LCA of mobility solutions: approaches and findings—66th LCA forum, Swiss Federal Institute of Technology, Zurich, 30 August, 2017

Frischknecht, Rolf; Bauer, Christian; Froemelt, Andreas; Hellweg, Stefanie; Biemann, K.; Buetler, Thomas; Cox, B.; Haan, Peter; Hoerl, Sebastian; Itten, René; Jungbluth, Niels; Ligen, Yorick; Mathys, Nicole A.; Schiess, Samuel; Schori, Salome; Loon, Patricia van; Wang, Jing; Wettstein, Sarah

doi:10.1007/s11367-017-1429-1

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…For example, the physical‐ and component‐based approach of the chosen building energy model will allow for computing detailed refurbishment scenarios, such as the effects of physical measures applied to specific buildings, specific components (e.g., roofs, walls, or windows), specific geographical regions, or specific homeowners (e.g., target groups which were identified due to their consumption behavior and living conditions). Likewise, the link to MATSim enables for the consideration of future mobility scenarios such as electric car penetration or autonomous vehicle systems (Frischknecht et al., ). In the context of assessing scenarios, we will also attempt to capture rebound effects of different policy applications.…”

Section: Discussionmentioning

confidence: 99%

Machine learning based modeling of households: A regionalized bottom‐up approach to investigate consumption‐induced environmental impacts

Froemelt

Buffat

Hellweg

2019

J of Industrial Ecology

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As major drivers of economy, households induce a large share of worldwide environmental impacts. The variability of local consumption patterns and associated environmental impacts needs to be quantified as an important starting point to devise targeted measures aimed at reducing household environmental footprints. The goal of this article is the development and appraisal of a comprehensive regionalized bottom-up model that assesses realistic environmental profiles for individual households in a specific region. For this purpose, a physically based building energy model, the results of an agent-based transport simulation, and a data-driven household consumption model were interlinked within a new probability-based classification framework and applied to the case of Switzerland. The resulting model predicts the demands in about 400 different consumption areas for each Swiss household by considering its particular circumstances and produces a realistic picture of variability in household environmental footprints. An analysis of the model results on a municipal level reveals per-capita income, population density, buildings' age, and household structure as possible drivers of municipal carbon footprints. Whilehigher-emission municipalities are located in rural areas and tend to show higher shares of older buildings, lower-emission communities have larger proportions of families and can be found in highly populated regions by trend. However, the opposing effects of various variables observed in this analysis confirm the importance of a model that is able to capture regional distinctions.The overall model constitutes a comprehensive information base supporting policymakers in understanding consumption patterns in their region and deriving environmental strategies tailored to their specific population.

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Section: Discussionmentioning

confidence: 99%

Machine learning based modeling of households: A regionalized bottom‐up approach to investigate consumption‐induced environmental impacts

Froemelt

Buffat

Hellweg

2019

J of Industrial Ecology

Self Cite

View full text Add to dashboard Cite

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“…A study comparing different LCAs of electric vehicles shows a high variety of results. The research demands a uniform approach [34]. Particularly regarding the production and recycling of the traction batteries, the charging infrastructure and the environmental impact of the different charging strategies, the results are often inconsistent due to a lack of data [34].…”

Section: Introductionmentioning

confidence: 99%

“…The research demands a uniform approach [34]. Particularly regarding the production and recycling of the traction batteries, the charging infrastructure and the environmental impact of the different charging strategies, the results are often inconsistent due to a lack of data [34]. In most cases, primary data regarding the whole life cycle of an electric vehicle are not available [35].…”

Section: Introductionmentioning

confidence: 99%

Life Cycle Analysis of an On-the-Road Modular Vehicle Concept

Ulrich,

Feinauer,

Bieber

et al. 2023

Sustainability

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In order to reduce the environmental impacts caused by the transport sector, autonomous and electrified on-the-road modular vehicles (otrm) could be a solution. By separating the drive unit from the transport unit, they enable use cases for various transport tasks and reduce individual and motorized transport and its generated emissions. Therefore, the goal of this study is to assess the environmental impacts from cradle to grave by applying the LCA methodology for a defined otrm—the U-Shift—vehicle fleet considering a specific use case relative to a reference vehicle fleet. The results indicate that the U-Shift fleet reduces the life cycle environmental impacts in a range of 3–28% for all of the seven impact categories, which are analyzed in detail. While emissions from the use phase are similar, U-Shift has an environmental benefit in the production phase due to a low amount of resource-intensive driveboards. Considering the early development stage of U-Shift, several measures are discussed, addressing the material and configuration aspects of the vehicles as well as optimized use case applications, which promise further impact-reduction potential.

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A life cycle assessment method to support cities in their climate change mitigation strategies

Dorr

François²,

Poulhès³

et al. 2022

Sustainable Cities and Society

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LCA of mobility solutions: approaches and findings—66th LCA forum, Swiss Federal Institute of Technology, Zurich, 30 August, 2017

Cited by 3 publications

References 6 publications

Machine learning based modeling of households: A regionalized bottom‐up approach to investigate consumption‐induced environmental impacts

Machine learning based modeling of households: A regionalized bottom‐up approach to investigate consumption‐induced environmental impacts

Life Cycle Analysis of an On-the-Road Modular Vehicle Concept

A life cycle assessment method to support cities in their climate change mitigation strategies

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