Uncovering driving forces of co-benefits achieved by eco-industrial development strategies at the scale of industrial park
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it