Technical Barriers to Trade: A Canadian Perspective on Ecolabelling
Bibliographic record
Abstract
Ecolabelling is a market-based instrument and an important element of international environmental policies. In our day and age, there is a wide range of ecolabels, which may complicate the decision-making process when looking for the best outcome for consumers and producers. The International Organization for Standardization (ISO) and Global Ecolabelling Network (GEN) suggest a solution to align the various ecolabelling programs. For instance, ISO launched the ISO 14,001 framework, which includes the requirements for Environmental Management Systems (EMSs). The GEN harmonizes international ecolabelling schemes and improves exchanges of information among its country members. This article addresses how unaligned and aligned regulations impact international trade. Consequently, a database including the ISO 14,001 certifications of all countries and containing the exports from 153 countries to Canada from 2001 to 2015 as a dependent variable was created. The remaining variables will serve as independent variables, including gravity variables such as market size, market similarity, distance, and some other core variables such as GEN membership of the exporting country, WTO membership, binding in Free Trade Agreements (FTA) and Mutual Recognition Agreements (MRA) with Canada. Findings show that holding ISO 14,001 certifications has a positive impact on exports to Canada; however, these impacts are not significant enough. Therefore, there is not strong evidence that ISO 14,001 creates barriers to export to Canada. In addition, GEN membership significantly promotes exports to Canada, especially for countries binding in an FTA or MRA with Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".