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The urgency of tackling global environmental issues calls for radical technological and behavioral changes. New prospective (or ex ante) methods are needed to assess the impacts of these changes. Prospective life cycle assessment (LCA) can contribute by detailed analysis of environmental consequences. A new stream of research has taken up the challenge to create prospective life cycle inventory (LCI) databases, building on projections of integrated assessment models to describe future changes in technology use and their underlying environmental performance. The present work extends on this by addressing the research question on how to project life cycle impact assessment methods for water scarcity consistent with prospective LCI modeling. Water scarcity characterization factors are projected from 2010–2050 using the AWARE method, based on SSP-RCP scenario results of the integrated assessment model IMAGE. This work is coupled with prospective LCI databases, where electricity datasets are adapted based on the energy component of IMAGE for the same scenario. Based on this, an LCA case study of water desalination for the steel industry in Spain is presented. The resulting regional characterization factors show that some regions (i.e., the Iberian Peninsula) could experience an increase in water scarcity in the future. Results of the case study show how this can lead to trade-offs between climate change and water scarcity impacts and how disregarding such trends could lead to biased assessments. The relevance and limitations are finally discussed, highlighting further research needs, such as the temporalization of the impacts.

Many decisions are made during the life cycle of a product, e.g., whether to replace a product or to keep using it. To pinpoint the “greener” choice, the consequential environmental effects of each choice should be estimated. Hereto, only activities occurring after the decision should be considered and, thus, not necessarily the complete product life cycle. Decisions are taken at different levels in the economy, ranging from small scale (e.g., few individual purchases) to large scale (e.g., policies). In this article, we introduce a framework that mainly focuses on the small-scale decision of replacement of a product in usage, illustrated in case of a petrol versus an electric car. For a fixed time span of usage (functional unit), the product system composition varies based on the selected time of replacement. Many market mechanisms occur within the cause-effect chain, for which argued presumptions are necessary. For example, the purchase of the alternative product is reasoned to possibly induce an additional product supply. Moreover, if the product is not obsolete after initial usage, it is sold in the secondhand market and will thus displace the average market mix. The developed framework for this small-scale replacement decision is different from the alternative consequential approach of ecoinvent that covers a different decision, namely, a marginal increase in demand, e.g., of car usage, at market level. In our framework, a ratio for the amount of newly car produced per purchase is introduced. For the case study, the global warming potential over 100 years is estimated, this using general data from databases and time-varying market mix (in composition), car technology, and electricity mix. A set of equations is introduced that serves as a first simplified framework. Bearing in mind limitations, if the petrol car is disposed of as waste after initial usage whereas the electric car resold, it is estimated that a replacement of the petrol car by the electric car at any time lowers the impact on climate change. A better characterization of impact over time and market and agent-based modeling should be at the core of further efforts, especially when upscaling to large-scale decisions. Framework and insights are provided on the modeling of the environmental effects of a replacement decision. Finally, this article may serve as a stepping-stone towards an integrated consequential modeling of sustainability impact of product-related decisions, looking beyond a normative consideration of a fixed product life cycle.

Cities consume almost 80 percent of world’s energy and account for 60 percent of all the emissions of carbon dioxide and significant amounts of other greenhouse gases (GHG). The ongoing rapid urbanization will further increase GHG emissions of cities. The quantification of the environmental impact generated in cities is an important step to curb the impact. In fact, quantifying the consumption activities taking place inside a city, if differentiated by socioeconomic and demographic groups, can provide important insights for sustainable-consumption policies. However, the lack of high-resolution data related to these activities makes it difficult to quantify urban GHG emissions (as well as other impacts). This paper presents a methodology that can quantify the carbon footprint of households in cities using consumption data from a national or European level, where the resource consumption is linked to socioeconomic attributes of a population. The methodology is applied to analyzing the environmental impact by household resource consumption in the city of The Hague in the Netherlands. The key insights reveal potential intervention areas regarding resource consumption categories and demographic groups that can be targeted to reduce GHG emissions due to consumption-driven activities in the city.

Life Cycle Assessment (LCA) methods for land use take both occupation and transformation impacts into account. However, for wetlands and impacts from water consumption, it is so far not possible to account for transformation impacts. It is our goal to close this research gap, by determining wetland recovery times and developing characterization factors for transformation. To do this, we conducted a meta-analysis of 59 studies analyzing biodiversity recovery in wetlands subject to passive and active restoration. Generalized linear models were fitted to the biodiversity data and age, along with other wetland characteristics (such as elevation, latitude, or climate class), and were used as predictor variables. The results indicate that elevation, latitude, type of wetland, and restoration method have the strongest effect on recovery speed. Recovery times vary from less than one year to a maximum of 107 years with passive restoration and 105 years with active restoration. Corresponding transformation characterization factors vary between 10–14 and 10–2 species-eq·year2/m3. Finally, recognizing the relevance of this work to real-world policy issues beyond LCA, we discuss the implications of our estimated restoration times on the feasibility of “biodiversity offsetting”. Offsetting utilizes restoration to replace biodiversity value lost due to development impacts. Our work can help stakeholders make informed decisions on whether offsetting represents a legitimate policy option in a particular context. This document is the Accepted Manuscript version of a Published Work that appeared in final form in [Environmental Science and Technology], copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.est.8b01501

The use of life cycle assessment (LCA) as a decision support tool can be hampered by the numerous uncertainties embedded in the calculation. The treatment of uncertainty is necessary to increase the reliability and credibility of LCA results. The objective is to provide an overview of the methods to identify, characterize, propagate (uncertainty analysis), understand the effects (sensitivity analysis), and communicate uncertainty in order to propose recommendations to a broad public of LCA practitioners. This work was carried out via a literature review and an analysis of LCA tool functionalities. In order to facilitate the identification of uncertainty, its location within an LCA model was distinguished between quantity (any numerical data), model structure (relationships structure), and context (criteria chosen within the goal and scope of the study). The methods for uncertainty characterization, uncertainty analysis, and sensitivity analysis were classified according to the information provided, their implementation in LCA software, the time and effort required to apply them, and their reliability and validity. This review led to the definition of recommendations on three levels: basic (low efforts with LCA software), intermediate (significant efforts with LCA software), and advanced (significant efforts with non-LCA software). For the basic recommendations, minimum and maximum values (quantity uncertainty) and alternative scenarios (model structure/context uncertainty) are defined for critical elements in order to estimate the range of results. Result sensitivity is analyzed via one-at-a-time variations (with realistic ranges of quantities) and scenario analyses. Uncertainty should be discussed at least qualitatively in a dedicated paragraph. For the intermediate level, the characterization can be refined with probability distributions and an expert review for scenario definition. Uncertainty analysis can then be performed with the Monte Carlo method for the different scenarios. Quantitative information should appear in inventory tables and result figures. Finally, advanced practitioners can screen uncertainty sources more exhaustively, include correlations, estimate model error with validation data, and perform Latin hypercube sampling and global sensitivity analysis. Through this pedagogic review of the methods and practical recommendations, the authors aim to increase the knowledge of LCA practitioners related to uncertainty and facilitate the application of treatment techniques. To continue in this direction, further research questions should be investigated (e.g., on the implementation of fuzzy logic and model uncertainty characterization) and the developers of databases, LCIA methods, and software tools should invest efforts in better implementing and treating uncertainty in LCA.

According to the Intergovernmental Panel on Climate Change (IPCC), in 2010 the transport sector was responsible for 23% of the total energy-related CO2 emissions (6.7 GtCO2) worldwide. Policy makers in Luxembourg are well-aware of the challenges and are setting ambitious objectives at country level for the mid and long term. However, a framework to assess environmental impacts from a life cycle perspective on the scale of transport policy scenarios, rather than individual vehicles, is lacking. We present a novel framework linking activity-based modeling with life cycle assessment (LCA) and a proof-of-concept case study for the French cross-border commuters working in Luxembourg. Our framework allows for the evaluation of specific policies formulated on the trip level as well as aggregated evaluation of environmental impacts from a life cycle perspective. The results of our proof-of-concept-based case study suggest that only a combination of: (1) policy measures improving the speed and coverage of the public transport system, (2) policy measures fostering electric mobility, and (3) external factors such as de-carbonizing the electricity mix will allow to counteract the expected increase in impacts due to the increase of mobility needs of the growing commuting population in the long term.

Integrated Environmental Models (IEMs) incorporating ecosystem services (ES) can be useful tools to support the decision-making. However, their successful application strongly depends upon the confidence decision-makers can place in their outputs. Uncertainty analysis (UA), including uncertainty location, identification, characterization, treatment and communication to decision-makers, can increase their confidence in the used model. To support the use of UA in practice we first focus on the purpose, concepts and methods underpinning UA in environmental modelling. As a main contribution, we then review existing UA frameworks and their coverage, before reviewing the current practice of performing UA for a set of 25 IEM tools. Based on our review we derive and discuss four perceived challenges and gaps whose significance may vary in different contexts: (1) the lack of consensus on terminologies for uncertainty locations; (2) characterization of uncertainty locations beyond variables; (3) treatment of heterogeneous uncertainty; and (4) the lack of a practical framework toward uncertainty communication. We suggest some steps forward for each of these challenges aiming at facilitating the application of UA to IEMs.