Kestävän teollisen ekosysteemin mallinnus
Bibliographic record
Abstract
Transforming business towards resource efficiency and the circular economy has been the key for sustainable development. The efforts in Finland for reducing waste and the carbon footprint are noteworthy. This research proposed an approach for modelling a sustainable industrial ecosystem, based on previous literature and following standard guidelines of life cycle assessment. The model entails identifying synergetic network connections to facilitate material exchange; quantifying environmental impact; life cycle cost assessment of products and waste management; analysis of material flow in the network; and energy optimisation. The model is implemented on a case study from Sodankylä, Finland where the municipality plans to establish new businesses to boost its local economy. Furthermore, the model investigates profitability of the construction of combined heat and power plants that deliver energy to the new businesses. The study also analyses the possibility of storing excess heat released from the power plants. Data and information relating to an industrial symbiosis network came from the Natural Resources Institute Finland. Data associated with the construction of the power plants were acquired from the municipality of Sodankylä. There is evaluation of two configurations of seasonal borehole thermal energy system. Both were validated with experimental measurements. Data for the first configuration were collected from Natural Resources Canada; data about the second were acquired from a pilot project in Kokkola, Finland. Synergetic relations of industrial participants are identified by a network matrix. The life cycle cost of products and waste management are projected at €115.20 million and €6.42 million respectively. Waste management cost reduction is one of the benefits of participating in a symbiotic environment. The potential cost saving from energy optimisation in the study is forecasted at €0.63 million. This optimisation included heat recovery and replacing fossil fuels with renewable fuel. Reduction in the region´s greenhouse gas emission is estimated at 53 % to 78 %. Furthermore, the evaluation of new power plants indicated they need a 16 % subsidy on investment to be profitable and that the proposed heat storage for excess heat should have a capacity of 280 kW.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.039 |
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".