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Record W3199907927 · doi:10.1016/j.jclepro.2021.129052

Regionalized climate footprints of battery electric vehicles in Europe

2021· article· en· W3199907927 on OpenAlex
Christine Roxanne Hung, Steve Völler, Maxime Agez, Guillaume Majeau‐Bettez, Anders Hammer Strømman

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Cleaner Production · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsPolytechnique Montréal
FundersNorges Forskningsråd
KeywordsElectricityBattery (electricity)Environmental scienceProduction (economics)Internal combustion engineCarbon footprintEnvironmental economicsFossil fuelBattery electric vehicleClimate changeLife-cycle assessmentElectricity generationElectric vehicleGreenhouse gasAutomotive engineeringNatural resource economicsEngineeringWaste managementEconomicsPower (physics)

Abstract

fetched live from OpenAlex

The climate mitigation benefits of battery electric vehicles (BEVs) relative to internal combustion engine vehicles (ICEVs) are highly dependent on the carbon intensity of the electricity consumed during their production and use-phase. A consistent and dynamic approach to grid-mix regionalization of BEV life-cycle assessments in Europe is therefore necessary to offer accurate guidance to consumers and policy makers. To this end, we present ReDyFEV, a simple open-source software tool that can be used to calculate attributional, regionalized lifecycle climate impacts of BEVs in Europe for user-defined time periods, including near real-time. We determine the national lifecycle carbon footprints across all EU states for four BEV size segments and compare them to those of fossil-fuelled vehicles of similar sizes. Simplified sensitivity analyses investigate the effect of lifetime assumptions, electricity demand in battery production, and of relocating battery production to Europe on the carbon footprints of BEVs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.207
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it