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Record W2259341646 · doi:10.11575/sppp.v8i0.42549

The Case for a Carbon Tax in Alberta

2017· article· en· W2259341646 on OpenAlexaffabout
Sarah Dobson, Jennifer Winter

Bibliographic record

VenueUniversity of Calgary · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGreenhouse gasCarbon taxNatural resource economicsJurisdictionFossil fuelPurchasingCarbon priceTonneCarbon fibersEconomicsAgricultural economicsBusinessEnvironmental scienceWaste managementOperations managementEngineeringPolitical science

Abstract

fetched live from OpenAlex

In 2007, Alberta demonstrated that it could be a leader in the effort to reduce greenhouse gas emissions by becoming the first North American jurisdiction to put a price on carbon. Given that the province had long been criticized for its central role in the carbon-based economy, Alberta’s move was important for its symbolism. Unfortunately, the emissions policy itself has delivered more in symbolism than it has in actually achieving meaningful reductions in greenhouse gas emissions. The Specified Gas Emitters Regulation (SGER), as the carbon-pricing system is formally called, has only helped Alberta achieve a three per cent reduction in total emissions, relative to what they would have been without the SGER. And emissions keep growing steadily, up by nearly 11 per cent between 2007 and 2014, with the SGER only slowing that growth by a marginal one percentage point. Alberta’s carbon-pricing policy simply fails to combat emissions growth; the province needs a new one. Lack of progress in reducing emissions appears to be partly attributable to the fact that many large emitters find it more economical to allow their emissions to rise beyond the provincially mandated threshold, and instead are purchasing amnesty at a lower cost through carbon offsets or by paying the levies that the SGER imposes on excess emissions. But it is also partly attributable to the fact that the SGER only applies to large emitters who annually produce 100,000 tonnes of CO2-equivalent all at one site: mainly oil sands operations and facilities that generate heat and electricity. This excludes operations that emit well over that threshold, but across diffuse locations. The transportation sector, which is typically spread out in just such a way, is the third-largest sector for emissions in Alberta. Its emissions are also growing faster than those of the mining and oil and gas sector, even as emissions in the electricity and heat generation sector are actually declining. And if we combine the emissions from the transportation sector with those of the manufacturing and industrial sector, which can also be characterized by scattered operations, they substantially exceed those of the electricity and heat generation sector. Indeed, over 58 per cent of Alberta emissions come from places other than oil and gas and mining. There will surely be those who prefer strengthening SGER to a carbon tax; this is not likely to make enough of a difference for Alberta to meet its carbon-reduction goal of 218 Mt by 2020. The government would make far more progress by implementing a broad carbon tax, similar to the one in British Columbia, which applies to all emitters and consumers. The cost to the economy would not be steep: For a $20 per tonne tax, the cost would be 0.9 per cent of gross output (or 1.7 per cent at $40 a tonne). And the cost to households would be less than $700 a year. As in B.C., the proceeds would be better recycled in the form of reduced corporate income taxes, personal taxes, and subsidies to lowincome households, to offset the extra burden and distortions a carbon tax would create. But unlike the current SGER, a carbon tax would succeed in being more than a symbolic, largely futile gesture.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

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

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 head0.005
GPT teacher head0.176
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.

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

Citations0
Published2017
Admission routes2
Has abstractyes

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