Demand-Pull Instruments to Support the Circular Economy: A Global Perspective
Bibliographic record
Abstract
In recent years, transitioning to a more circular economy has been introduced as a policy objective in many jurisdictions across the globe with a view to achieving a sustainable society. However, the increasing attention paid to this issue has so far not led to a large-scale transformation of production processes and consumption. Instead, many circular economy innovations have remained niche and have not become the mainstream solutions. A plethora of regulatory, market, cultural and technological barriers limit the demand for, and consequently wide-scale adoption of, circular solutions. This article examines the potential offered by regulatory demand-pull instruments to overcome such barriers and to mainstream circular economy solutions. In particular, the article investigates innovative demand-pull instruments that have been used in various jurisdictions globally. This article analyses the instruments according to their types – i.e., command-and-control measures, economic incentives, information tools and public procurement – to gain a better understanding of the rationales, strengths, and limitations of these categories of instruments in creating a stable demand for the circular economy. The lessons learned from the regulatory innovations enable a more critical approach in determining the best combination of instruments and tools to implement sustainable circular solutions on a larger scale.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".