Evaluating the Barriers to Industrial Symbiosis Using a Group AHP-TOPSIS Model
Bibliographic record
Abstract
Industrial symbiosis (IS) can contribute to achieving a win-win situation between industry and environment for local and regional circular economies. Many authors have recognized that a variety of barriers can hinder the implementation of IS. However, there is very limited research on quantitatively evaluating the IS barriers. In this paper, we propose a model which combines the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate the IS barriers semi-quantitatively. This model assists in identifying and prioritizing the fundamental barriers for implementation of IS in a comprehensive manner. An operating IS, the Hai Hua Group (HHG), in Shandong Province, China is used as a case study to test the proposed model. The results show that the top four generic barriers are technological barriers, economic barriers, safety barriers, and informational barriers. More specifically, the barriers are information platforms, human safety and health, technology involved with extending industrial chains, product added value, and costs. The paper concludes by discussing managerial implications for promoting the establishment and operation of IS.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".