respectively. SIGNIFICANCENot only has the ASD-based LCI method proven powerful when building LCI in agriculture at the regional scale with reduced uncertainty, but it is also suited to the quantification of material flow exchanges within the farm and across farms, which is of valuable service when assessing agroecological productions, which promotes circularity.

Method to decompose uncertainties in LCA results into contributing factors

PurposeUnderstanding uncertainty is essential in using life cycle assessment (LCA) to support decisions. Monte Carlo simulation (MCS) is widely used to characterize the variability in LCA results, be them life cycle inventory (LCI), category indicator results, normalized results, or weighted results. In this study, we present a new method to decompose MCS results into underlying contributors using the logarithmic mean Divisia index (LMDI) decomposition method with a case study on natural gas focusing on two impact categories: global warming and USETox human health impacts.MethodsFirst, after each run of MCS, the difference in simulated and deterministic results is decomposed using the LMDI decomposition method, which returns the contribution of each factor to the difference of the run. After repeating this for 1000 MCS runs, the statistical properties of the contributions by each factor are analyzed. The method quantifies the contribution of underlying variables, such as characterization factors and LCI items, to the overall variability of the result, such as characterized results.ResultsThe method presented can decompose the variabilities in LCI, characterized, normalized, or weighted results into LCI items, characterization factors, normalization references, weighting factors, or any subset of them. As an illustrative example, a case study on natural gas LCA was conducted, and the variabilities in characterized results were decomposed into underlying LCI items and characterization factors. The results show that LCI and characterization phases contribute 65% and 35%, respectively, to the uncertainty of the characterized result for global warming. For the human health impact category, LCIs and characterization factors contribute 32% and 68%, respectively, to the overall uncertainty. In particular, methane emissions in LCI contributed the most to the overall uncertainties in global warming impact, while the characterization factor of chromium was identified as the main driver of the overall uncertainties in human health impact of natural gas. Conclusions and discussionUsing this approach, LCA practitioners can decompose the overall variability in the results to the underlying contributors under the MCS setting, which can help prioritize the parameters that need further refinement to reduce overall uncertainty in the results. The method reliably estimates the uncertainty contributions of the variables with large variabilities without the need for large computational resources, and it can be applied to any stage of an LCA calculation including normalization and weighting, or to other fields than LCA such as material flow analysis and risk assessment.

Integrating life cycle assessment (LCA) and quantitative risk assessment (QRA) to address model uncertainties: defining a landfill reference case under varying environmental and engineering conditions

PurposeModel uncertainties in life cycle assessment (LCA) can arise due to the lack or limited knowledge of the system (epistemic), or due to the inherent spatial and/or temporal variability of the processes that characterize it (stochastic). Scenario analysis is often used to address such uncertainties. However, the analysis does not provide information on which scenario is more representative of the system, or more likely to occur. To account for model uncertainties in LCA, the approach presented here estimates the likelihood of occurrence of the scenarios by assigning probabilities to the events characterizing them.MethodAn integrated life cycle assessment (LCA) and quantitative risk assessment (QRA) approach was developed to include the likelihood of occurrence of future events, and their consequences, in the definition of one representative scenario. The integration of the two tools was conceived at a framework level, with the inclusion of probability estimates as weighting factors for the scenario results. These probability estimates for each scenario allow improving the spatial and temporal representativeness of the model of the system.The approach was applied to a landfill case study to test its applicability in defining the impacts of a landfill reference scenario. Twelve scenarios were developed to represent the potential variation of landfill conditions over time. In particular, the degradation and failure of the containment systems, and the flooding of the site, were the main events considered in the scenario definition. An event tree was built to estimate the probability of occurrence of each scenario.ResultsExpected values and standard deviations were computed for all impact categories based on the impacts and their probabilities. Additional information on the probability of obtaining a certain impact was further provided by the impact curve built as function of the impacts and their cumulative probability.ConclusionsOverall, the results of the study highlight the potential of the approach and the relevance of the information that the results can provide on the impact of a landfill, and more generally a system, under varying conditions over time.

Accounting for correlational structures in stochastic comparative life cycle assessments through copula modeling

The presence of correlations between input parameters in a life cycle assessment (LCA) is a well-known issue. On top of that, the univariate distribution of environmental indicators, in most cases, does not follow the Gaussian nor the lognormal distribution. In this article, we introduce the copula modeling to build joint multivariate sampling spaces, irrespective of the marginal distributions of the environmental indicators, for LCA uncertainty analyses. To exemplify the proposed method, we integrate the copula modeling to the stochastic multiattribute analysis (SMAA) method to perform the normalization and weighting steps in a comparative agricultural LCA. An attributional LCA was performed to compare the environmental impact of two tomato production systems (GH, greenhouse; OF, open field) with different intensification levels. To choose the best environmental performance system, we implemented the outranking procedure of the SMAA method but based on the true multivariate distribution of the environmental indicators. As required by the SMAA method, initially, we fitted skewed multivariate distributions among the environmental indicators but accounting for their correlation structure through the copula method. Afterwards, the standard SMAA procedure was followed, leading to the calculation of overall scores indicating the environmental performance of the systems under comparison. After individual LCAs were performed for each grower, the variability observed in the primary data was propagated to the environmental indicators. The marginal distributions of the environmental indicators showed a right skewed trend which were fitted to gamma, log-normal, or Weibull distributions as applicable. The application of the copula method for the environmental indicators of the GH and OF systems resulted in D-vine models consisting of 46 and 45 bivariate copulas requiring 47 parameters each, respectively. Sampling the multivariate space configured by the D-vine models and integrating it with the SMAA method indicated that the OF system is more likely to have a better environmental performance with a rank acceptability index (RAI) of 57.6% while the GH system yielded a lower RAI (42.4%). We applied a stochastic unbiased approach to compare the environmental performance of agricultural systems but recognizing the correlation structure of the indicators. The copula method introduced here can be applied to uncertainty or multicriteria decision analysis where correlation needs to be accounted for. Joining the copula and the SMAA methods to produce an unbiased preference indicator allows to evaluate scenarios in a realistic way, producing results that can easily be communicated.

Irreversible entropy to account for environmental impacts and sustainability

The weighting and standardisation of several incomparable physical-chemical quantities lead to great uncertainties in life cycle assessment. Aim of our current research is to establish entropy production as inambiguous parameter for sustainability accounting and, more specifically, for LCA practice. Entropy production is a measure of irreversibility, and can therefore measure the overall changes of the environment caused by any process. The quantity is inherent in every process, it can be calculated directly and unambiguously, and can thus make Life Cycle Assessments comparable without de-tour. The entropy life cycle assessment (Entropy-LCA) is intended to serve as screening instrument focusing on the entire ecological footprint, delivering scientifically consistent and therefore mutually comparable studies. As such, an Entropy-LCA is meaningful on its own, and can also serve as an evaluation and interpretation aid for conventional LCA. An ongoing research project investigates the suitability of entropy production as impact indicator for LCA, with respect to practical feasibility, and to how an Entropy-LCA can be aligned with the current state of knowledge about LCA and entropy accounting. Current research is about data generation, coverage of impacts by entropy generation, and balance limits. The latter includes also the relation of thermodynamic entropy to alternative approaches, including established ecological, social and economic entropy theories and calculations. In this way, we intend to also show ways towards general sustainability and its quantification.

Perceived uncertainties of characterization in LCA: a survey

Uncertainty analyses in life cycle assessment (LCA) literature have focused primarily on the life cycle inventory (LCI) phase, but LCA experts generally agree that the life cycle impact assessment (LCIA) phase is likely to contribute even more to the overall uncertainty of an LCA result. The magnitude of perceived uncertainties in characterization relative to that in LCI, however, has not been examined in the literature. Here, we use the pedigree approach to gauge the perceived uncertainty in the characterization phase relative to the LCI phase. In addition, we evaluate the level of approval on the pedigree approach as a means to characterize uncertainty in LCA. Applying the Numeral Unit Spread Assessment Pedigree (NUSAP) approach to environmental risk assessment literature, we extracted the criteria for evaluating the uncertainty in the characterization phase. We used expert elicitation to identify a pool of experts and conducted a survey, to which 47 LCA practitioners from 12 countries responded. In order to reduce personal biases in perceived geometric standard deviation (GSD) values, we used two reference questions on weight and life expectancy at birth for calibration. Nearly half (49%) of respondents expressed their approval to the pedigree matrix approach as a means of characterizing uncertainties in LCA, and responses were highly sensitive to the respondent’s familiarity with the pedigree matrix. For instance, respondents who are highly familiar with the pedigree matrix were more polarized, with 15% and 19% of them expressing either strong approval or strong disapproval, respectively. Respondents less familiar with the pedigree approach were generally more favorable to its use. Compared with LCI, variability in characterization factors was influenced more strongly by geographical correlation and reliability of the underlying model, which showed 11 to 16% larger average GSDs when compared with the comparable criteria for LCI. Conversely, temporal correlation criterion was a less significant factor in characterization than in LCI. Overall, survey respondents viewed LCIA characterization as only marginally more uncertain than LCI, but with a wider variability in responses on characterization than LCI. This finding indicates the need for additional research to develop more thorough methods for characterizing uncertainties in life cycle impact assessment that are compatible with the uncertainty measures in LCI.

Managing Choice Uncertainties in Life-Cycle Assessment as a Decision-Support Tool for Building Design: A Case Study on Building Framework

To establish a circular economy in society, it is crucial to incorporate life-cycle studies, such as life-cycle assessment (LCA), in the design process of products in order to mitigate the well-recognized problem of the design paradox. The aim of the study was to provide means in a structured way to highlight choice uncertainty present in LCA when used as decision support, as well as to mitigate subjective interpretations of the numerical results leading to arbitrary decisions. The study focused on choices available when defining the goal and scope of a life-cycle assessment. The suggested approach is intended to be used in the early design phases of complex products with high levels of uncertainty in the product life-cycle. To demonstrate and evaluate the approach, a life-cycle assessment was conducted of two design options for a specific building. In the case study two types of building frameworks were compared from an environmental perspective by calculating the global warming potential, eutrophication potential, acidification potential, stratospheric ozone depletion potential and photochemical oxidants creation potential. In the study, a procedure named the Decision Choices Procedure (DCP) was developed to improve LCA as an effective tool for decision support concerning design alternatives when less information is available. The advantages and drawbacks of the proposed approach are discussed to spur further improvements in the use of LCA as a decision-support tool.

Uncertainty of building elements’ service lives in building LCA & LCC: What matters?

The existing methods of evaluating the environmental life cycle assessment (LCA) and life cycle costs (LCC) performance of a building concept have been widely used, since they offer the possibility to consider the system globally and avoid, thus, the hidden impacts. However, many studies have indicated the possible uncertainties of the input parameters and recommended the use of probabilistic methods, to deal with these issues. The current study continues towards this direction and presents a systematic way to take into account the uncertainties of the building elements’ service life within a stochastic framework, by defining the corresponding probability density functions, based on a new service life database. Applying this methodology for the calculation of the LCA & LCC for multifamily houses in Switzerland revealed that the replacement stage contributes with a share of up to 36% on the GHG emissions. Furthermore, through a global sensitivity analysis, the uncertainty on the replacement rate of six building elements was found to mainly influence the LCA & LCC uncertainty, i.e. compact façade (external insulation), windows, roofing, flooring, internal layout and ceiling covering. Finally, it was found that the reference service life of the building significantly influences the LCA and LCC uncertainty. In order to increase the reliability on the building LCA and LCC results, it is recommended to take into account probabilistically the service lives of the building elements, which mostly influence the LCA uncertainty.

Consideration of uncertainties in LCA for infrastructure using probabilistic methods

The construction and usage of transport infrastructure are major causes of greenhouse gas emissions and energy consumption. The effects of resource consumption and pollutant emissions are often quantified through Life Cycle Assessment (LCA) models. All decisions made in infrastructure projects during the whole life cycle are afflicted by uncertainty, e.g. physical properties of materials or amount of pollutants emitted by certain processes. The pervasive role of uncertainty is reflected in LCA models, which therefore should consider uncertainty from various sources and provide a sound quantification of their effects. The aim of the work presented in this paper is to give an overview of different sources of uncertainty in LCA of infrastructure projects and to describe systematic methods to evaluate their influence on the results. The possibility of including uncertainty in a LCA-tool for infrastructure is presented, studying the sensitivity of the model output to the input parameters and two alternative approaches for propagation of uncertainty using two case studies. It is shown that, besides the influence of uncertainty in emission factors, other inputs such as material amounts and service life could contribute significantly to the variability of model output and has to be considered if reliable results are sought.

System-Level Approach for Identifying Main Uncertainty Sources in Pavement Construction Life-Cycle Assessment for Quantifying Environmental Impacts

Poor data quality in pavement construction life-cycle inventory (LCI) causes uncertainty in quantifying the associated environmental impact through life-cycle assessment (LCA). To reduce such LCA uncertainty while enhancing the reliability, several studies have been conducted on a screening procedure based on a quality assessment of the LCI input data to identify main sources of the resulting uncertainty. However, they often create additional uncertainty in the screening process and thus result in erroneous outcomes in identifying main uncertainty sources. This paper proposes a new system-level approach that enables the identification of main uncertainty sources through input data quality assessment upon reducing additional uncertainty. Based on the proposed preset criteria and by leveraging environmental emission quantities associated with each process, the authors first propose to achieve a consistent weighting process and then derive the system-level aggregated data quality indicator (ADQI). By utilizing the ADQI, system-level LCA uncertainty information is obtained through a modified beta distribution. The proposed method was evaluated through case studies on real-world pavement construction projects of the Illinois Tollway, and the main uncertainty sources, named key processes, were identified through sensitivity analyses. In the case studies, the plant operation, cement production, and binder production were identified as key processes in the given pavement construction project, contributing more than half of the total uncertainty resulting from poor data quality. Based on these findings, the proposed work is expected to help practitioners improve the reliability of pavement construction LCA through uncertainty-informed decision making to better reflect real project characteristics in the identified key processes.

Uncertainty in the life cycle assessment of building emissions: A comparative case study of stochastic approaches

Life cycle assessment of buildings has become popular recently, due to the significant potential of emission reduction. Previous studies have contributed much to the quantification of emissions, yet the issue of uncertainties needs to be further investigated. In general, the uncertainty in life cycle assessment is mainly due to the errors in input parameters, definition of system boundary and scenario assumptions, and choice of analytical models, which could be summarized as parameter, scenario, and model uncertainties. In the present study, data quality indicators were adopted and a semi-quantitative method was modeled accordingly, relevant to the uncertainty analysis of building emission assessment. A case study building was analyzed comparing the deterministic and stochastic approaches, and the results indicate that the uncertainty in input parameters could result in a standard deviation of 3106 tCO2e for the sample mean of 61504 tCO2e, which is in line with the deterministic results. Further scenario analyses investigated the influence of scenario and modelling alternatives. Relevant results emphasized the definition of system boundary and energy efficiency to control uncertainties, and suggested applying statistical distribution for key parameters in analysis. Overall, the present study could provide useful information on the uncertainty of life cycle emission assessment, and, therefore, be helpful in decision-making regarding the low-carbon development of building industry.

How to treat uncertainties in life cycle assessment studies?

The use of life cycle assessment (LCA) as a decision support tool can be hampered by the numerous uncertainties embedded in the calculation. The treatment of uncertainty is necessary to increase the reliability and credibility of LCA results. The objective is to provide an overview of the methods to identify, characterize, propagate (uncertainty analysis), understand the effects (sensitivity analysis), and communicate uncertainty in order to propose recommendations to a broad public of LCA practitioners. This work was carried out via a literature review and an analysis of LCA tool functionalities. In order to facilitate the identification of uncertainty, its location within an LCA model was distinguished between quantity (any numerical data), model structure (relationships structure), and context (criteria chosen within the goal and scope of the study). The methods for uncertainty characterization, uncertainty analysis, and sensitivity analysis were classified according to the information provided, their implementation in LCA software, the time and effort required to apply them, and their reliability and validity. This review led to the definition of recommendations on three levels: basic (low efforts with LCA software), intermediate (significant efforts with LCA software), and advanced (significant efforts with non-LCA software). For the basic recommendations, minimum and maximum values (quantity uncertainty) and alternative scenarios (model structure/context uncertainty) are defined for critical elements in order to estimate the range of results. Result sensitivity is analyzed via one-at-a-time variations (with realistic ranges of quantities) and scenario analyses. Uncertainty should be discussed at least qualitatively in a dedicated paragraph. For the intermediate level, the characterization can be refined with probability distributions and an expert review for scenario definition. Uncertainty analysis can then be performed with the Monte Carlo method for the different scenarios. Quantitative information should appear in inventory tables and result figures. Finally, advanced practitioners can screen uncertainty sources more exhaustively, include correlations, estimate model error with validation data, and perform Latin hypercube sampling and global sensitivity analysis. Through this pedagogic review of the methods and practical recommendations, the authors aim to increase the knowledge of LCA practitioners related to uncertainty and facilitate the application of treatment techniques. To continue in this direction, further research questions should be investigated (e.g., on the implementation of fuzzy logic and model uncertainty characterization) and the developers of databases, LCIA methods, and software tools should invest efforts in better implementing and treating uncertainty in LCA.

Erratum to: A global approach for sparse representation of uncertainty in Life Cycle Assessments of waste management systems

Identification of key inputs and their effect on results from Life Cycle Assessment (LCA) models is fundamental. Because parameter importance varies greatly between cases due to the interaction of sensitivity and uncertainty, these features should never be defined a priori. However, exhaustive parametrical uncertainty analyses may potentially be complicated and demanding, both with analytical and sampling methods. Therefore, we propose a systematic method for selection of critical parameters based on a simplified analytical formulation that unifies the concepts of sensitivity and uncertainty in a Global Sensitivity Analysis (GSA) framework. The proposed analytical method based on the calculation of sensitivity coefficients (SC) is evaluated against Monte Carlo sampling on traditional uncertainty assessment procedures, both for individual parameters and for full parameter sets. Three full-scale waste management scenarios are modelled with the dedicated waste LCA model EASETECH and a full range of ILCD recommended impact categories. Common uncertainty ranges of 10 % are used for all parameters, which we assume to be normally distributed. The applicability of the concepts of additivity of variances and GSA is tested on results from both uncertainty propagation methods. Then, we examine the differences in discernibility analyses results carried out with varying numbers of sampling points and parameters. The proposed analytical method complies with the Monte Carlo results for all scenarios and impact categories, but offers substantially simpler mathematical formulation and shorter computation times. The coefficients of variation obtained with the analytical method and Monte Carlo differ only by 1 %, indicating that the analytical method provides a reliable representation of uncertainties and allows determination of whether a discernibility analysis is required. The additivity of variances and the GSA approach show that the uncertainty in results is determined by a limited set of important parameters. The results of the discernibility analysis based on these critical parameters vary only by 1 % from discernibility analyses based on the full set, but require significantly fewer Monte Carlo runs. The proposed method and GSA framework provide a fast and valuable approximation for uncertainty quantification. Uncertainty can be represented sparsely by contextually identifying important parameters in a systematic manner. The proposed method integrates with existing step-wise approaches for uncertainty analysis by introducing a global importance analysis before uncertainty propagation.

Inclusion of uncertainty in the LCA comparison of different cherry tomato production scenarios

Purpose Knowledge regarding environmental impacts of agricultural systems is required. Consideration of uncertainty in life cycle assessment (LCA) provides additional scientific information for decision making. The aims of this study were to compare the environmental impacts of different growing cherry tomato cultivation scenarios under Mediterranean conditions and to assess the uncertainty associated to the different agricultural production scenarios.