Regenerative economics for planetary health: A scoping review
Bibliographic record
Abstract
Introduction: The relationship between humans and our planet is conditioned by an economic system that undermines rather than supports health. There has been an emerging focus on the relationship between economic structures and planetary health, but alternative economic approaches to support health for people and the planet require further development. Regenerative economics offers a compelling approach to transform humankind’s relationships with each other and their environment. Regenerative economics fosters grounded, pragmatic solutions to wider human and ecological crises that moves beyond a sustainability discourse towards one of regeneration. While there are, notionally, large areas of overlap between regenerative economics and planetary health, to date these have not been systematically articulated. Methods: A scoping review was performed to examine the background, principles, and applications of regenerative economics, and their implications for planetary health. Five databases (SCOPUS, Ovid Medline, Web of Science, Geobase, IEEE Xplore) were searched for peer-reviewed literature using key terms relating to regenerative economics and planetary health. Findings were reported using thematic synthesis. Results: The review identified a total of 121 articles and included 30 papers in the final review, from economics, industrial design, business, tourism, education, urban design and architecture, energy, technology, and food and agriculture. The principles of regenerative economics focused on people, place, planet, position, peace, plurality, and progress. Putting these principles into action requires identifying and valuing different forms of capital, taking a dynamic systems approach, applying regenerative design, developing a true circular economy, good governance, and transdisciplinary education and advocacy. Conclusions: While the principles of regenerative economics and planetary health are well aligned, the tools and actions of each field differ substantially. Planetary health can learn from regenerative economics’ grounding in natural design principles, systems-based approaches, actions at the right scale and cadence, respect for diversity, community and place, and mindset that moves beyond sustainability towards a regenerative future.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".