Six Principles for Integrating Non-Governmental Environmental Standards into Smart Regulation
Bibliographic record
Abstract
Ontario recently introduced environmental penalties (EPs), the environmental equivalent of speeding tickets. EPs are widely understood as part of a move toward "smarter" environmental regulation. As part of the EPs regime, facilities with an environmental management system aligned with ISO 14001 or Responsible Care qualify for reduced penalties. The Ontario government's attempt to incorporate voluntary standards-such as ISO 14001-into its EPs regulations was not very smart, however, because it failed to observe six principles that, in our view, should guide the incorporation of standards into smart regulation. First, do not reinvent the wheel. If an existing standard fulfills the objectives of a proposed regulation, and was developed by a recognized standards body through a multi-stakeholder consensus process, it would be "smart" to incorporate the standard into the regulatory scheme as far as possible and appropriate, rather than drafting a new standard from scratch. Second, avoid unexplained discrepancies between the regulation and the standard. Third, if an existing, widely accepted standard does not, on its own, meet all of the public policy goals of the proposed regulation, indicate clearly how the standard is deficient and what more is required to meet public policy objectives. Fourth, consult relevant standardization bodies when developing regulations; they are experts on the topic. Fifth, participate in standardization processes in order to keep abreast of developments and influence the content of the standards. Finally, where both regulators and standards development bodies have failed to take into account the special characteristics and challenges of small businesses, they must now address these important factors. A critical period for small business and sustainability is about to unfold.
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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 | 0.108 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.058 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.022 | 0.032 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".