Structural and Environmental Performance of Evolving Industrial Symbiosis: A Multidimensional Analysis
Bibliographic record
Abstract
Industrial symbiosis (IS) involves networks of organizations collaborating through flow exchanges. Scientific research has shown that such systems are able to provide benefits at the environmental level. Structural organization and stability were also studied, as they are linked to resilience (maintenance of activity over time), especially with ecological network analysis (ENA), which considers several dimensions in the assessment of a network organization. Studies combining ENA and environmental assessment are lacking in the literature; therefore, the links between the two dimensions are not well documented. The intention of this study was to fill this gap by analyzing structural and environmental performance simultaneously using ENA and a life-cycle-analysis-based approach focusing on the structural topology of IS. The results show that the two dimensions do not strictly influence each other. Structural performance was found to vary depending on the network structure topology, whereas environmental performance was influenced by the network complexity. To ensure the continuation of IS benefits, the two dimensions should be considered in the decision-making process in IS planification, even if they are independent evaluation criteria. Tradeoffs should be based on IS development possibilities and territorial needs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".