MétaCan
Menu
Back to cohort
Record W2996972401 · doi:10.3138/cpp.2017-074

Carbon Pricing and Competitiveness Pressures: The Case of Cement Trade

2020· article· en· W2996972401 on OpenAlexvenueaboutno aff
Vincent Thivierge

Bibliographic record

VenueCanadian Public Policy · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasCementEconomicsConsumption (sociology)Production (economics)Emissions tradingBalance of tradeCarbon taxInternational tradeInternational economicsNatural resource economicsAgricultural economicsMacroeconomics

Abstract

fetched live from OpenAlex

The impact of unilateral carbon pricing on domestic industry is a central element in current policy debates dealing with mitigation of greenhouse gas emissions. This is especially the case for industries that are both emission intensive and trade exposed. A poster child for these vulnerable industries is the cement industry. In this article, I examine the impact of British Columbia’s carbon tax on cement trade. I use quarterly data on volume of imports, exports, and net exports of cement between cement-producing Canadian provinces and the United States, as well as the world. Through a series of econometric models, findings suggest that net exports were reduced as a result of the policy. As a share of average domestic cement production, estimates point to reduced net exports between 13 and 18 percent. Further empirical investigation suggests that reductions in domestic production due to the policy stems from this trade effect, not reductions in domestic consumption. I therefore discuss policy options to address these competitiveness pressures in the cement industry.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.302

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0030.006
Scholarly communication0.0070.004
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.101
GPT teacher head0.238
Teacher spread0.137 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Public PolicySame topicClimate Change Policy and EconomicsFrench-language works237,207