How uncertainties are handled in LCA – focus on the wastewater and textile sectors

Life cycle assessment (LCA) relies on large data samples and includes numerous choices and assumptions. This study aimed at reviewing to what extent relevant uncertainties are communicated and considered when interpreting LCA results, looking at current practices in LCAs on wastewater and textile systems. Our review showed that uncertainties are seldom communicated or considered in relation to the conclusions of the study, despite the availability of methods for propagating uncertainties in LCAs. We discuss that uncertainties and variation should at least be qualitatively assessed, and ideally be propagated from the life cycle inventory through the impact assessment

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

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

Implementation of stochastic multi attribute analysis (SMAA) in comparative environmental assessments

The selection of an alternative based on the results of a comparative environmental assessment such as life cycle assessment (LCA), environmental input-output analysis (EIOA) or integrated assessment modelling (IAM) is challenging because most of the times there is no single best option. Most comparative cases contain trade-offs between environmental criteria, uncertainty in the performances and multiple diverse values from decision makers. To circumvent these challenges, a method from decision analysis, namely stochastic multi attribute analysis (SMAA), has been proposed instead. SMAA performs aggregation that is partially compensatory (hence, closer to a strong sustainability perspective), incorporates performance uncertainty in the assessment, is free from external normalization references and allows for uncertainties in decision maker preferences. This paper presents a thorough introduction of SMAA for environmental decision-support, provides the mathematical fundamentals and offers an Excel platform for easy implementation and access

Embodied energy data implications for optimal specification of building envelopes

Highly insulated building envelopes have become more commonplace as environmental imperatives require reduction of building carbon footprints. Whilst increased insulation levels reduce operational energy demand, the additional embodied energy investment can increase the buildings’ overall environmental impact. The embodied energy consideration can determine whether, and to what extent, additional insulation is justified. The following paper investigates the impact of uncertainties of embodied energy data on the cumulative operational and embodied energy analyses and holistically appraises its implications for different stakeholders involved with the construction sector. Limitations in current life cycle assessment (LCA) calculation methods and high uncertainty of available data are recognized and reflected in the analyses through studying available environmental product declarations of various types of insulation materials and by modelling a typical semi-detached residential building in the UK as the case study. The results of such approach illustrate ‘optimum insulation thicknesses’ beyond which the embodied energy penalty outweighs operational energy savings. These essentially represent idealized levels of building envelope insulation that can inform the development of future standards for low energy/carbon buildings and support the adoption of LCAs as decision-making tools in informing the urgent debate of optimal insulation requirements of buildings

Multi-level Uncertain Fatigue Analysis of a Truss under Incomplete Available Information

We predict the fatigue life of a planar tubular truss when geometrical parameters, material properties, and live loads are non-deterministic. A multi-level calculation uncertainty quantification framework code was designed to aggregate the finite element method and fatigue-induced sequential failures. Due to the incompleteness of the aleatory-type inputs, the maximum entropy principle was applied. Two sensitivity analyses were performed to report the most influencing factors. In terms of variance, the results suggest that the slope of the curve crack growth rate × stress intensity factor range is the most influencing factor related to fatigue life. Furthermore, due to the application of the entropy concept, the fatigue crack growth boundaries and fatigue crack size boundaries obtained provide the most unbiased fatigue crack design mapping. These boundaries allow the designer to select the worst-case fatigue scenario, besides being able to predict the crack behavior at a required confidence level

Life cycle assessment of pavement construction: A case study

Road construction is often associated with carbon emissions from direct and indirect sources, primarily due to construction and maintenance activities. Currently, there is a lack of comprehensive Life Cycle Assessment (LCA) benchmarks to evaluate flexible composite pavement, fully flexible pavement and pavement rehabilitation options under various ground conditions. The objective of this study is to investigate the environmental impact associated with different pavement designs over a 60-year analysis period, comprising a 40-year basic design period with maintenance extended up to 60 years. This research paper encompasses a literature review on pavement LCA and conducts and LCA on various pavement design and construction options, following the ISO 14040 framework and PAS 2080 methodology. The LCA in this study specifically focuses on material production, transportation, construction, maintenance, and end-of-life phases. Using global warming potential as an environmental indicator, the study calculates and compares a range of potential impacts for each component. In terms of carbon emissions, the rehabilitation option was found to be most favourable when compared to other full-depth reconstruction options, while the flexible composite pavement option exhibited the highest carbon emission value compared to other pavement build-ups assessed. Additionally, a sensitivity analysis was conducted to identify ‘hotspots’ in the study, which increase the confidence level of the results

A pseudo-statistical approach to treat choice uncertainty: the example of partitioning allocation methods

Purpose Despite efforts to treat uncertainty due to methodological choices in life cycle assessment (LCA) such as standardization, one-at-a-time (OAT) sensitivity analysis, and analytical and statistical methods, no method exists that propagate this source of uncertainty for all relevant processes simultaneously with data uncertainty through LCA. This study aims to develop, implement, and test such a method, for the particular example of the choice of partitioning methods for allocation in LCA, to be used in LCA calculations and software. Methods Monte Carlo simulations were used jointly with the CMLCA software for propagating into distributions of LCA results, uncertainty due to the choice of allocation method together with uncertainty of unit process data. In this study, a methodological preference is assigned to each partitioning method, applicable to multi-functional processes in the system. The allocation methods are sampled per process according to these preferences. A case study on rapeseed oil focusing on three greenhouse gas (GHG) emissions and their global warming impacts is presented to illustrate the method developed. The results of the developed method are compared with those for the same case similarly quantifying uncertainty of unit process data but accompanied by separate scenarios for the different partitioning choices. Results and discussion The median of the inventory flows (emissions) for separate scenarios varies due to the partitioning choices and unit process data uncertainties. Inventory variations are reflected in the global warming results. Results for the approach of this study vary with the methodological preference assigned to the different allocation methods per multi-functional process and with the continuous distribution of unit process data. The method proved feasible and implementable. However, absolute uncertainties only further increased. Therefore, it should be further researched to reflect relative uncertainties, more relevant for comparative LCAs. Conclusions Propagation of uncertainties due to the choice of partitioning methods and to unit process data into LCA results is enabled by the proposed method, while capturing variability due to both sources. It is a practical proposal to tackle unresolved debates about partitioning choices increasing robustness and transparency of LCA results. Assigning a methodological preference to each allocation method of multi-functional processes in the system enables pseudo-statistical propagation of uncertainty due to allocation. Involving stakeholders in determining these methodological preferences allows for participatory approaches. Eventually, this method could be expanded to also cover other ways of dealing with allocation and to other methodological choices in LCA.  Industrial Ecolog

Methods for global sensitivity analysis in life cycle assessment

Industrial Ecolog

Quantified Uncertainties in Comparative Life Cycle Assessment: What Can Be Concluded?

Industrial Ecolog