Development of an electricity system model allowing dynamic and marginal approaches in LCA—tested in the French context of space heating in buildings

Roux, Charlotte; Schalbart, Patrick; Peuportier, Bruno

doi:10.1007/s11367-016-1229-z

Cited by 55 publications

(23 citation statements)

References 48 publications

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“…The problem of identifying the correct emission factor is common to many studies, that do not necessarily refer to EVs as product system. Some studies analyse it from an energetic point of view [80,83,85], while others are interested in the topic because the energy use is a relevant part in their product system-such as, the building sector [59], chemicals produced via chlor-alkali electrolysis [60], and aluminium production [61]. Another famous review is the one by Ryan et al [85].…”

Section: Short Term Marginal Mix-calculation Methodsmentioning

confidence: 99%

“…Roux et al [59] reported that GHG of French mix varied from one year to another, due to economic and meteorological hazards, thus suggesting the need to define a reference year to mitigate these variations and to convey more representative average impacts. They also highlight that using EcoInvent v3.1 data for low voltage supply mix in order to evaluate the impact of the average mix for year 2014 leads to an overestimation of the carbon footprint by 25%.…”

Section: Temporal Boundarymentioning

confidence: 99%

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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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Section: Short Term Marginal Mix-calculation Methodsmentioning

confidence: 99%

Section: Temporal Boundarymentioning

confidence: 99%

Electricity Generation in LCA of Electric Vehicles: A Review

et al. 2018

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“…A second approach relies on the use of energy system models or energy simulation tools (Dandres et al 2017;Frischknecht 2016;Roux et al 2017). In this approach, a base case scenario describing the future electricity production of one or several electricity markets is first defined using the energy modeling tools.…”

Section: Introductionmentioning

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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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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“…One (4%) used qualitative analysis, four (15%) used scenario analysis, five (19%) sensitivity analysis and one (4%) did not report the method of uncertainty analysis. Roux et al (2017) used MC analysis to propagate uncertainty based on the nine most sensitive parameters related to the marginal electricity mix. However, the majority of studies did not perform uncertainty analysis nor attempt to account for the uncertainty associated with market scenarios.…”

Section: Larreamentioning

confidence: 99%

Comparing sources and analysis of uncertainty in consequential and attributional life cycle assessment: review of current practice and recommendations

Bamber

Turner

Arulnathan

et al. 2019

Int J Life Cycle Assess

142

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Purpose Life cycle assessment (LCA) is intended as a quantitative decision support tool. However, the large amount of uncertainty characteristic of LCA studies reduces confidence in results. To date, little research has been reported regarding the comparative sources of uncertainty (and their relative importance) and how, or how commonly, they are quantified in attributional and consequential LCA. This paper answers these questions based on a review of recent LCA studies and methods papers, and advances recommendations for improved practice. Methods All relevant LCA methods papers as well as case studies (amounting to 2687 journal articles) published from 2014 to 2018 in the top seven journals publishing LCA studies were reviewed. Common sources and methods for analysis of uncertainty in both attributional and consequential LCA were described, and their frequency of application evaluated. Observed practices were compared to best practice recommendations from methods papers, and additional recommendations were advanced. Results and discussion Less than 20% of LCA studies published in the past five years reported any kind of uncertainty analysis. There are many different sources of uncertainty in LCA, which can be classified as parameter, scenario or model uncertainty. Parameter uncertainty is most often reported, although the other types are considered equally important. There are also sources of uncertainty specific to each kind of LCA-in particular related to the resolution of multi-functionality problems (i.e. allocation in attributional LCA versus the definition of market-mediated substitution scenarios in consequential LCA). However, there are currently no widely applied methods to specifically account for these sources of uncertainty other than sensitivity analysis. Monte Carlo sampling was the most popular method used for propagating uncertainty results, regardless of LCA type. Conclusions Data quality scores and inherent (i.e. stochastic) uncertainty data are widely available in LCI databases, and researchers should generally be able to define comparable uncertainty information for their primary data. Moreover, uncertainty propagation for parameter uncertainty is supported by LCA modelling software. There are hence no obvious barriers to quantifying parameter uncertainty in LCA studies. More standardized methods based upon context-specific data that strike the right balance between comprehensiveness and usability are, however, necessary in order to better account for both the shared and unique sources of uncertainty in attributional and consequential LCAs. More frequent and comprehensive reporting of uncertainty analysis is strongly recommended for published LCA studies. Improved practices should be encouraged and supported by peer-reviewers, editors, LCI databases and LCA software developers.

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Development of an electricity system model allowing dynamic and marginal approaches in LCA—tested in the French context of space heating in buildings

Cited by 55 publications

References 48 publications

Electricity Generation in LCA of Electric Vehicles: A Review

Electricity Generation in LCA of Electric Vehicles: A Review

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

Comparing sources and analysis of uncertainty in consequential and attributional life cycle assessment: review of current practice and recommendations

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