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Record W2147181211 · doi:10.1504/ijesd.2002.000731

Designing emissions trading programs in Canada: the implications of market power on economic and environmental outcomes

2002· article· en· W2147181211 on OpenAlexaboutno aff
David A. Sawyer

Bibliographic record

VenueInternational Journal of Environment and Sustainable Development · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsEmissions tradingMarket powerKyoto ProtocolConstraint (computer-aided design)Environmental economicsIndustrial organizationBusinessMarginal costEconomicsMarket shareEnvironmental policyNatural resource economicsMicroeconomicsGreenhouse gasFinance

Abstract

fetched live from OpenAlex

Environmental policy often assumes that firms will participate in a competitive emissions trading market to cost-effectively achieve their emission reduction constraint. Indeed, the competitive market assumption is central to the notion that emission trading is the least-cost management option to achieve an environmental objective. This paper uses two optimisation models based on marginal abatement cost functions to investigate how market power distortions within an emission trading system can impact overall compliance costs and environmental performance. One model is based on the solvent degreasing sector in Canada and the other is for the Parties to the Kyoto Protocol. The analysis indicates that the presence of market power can result in a sub-optimal economic outcome while improving environmental performance. Given this result, policy makers should consider the implications of market power when designing emissions trading programmes. This is particularly the case if a few firms control a large share of the overall emission reduction target.

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 score0.031
Threshold uncertainty score0.613

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)0.0010.000

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 head0.044
GPT teacher head0.208
Teacher spread0.164 · 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

Citations1
Published2002
Admission routes1
Has abstractyes

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