Recent advances in research have made global sensitivity analysis of very large and highly linear life cycle assessment systems feasible. In this paper, we build on these developments to include sensitivity analysis of correlated parameters and nonlinear models. We augment numerical uncertainty propagation with Monte Carlo simulations (i) to include propagation of uncertainty from uncertain variables in parameterized inventory datasets; (ii) to account for correlations between process inputs and outputs and in particular incorporate the carbon balance of combustion activities; (iii) to employ published time-series data instead of static values for electricity generation market mixes in Europe; (iv) to ensure that inputs which are supposed to reach a fixed total (e.g., the percentage contributions of power sources to an electricity mix) actually do so consistently by using the Dirichlet distribution. We then iterate on existing global sensitivity analysis protocols for high-dimensional systems to improve their computational performance. To correctly calculate sensitivity rankings for correlated inputs, we use SHapley Additive exPlanations as feature importance metrics with gradient boosted trees. Our results for a case study of climate change impacts of an average Swiss household confirm that neglecting correlations limits the validity of uncertainty and sensitivity analysis. Our methodology and correlated sampling modules are given as open source code.

We develop a comprehensive computational framework for matrix-based regionalized life cycle assessment (LCA). When life cycle inventories and impact assessment methods have different spatial scales, spatial allocation is needed to map inventory locations to impact assessment spatial units. We review spatial allocation based on intersected areas and existing background emissions, and propose using additional spatial inventory data as a third type of allocation, which we call extension tables. Extension tables allow for detailed maps of individual processes, or even separate maps for specific process emissions — a significant improvement over the assumed uniform spatial density of process datasets. Extension tables can also be applied to existing process datasets. New LCA matrix formulae are developed for all three forms of spatial allocation, and these formulae allow for the expression of results on multiple spatial scales. As the final calculation result is a matrix, instead of a single value, the most damaging processes, emissions, and spatial units for every spatial scale used can be easily identified and combined. A case study of ecosystem damage due to freshwater consumption from irrigation of cotton in the United States is used to illustrate the different approaches. We implement our framework in an accessible open-source software package, including multiple additional examples.

Purpose In prospective life cycle assessment (pLCA), inventory models represent a future state of a production system and therefore contain assumptions about future developments. Scientific quality should be ensured by using foresight methods for handling these future assumptions during inventory modelling. We present a stepwise approach for integrating future scenario development into inventory modelling for pLCA studies. Methods A transdisciplinary research method was used to develop the SIMPL approach for scenario-based inventory modelling for pLCA. Our interdisciplinary team of LCA and future scenario experts developed a first draft of the approach. Afterwards, 112 LCA practitioners tested the approach on prospective case studies in group work projects in three courses on pLCA. Lessons learned from application difficulties, misunderstandings and feedback were used to adapt the approach after each course. After the third course, reflection, discussion and in-depth application to case studies were used to solve the remaining problems of the approach. Ongoing courses and this article are intended to bring the approach into a broader application. Results and discussion The SIMPL approach comprises adaptations and additions to the LCA goal and scope phase necessary for prospective inventory modelling, particularly the prospective definition of scope items in reference to a time horizon. Moreover, three iterative steps for combined inventory modelling and scenario development are incorporated into the inventory phase. Step A covers the identification of relevant inventory parameters and key factors, as well as their interrelations. In step B, future assumptions are made, by either adopting them from existing scenarios or deriving them from the available information, in particular by integrating expert and stakeholder knowledge. Step C addresses the combination of assumptions into consistent scenarios using cross-consistency assessment and distinctness-based selection. Several iterations of steps A–C deliver the final inventory models. Conclusion The presented approach enables pLCA practitioners to systematically integrate future scenario development into inventory modelling. It helps organize possible future developments of a technology, product or service system, also with regard to future developments in the social, economic and technical environment of the technology. Its application helps to overcome implicit bias and ensures that the resulting assessments are consistent, transparently documented and useful for drawing practically relevant conclusions. The approach is also readily applicable by LCA practitioners and covers all steps of prospective inventory modelling.

The Brumadinho tailings dam collapse in 2019 killed hundreds and caused extensive damage to the surrounding area, including long-lasting environmental damage.Yet, this catastrophic event also triggered numerous proposals and activities to increase transparency related to environmental, social and governance (ESG) risks and sustainability in a broader context.Recently, several studies have proposed indicator-based approaches, but they generally lack a coherent aggregation and analysis of trade-offs and synergies between different sustainability criteria.Against this background, the current study seeks to create a global sustainability comparison of tailings dams at a country level, by combining harmonized data from multiple input sources through an iterative, multi-stage process.First, a comprehensive set of criteria and indicators is established that includes, among others, the impact on the environment, accident risks, and socio-political and governance aspects.Second, a dedicated Multi-Criteria Decision Analysis (MCDA) framework based on an outranking sorting approach (i.e., ELECTRE-TRI) is developed.Third, the evaluation system is applied to 43 countries that experienced at least one tailings dam failure since 1970, providing fact-based and transparent decision support to stakeholders and policy makers.

Reducing the climate impacts of passenger cars has a high priority on the political agenda, especially in the EU. However, there is disagreement on how this can best be achieved – with battery or fuel cell electric vehicles, or rather with combustion engine vehicles using electricity-based synthetic liquid fuels. To answer this question and to quantify potential environmental co-benefits and trade-offs, this paper introduces carculator, a Python library to conduct environmental life cycle assessments of current and future passenger vehicles. Because carculator is open-source and equipped with an easy-to-use online graphical user interface, it produces context-specific results, deemed more relevant than results otherwise published in more static formats. carculator supports for several powertrains, vehicle size categories and fuel types, for any year between 2000 and 2050, as well as error propagation from input parameters. We demonstrate carculator with an analysis of the expected evolution of life cycle greenhouse gas emissions of hybrid vehicles powered by fossil or synthetic gasoline and battery electric vehicles between 2020 and 2050, for all European countries and Brazil, China, India, Japan and the United States. Results show that current battery electric vehicles perform better than gasoline-powered vehicles in 26 out of the 35 countries considered. In the future, electricity-based synthetic fuels show the potential to reduce climate impacts due to the expected massive decarbonization of electricity supply. However, due to their comparatively inefficient supply and use, limited renewable resources represent a challenge and should better be used for other purposes.

In recent years many Life Cycle Assessment (LCA) studies have been conducted to quantify the environmental performance of products and services. Some of these studies propagated numerical uncertainties in underlying data to LCA results, and several applied Global Sensitivity Analysis (GSA) to some parts of the LCA model to determine its main uncertainty drivers. However, only a few studies have tackled the GSA of complete LCA models due to the high computational cost of such analysis and the lack of appropriate methods for very high-dimensional models. This study proposes a new GSA protocol suitable for large LCA problems that, unlike existing approaches, does not make assumptions on model linearity and complexity and includes extensive validation of GSA results. We illustrate the benefits of our protocol by comparing it with an existing method in terms of filtering of noninfluential and ranking of influential uncertainty drivers and include an application example of Swiss household food consumption. We note that our protocol obtains more accurate GSA results, which leads to better understanding of LCA models, and less data collection efforts to achieve more robust estimation of environmental impacts. Implementations supporting this work are available as free and open source Python packages.

Geothermal energy is a renewable source of base-load power that could facilitate decarbonising the power generation sector. This work proposes novel simplified models based on Life Cycle Assessment (LCA) that enable rapid but accurate estimates of the environmental impacts of geothermal power. The proposed approach not only reduces the variability of LCA estimates due to methodological choices, but also substantially facilitates data collection by identifying the most important input parameters. These parameters are selected using Sobol’ total order indices from Global Sensitivity Analysis to a general parametric model. The models are applicable to both conventional and enhanced geothermal technologies, and cover numerous environmental impact categories. We determine the level of correlation between the simplified models and the general model. Our analysis shows that the simplified models correlate well with the general model, with correlation coefficients above 0.75 for both types of geothermal technologies and for all environmental categories. We also evaluate the performance of the simplified models by comparison with literature data. The results are positive, especially for conventional technologies where the relative difference with literature data on climate change impacts averages 14%. Finally, we identify the most appropriate model for each technology archetype and environmental category.

Global sensitivity analysis (GSA) is a valuable tool for filtering out non-influential model inputs. In combination with robustness, convergence and validation analyses, GSA can be particularly beneficial in interpreting and simplifying models with tens of thousands of independent inputs. However, there is lack of research on robust screening of such large models, where the curse of dimensionality can make existing analyses obsolete. We aim to close this gap by evaluating the computational performance of Spearman rank correlation coefficients, Sobol and delta indices, and gradient boosted trees regression. Numerical experiments are conducted for the Morris test function and a life cycle assessment model with 10'000 inputs each. Our results enable us to recommend a standardized procedure for higher-dimensional models which efficiently tests for model linearity, GSA screening, and convergence and robustness analyses of sensitivity indices, screening and rankings.

Prospective Life Cycle Assessment (pLCA) is useful to evaluate the environmental performance of current and emerging technologies in the future. Yet, as energy systems and industries are rapidly shifting towards cleaner means of production, pLCA requires an inventory database that encapsulates the expected changes in technologies and the environment at a given point in time, following specific socio-techno-economic pathways. To this end, this study introduces premise, a tool to streamline the generation of prospective inventory databases for pLCA by integrating scenarios generated by Integrated Assessment Models (IAM). More precisely, premise applies a number of transformations on energy-intensive activities found in the inventory database ecoinvent according to projections provided by the IAM. Unsurprisingly, the study shows that, within a given socio-economic narrative, the climate change mitigation target chosen affects the performance of nearly all activities in the database. This is illustrated by focusing on the effects observed on a few activities, such as systems for direct air capture of CO2, lithium-ion batteries, electricity and clinker production as well as freight transport by road, in relation to the applied sector-based transformation and the chosen climate change mitigation target. This work also discusses the limitations and challenges faced when coupling IAM and LCA databases and what improvements are to be brought in to further facilitate the development of pLCA.

For light-duty vehicles (LDVs), alternative powertrains and liquid fuels based on renewable electricity are competing options considered by policymakers and stakeholders for achieving necessary CO 2 emission reductions in the transport sector. While the urgency of climate change and the need to reach mitigation targets are well understood, system-wide implications along other sustainability dimensions need further exploration. We integrate a detailed transport system model into an integrated assessment framework and couple it with prospective life cycle impact analysis. This allows to assess different technological pathways of the European LDV fleet until 2050 for a comprehensive set of environmental and resource depletion indicators. Results indicate that greenhouse gas emissions drop significantly in all mitigation scenarios. However, impacts increase in several non-climate change impact categories even with fully renewable electricity supply. Additional impacts arise from the production of battery and fuel-cell components, and from a significant rise in electricity demand, most prominently for synthetic fuels. We consequently find that changes in mobility life-styles and in the relevant industrial processes are paramount to reduce environmental impacts from a climate-friendly LDV fleet across all categories.

The life-cycle environmental impacts of geothermal power generation are highly variable and depend on many site-specific conditions. The objective of this work is the identification of the most influential parameters for estimating the environmental impacts of geothermal electricity production. First, we developed a general model for computing the impacts of both conventional and enhanced geothermal technologies. The model is validated against selected literature studies for the climate change category. We then use Global Sensitivity Analysis (GSA) to evaluate the contribution of each parameter to the overall variance of the model's output. The results of the GSA suggest that i) the uncertainty of environmental impact estimates can be significantly reduced by obtaining more accurate values for a small number of key parameters, such as the installed capacity of the plant, operational emissions of CO2 and the depth and capacity of wells; and ii) the majority of parameters do not affect significantly the environmental impact estimates and therefore can be fixed anywhere within their range of variability. Finally, we discuss some of the limitations of the present study and propose approaches that could be implemented to overcome such limitations.

Introduction The flexibility of life cycle inventory (LCI) background data selection is increasing with the increasing availability of data, but this comes along with the challenge of using the background data with primary life cycle inventory data. To relieve the burden on the practitioner to create the linkages and reduce bias, this study aimed at applying automation to create foreground LCI from primary data and link it to background data to construct product system models (PSM). Methods Three experienced LCA software developers were commissioned to independently develop software prototypes to address the problem of how to generate an operable PSM from a complex product specification. The participants were given a confidential product specification in the form of a Bill of Materials (BOM) and were asked to develop and test prototype software under a limited time period that converted the BOM into a foreground model and linked it with one or more a background datasets, along with a list of other functional requirements. The resulting prototypes were compared and tested with additional product specifications. Results Each developer took a distinct approach to the problem. One approach used semantic similarity relations to identify best-fit background datasets. Another approach focused on producing a flexible description of the model structure that removed redundancy and permitted aggregation. Another approach provided an interactive web application for matching product components to standardized product classification systems to facilitate characterization and linking. Discussion Four distinct steps were identified in the broader problem of automating PSM construction: creating a foreground model from product data, determining the quantitative properties of foreground model flows, linking flows to background datasets, and expressing the linked model in a format that could be used by existing LCA software. The three prototypes are complementary in that they address different steps and demonstrate alternative approaches. Manual work was still required in each case, especially in the descriptions of the product flows that must be provided by background datasets. Conclusion The study demonstrates the utility of a distributed, comparative software development, as applied to the problem of LCA software. The results demonstrate that the problem of automated PSM construction is tractable. The prototypes created advance the state of the art for LCA software.

Duckweeds are efficient aquatic plants for wastewater treatment due to their high nutrient uptake capabilities, growth rates, and resilience to severe environmental conditions. The high starch and cellulose contents of duckweed species make them an attractive feedstock for biofuels and biochemicals. Experimental studies have shown that sequential anaerobic bioprocessing of duckweed into ethanol, carboxylates, methane, and soil amendment in a biorefinery system is technically feasible. This study aims to identify challenges and opportunities for large-scale wastewater-derived duckweed biorefineries as a way to promote a circular bioeconomy. The most suitable end products from wastewater-derived duckweed biomass, determined in a series of previously reported laboratory batch experiments, were used to estimate the bioproduct yields during the hypothetical operation of a large-scale biorefinery. Techno-economic analysis (TEA) revealed a minimum duckweed selling price of $7.69 Mg-1 dry matter and a minimum ethanol selling price of $2.17/L or $8.23 gal(-1). Duckweed pond construction and duckweed harvesting accounted for the largest share of capital (55.6%) and operating expenses (90.4%), respectively. A cradle-to-gate life cycle assessment (LCA) revealed that duckweed pond construction led to increased land use change impacts, but water-quality and eutrophication impacts could be significantly reduced with this integrated system through efficient nutrient upcycling.

In this analysis, life cycle environmental burdens and total costs of ownership (TCO) of current (2017) and future (2040) passenger cars with different powertrain configurations are compared. For all vehicle configurations, probability distributions are defined for all performance parameters. Using these, a Monte Carlo based global sensitivity analysis is performed to determine the input parameters that contribute most to overall variability of results. To capture the systematic effects of the energy transition, future electricity scenarios are deeply integrated into the ecoinvent life cycle assessment background database. With this integration, not only the way how future electric vehicles are charged is captured, but also how future vehicles and batteries are produced. If electricity has a life cycle carbon content similar to or better than a modern natural gas combined cycle powerplant, full powertrain electrification makes sense from a climate point of view, and in many cases also provides reductions in TCO. In general, vehicles with smaller batteries and longer lifetime distances have the best cost and climate performance. If a very large driving range is required or clean electricity is not available, hybrid powertrain and compressed natural gas vehicles are good options in terms of both costs and climate change impacts. Alternative powertrains containing large batteries or fuel cells are the most sensitive to changes in the future electricity system as their life cycles are more electricity intensive. The benefits of these alternative drivetrains are strongly linked to the success of the energy transition: the more the electricity sector is decarbonized, the greater the benefit of electrifying passenger vehicles.

La fermeture des centrales nucléaires et le développement de l’énergie solaire et éolienne rendent la production d’électricité plus volatile. De nouveaux systèmes de stockage sont nécessaires pour s’assurer que l’électricité est disponible au moment où elle est nécessaire. Le stockage adiabatique d’air comprimé représente une technologie prometteuse. Il utilise l’excédent de production des installations solaires et éoliennes pour comprimer l’air ambiant et le stocker dans une cavité souterraine. Au besoin, l’air comprimé est à nouveau détendu et entraîne alors une turbine qui produit de l’électricité. En tirant profit de la chaleur générée lors de la compression, cette technologie atteint un rendement de 65 à 75 %, ce qui est semblable à celui obtenu avec l’accumulation par pompage. En termes de potentiel d’émission de gaz à effet de serre et de dommages aux écosystèmes, la compatibilité environnementale des réservoirs d’air comprimé est également comparable à celle des systèmes à accumulation par pompage. Les réservoirs d’air comprimé sont techniquement réalisables. Les composants importants, comme les turbomachines et les accumulateurs thermiques, sont déjà disponibles sur le marché ou ont été testés dans une installation pilote. La construction de cavités bénéficie de l’expérience acquise lors de la réalisation de tunnels et de cavernes. Les réservoirs adiabatiques d’air comprimé constituent par conséquent une solution de stockage efficace, écologique et techniquement réalisable. En raison de leurs coûts d’investissement élevés et du manque de clarté qui entoure leur cadre économique et juridique, leur rentabilité demeure toutefois incertaine. Cela complique également le financement d’une installation de démonstration.

Der Verzicht auf Kernkraftwerke und der Ausbau von Solar- und Windenergie führen dazu, dass die Stromproduktion volatiler wird. Damit Strom dann zur Verfügung steht, wenn er gebraucht wird, braucht es neue Speichersysteme. Eine vielversprechende Technologie ist die adiabatische Druckluftspeicherung. Sie nutzt überschüssigen Strom aus Solar- und Windanlagen, um Umgebungsluft zu komprimieren und diese in einem unterirdischen Hohlraum zu speichern. Bei Bedarf wird die komprimierte Luft wieder expandiert; sie treibt dabei eine Turbine an und erzeugt wieder Strom. Da die bei der Komprimierung entstandene Wärme genutzt wird, beträgt die Effizienz 65 bis 75 Prozent; das ist ein ähnlicher Wert wie jener, den Pumpspeicher erreichen. Auch die Umweltverträglichkeit von Druckluftspeichern ist, gemessen am Treibhausgaspotenzial und an Schäden an Ökosystemen, vergleichbar mit jener von Pumpspeichern. Druckluftspeicher sind technisch machbar. Wichtige Komponenten wie Turbomaschinen und Wärmespeicher sind entweder bereits auf dem Markt erhältlich oder wurden in einer Pilotanlage erprobt. Der Bau von Hohlräumen ist zudem durch die Erfahrungen im Tunnel- und Kavernenbau ausgereift. Adiabatische Druckluftspeicher sind also eine effiziente, umweltverträgliche und technisch machbare Speicherlösung. Wegen der hohen Kapitalkosten sowie der unklaren wirtschaftlichen und rechtlichen Rahmenbedingungen ist allerdings ungewiss, ob sie wirtschaftlich sein können. Dies erschwert auch die Finanzierung einer Demonstrationsanlage.

Life cycle impact assessment (LCIA) is a lively field of research, and data and models are continuously improved in terms of impact pathways covered, reliability, and spatial detail. However, many of these advancements are scattered throughout the scientific literature, making it difficult for practitioners to apply the new models. Here, we present the LC-IMPACT method that provides characterization factors at the damage level for 11 impact categories related to three areas of protection (human health, ecosystem quality, natural resources). Human health damage is quantified as disability adjusted life years, damage to ecosystem quality as global species extinction equivalents (based on potentially disappeared fraction of species), and damage to mineral resources as kilogram of extra ore extracted. Seven of the impact categories include spatial differentiation at various levels of spatial scale. The influence of value choices related to the time horizon and the level of scientific evidence of the impacts considered is quantified with four distinct sets of characterization factors. We demonstrate the applicability of the proposed method with an illustrative life cycle assessment example of different fuel options in Europe (petrol or biofuel). Differences between generic and regionalized impacts vary up to two orders of magnitude for some of the selected impact categories, highlighting the importance of spatial detail in LCIA. This article met the requirements for a gold – gold JIE data openness badge described at http://jie.click/badges.