Empirical analysis of eco‐industrial development in China
Bibliographic record
Abstract
Abstract The increasing resource and environmental pressures have impeded China's efforts to quickly promote its people's quality of life, while protecting its natural environment. Due to lack of resources, technologies and capital, China needs to seek a more integrated development strategy. Industrial ecology (IE) may be one solution as it aims at optimizing the use of materials and energy in products, processes, industrial sectors and economies by systemically mimicking natural systems in an industrial setting. The relevant practices and experiences in the developed world have proved that there is a degree of effectiveness and efficiency to development through the application of IE. It is even more critical to apply the principles of IE in China, where resources are scarce. However, compared with developed nations, China faces different environmental, economic and social constraints. Therefore, China has to adopt different approaches to implement the concept of IE. In this paper, we first review the current practices in eco‐industrial development in China. Then the advantages and barriers to applying IE in China are analyzed and recommendations are provided. Copyright © 2006 John Wiley & Sons, Ltd and ERP Environment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".