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Record W4416630575 · doi:10.1257/aeri.20240476

Electric Vehicles and the Energy Transition: Unintended Consequences of Time-of-Use Pricing

2025· article· en· W4416630575 on OpenAlex

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

VenueAmerican Economic Review Insights · 2025
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsUnintended consequencesElectricity pricingElectricityElectric vehicleDemand responseElectric energyShadow priceEnergy (signal processing)Distribution (mathematics)

Abstract

fetched live from OpenAlex

The growth of electric vehicles (EVs) raises new challenges for electricity systems. We implement a field experiment to assess the effect of time-of-use (TOU) pricing and managed charging on EV charging behavior. We find that while TOU pricing is effective at shifting EV charging into off-peak hours, it unintentionally induces new and larger “shadow peaks” of simultaneous charging. These shadow peaks lead to greater exceedance of local capacity constraints and advance the need for distribution network upgrades. In contrast, centrally managed charging solves the coordination problem, reducing transformer capacity requirements, and is well tolerated by consumers in our setting. (JEL C93, D91, L62, L94, Q42)

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.004
GPT teacher head0.196
Teacher spread0.192 · 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