Goal and Background Uncertainty is commonly not taken into account in LCA studies, which downgrades their usability for decision support. One
often stated reason is a lack of method. The aim of this paper is to develop a method for calculating the uncertainty propagation
in LCAs in a fast and reliable manner.
Approach The method is developed in a model that reflects the calculation of an LCA. For calculating the uncertainty, the model combines
approximation formulas and Monte Carlo Simulation. It is based on virtual data that distinguishes true values and random errors
or uncertainty, and that hence allows one to compare the performance of error propagation formulas and simulation results.
The model is developed for a linear chain of processes, but extensions for covering also branched and looped product systems
are made and described.
Results The paper proposes a combined use of approximation formulas and Monte Carlo simulation for calculating uncertainty in LCAs,
developed primarily for the sequential approach. During the calculation, a parameter observation controls the performance
of the approximation formulas. Quantitative threshold values are given in the paper. The combination thus transcends drawbacks
of simulation and approximation.
Conclusions and Outlook The uncertainty question is a true jigsaw puzzle for LCAs and the method presented in this paper may serve as one piece in
solving it. It may thus foster a sound use of uncertainty assessment in LCAs. Analysing a proper management of the input uncertainty,
taking into account suitable sampling and estimation techniques; using the approach for real case studies, implementing it
in LCA software for automatically applying the proposed combined uncertainty model and, on the other hand, investigating about
how people do decide, and should decide, when their decision relies on explicitly uncertain LCA outcomes-these all are neighbouring
puzzle pieces inviting to further work. 相似文献
The International Journal of Life Cycle Assessment - This study aims to present the availability of studies that are related to life cycle assessments (LCA) in Austria since 2000. This study also... 相似文献
Highlights► Biofuel LCAs have high uncertainty and variability, particularly prospective LCAs. ► Variability in biofuel LCAs has two sources: model and methods-induced, and real. ► LCAs of 2nd-generation fuels are so uncertain that no pathway can clearly be preferred. ► Algae biodiesel LCA has greatest variability due to diverse technology, assumptions. 相似文献
Background, Aims and Scope Although LCA is frequently used in product comparison, many practitioners are interested in identifying and assessing improvements
within a life cycle. Thus, the goals of this work are to provide guidelines for scenario formulation for process and material alternatives
within a life cycle inventory and to evaluate the usefulness of decision tree and matrix computational structures in the assessment
of material and process alternatives. We assume that if the analysis goal is to guide the selection among alternatives towards
reduced life cycle environmental impacts, then the analysis should estimate the inventory results in a manner that: (1) reveals
the optimal set of processes with respect to minimization of each impact of interest, and (2) minimizes and organizes computational
and data collection needs.
Methods A sample industrial system is used to reveal the complexities of scenario formulation for process and material alternatives
in an LCI. The system includes 4 processes, each executable in 2 different ways, as well as 1 process able to use 2 different
materials interchangeably. We formulate and evaluate scenarios for this system using three different methods and find advantages
and disadvantages with each. First, the single branch decision tree method stays true to the typical construction of decision
trees such that each branch of the tree represents a single scenario. Next, the process flow decision tree method strays from
the typical construction of decision trees by following the process flow of the product system, such that multiple branches
are needed to represent a single scenario. In the final method, disaggregating the demand vector, each scenario is represented
by separate vectors which are combined into a matrix to allow the simultaneous solution of the inventory problem for all scenarios.
Results For both decision tree and matrix methods, scenario formulation, data collection, and scenario analysis are facilitated in
two ways. First, process alternatives that cannot actually be chosen should be modeled as sub-inventories (or as a complete
LCI within an LCI). Second, material alternatives (e.g., a choice between structural materials) must be maintained within
the analysis to avoid the creation of artificial multi-functional processes. Further, in the same manner that decision trees
can be used to estimate ‘expected value’ (the sum of the probability of each scenario multiplied by its ‘value’), we find
that expected inventory and impact results can be defined for both decision tree and matrix methods.
Discussion For scenario formulation, naming scenarios in a way that differentiate them from other scenarios is complex and important
in the continuing development of LCI data for use in databases or LCA software. In the formulation and assessment of scenarios,
decision tree methods offer some level of visual appeal and the potential for using commercially available software/ traditional
decision tree solution constructs for estimating expected values (for relatively small or highly aggregated product systems).
However, solving decision tree systems requires the use of sequential process scaling which is difficult to formalize with
mathematical notation. In contrast, preparation of a demand matrix does not require use of the sequential method to solve
the inventory problem but requires careful scenario tracking efforts.
Conclusions Here, we recognize that improvements can be made within a product system. This recognition supports the greater use of LCA
in supply chain formation and product research, development, and design. We further conclude that although both decision tree
and matrix methods are formulated herein to reveal optimal life cycle scenarios, the use of demand matrices is preferred in
the preparation of a formal mathematical construct. Further, for both methods, data collection and assessment are facilitated
by the use of sub-inventories (or as a complete LCI within an LCI) for process alternatives and the full consideration of
material alternatives to avoid the creation of artificial multi-functional processes.
Recommendations and Perspectives The methods described here are used in the assessment of forest management alternatives and are being further developed to
form national commodity models considering technology alternatives, national production mixes and imports, and point-to-point
transportation models.
ESS-Submission Editor: Thomas Gloria, PhD (t.gloria@fivewinds.com) 相似文献
In recent years several different approaches towards Social Life Cycle Assessment (SLCA) have been developed. The purpose
of this review is to compare these approaches in order to highlight methodological differences and general shortcomings. SLCA
has several similarities with other social assessment tools, although, in order to limit the expanse of the review, only claims
to address social impacts from an LCA-like framework are considered. 相似文献
Several life cycle assessments (LCAs) of wind energy published in recent years are reviewed to identify methodological differences and underlying assumptions.
Methods
A full comparative analysis of 12 studies were undertaken (ten peer-reviewed papers, one conference paper, and one industry report) regarding six fundamental factors (methods used, energy use accounting, quantification of energy production, energy performance and primary energy, natural resources, and recycling). Each factor is discussed in detail to highlight strengths and shortcomings of various approaches.
Results
Several potential issues are found concerning the way LCA methods are used for assessing energy performance and environmental impact of wind energy, as well as dealing with natural resource use and depletion. The potential to evaluate natural resource use and depletion impacts from wind energy appears to be poorly exploited or elaborated on in the reviewed studies. Estimations of energy performance and environmental impacts are critically analyzed and found to differ significantly.
Conclusions and recommendations
A continued discussion and development of LCA methodology for wind energy and other energy resources are encouraged. Efforts should be made to standardize methods and calculations. Inconsistent use of terminology and concepts among the analyzed studies are found and should be remedied. Different methods are generally used and the results are presented in diverse ways, making it difficult to compare studies with each other, but also with other renewable energy sources. 相似文献
A relatively broad consensus has formed that the purpose of developing and using the social life cycle assessment (SLCA) is
to improve the social conditions for the stakeholders affected by the assessed product’s life cycle. To create this effect,
the SLCA, among other things, needs to provide valid assessments of the consequence of the decision that it is to support.
The consequence of a decision to implement a life cycle of a product can be seen as the difference between the decision being
implemented and ‘non-implemented’ product life cycle. This difference can to some extent be found using the consequential
environmental life cycle assessment (ELCA) methodology to identify the processes that change as a consequence of the decision.
However, if social impacts are understood as certain changes in the lives of the stakeholders, then social impacts are not
only related to product life cycles, meaning that by only assessing impacts related to the processes that change as a consequence
of a decision, not all changes in the life situations of the stakeholders will be captured by an assessment following the
consequential ELCA methodology. This article seeks to identify these impacts relating to the non-implemented product life
cycle and establish indicators for their assessment. 相似文献
Background, aim, and scope Management of the medical waste produced in hospitals or health care facilities has raised concerns relating to public health,
occupational safety, and the environment. Life cycle assessment (LCA) is a decision-supporting tool in waste management practice;
but relatively little research has been done on the evaluation of medical waste treatment from a life cycle perspective. Our
study compares the environmental performances of two dominant technologies, hazardous waste incineration (HWI) as a type of
incineration technology and steam autoclave sterilization with sanitary landfill (AL) as a type of non-incineration technology,
for specific medical waste of average composition. The results of this study could support the medical waste hierarchy.
Materials and methods This study implemented the ISO 14040 standard. Data on steam autoclave sterilization were obtained from an on-site operations
report, while inventory models were used for HWI, sanitary landfill, and residues landfill. Background data were from the
ecoinvent database. The comparative LCA was carried out for five alternatives: HWI with energy recovery efficiencies of 0%,
15%, and 30% and AL with energy recovery efficiencies of 0% and 10%.
Results The assumptions on the time frame for landfill markedly affect the impact category scores; however, the orders of preference
for both time frames are almost the same. HWI with 30% energy recovery efficiency has the lowest environmental impacts for
all impact categories, except freshwater ecotoxicity. Incineration and sanitary landfill processes dominate global warming,
freshwater aquatic ecotoxicity, and eutrophication of incineration and non-incineration alternatives, respectively. Dioxin
emissions contribute about 10% to human toxicity in HWI without energy recovery alternatives, and a perturbation analysis
yielded identical results. As regards eutrophication, non-incineration treatments have an approximately sevenfold higher impact
than incineration treatments.
Discussion The differences between short-term and long-term time frame assumptions mainly are decided by heavy metals dissolved in the
future leachate. The high heat value of medical waste due to high contents of biomass, plastic, and rubber materials and a
lower content of ash, results in a preference for incineration treatments. The large eutrophication difference between incineration
and non-incineration treatments is caused by different N element transformations. Dioxin emission from HWI is not the most
relevant to human toxicity; however, large uncertainties could exist.
Conclusions From a life cycle perspective, the conventional waste hierarchy, implying incineration with energy recovery is better than
landfill, also applies to the case of medical waste. The sanitary landfill process is the key issue in non-incineration treatments,
and HWI and the subsequent residues landfill processes are key issues in incineration treatments.
Recommendations and perspectives Integrating the medical waste hierarchy and constructing a medical waste framework require broader technologies to be investigated
further, based on a life cycle approach.
Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. 相似文献
Lightweight design is a common means of reducing a passenger car's fuel consumption. In order to calculate the resulting fuel
savings, one has to estimate the total energy that is needed to move a certain weight over a defined distance in a distinct
way, and express this energy in liter of gasoline or diesel. This can be accomplished by the so-called fuel reduction value
(FRV) and based on a standardized driving cycle, e.g., the New European Driving Cycle (NEDC). The aim of this paper is to
explain the theoretical background of the calculation of fuel savings in automotive lightweight life cycle assessments (LCAs)
of internal combustion engine (ICE) vehicles in greater detail than it has been done before, to describe the resulting factors
and their different applications, and to point out some notable particularities that need to be taken into account when conducting
this type of LCA study. 相似文献
The main purpose of this review is to investigate the methodology of social life cycle assessment (SLCA) through its application to case studies. In addition, the following research aims to define the trends related to the SLCA by researchers and consultants. This study will help to map the current situation and to highlight the hot spots and weaknesses of the application of the SLCA theory.
Methods
The SLCA could be considered as a useful methodology to provide decision support in order to compare products and/or improve the social effects of the life cycle of a product. Furthermore, the results of the case studies analyzed may influence decision makers significantly. For this reason, a systematic literature review of case studies was carried out in which SLCA was applied in order to analyze closely the application of the stages of this methodology. In this study, the major phases of the technical framework for a SLCA were analyzed. Specific attention was paid to detect the positive impacts that emerged in the case studies, which were also studied by administering a questionnaire to the authors of the analyzed case studies and to a number of experts in the field of SLCA.
Results and discussion
The 35 case studies examined in this paper, even though they do not deviate from the 40 identified by the previous processing, are still significantly different in terms of outcome produced. It is important to clarify that the authors who developed the case studies considered the steps defined in the SETAC/SETAC guidelines, borrowed from the ISO 14044 standard.
Conclusions
The data resulting from this analysis could help both practitioners and researchers to understand what the issues are, on which it is still necessary to investigate and work, in order to solidify the SLCA methodology and define its role in the context of life cycle sustainability assessment (LCSA).
The main goal of this paper is to present the feasibility of the quantitative method presented in the Product Social Impact Assessment (PSIA) handbook throughout a case study. The case study was developed to assess the social impacts of a tire throughout its entire life cycle. We carried out this case study in the context of the Roundtable for the Product Social Metrics project in which 13 companies develop two methodologies, a qualitative and a quantitative one, for assessing the social impact of product life cycle.
Methods
The quantitative methodology implemented for assessing the social impact of a Run On Flat tire mounted in a BMW 3 series consists of 26 indicators split in three groups. Each group represents a stakeholder group. Primary data of the quantitative indicators were collected along the product life cycle of the Run On Flat by involving the companies, which owned the main steps of the product life cycle. Throughout this case study, an ideal/worst-case scenario was defined for the distance-to-target approach to compare the social performances of more products when they are available.
Results and discussion
The implementation of the PSIA quantitative method to a Run On Flat illustrated the necessity to have a referencing step in order to interpret the results. This is particularly important when the results are used to support decision-making process in which no experts are involved. It frequently happens in a big company where the management level has to take often decisions on different topics. Reference values were defined using ideal or worst-case-target scenarios (Fontes et al. 2014). For those topics where it was possible, an ideal/ethical scenario was defined, e.g., 0 h of child labor per product. In other cases, we defined a worst-case scenario, e.g., 0 training hours per product. It was then possible to interpret the results using a distance-to-target approach. A matrix was developed in the case study for identifying in which step of the product life cycle data is not available; that means we need more transparency in the supply chain.
Conclusions
Each value of the matrix can be compared to the ideal/worst scenario to compare the step to each other and to identify along the product life cycle which step and the relative supplier that needs further measures to improve the product performance. Furthermore, a quantitative value for each indicator related to the product life cycle is calculated and compared with the ideal/worst scenario. The case study on Run On Flat represents the first implementation of the quantitative method of PSIA.
The International Journal of Life Cycle Assessment - The presence of correlations between input parameters in a life cycle assessment (LCA) is a well-known issue. On top of that, the univariate... 相似文献
Life cycle assessments (LCAs) are considered common quantitative environmental techniques to analyze the environmental impact of products and/or services throughout their entire life cycle. A few LCA studies have been conducted in West Africa. This study aimed to discuss the availability of LCA (and similar) studies in Nigeria, Ghana, and Ivory Coast.
Methods
An online literature review of reports published between 2000 and 2016 was conducted using the following keywords: “life cycle assessment,” “carbon footprinting,” “water footprinting,” “environmental impact,” “Nigeria,” “Ghana” and “Ivory Coast.”
Results and discussion
A total of 31 LCA and environmental studies in Nigeria, Ghana, and Ivory Coast were found; all but one were conducted after 2008. These were mainly academic and most were publicly available. The industries studied included energy sector, waste management, real estate, food sector, and others such as timber and gold. The minimal number of studies on LCAs and environmental impacts in these West African states could be because companies are failing to promote quantitative environmental studies or studies are kept internally for the use of other assessment techniques. Furthermore, it could be that academic research institutions lack cutting-edge research resources for LCA, environmental impact, carbon, and water footprinting studies.
Conclusions
Further quantitative environmental studies should be conducted in Nigeria, Ghana, and Ivory Coast to increase the understanding of environmental impacts. In these countries, the existence of LCA studies (and by association the localized life cycle inventory (LCI) datasets) is crucial as more companies request this information to feed into background processes.
Climate change is expected to impact both the operational and structural performance of infrastructures such as roads, bridges, and buildings. However, most past life cycle assessment (LCA) studies do not consider how the operational/structural performance of infrastructure will be affected by a changing climate. The goal of this research was to develop a framework for integrating climate change impacts into LCA of infrastructure systems. To illustrate this framework, a flexible pavement case study was considered where life‐cycle environmental impacts were compared across a climate change scenario and several time horizons. The Mechanistic‐Empirical Pavement Design Guide (MEPDG) was utilized to capture the structural performance of each pavement performance scenario and performance distresses were used as inputs into a pavement LCA model that considered construction and maintenance/rehabilitation materials and activities, change in relative surface albedo, and impacts due to traffic. The results from the case study suggest that climate change will likely call for adaptive design requirements in the latter half of this century but in the near‐to‐mid term, the international roughness index (IRI) and total rutting degradation profile was very close to the historical climate run. While the inclusion of mechanistic performance models with climate change data as input introduces new uncertainties to infrastructure‐based LCA, sensitivity analyses runs were performed to better understand a comprehensive range of result outcomes. Through further infrastructure cases the framework could be streamlined to better suit specific infrastructures where only the infrastructure components with the greatest sensitivity to climate change are explicitly modeled using mechanistic‐empirical modeling routines. 相似文献
