Uncertainty Quantification in Life Cycle Assessments: Exploring Distribution Choice and Greater Data Granularity to Characterize Product Use

The life cycle environmental profile of energy‐consuming products is dominated by the products’ use stage. Variation in real‐world product use can therefore yield large differences in the results of life cycle assessment (LCA). Adequate characterization of input parameters is paramount for uncertainty quantification and has been a challenge to wider adoption of the LCA method. After emphasis in recent years on methodological development, data development has become the primary focus again. Pervasive sensing presents the opportunity to collect rich data sets and improve profiling of use‐stage parameters. Illustrating a data‐driven approach, we examine energy use in domestic cooling systems, focusing on climate change as the impact category. Specific objectives were to examine: (1) how characterization of the use stage by different probability distributions and (2) how characterizing data aggregated at successively higher granularity affects LCA modeling results and the uncertainty in output. Appliance‐level electricity data were sourced from domestic residences for 3 years. Use‐stage variables were propagated in a stochastic model and analyses simulated by Monte Carlo procedure. Although distribution choice did not necessarily significantly impact the estimated output, there were differences in the estimated uncertainty. Characterization of use‐stage power consumption in the model at successively higher data granularity reduced the output uncertainty with diminishing returns. Results therefore justify the collection of high granularity data sets representing the life cycle use stage of high‐energy products. The availability of such data through proliferation of pervasive sensing presents increasing opportunities to better characterize data and increase confidence in results of LCA.     

EU‐Average Impacts of Wheat Production: A Meta‐Analysis of Life Cycle Assessments

Wheat is an important commodity in Europe. With a production of 133 million tonnes per year and annual import and export accounting for 6.3 and 5.3 billion US$, respectively, wheat is the most important cereal in Europe. Wheat cultivation further feeds into a wide variety of products ranging from bread, over imitation meat, to biofuels and bio‐based materials. Therefore, it is desirable to have a synthetic life cycle assessment (LCA) of the impacts of an average kilogram (kg) of wheat produced in Europe. This article aims to provide such a synthesis using two strategies. In the first strategy, we give an overview of published LCA impacts of wheat production. A second strategy is a meta‐analysis in which a re‐evaluation is made of 20 available life cycle inventories representing cases in 11 different European countries. Based on the production shares of these countries in the total European production, weighted average impacts are calculated. These weighted averages of the re‐evaluated inventories show that an average kg of wheat grain produced in Europe demands 3.25 megajoules of nonrenewable, fossil energy, emits 0.61 to 0.65 kg carbon dioxide equivalents, triggers terrestrial acidification of 4.94 to 6.51 grams (g) sulphur dioxide equivalents, freshwater eutrophication of 0.08 to 0.09 g phosphorous equivalents, marine eutrophication of 4.97 to 7.60 g nitrogen equivalents, and occupies 1.63 square meter years of agricultural land. The re‐evaluation of studies results in similar impacts as the mere reviewing of energy demands and global warming potentials. Given the many applications of wheat, the presented meta‐analysis is interesting to evaluate the average and range of environmental performance of wheat production in Europe, but is also useful as an input in assessing impacts of wheat‐based products.     

Analytical Propagation of Uncertainty in Life Cycle Assessment Using Matrix Formulation

Inventory data and characterization factors in life cycle assessment (LCA) contain considerable uncertainty. The most common method of parameter uncertainty propagation to the impact scores is Monte Carlo simulation, which remains a resource‐intensive option—probably one of the reasons why uncertainty assessment is not a regular step in LCA. An analytical approach based on Taylor series expansion constitutes an effective means to overcome the drawbacks of the Monte Carlo method. This project aimed to test the approach on a real case study, and the resulting analytical uncertainty was compared with Monte Carlo results. The sensitivity and contribution of input parameters to output uncertainty were also analytically calculated. This article outlines an uncertainty analysis of the comparison between two case study scenarios. We conclude that the analytical method provides a good approximation of the output uncertainty. Moreover, the sensitivity analysis reveals that the uncertainty of the most sensitive input parameters was not initially considered in the case study. The uncertainty analysis of the comparison of two scenarios is a useful means of highlighting the effects of correlation on uncertainty calculation. This article shows the importance of the analytical method in uncertainty calculation, which could lead to a more complete uncertainty analysis in LCA practice.     

Uncertainty Analysis in Biofuel Systems

This article evaluates the implications of uncertainty in the life cycle (LC) energy efficiency and greenhouse gas (GHG) emissions of rapeseed oil (RO) as an energy carrier displacing fossil diesel (FD). Uncertainties addressed include parameter uncertainty as well as scenario uncertainty concerning how RO coproduct credits are accounted for (uncertainty due to modeling choices). We have carried out an extensive data collection to build an LC inventory accounting for parameter uncertainty. Different approaches for carbon stock changes associated with converting set‐aside land to rapeseed cultivation have been considered, which result in different values: from ?0.25 t C/ha.yr (carbon uptake by the soil in tonnes per hectare year) to 0.60 t C/ha.yr (carbon emission). Energy renewability efficiency and GHG emissions of RO are presented, which show the influence of parameter versus scenario uncertainty. Primary energy savings and avoided GHG emissions when RO displaces FD have also been calculated: Avoided GHG emissions show considerably higher uncertainty than energy savings, mainly due to land use (nitrous oxide emissions from soil) and land use conversion (carbon stock changes). Results demonstrate the relevance of applying uncertainty approaches; emphasize the need to reduce uncertainty in the environmental life cycle modeling, particularly GHG emissions calculation; and show the importance of integrating uncertainty into the interpretation of results.     

Uncertainty and Variability in Product Carbon Footprinting

Recent years have seen increasing interest in life cycle greenhouse gas emissions accounting, also known as carbon footprinting, due to drivers such as transportation fuels policy and climate‐related eco‐labels, sometimes called carbon labels. However, it remains unclear whether applications of greenhouse gas accounting, such as carbon labels, are supportable given the level of precision that is possible with current methodology and data. The goal of this work is to further the understanding of quantitative uncertainty assessment in carbon footprinting through a case study of a rackmount electronic server. Production phase uncertainty was found to be moderate (±15%), though with a high likelihood of being significantly underestimated given the limitations in available data for assessing uncertainty associated with temporal variability and technological specificity. Individual components or subassemblies showed varying levels of uncertainty due to differences in parameter uncertainty (i.e., agreement between data sets) and variability between production or use regions. The use phase displayed a considerably higher uncertainty (±50%) than production due to uncertainty in the useful lifetime of the server, variability in electricity mixes in different market regions, and use profile uncertainty. Overall model uncertainty was found to be ±35% for the whole life cycle, a substantial amount given that the method is already being used to set policy and make comparative environmental product declarations. Future work should continue to combine the increasing volume of available data to ensure consistency and maximize the credibility of the methods of life cycle assessment (LCA) and carbon footprinting. However, for some energy‐using products it may make more sense to increase focus on energy efficiency and use phase emissions reductions rather than attempting to quantify and reduce the uncertainty of the relatively small production phase.     

Uncertainty and Sensitivity in the Carbon Footprint of Shopping Bags

Carbon footprints for several shopping bag alternatives (polyethylene, paper, cotton, biodegradable modified starch, and recycled polyethylene) were compared with life cycle assessment. Stochastic uncertainty analysis was used to study the sensitivity of the comparison to scenario and parameter uncertainty. On the basis of the results, we could give only a few robust conclusions without choosing a waste treatment scenario or limiting the parameter space. Given the scenario of current waste infrastructure in Finland, recycled polyethylene bags seem to be the most preferable (?7 to 24 g CO₂ eq./bag) and biodegradable bags the least preferable (38 to 60 g CO₂ eq./bag) option. In each analyzed waste treatment scenario, a few parameters dominated the uncertainty of results. Most of these parameters were downstream of the shopping bag manufacturing (consumer behavior, landfill conditions, method of waste combustion, etc.). The choice of waste treatment scenario had a greater effect on the ranking of bags than parameter uncertainty within scenarios. This result highlights the importance of including several scenarios in comparative life cycle assessments.     

Life Cycle Assessment of Frozen Tilapia Fillets From Indonesian Lake‐Based and Pond‐Based Intensive Aquaculture Systems

We used life cycle assessment to evaluate a subset of the cradle‐to‐destination‐port environmental impacts associated with the production, processing, and transportation of frozen, packaged Indonesian tilapia (Oreochromis niloticus) fillets to ports in Chicago and Rotterdam. Specifically, we evaluated the cumulative energy use; biotic resource use; and global warming, acidifying, and eutrophying emissions at each life cycle stage and in aggregate. We identify the importance of least environmental cost feed sourcing for reducing supply chain environmental impacts. We also highlight the need for more effective nutrient cycling in intensive aquaculture. The environmental trade‐offs inherent in substituting technological inputs for ecosystem services in intensive pond‐based versus lake‐based production systems are discussed. We further call for more nuanced considerations of comparative environmental advantage in the production and interregional trade of food commodities than has been characteristic of historic food miles discussions. Significant opportunities exist for improving environmental performance in tilapia aquaculture. This product compares favorably, however, with several other fishery, aquaculture, and animal husbandry products, according to the suite of impact categories considered in this study.     

Comparative Life Cycle Assessment of Charcoal,Biogas, and Liquefied Petroleum Gas as Cooking Fuels in Ghana

Standard life cycle assessment (LCA) methodology has been used to determine and compare the environmental impacts of three different cooking fuels used in Ghana, namely, charcoal, biogas, and liquefied petroleum gas (LPG). A national policy on the use of cooking fuels would have to look at the environmental, social, and cost implications associated with the fuel types. This study looked at the environmental aspect of using these fuels. The results showed that global warming and human toxicity were the most significant overall environmental impacts associated with them, and charcoal and LPG, respectively, made the largest contribution to these impact categories. LPG, however, gave relatively higher impacts in three other categories of lesser significance—that is, eutrophication, freshwater aquatic ecotoxicity, and terrestrial ecotoxicity potentials. Direct comparison of the results showed that biogas had the lowest impact in five out of the seven categories investigated. Charcoal and LPG had only one lowest score each. From the global warming point of view, however, LPG had a slight overall advantage over the others, and it was also the most favorable at the cooking stage, in terms of its effect on humans.     

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.     

Uncertainty Analysis in Life Cycle Assessment (LCA): Case Study on Plant-Protection Products and Implications for Decision Making (9 pp + 3 pp)

Goal, Scope, and Background Uncertainty analysis in LCA is important for sound decision support. Nevertheless, the actual influence of uncertainty on decision making in specific LCA case-studies has only been little studied so far. Therefore, we assessed the uncertainty in an LCA comparing two plant-protection products.Methods Uncertainty and variability in LCI flows and characterization factors (CML-baseline method) were expressed as generic uncertainty factors and subsequently propagated into impact scores using Monte-Carlo simulation. Uncertainty in assumptions on production efficiency for chemicals, which is of specific interest for the case study, was depicted by scenarios. Results and Discussion Impact scores concerning acidification, eutrophication, and global warming display relatively small dispersions. Differences in median impact scores of a factor of 1.6 were sufficient in the case study for a significant distinction of the products. Results of toxicity impact-categories show large dispersions due to uncertainty in characterization factors and in the composition of sum parameters. Therefore, none of the two products was found to be significantly environmentally preferable to the other. Considering the case study results and inherent characteristics of the impact categories, a tentative rule of thumb is put forward that quantifies differences in impact scores necessary to obtain significant results in product comparisons.Conclusion Published LCA case-studies may have overestimated the significance of results. It is therefore advisable to routinely carry out quantitative uncertainty analyses in LCA. If this is not feasible, for example due to time restrictions, the rule of thumb proposed here may be helpful to evaluate the significance of results for the impact categories of global warming, acidification, eutrophication, and photooxidant creation.     

Environmental Performance Analysis of Eco‐Industrial Parks in China: A Data Envelopment Analysis Approach

In pursuit of more sustainable development of industry, China has been actively developing eco‐industrial parks (EIPs) for more than a decade. However, the environmental value of these EIPs remains largely unverified. This study aimed to evaluate the environmental performance of national EIPs in China using data envelopment analysis. Eco‐efficiency and environmental performance indices were used to represent the static and dynamic environmental performance of EIPs, respectively. An environmental performance index was formed by combining measures of eco‐efficiency in a dynamic setting with the sequential Malmquist index approach. We obtained three main empirical findings. First, 34 national EIPs exhibited a cumulative environmental performance improvement of 89.4% from 2007 to 2010, which is primarily the result of eco‐efficiency change rather than environmental technical change. Second, compared with the trial EIPs, the demonstration EIPs had a higher average eco‐efficiency (0.611 vs. 0.446 in 2010) and experienced greater average environmental performance improvement (129% vs. 60%). Third, the EIPs retrofitted from high‐tech industrial development zones exhibited much higher average eco‐efficiency (0.798 vs. 0.440 in 2010) than those retrofitted from economic and technical development zones. The key measures supporting the performance improvement and policy implications for the development of EIPs are also discussed.     

Beyond Global Warming Potential: A Comparative Application of Climate Impact Metrics for the Life Cycle Assessment of Coal and Natural Gas Based Electricity

In the ongoing debate about the climate benefits of fuel switching from coal to natural gas for power generation, the metrics used to model climate impacts may be important. In this article, we evaluate the life cycle greenhouse gas emissions of coal and natural gas used in new, advanced power plants using a broad set of available climate metrics in order to test for the robustness of results. Climate metrics included in the article are global warming potential, global temperature change potential, technology warming potential, and cumulative radiative forcing. We also used the Model for the Assessment of Greenhouse‐gas Induced Climate Change (MAGICC) climate‐change model to validate the results. We find that all climate metrics suggest a natural gas combined cycle plant offers life cycle climate benefits over 100 years compared to a pulverized coal plant, even if the life cycle methane leakage rate for natural gas reaches 5%. Over shorter time frames (i.e., 20 years), plants using natural gas with a 4% leakage rate have similar climate impacts as those using coal, but are no worse than coal. If carbon capture and sequestration becomes available for both types of power plants, natural gas still offers climate benefits over coal as long as the life cycle methane leakage rate remains below 2%. These results are consistent across climate metrics and the MAGICC model over a 100‐year time frame. Although it is not clear whether any of these metrics are better than the others, the choice of metric can inform decisions based on different societal values. For example, whereas annual temperature change reported may be a more relevant metric to evaluate the human health effects of increased heat, the cumulative temperature change may be more relevant to evaluate climate impacts, such as sea‐level rise, that will result from the cumulative warming.     

Structured Under‐Specification of Life Cycle Impact Assessment Data for Building Assemblies

The existence of uncertainties and variations in data represents a remaining challenge for life cycle assessment (LCA). Moreover, a full analysis may be complex, time‐consuming, and implemented mainly when a product design is already defined. Structured under‐specification, a method developed to streamline LCA, is here proposed to support the residential building design process, by quantifying environmental impact when specific information on the system under analysis cannot be available. By means of structured classifications of materials and building assemblies, it is possible to use surrogate data during the life cycle inventory phase and thus to obtain environmental impact and associated uncertainty. The bill of materials of a building assembly can be specified using minimal detail during the design process. The low‐fidelity characterization of a building assembly and the uncertainty associated with these low levels of fidelity are systematically quantified through structured under‐specification using a structured classification of materials. The analyst is able to use this classification to quantify uncertainty in results at each level of specificity. Concerning building assemblies, an average decrease of uncertainty of 25% is observed at each additional level of specificity within the data structure. This approach was used to compare different exterior wall options during the early design process. Almost 50% of the comparisons can be statistically differentiated at even the lowest level of specificity. This data structure is the foundation of a streamlined approach that can be applied not only when a complete bill of materials is available, but also when fewer details are known.     

Import‐based Indicator for the Geopolitical Supply Risk of Raw Materials in Life Cycle Sustainability Assessments

There is a growing concern over the security and sustainable supply of raw material among businesses and governments of developed, material‐intensive countries. This has led to the development of a systematic analysis of risk incorporated with raw materials usage, often referred as criticality assessment. In principle, this concept is based on the material flow approach. The potential role of life cycle assessment (LCA) to integrate resource criticality through broadening its scope into the life cycle sustainability assessment (LCSA) framework has been discussed within the LCA communities for some time. In this article, we aim at answering the question of how to proceed toward integration of the geopolitical aspect of resource criticality into the LCSA framework. The article focuses on the assessment of the geopolitical supply risk of 14 resources imported to the seven major advanced economies and the five most relevant emerging countries. Unlike a few previous studies, we propose a new method of calculation for the geopolitical supply risk, which is differentiated by countries based on the import patterns instead of a global production distribution. Our results suggest that rare earth elements, tungsten, antimony, and beryllium generally pose high geopolitical supply risk. Results from the Monte Carlo simulation allow consideration of data uncertainties for result interpretation. Issues concerning the consideration of the full supply chain are exemplarily discussed for cobalt. Our research broadens the scope of LCA from only environmental performance to a resource supply‐risk assessment tool that includes accessibility owing to political instability and market concentration under the LCSA framework.     

The Life Cycle Assessment of an Energy‐Positive Peri‐Urban Residence in a Tropical Regime

Urban settlements are home to the greatest levels of greenhouse gas emissions and energy consumption globally, with unprecedented rates of urban expansion occurring today. With the majority of global urbanization occurring along the periphery of urban areas in developing countries, investigation of “green” building practices designed specifically for “peri‐urban” regions is critical for a low‐emitting future society. This study assesses a state‐of‐the‐art residence designed for a middle‐class family of four residing in the peri‐urban region of Bangkok, Thailand. The residence employs both demand‐side management strategies and low‐emitting energy supply technology to achieve energy‐positive status. To elucidate the influence that key design decisions have on the life cycle sustainability of the home, several variants of the residence are modeled. A process‐based life cycle assessment consistent with the International Organization for Standardization (ISO) 14044:2006 standard and following ReCiPe Midpoint life cycle impact assessment methodology is used to quantify the life cycle impacts per square meter of conditioned residence floor area for climate change (582 kilograms [kg] carbon dioxide equivalent), terrestrial acidification (4.01 kg sulfur dioxide equivalent), freshwater eutrophication (30.4 grams phosphorous equivalent), fossil depletion (362 kg iron equivalent), and metal depletion (186 kg oil equivalent) impacts. We model multiple scenarios in which varying proportions of Bangkok's peri‐urban detached housing demand are fulfilled by the energy‐positive residence variants. Under the best‐case replacement scenario (i.e., 100% replacement of future peri‐urban detached housing), significant reductions are achieved across the life cycle climate change (80%), terrestrial acidification (82%), and fossil depletion (81%) impact categories for the steel‐framed, energy‐positive residence.     

Truncation Error Estimates in Process Life Cycle Assessment Using Input‐Output Analysis

Process life cycle assessment (PLCA) is widely used to quantify environmental flows associated with the manufacturing of products and other processes. As PLCA always depends on defining a system boundary, its application involves truncation errors. Different methods of estimating truncation errors are proposed in the literature; most of these are based on artificially constructed system complete counterfactuals. In this article, we review the literature on truncation errors and their estimates and systematically explore factors that influence truncation error estimates. We classify estimation approaches, together with underlying factors influencing estimation results according to where in the estimation procedure they occur. By contrasting different PLCA truncation/error modeling frameworks using the same underlying input‐output (I‐O) data set and varying cut‐off criteria, we show that modeling choices can significantly influence estimates for PLCA truncation errors. In addition, we find that differences in I‐O and process inventory databases, such as missing service sector activities, can significantly affect estimates of PLCA truncation errors. Our results expose the challenges related to explicit statements on the magnitude of PLCA truncation errors. They also indicate that increasing the strictness of cut‐off criteria in PLCA has only limited influence on the resulting truncation errors. We conclude that applying an additional I‐O life cycle assessment or a path exchange hybrid life cycle assessment to identify where significant contributions are located in upstream layers could significantly reduce PLCA truncation errors.     

Hybrid Input‐Output Life Cycle Assessment of First‐ and Second‐Generation Ethanol Production Technologies in Brazil

A hybrid approach combining life cycle assessment and input‐output analysis was used to demonstrate the economic and environmental benefits of current and future improvements in agricultural and industrial technologies for ethanol production in Brazilian biorefineries. In this article, three main scenarios were evaluated: first‐generation ethanol production with the average current technology; the improved current technology; and the integration of improved first‐ and second‐generation ethanol production. For the improved first‐generation scenario, a US$1 million increase in ethanol demand can give rise to US$2.5 million of total economic activity in the Brazilian economy when direct and indirect purchases of inputs are considered. This value is slightly higher than the economic activity (US$1.8 million) for an energy equivalent amount of gasoline. The integration of first‐ and second‐generation technologies significantly reduces the total greenhouse gas emissions of ethanol production: 14.6 versus 86.4 grams of carbon dioxide equivalent per megajoule (g CO₂‐eq/MJ) for gasoline. Moreover, emissions of ethanol can be negative (–10.5 g CO₂‐eq/MJ) when the system boundary is expanded to account for surplus bioelectricity by displacement of natural gas thermal electricity generation considering electricity produced in first‐generation optimized biorefineries.     

Willingness to Pay for Eco‐Certified Refurbished Products: The Effects of Environmental Attitudes and Knowledge

Refurbishing products, which are increasingly sold in business‐to‐consumer markets, is a key strategy to reduce waste. Nevertheless, research finds that consumers’ willingness to pay (WTP) for refurbished products is low. Strategies for a higher WTP are needed in order to grow consumer markets for refurbished products. Eco‐certification of refurbished products may be a key strategy here. Drawing on the consumer WTP literature concerning “green” products, we investigate the impact of independent eco‐certificates. Our analysis is based on a survey of 231 potential customers. The results suggest that, across various product categories, the WTP for products with refurbished components is significantly lower. Adding an eco‐certificate tends to return the WTP toward the virgin product level. We show that consumers with proenvironmental attitudes particularly exhibit green buying behavior. Our findings indicate that eco‐certification is often worthwhile because it enhances the business rationale for producing products with refurbished components.