Potential for Eco-Industrial Park Development in Moncton, New Brunswick (Canada): A Comparative Analysis
Bibliographic record
Abstract
Eco-industrial development projects are increasingly popular because of their ability to transform the traditional model of industrial parks into more sustainable forms of economic development. Still, few industrial parks worldwide have achieved the high degree of eco-transformation that characterizes eco-industrial parks (EIPs). Assessing the potential for eco-industrial development at the park or regional scale is an important step towards this goal. This study aimed to assess the potential for ecological development of a growing industrial park (Caledonia Industrial Estates (CIE), province of New Brunswick, Canada) following the principles of industrial ecology. A baseline survey of CIE businesses was conducted. The results were compared to results from similar assessments in three other industrial parks across Canada, located in Nova Scotia, Ontario and Saskatchewan. The main categories used for comparison were business variety and size, public transportation, green spaces, energy and material use, and environmental management organization. While showing that CIE has EIP potential, the results revealed similarities and differences between the industrial parks studied, some of which were related to barriers limiting the efficient use and sharing of resources. One way that was identified that could help CIE incorporate eco-industrial activities into their operations would be the appointment of an environmental management organization or a shared environmental manager. Strategies to foster EIP development, in general, are also identified. These findings, based on actual business experiences, can help determine which actions and activities are suitable for CIE and other business communities that consider eco-industrial development as their next phase. They are particularly relevant to industrial parks in a development or redevelopment phase.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".