Uncovering driving forces of co-benefits achieved by eco-industrial development strategies at the scale of industrial park
Bibliographic record
Abstract
Co-benefits are used to reflect multiple important benefits that could be achieved by a single policy or measure. In recent years, researches on co-benefits have developed rapidly in various fields, but there is limited research associated with eco-industrial development. In order to investigate the driving forces of co-benefits in the field of eco-industrial development, this study established an emergy-based hybrid model for such a research objective. In order to verify this model, Suzhou industrial park in China has been selected as a case study. The results showed that co-benefits achieved in 2015 through eco-industrial development-based strategies in Suzhou industrial park were more than that were in 2010. Waste reutilization environmental efficiency effect was the most significant positive driving forces, while energy consumption efficiency effect had the least impact on generating co-benefits in Suzhou industrial park. Policy implications such as strengthening eco-industrial network and further industrial structure promotion are proposed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".