Understanding the Evolution of Industrial Symbiosis with a System Dynamics Model: A Case Study of Hai Hua Industrial Symbiosis, China
Bibliographic record
Abstract
Industrial symbiosis (IS) is a key sub-field in industrial ecology. The field itself assists in developing strategies that support local and regional sustainable development. The evolution of IS is an important topic to be explored. In this paper, we use the system dynamics (SD) method to establish a model of the evolution of industrial symbiosis in the case of Hai Hua Industrial Symbiosis (HHIS). In this model, different scenarios for energy consumption, solid waste utilization, and wastewater utilization were set by changing the parameter values of certain input variables to analyze the evolution of HHIS. In addition, the drivers for IS evolution were investigated qualitatively in this study. The SD model was helpful for visualizing the effects and benefits of reducing the energy consumption, solid waste stock, and wastewater stock that were achieved by establishing symbiotic relationships during the evolution of IS. The results show that the optimization of energy and material flows and other symbiotic benefits can be achieved in HHIS. There are still some challenges that restrict the further evolution of HHIS. Some suggestions are proposed to promote its further evolution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".