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Record W4410201905 · doi:10.1016/j.erss.2025.104119

Passing the check: How overlapping climate policies shift the distribution of costs in California

2025· article· en· W4410201905 on OpenAlexafffund
William A. Scott

Bibliographic record

VenueEnergy Research & Social Science · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of CanadaStanford School of Earth, Energy and Environmental SciencesStanford University
KeywordsDistribution (mathematics)Environmental scienceEconomicsMathematics

Abstract

fetched live from OpenAlex

Energy and transportation services represent a significant portion of household budgets, particularly for low-income households. Climate policies that increase the cost of energy and transportation services can lead to disparate impacts across income and demographic groups. When multiple overlapping climate policies are in place, they may interact to compound or shift the distribution of costs. This study employs household expenditure microdata from the Consumer Expenditure Survey to examine the distributional costs of two overlapping policies in California: the cap-and-trade program (CAT) and the low carbon fuel standard (LCFS). Findings indicate that the LCFS is regressive and that the CAT, while highly regressive at initial incidence, is similar in net incidence to the LCFS after returning some revenue to households through utility rebates. Yet the CAT program leads to greater differences in impacts on households within income groups, even exceeding differences between groups. Interactions between the two policies lead to higher costs for households across income deciles compared to achieving the same level of emissions reduction with the CAT alone. This suggests there is no equity-efficiency tradeoff in choosing between these policy instruments.

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.

How this classification was reachedexpand

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.114
GPT teacher head0.354
Teacher spread0.241 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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