Voluntary Environmental Programs: A Canadian Perspective
Bibliographic record
Abstract
In light of the increasing number of environmental problems necessitating government's attention and the limited scope and budget for addressing these issues, environmental protection has, and continues to evolve as more flexible approaches to regulation are being sought and embraced by governments throughout the world. Voluntary environmental programs (VEPs) are a pragmatic response by both governments and business to find a more flexible way to protect the environment. We discuss the theoretical motivations for firms to adopt VEPs in general and examine Canada's experience with three types of VEPs, public, negotiated, and unilateral agreements, to assess whether the motivating factors are present. We then argue that the institutional, political, and regulatory framework governing environmental policy in Canada does not provide the conditions necessary to effectively promote superior corporate environmental protection across jurisdictions. Despite the lack of government‐directed VEPs, there has been considerable interest by both the private sector and civil society who have taken the lead by developing unilateral agreements. Using existing literature and our current research, we examine the factors that motivate firms in Canada to participate in unilateral agreements and the characteristics of firms with the higher environmental performance and suggest some policy implications.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".