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Record W2898028542 · doi:10.1139/facets-2017-0109

Investigating the impacts of plausible Canadian policies and their supporting mechanisms on export-based regional air pollution in China: A cement manufacturing case study

2018· article· en· W2898028542 on OpenAlexaffvenueabout
Darren Brown, Rehan Sadiq, Kasun Hewage

Bibliographic record

VenueFACETS · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsChinaBusinessAir quality indexPollutionAir pollutionNatural resource economicsPolitical scienceEconomicsGeography

Abstract

fetched live from OpenAlex

The Canadian Environmental Protection Act (CEPA) enables the Minister of Environment and Climate Change to develop policy to curtail international air pollution. However, regional air pollution generated during the manufacturing of products outside of Canada is not addressed in CEPA. Using cement manufacturing in China as a case study, three policy options were devised to manage export-based regional air pollution. The options investigated included Policy 1—an open border with direct support for domestic cement manufacturers, Policy 2—a restricted border with no support for domestic cement manufacturers, and Policy 3—a selective border with partial support for domestic cement manufacturers. An analytic hierarchy process, in conjunction with the three actionable solidarities of cultural theory, was applied to the policy options and their supporting mechanisms. Results indicated that Policy 3 was strongly favoured (52.5%), followed by Policy 2 (33.4%), with Policy 1 being the least favoured (14.2%). Regarding policy mechanisms, a verification process was preferred by all three solidarities. From the standpoint of a universal approach to trade it is recommended that an air quality agreement between China and Canada under CEPA be established with a framework to eventually incorporate environmental production declarations. With respect to cement exports, it is recommended that manufacturers in China provide emissions intensities and winter smog assessments.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.318

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0030.003
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.262
Teacher spread0.244 · 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 designSimulation or modeling
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
Published2018
Admission routes3
Has abstractyes

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