Environmental practice and its effect on the sustainable development of eco-industrial parks in China
Bibliographic record
Abstract
After more than a decade of development, eco-industrial parks (EIPs) have made significant progress in saving resources and protecting the environment in China. Meanwhile, many problems have emerged, such as poor stability, poor profitability and weak eco-industrial chains, which have impeded the EIP's sustainable development. Faced with variable environmental practices and limited resources and capital, EIPs need to address suitable environmental practices seriously to configure resources reasonably and ultimately realize sustainable development. Therefore, based on an analysis of the elements of environmental practice and sustainable development level, this article aims at identifying those environmental practices that can improve the sustainable development level of EIPs and to analyze the impact of those different environmental practices on the sustainable development level of EIPs using factor analysis and analytic hierarchy process. The results could provide theoretical guidance and reference for decision-making to Chinese government and administration committees of EIPs for choosing and implementing environmental practices.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".