Hybrid Framework for Managing Uncertainty in Life Cycle Inventories

Life cycle assessment (LCA) is increasingly being used to inform decisions related to environmental technologies and polices, such as carbon footprinting and labeling, national emission inventories, and appliance standards. However, LCA studies of the same product or service often yield very different results, affecting the perception of LCA as a reliable decision tool. This does not imply that LCA is intrinsically unreliable; we argue instead that future development of LCA requires that much more attention be paid to assessing and managing uncertainties. In this article we review past efforts to manage uncertainty and propose a hybrid approach combining process and economic input–output (I‐O) approaches to uncertainty analysis of life cycle inventories (LCI). Different categories of uncertainty are sometimes not tractable to analysis within a given model framework but can be estimated from another perspective. For instance, cutoff or truncation error induced by some processes not being included in a bottom‐up process model can be estimated via a top‐down approach such as the economic I‐O model. A categorization of uncertainty types is presented (data, cutoff, aggregation, temporal, geographic) with a quantitative discussion of methods for evaluation, particularly for assessing temporal uncertainty. A long‐term vision for LCI is proposed in which hybrid methods are employed to quantitatively estimate different uncertainty types, which are then reduced through an iterative refinement of the hybrid LCI method.     

Prioritizing risks and uncertainties from intentional release of selected Category A pathogens

This paper synthesizes available information on five Category A pathogens (Bacillus anthracis, Yersinia pestis, Francisella tularensis, Variola major and Lassa) to develop quantitative guidelines for how environmental pathogen concentrations may be related to human health risk in an indoor environment. An integrated model of environmental transport and human health exposure to biological pathogens is constructed which 1) includes the effects of environmental attenuation, 2) considers fomite contact exposure as well as inhalational exposure, and 3) includes an uncertainty analysis to identify key input uncertainties, which may inform future research directions. The findings provide a framework for developing the many different environmental standards that are needed for making risk-informed response decisions, such as when prophylactic antibiotics should be distributed, and whether or not a contaminated area should be cleaned up. The approach is based on the assumption of uniform mixing in environmental compartments and is thus applicable to areas sufficiently removed in time and space from the initial release that mixing has produced relatively uniform concentrations. Results indicate that when pathogens are released into the air, risk from inhalation is the main component of the overall risk, while risk from ingestion (dermal contact for B. anthracis) is the main component of the overall risk when pathogens are present on surfaces. Concentrations sampled from untracked floor, walls and the filter of heating ventilation and air conditioning (HVAC) system are proposed as indicators of previous exposure risk, while samples taken from touched surfaces are proposed as indicators of future risk if the building is reoccupied. A Monte Carlo uncertainty analysis is conducted and input-output correlations used to identify important parameter uncertainties. An approach is proposed for integrating these quantitative assessments of parameter uncertainty with broader, qualitative considerations to identify future research priorities.     

Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low‐quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision‐making framework will result in better‐informed, more robust decisions.     

Comparative analysis of passenger transport sustainability in European cities

Sustainable development in its three dimensions – economic, social and environmental – has become a major concern on an international scale. The problem is global, but must be solved locally. Most of the world’s population lives in cities that act as centres of economic growth and productivity, but which – if they develop in the wrong direction – can cause social inequalities, or irreversibly harm the environment. Urban transport causes a number of negative impacts that can affect sustainability targets. The objective of this study is to propose an analysis of sustainability of urban passenger transport systems based on available indicators in most cities. This will serve to benchmark the practices of different cities and manage their transport systems. This work involves the creation of composite indicators (CI) to measure the sustainability of urban passenger transport systems. The methodology is applied to 23 European cities. The indicators are based on a benchmarking approach, and the evaluation of each aspect in each case therefore depends on the performance of the whole sample. The CI enabled us to identify which characteristics have the greatest influence on the sustainability of a city’s transport system, and to establish transport policies that could potentially improve its shortcomings. Finally, the cities are clustered according to the values obtained from the CIs, and thus according to the weaknesses and strengths of their transport systems.     

Part II: Dealing with parameter uncertainty and uncertainty due to choices in life cycle assessment

Results of product assessments are often criticised as to their handling of uncertainty. Therefore, it is necessary to develop a comprehensive methodology reflecting parameter uncertainty in combination with uncertainty due to choices in the outcome of LCAs. This paper operationalises the effect of combined parameter uncertainties in the inventory and in the characterisation factors for global warming and acidification for the comparison of two exemplary types of roof gutters. For this purpose, Latin Hypercube sampling is used in the matrix (inventory) method. To illustrate the influence of choices, the effect on LCA outcomes is shown of two different allocation procedures in open-loop recycling and three time horizons for global warming potentials. Furthermore, an uncertainty importance analysis is performed to show which parameter uncertainties mainly contribute to uncertainties in the comparison and the separate environmental profiles of the product systems. These results can be used to prioritise further data research.     

Critical Components of Uncertainty Communication in Life Cycle Assessments of Emerging Technologies

Because of their recognition as a comprehensive tool of environmental assessments and their increasing use by governments and industries, life cycle assessments (LCAs) are positioned to be prominent sources of mass media information on new products and technologies. The LCA studies underlying media reports are often viewed by nonexperts after the initial reporting. However, uncertainty is rife in early assessments of emerging technologies, and LCA's ability to inform environmental opinions and decisions is limited without the accompanying communication on uncertainty. Though approaches to the technical aspects of uncertainty analysis in LCA are available in the literature, those on communicating that uncertainty, in ways that are cognitively accessible to the nonexperts, are still lacking despite their highlighted importance across various disciplines. With the focus on communication, this article uses the existing literature to derive five criteria for making uncertainty communication accessible to a nonexpert audience. Then, LCAs on engineered nanomaterial (ENM) and ENM‐enabled products, as a case study of emerging technologies where uncertainties abound, are reviewed for whether they meet these five criteria. The study concludes with recommendations for communicating uncertainty in LCAs in order to enhance their role as decision‐ and public opinion–informing assessments.     

From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure,function, and service: Part II—Harnessing data

Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in “data desert” regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.     

青藏高原重大建设工程生态修复综合效益评估指标体系

青藏高原作为我国重要的生态屏障和战略资源基地,兼顾经济社会的发展和生态环境的保护,是当前区域发展的主要目标。为实现该目标,国家先后开展了多项推动当地经济发展的重大建设工程项目,并在项目开展的过程中,针对出现的生态问题,实施了一系列的生态修复措施。目前针对青藏高原重大建设工程生态修复的研究,在多工程类型修复效益的融合,以及宏观空间布局尺度上对生态修复成效的评估还相对较少,并且缺乏统一的评价标准。故本研究选取青藏高原道路、水电和矿产开发三类典型的重大建设工程,结合区域及工程特点,通过文献调研、业界专家咨询等方法手段,以生态修复后的生态系统结构、质量和服务为核心架构,筛选能够综合客观的反映该地区生态修复效益的具体指标,最终构建系统、合理和科学的青藏高原重大建设工程生态修复综合效益评估指标体系。该评估指标体系涉及生态系统结构、质量和服务三大指标类别,包括10个主题指标,21个具体指标。以期为青藏高原重大建设工程生态修复效益的总体认知和长期监测,以及未来生态修复工程的制定、建设工程的布局及其他生态系统管理措施的实施,和未来生态修复评估的相关研究提供科学参考和理论支撑。     

Characterizing,Propagating, and Analyzing Uncertainty in Life‐Cycle Assessment: A Survey of Quantitative Approaches

Life‐cycle assessment (LCA) practitioners build models to quantify resource consumption, environmental releases, and potential environmental and human health impacts of product systems. Most often, practitioners define a model structure, assign a single value to each parameter, and build deterministic models to approximate environmental outcomes. This approach fails to capture the variability and uncertainty inherent in LCA. To make good decisions, decision makers need to understand the uncertainty in and divergence between LCA outcomes for different product systems. Several approaches for conducting LCA under uncertainty have been proposed and implemented. For example, Monte Carlo simulation and fuzzy set theory have been applied in a limited number of LCA studies. These approaches are well understood and are generally accepted in quantitative decision analysis. But they do not guarantee reliable outcomes. A survey of approaches used to incorporate quantitative uncertainty analysis into LCA is presented. The suitability of each approach for providing reliable outcomes and enabling better decisions is discussed. Approaches that may lead to overconfident or unreliable results are discussed and guidance for improving uncertainty analysis in LCA is provided.     

What can and can't we say about indirect land‐use change in Brazil using an integrated economic – land‐use change model?

It is commonly recognized that large uncertainties exist in modelled biofuel‐induced indirect land‐use change, but until now, spatially explicit quantification of such uncertainties by means of error propagation modelling has never been performed. In this study, we demonstrate a general methodology to stochastically calculate direct and indirect land‐use change (dLUC and iLUC) caused by an increasing demand for biofuels, with an integrated economic – land‐use change model. We use the global Computable General Equilibrium model MAGNET, connected to the spatially explicit land‐use change model PLUC. We quantify important uncertainties in the modelling chain. Next, dLUC and iLUC projections for Brazil up to 2030 at different spatial scales and the uncertainty herein are assessed. Our results show that cell‐based (5 × 5 km²) probabilities of dLUC range from 0 to 0.77, and of iLUC from 0 to 0.43, indicating that it is difficult to project exactly where dLUC and iLUC will occur, with more difficulties for iLUC than for dLUC. At country level, dLUC area can be projected with high certainty, having a coefficient of variation (cv) of only 0.02, while iLUC area is still uncertain, having a cv of 0.72. The latter means that, considering the 95% confidence interval, the iLUC area in Brazil might be 2.4 times as high or as low as the projected mean. Because this confidence interval is so wide that it is likely to straddle any legislation threshold, our opinion is that threshold evaluation for iLUC indicators should not be implemented in legislation. For future studies, we emphasize the need for provision of quantitative uncertainty estimates together with the calculated LUC indicators, to allow users to evaluate the reliability of these indicators and the effects of their uncertainty on the impacts of land‐use change, such as greenhouse gas emissions.     

Improved emergy indices for the evaluation of industrial systems incorporating waste management

Emergy analysis is able to account for ecosystems’ contribution to industrial activity. Accordingly, it is an ecologically conscious tool useful for assessing the environmental impact and sustainability of industrial systems. The emergy-based approach requires proper system boundary definitions and uses several standard indices. In this article some perspectives on the deficiencies of three standard emergy indicators - environmental loading ratio (ELR), emergy yield ratio (EYR) and emergy index of sustainability (EIS) - when applied to industrial systems involving waste management are put forward and suggestions for overcoming them given. In addition, in order to account for the impact of waste emissions on the environment, a simple impact amplification factor is proposed for inclusion in the improved emergy indicators. To demonstrate their usefulness and highlight their superiority over standard indices, the improved emergy indicators are used to evaluate the interaction between a commercial polyethylene production process incorporating waste management and its surrounding environment.     

Fuzzy-based methodological proposal for participatory diagnosis in the linear parks management

Participatory decision-making in the management of linear parks can provide better outcomes than classical top-down procedures. However, the integration of technical knowledge with the understanding of the community requires an approach able to deal with uncertainty, due to the issues of vagueness and subjectivity. Therefore, the present paper introduces a fuzzy-based proposal for supporting participatory diagnosis. For this purpose, five Mamdani-type systems were built based on the knowledge of experts, including as indicators the species richness of fauna and flora, land cover, basic and recreational facilities, support services, and the park users’ perceptions. By integrating such indicators, three indexes were obtained for assessing the natural condition, infrastructure, and functional performance of a linear park. The proposed system was then applied in a case study of the Tiquatira linear park in São Paulo city, Brazil. The results indicated that the park has high functional performance, but that improvements in its natural condition and infrastructure are needed, such as the reclamation of disturbed areas and ongoing maintenance of the facilities. The use of fuzzy modeling enabled integration of the technical assessment by experts and the users’ perception, in order to support a participatory diagnosis taking into account the existing uncertainties. In conclusion, this fuzzy-based proposal can be considered a promising approach for the participatory management of linear parks. In future studies, techniques for the case-by-case weighting of variables could be assessed, in order to strengthen the systems described in the present paper.     

Applying Fuzzy and Probabilistic Uncertainty Concepts to the Material Flow Analysis of Palladium in Austria

Material flow analysis (MFA) is a widely applied tool to investigate resource and recycling systems of metals and minerals. Owing to data limitations and restricted system understanding, MFA results are inherently uncertain. To demonstrate the systematic implementation of uncertainty analysis in MFA, two mathematical concepts for the quantification of uncertainties were applied to Austrian palladium (Pd) resource flows and evaluated: (1) uncertainty ranges expressed by fuzzy sets and (2) uncertainty ranges defined by normal distributions given as mean values and standard deviations. Whereas normal distributions represent the traditional approach for quantifying uncertainties in MFA, fuzzy sets may offer additional benefits in relation to uncertainty quantification in cases of scarce information. With respect to the Pd case study, the fuzzy representation of uncertain quantities is more consistent with the actual data availability in cases of incomplete databases, and fuzzy sets serve to highlight the effect of uncertainty on resource efficiency indicators derived from the MFA results. For both approaches, data reconciliation procedures offer the potential to reduce uncertainty and evaluate the plausibility of the model results. With respect to Pd resource management, improved formal collection of end‐of‐life (EOL) consumer products is identified as a key factor in increasing the recycling efficiency. In particular, the partial export of EOL vehicles represents a substantial loss of Pd from the Austrian resource system, whereas approximately 70% of the Pd in the EOL consumer products is recovered in waste management. In conclusion, systematic uncertainty analysis is an integral part of MFA required to provide robust decision support in resource management.     

Review and downscaling of life cycle decision support tools for the procurement of low-value products

Purpose

In this article, we analyze how environmental aspects can be derived from life cycle management instruments for procurement decisions of low-value products. For our analysis, we chose the case of operating room textiles. The review includes the life cycle management instruments: life cycle assessment, environmental labels, and management systems applied within the textile industry. We do so in order to identify the most important environmental decision criteria based on which the procurer of low-value products can decide for the most environmentally friendly option.

Methods

We conducted a systematic literature search in the relevant literature databases. We critically evaluate the identified life cycle assessment studies for sound methodology, verifiability, completeness, and actuality. Based on this review, we analyze and compare the results of the three most comprehensive studies in more detail and derive the most important environmental aspects of operating room textiles. In a second step, we extend the operational perspective via the strategic perspective, namely environmental management systems and further existing life cycle instruments such as eco-labels. We then synthesize the gathered information into a decision vector. Finally, we discuss how the gathered data can be further exploited and give suggestions for a more sophisticated assessment.

Results and discussion

The review of the existing life cycle assessments on operating room textiles showed that procurers should not base their decisions exclusively on existing life cycle assessments. In addition to problems such as methodological weakness, incompleteness, outdated data, and poor verifiability, the information provided is far too complex to prepare procurement decisions regarding low-value products. Furthermore, the results for the textiles assessed in the existing life cycle assessments are not necessarily transferrable to the textiles considered by the procurer because of restrictive assumptions. Therefore, it is necessary to downscale the available information and synthesize it in an applicable decision support tool. Our decision vector consists of the key environmental aspects water, CO₂, energy, and waste and is completed by environmental management systems, eco-labels, and the countries of origin that matters for environmental and social aspects as well.

Conclusions

The decision vector supports procurers when considering environmental aspects in procurement decisions and provides a mechanism for balancing the information between overcomplexity and oversimplification. Therefore, it should be the basis for future development of an eco-label for operating room textiles.     

Consequential Life Cycle Assessment With Market‐Driven Design

This article describes the development of a consequential life cycle assessment (cLCA) with endogenous market‐driven design (MDD). Incorporation of MDD within cLCA (cLCA‐MDD) is beneficial because design decisions, influenced by market forces, are a major source of environmental emissions and resource consumption in many life cycle systems. cLCA‐MDD captures the environmental impact of these design responses resulting from industrial and policy decisions. We begin by developing the concept of cLCA‐MDD, then present a case study that demonstrates how design responses can be endogenously captured in a cLCA analysis. The case study is in two parts: First, we incorporate endogenous design responses into a cLCA of a mid‐size vehicle and, second, we conduct a policy analysis using a cLCA‐MDD approach. The case study illustrates that cLCA‐MDD can capture multiple “ripple effects” resulting from an industrial decision (e.g., downsizing a vehicle's engine) or a policy decision (e.g., raising gasoline taxes) and that these effects significantly influence results. A key challenge of the approach is appropriately managing and communicating uncertainties associated with the choice of economic parameters or models. We discuss sources of uncertainty in cLCA‐MDD and demonstrate a presentation scheme to facilitate communication of result sensitivity to uncertainties from input parameters, models, and model structure.     

Disentangling effects of uncertainties on population projections: climate change impact on an epixylic bryophyte

Assessment of future ecosystem risks should account for the relevant uncertainty sources. This means accounting for the joint effects of climate variables and using modelling techniques that allow proper treatment of uncertainties. We investigate the influence of three of the IPCC's scenarios of greenhouse gas emissions (special report on emission scenarios (SRES)) on projections of the future abundance of a bryophyte model species. We also compare the relative importance of uncertainty sources on the population projections. The whole chain global climate model (GCM)-regional climate model-population dynamics model is addressed. The uncertainty depends on both natural- and model-related sources, in particular on GCM uncertainty. Ignoring the uncertainties gives an unwarranted impression of confidence in the results. The most likely population development of the bryophyte Buxbaumia viridis towards the end of this century is negative: even with a low-emission scenario, there is more than a 65 per cent risk for the population to be halved. The conclusion of a population decline is valid for all SRES scenarios investigated. Uncertainties are no longer an obstacle, but a mandatory aspect to include in the viability analysis of populations.     

Uncertainty Quantification in Life Cycle Assessments: Interindividual Variability and Sensitivity Analysis in LCA of Air‐Conditioning Systems

The life cycle environmental profile of energy‐consuming products, such as air conditioning, is dominated by the products’ use phase. Different user behavior patterns can therefore yield large differences in the results of a cradle‐to‐grave assessment. Although this variation and uncertainty is increasingly recognized, it remains often poorly characterized in life cycle assessment (LCA) studies. Today, pervasive sensing presents the opportunity to collect rich data sets and improve profiling of use‐phase parameters, in turn facilitating quantification and reduction of this uncertainty in LCA. This study examined the case of energy use in building cooling systems, focusing on global warming potential (GWP) as the impact category. In Singapore, building cooling systems or air conditioning consumes up to 37% of national electricity demand. Lack of consideration of variation in use‐phase interaction leads to the oversized designs, wasted energy, and therefore reducible GWP. Using a high‐resolution data set derived from sensor observations, energy use and behavior patterns of single‐office occupants were characterized by probabilistic distributions. The interindividual variability and use‐phase variables were propagated in a stochastic model for the life cycle of air‐conditioning systems and simulated by way of Monte Carlo analysis. Analysis of the generated uncertainties identified plausible reductions in global warming impact through modifying user interaction. Designers concerned about the environmental profile of their products or systems need better representation of the underlying variability in use‐phase data to evaluate the impact. This study suggests that data can be reliably provided and incorporated into the life cycle by proliferation of pervasive sensing, which can only continue to benefit future LCA.     

Uncertainty in environmentally conscious decision making: beer or wine?

Purpose

Life cycle assessment (LCA) is being used increasingly in decision support situations. In actual cases, the sources of uncertainty are easily hidden in the complexity. Methods for taking uncertainty into account are recommended by LCA guidelines, but actual application remains rare. The aim of this study is to demonstrate the sources of uncertainty in a practical simple selection case wherein a customer makes a decision between beer and wine in a restaurant, considering the selected criteria and the given information. The uncertainty in LCA results is connected to the broader scope of decision analysis.

Methods

Life cycle inventories were collected for beer and wine production from existing literature. The functional unit was chosen to be one serving of alcohol: beer or wine. For illustrative purposes, only the global warming potential indicator was included in the LCA through carbon footprint (CF). Probabilistic uncertainty analysis was applied to the CF system using Monte Carlo simulation. Water footprint was also roughly considered. In addition, three non-environmental indicators were included in the decision: weight control, price, and taste. The comparison between the two products was constructed as a multiple-criteria decision analytical problem.

Results and discussion

The results indicated that beer had, on average, a higher CF value than wine did. However, the difference was not significant, and within the uncertainty range, also the opposite conclusion was possible. The ratio of wine to beer CF was dominated by the uncertainty in the N₂O emissions of wine production. When all of the decision criteria were included, the level of uncertainty prevented robust overall conclusions about preference for beer or wine. However, depending on the utility differences assigned to subjective indicators, there existed also cases wherein decisions could be made at a 10?% risk level regardless of high overall uncertainty.

Conclusions

In many cases, the uncertainties of LCA are dwarfed by the overall uncertainty of the decision situation. However, as shown by our example, in many cases, reasonable decisions can be made in spite of high uncertainties. The uncertainties of single LCA indicators should be considered in relation to the decision-making problem, which depends on the uncertainty of LCA indicators but also significantly on the weighting of the indicators and the related uncertainty. Successful decision making depends on both the magnitude of uncertainty and the differences in expected utility value between alternatives. More attention should be paid to uncertainty analysis considering the weighting factors.     

Uncertainty in life cycle costing for long-range infrastructure. Part I: leveling the playing field to address uncertainties

Purpose

Life cycle costing (LCC) is a state-of-the-art method to analyze investment decisions in infrastructure projects. However, uncertainties inherent in long-term planning question the credibility of LCC results. Previous research has not systematically linked sources and methods to address this uncertainty. Part I of this series develops a framework to collect and categorize different sources of uncertainty and addressing methods. This systematization is a prerequisite to further analyze the suitability of methods and levels the playing field for part II.

Methods

Past reviews have dealt with selected issues of uncertainty in LCC. However, none has systematically collected uncertainties and linked methods to address them. No comprehensive categorization has been published to date. Part I addresses these two research gaps by conducting a systematic literature review. In a rigorous four-step approach, we first scrutinized major databases. Second, we performed a practical and methodological screening to identify in total 115 relevant publications, mostly case studies. Third, we applied content analysis using MAXQDA. Fourth, we illustrated results and concluded upon the research gaps.

Results and discussion

We identified 33 sources of uncertainty and 24 addressing methods. Sources of uncertainties were categorized according to (i) its origin, i.e., parameter, model, and scenario uncertainty and (ii) the nature of uncertainty, i.e., aleatoric or epistemic uncertainty. The methods to address uncertainties were classified into deterministic, probabilistic, possibilistic, and other methods. With regard to sources of uncertainties, lack of data and data quality was analyzed most often. Most uncertainties having been discussed were located in the use stage. With regard to methods, sensitivity analyses were applied most widely, while more complex methods such as Bayesian models were used less frequently. Data availability and the individual expertise of LCC practitioner foremost influence the selection of methods.

Conclusions

This article complements existing research by providing a thorough systematization of uncertainties in LCC. However, an unambiguous categorization of uncertainties is difficult and overlapping occurs. Such a systemizing approach is nevertheless necessary for further analyses and levels the playing field for readers not yet familiar with the topic. Part I concludes the following: First, an investigation about which methods are best suited to address a certain type of uncertainty is still outstanding. Second, an analysis of types of uncertainty that have been insufficiently addressed in previous LCC cases is still missing. Part II will focus on these research gaps.