A Framework for Integrating Ecosystem Services into China’s Circular Economy: The Case of Eco-Industrial Parks
Bibliographic record
Abstract
Identified as critical for sustainable development, ecosystem services are increasingly being put on the policy agendas of governments and corporations. China is now facing serious environmental challenges caused by losses of ecosystem services and recently has recognized that the country is reaching its environmental capacity. The circular economy (CE) has been positioned as a key strategy for national economic and social development by the national government as a way to resolve problems of resource depletion and environmental pollution. It will be increasingly critical to link ecosystem services to the CE. This means that the CE needs to be expanded to include restoration and regeneration of ecosystem services. This paper proposes a framework comprised of components including policies, governance, techniques and technologies, business development, key actors and support organizations for incorporating ecosystem services into the CE and focuses on industrial ecosystems, specifically eco-industrial parks (EIPs), as microcosms of a CE. Taking China as an example, this paper explores whether this framework can be applied to EIPs. The paper concludes that there are many opportunities to apply the framework to China’s EIPs.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".