Will alternative fuelled vehicles contribute to solving the sustainability problem? A framework for assessing future vehicle demand [Abstract]
Bibliographic record
Abstract
It is widely accepted that the transportation system is a major contributor of greenhouse gas emissions, leading to an unsustainable future. While the problem is more intense in the developed countries, in which the transportation sector is responsible for approximately twenty five to forty percent of the overall greenhouse emissions, the developing world is following suit. Recent trends indicate an increasing demand for automobiles, in these countries, with potentially dire environmental consequences for the planet. Meeting the growing demand for personal mobility and transport of goods in a sustainable way represents a wide range of interrelated technological and public policy challenges. A number of strategies for fulfilling sustainability targets impose improvements in vehicle technology and fuels. Alternative fuelled vehicles offer hope for drastic reductions in air pollution and greenhouse gas emissions. One the most important challenges regarding alternative fuelled vehicles is devising strategies that will lead to the success of these vehicles in the market. In this respect, an important dimension is to understand household preferences regarding certain vehicle characteristics, fuel availability and policies affecting a vehicle’s end price. Since alternative fuelled vehicles are not widely available, researchers resort to innovative approaches for assessing their demand. One such method is known as stated choices. The method uses experimental design to create sets of hypothetical vehicle options using a number of vehicle attributes. This paper describes a framework for assessing the demand for alternative fuelled vehicles and its application to the Census Metropolitan Area of Hamilton, Canada. Data collection is conducted using contemporary internet-based surveying techniques and respondents are recruited via e-mail. The overall procedure involves two stages. First, respondents are asked to provide information about their household, residence and vehicles currently owned. A second stage in the survey employs the stated choices experiment where respondents choose the vehicle they would most likely buy from a hypothetical set of vehicles that use gasoline, a mix of fuels (hybrid) or an alternative fuel. Results from this study will contribute to scenario building with the purpose of examining the implications of policies, regarding the potential contribution of alternative fuelled vehicles to emission reduction objectives and the sustainability of the transportation system.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".