LCA and decision making: when and how to use consequential LCA; 62nd LCA forum, Swiss Federal Institute of Technology, Zürich, 9 September 2016

Frischknecht, Rolf; Benetto, Enrico; Dandres, Thomas; Heijungs, Reinout; Roux, Charlotte; Schrijvers, Dieuwertje; Wernet, Gregor; Yang, Yi; Messmer, Annika; Tschuemperlin, Laura

doi:10.1007/s11367-016-1248-9

Cited by 30 publications

(22 citation statements)

References 14 publications

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“…These approaches all come with a number of simplifications given the difficulty of the task at hand (Majeau-Bettez et al 2018;. For example, economic models, which are often associated with CLCA and are behind most of the energy scenarios used in this work, face different shortcomings, including insufficient representation of future technologies, lack of transparency, difficulty to evaluate uncertainty, reliance on perfect competition assumption and the inability to capture certain non-economic elements (Marvuglia et al 2013;Zamagni 2013;Menten et al 2015;Wikipedia 2016;Frischknecht et al 2017). The consequential system model of the ecoinvent database was built to reflect long-term consequences of small-scale decisions.…”

Section: Discussionmentioning

confidence: 99%

“…Substitution, which is used systematically to handle multifunctionality in CLCA, relies on one-to-one ratios that are revealed to be inaccurate (Chalmers et al 2015;Vadenbo et al 2017). This issue stems from the linearity assumption generally applied in the field of LCA and could be lifted with the introduction of elasticities to characterize the substitution and supply links between products (Frischknecht et al 2017;Yang and Heijungs 2018b). Nevertheless, it remains unclear how to implement this option at the scale of an LCI database.…”

Section: Discussionmentioning

confidence: 99%

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The integration of long-term marginal electricity supply mixes in the ecoinvent consequential database version 3.4 and examination of modeling choices

Vandepaer

Treyer

Mutel

et al. 2018

Int J Life Cycle Assess

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Purpose The long-term marginal electricity supply mixes of 40 countries were generated and integrated into version 3.4 of the ecoinvent consequential database. The total electricity production originating from these countries accounts for 77% of the current global electricity generation. The goal of this article is to provide an overview of the methodology used to calculate the marginal mixes and to evaluate the influence of key parameters and methodological choices on the results. Methods The marginal mixes are based on public energy projections from national and international authorities and reflect the accumulated effect of changes in demand for electricity on the installation and operation of new-generation capacities. These newly generated marginal mixes are first examined in terms of their compositions and environmental impacts. They are then compared to several sets of alternative electricity supply mixes calculated using different methodological choices or data sources. Results and discussion Renewable energy sources (RES) as well as natural gas power plants show the highest growth rates and usually dominate the marginal mixes. Nevertheless, important variations may exist between the marginal mixes of the different countries in terms of their technological compositions and environmental impacts. The examination of the modeling choices reveals substantial variations between the marginal mixes integrated into the ecoinvent consequential database version 3.4 and marginal mixes generated using alternative modeling options. These different modeling possibilities include changes in the methodology, temporal parameters, and the underlying energy scenarios. Furthermore, in most of the impact categories, average (i.e., attributional) mixes cause higher impact scores than marginal mixes due to higher shares of RES in marginal mixes. Conclusions Accurate and consistent data for electricity supply is integrated into a consequential database providing a strong basis for the development of consequential Life Cycle Assessments. The methodology adopted in this version of the database eliminates several shortcomings from the previous approach which led to unrealistic marginal mixes in several countries. The use of energy scenarios allows the evolution of the electricity system to be considered within the definition of the marginal mixes. The modeling choices behind the electricity marginal mix should be adjusted to the goal and scope of individual studies and their influence on the results evaluated. Keywords CLCA. Ecoinvent. Life cycle inventory (LCI) database. Long-term marginal electricity supply mix 1 Using the IPCC Global Warming Potentials (GWP) characterization factors with a time horizon of 100 years and the calculation approach detailed in Mutel (2018).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The integration of long-term marginal electricity supply mixes in the ecoinvent consequential database version 3.4 and examination of modeling choices

Vandepaer

Treyer

Mutel

et al. 2018

Int J Life Cycle Assess

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“…Consequential LCA should be more than the use of marginal mixes, as pointed out in the 62 • LCA conference [68]. However, in the LCA of EV, it emerges that the difference between ALCA and CLCA is often identified with the difference between average and marginal electricity mixes, and the terms are sometimes used as synonyms.…”

Section: Clca In Electric Mobilitymentioning

confidence: 99%

Electricity Generation in LCA of Electric Vehicles: A Review

et al. 2018

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: Life Cycle assessments (LCAs) on electric mobility are providing a plethora of diverging results. 44 articles, published from 2008 to 2018 have been investigated in this review, in order to find the extent and the reason behind this deviation. The first hurdle can be found in the goal definition, followed by the modelling choice, as both are generally incomplete and inconsistent. These gaps influence the choices made in the Life Cycle Inventory (LCI) stage, particularly in regards to the selection of the electricity mix. A statistical regression is made with results available in the literature. It emerges that, despite the wide-ranging scopes and the numerous variables present in the assessments, the electricity mix’s carbon intensity can explain 70% of the variability of the results. This encourages a shared framework to drive practitioners in the execution of the assessment and policy makers in the interpretation of the results.

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“…A survey by Zanghelini et al [48] found that multicriteria decision analysis is typically used in three different stages in an LCA: life cycle impact assessment (e.g., weighting), life cycle inventory, and goal and scope definition. Decisions can also be made for different systems of interest, for example, the neighbourhood level [49], building level [39,40], component level [50], and material level [51,52]. A general challenge is to balance precision with timeliness, or to know "when 'good enough' is best" as formulated by Bala et al [53].…”

Section: Procedural Methodsmentioning

confidence: 99%

From Zero Emission Buildings (ZEB) to Zero Emission Neighbourhoods (ZEN): A Mapping Review of Algorithm-Based LCA

2018

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Abstract:The building industry is responsible for approximately 40% of energy consumption and 36% of greenhouse gas emissions in the European Union (EU). The most efficient way of reducing a building's environmental impact is addressing it in the design stage. Here, design freedom is the greatest, but uncertainty is high and there is a nearly limitless number of design options. Based on experiences with zero emission buildings (ZEB) and zero emission neighbourhoods (ZEN), a mapping review has been conducted to analyse how parametric life cycle assessment (LCA) and algorithms have been used to address neighbourhoods, buildings, and construction materials. Results have identified a general gap of knowledge regarding the use of parametric LCA models for decision-support purposes, demonstrated by the substantial focus on analytical methods compared to procedural methods. Implications for the evolution from ZEB to ZEN are twofold: (i) an integrated approach with multiple tools and methods is required, and (ii) further development of algorithms in the tool are needed to address complexity, sensitivity, and uncertainty. This study is expected to foster the development of algorithmic approaches to improve the ZEB tool as a decision-support tool. Further research should address the key questions of when and how.

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LCA and decision making: when and how to use consequential LCA; 62nd LCA forum, Swiss Federal Institute of Technology, Zürich, 9 September 2016

Cited by 30 publications

References 14 publications

The integration of long-term marginal electricity supply mixes in the ecoinvent consequential database version 3.4 and examination of modeling choices

The integration of long-term marginal electricity supply mixes in the ecoinvent consequential database version 3.4 and examination of modeling choices

Electricity Generation in LCA of Electric Vehicles: A Review

From Zero Emission Buildings (ZEB) to Zero Emission Neighbourhoods (ZEN): A Mapping Review of Algorithm-Based LCA

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