Bibliographic record
Abstract
Waste is widely used as a fuel in the Swedish district heating (DH) systems, thereby linking waste management and the energy system.This paper summarizes earlier studies by the author on the role of waste as a fuel in DH systems.The method used is case studies of three Swedish municipalities that utilise waste in their DH systems.Economic optimisations of the DH systems are made using the linear programming model MODEST, and environmental effects in terms of carbon dioxide emissions are assessed.It is economically advantageous to use waste as a fuel due to regulations in the waste management sector and high taxes on fossil fuels.There can be a conflict between combined heat and power (CHP) production in DH systems and waste incineration, since the latter can remove the heat sink for other CHP plants in combination with low electrical efficiency in waste incineration plants.CHP is the main measure to decrease carbon dioxide emissions in DH systems on the assumption that locally produced electricity replaces electricity in coal condensing plants.It can be difficult to design policy instruments for waste incineration due to conflicting goals for waste management and energy systems.To put costs on environmental effects, so called external costs, is one way to include them but the method has drawbacks, for example the limited range of environmental effects included.Comparing the energy efficiency of material recovery and energy recovery from waste incineration is one way to assess the resource efficiency of the waste treatment methods.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".