The Global Energy, CO2 Emissions, and Economic Impact of Vehicle Fuel Economy Standards
Bibliographic record
Abstract
Fuel economy standards have been recently tightened in the United States and Europe, and have been adopted in Japan, Korea, China, Australia, and Canada. This analysis uses a global computable general equilibrium model to analyze the combined effect of existing national or regional fuel economy standards on global demand for petroleum-based fuels, CO2 emissions, and welfare. We also examine the impact of more aggressive targets for fuel economy through 2050 for all regions, and compare it to a market-based (cap-and-trade) instrument that achieves identical reductions. We find that while fuel economy standards reduce demand for petroleum-based refined fuels and lead to a net decrease in global CO2 emissions, the standards are not cost effective in part because they indirectly subsidize the use of these fuels in unconstrained sectors and regions. Refined oil demand even increases in India, Indonesia (Rest of East Asia), and Africa due to lower refined oil prices, as fuel economy standards reduce demand in other parts of the world. Fuel economy standards are also a relatively expensive way of reducing global CO2 emissions—year-on-year consumption loss reaches 10% in 2050 with fuel economy standards, as opposed to 6% by 2050 with a global cap-and-trade system that achieves comparable total CO2 reductions. This study underscores how the effects of national and regional fuel economy standards can propagate through global fuels markets to offset petroleum or CO2 reductions at the global level, as well as lead to surprising outcomes in unconstrained countries or regions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".