Fuel Prices vs. Automobile Fuel Economy Standards in a CO2-Constrained Transport Sector
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".