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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".