Industry hybrid regulation: Exploring a model for business-driven circular economy
Bibliographic record
Abstract
• Closing resource loops requires urgent innovative solutions which are difficult to achieve. • Conventional policy-making for circular economy may not motivate businesses to innovate. • The proposed hybrid model can resolve the shortcomings of conventional policy. • The explored model involves both business and government to secures effectiveness and efficiency of actions and innovative solutions. • The model can also be used for other costly sustainability initiatives that need business proactivity. Government is often seen as the arbiter for environmental protection. Alternatively, firms can volunteer to proactively take collective action toward sustainability, called industry self-regulation. But, what happens when neither of the two alternatives can deliver the expected outcomes? This inductive study addresses such a situation in managing hazardous consumer waste in the province of Ontario, Canada, where waste management and later circular economy have been on the agenda since the 1980s. However, both self- and government regulation failed to spur the advancements required to close material loops effectively and efficiently. Finally, after three decades, actors developed a new path to transition to circular economy. This longitudinal process study focuses on this process to explore the changes in business-policy interactions that realized this transition. I analyze extensive qualitative data, including 55 interviews with top-level decision-makers in all stakeholder groups (businesses, policy-makers, NGOs, consultants, etc.). Based on the unearthed patterns, I propose a hybrid model for regulation. In this model, both business and government coordinate throughout the process to set the rules and enforce them. By allowing organically shaped competition, this model can spur proactivity and innovation, which are crucial for the transition to circular economy but are hard to incentivize in conventional policy-making. The model can be used in any situation where an urgent issue needs immediate proactive responses by business.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".