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Record W2789070122 · doi:10.55016/ojs/sppp.v11i1.43288

Reducing Greenhouse Gas Emissions in Transport: All in One Basket?

2018· article· en· W2789070122 on OpenAlexaboutno aff
Nicholas Rivers, Randall Wigle

Bibliographic record

VenueThe School of Public Policy Publications · 2018
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Analysis after analysis has shown consistently that if policy-makers aiming to meet climate goals are looking for the most-efficient, least-distortionary way to target emissions growth, there is simply nothing better than abandoning all emissions regulations except for one: A straight, revenue-neutral carbon tax. Nothing works through more channels, at a lower cost. Alas, policy-makers are not always looking for the most-efficient, least-distortionary way to target emissions growth. That’s because many of those same analyses show that in order to reach emissions targets, the price on carbon would have to be so punitive as to be politically unbearable, raising the price of gasoline, for example, by about a dollar a litre. That leads politicians to mix in other policies that are less visible to the consumer but also less efficient, less effective and more expensive in abating carbon dioxide. The recently negotiated Pan-Canadian Framework on Clean Growth and Climate Change intends to follow that model, relying on a blend of different policies to help reach Canada’s Paris climate targets. But while the government seems therefore determined to rule out the possibility of a nothing-but-a-carbon-tax plan, it is possible, through the careful application of just the right sort of emission-reduction approaches, to reduce the costs of abatement in a key policy target — namely, road transportation — to a level that at least approaches the lower cost of a carbon tax. The government will likely consider several options in trying to reduce emissions from road transportation. Typical tools include requiring manufacturers to meet standards for new vehicles that mandate fuel economy and greenhouse gas emissions; gasoline taxes; taxes on emissions-intensive vehicles; subsidies for low-emission or zero-emission vehicles; and subsidies for public transit. Indications are that a low-carbon fuel standard (LCFS) will play a significant role in the Pan-Canadian Framework. Applied carefully, an LCFS combined with a mandate for automakers to sell more electric vehicles would be an appropriate policy for Canada to achieve meaningful emissions reductions at a tolerable cost, given other policy measures already committed to. Subsidies for electric vehicles, however, should be avoided as they turn out to be one of the least cost-effective policies to reduce emissions. Requiring car makers to sell more electric vehicles will lead to higher prices for standard internal-combustion vehicles as automakers are forced to spread the cost of the electric-vehicle mandate across their non-electric models. That in turn will lead to cheaper electric cars and pricier non-electric cars, making it likelier that consumers will gravitate in increasing numbers to electric cars, helping reduce emissions. Meanwhile, as the LFCS standard is raised, drivers of internal-combustion vehicles will face an even higher cost of filling up, again prompting more drivers to consider switching to electric vehicles. The combined effect — achievable at close to the cost of a carbon tax — will make it more expensive to drive a gasoline-powered car, similar to the effect of a carbon tax on drivers, but less visible and so less politically risky.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.293
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2018
Admission routes1
Has abstractyes

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