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Record W7047405651

Fuel Prices vs. Automobile Fuel Economy Standards in a CO2-Constrained Transport Sector

2012· article· en· W7047405651 on OpenAlexaboutno aff

Bibliographic record

VenueKtisis at Cyprus University of Technology (Cyprus University of Technology) · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsGasolineOrder (exchange)Work (physics)Automotive industryFuel efficiencyVariable (mathematics)Eu countries
DOInot available

Abstract

fetched live from OpenAlex

One way to raise the fuel efficiency and reduce CO2 emissions of new cars is through fuel economy (FE) standards; more than 20 countries worldwide currently implement such standards. A second way is to increase fuel taxation in order to induce purchases of more efficient cars and discourage private car travel. Although the adoption of standards has induced FE improvements, there are arguments against standards and in favor of fuel tax increases.
\nThe aim of this paper is to analyze the impact of FE standards and fuel prices in new car fuel economy with the aid of cross-section time series analysis of data from 18 countries. Similar work was previously conducted for the U.S. only, and mostly with data up to 1990.
\nWe estimated a log-linear equation with new-car FE as the dependent variable and the following explanatory variables: FE standard, real gasoline price (with lags of 0 to –3), and a time trend to capture autonomous technical progress and changing consumer preferences. Data were obtained from official sources such as the U.S. EPA, the IEA and the European Commission, covering the U.S. (cars and trucks), Canada (cars and trucks), Australia, Japan, Switzerland and 13 EU countries, thus building an unbalanced panel of 279 observations. For Japan and some EU countries, we employed Chow tests to test for the existence of a structural break between two periods: one for the years up to 1995 (approximately the time of adoption of the first FE target values in both Japan and the EU), and one for the post-1995 ‘with standards’ period. For all those countries, the hypothesis of no break was clearly rejected. Therefore, we ran separate regressions for the ‘pre-standard’ and the ‘with standards’ sample using the above mentioned variables through pooled least squares with country fixed effects.
\nIn both samples, only one price variable was found to be statistically significant, that of lag 1. Estimated coefficients (i.e. ‘elasticities’) for the ‘with standards’ panel were approximately 0.7 for FE standards, -0.1 for price and -0.002 for the time trend and were all significant.
\nUsing the ‘pre-standard’ sample of 41 observations with lagged gasoline price and time trend as regressors, we estimated statistically significant coefficients of –0.3 and –0.007 respectively.
\nThen we selected those countries for which both pre- and post-standard observations were available. Running the same regression for these countries and the whole period (pre- and post-standard), the price and time trend coefficients were almost the same as previously (–0.3 and –0.008 respectively). In all estimations, heteroskedasticity and serial correlation consistent standard errors were calculated.
\nThe results have significant policy implications: Firstly, they help to assess how much fuel prices should be raised in order to achieve future FE targets without resorting to higher FE standards. Secondly, they provide an indication about how FE might evolve without stricter standards. This is a very relevant issue as several European long-term energy/transport models assume that automobile FE will continue to improve at fast rates even without post-2010 FE regulations. Results show that without stricter FE standards and at fuel prices not higher than $50(in 2004 prices) per barrel, one could expect only minor FE improvements between 2010 and 2020. Still, the cross-section time series analysis shown here cannot help to draw conclusions on the cost-effectiveness and the welfare impact of alternative policy paths.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.174
Teacher spread0.171 · 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 designObservational
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

Citations0
Published2012
Admission routes1
Has abstractyes

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