WTE plants installed in European cities: a review of success stories
Bibliographic record
Abstract
Purpose – Waste is a resource. Generating energy from waste instead of sending it to landfill avoids methane gas which equals 25 times CO2 in mass. In combination with the energy efficiency thresholds set in Waste Framework Directive, this could prevent up to a further 45 million tons of CO2 eq. per year. The purpose of this paper is to present the waste-to-energy (WTE) plants installed in ten European cities which have been selected among the most sustainable cities or among the best cities to live in. Design/methodology/approach – The work is based on literature review and a combination of several statistical data and reports that include the required data. Findings – The European Directives, along with the general thinking that wastes are resources and the effort to reduce the environmental impact in urban environment from waste management, were the driving forces. The most sustainable cities in EU considered that their sustainability is based also in energy recovery from wastes. All of them are using WTE facilities to treat a significant part of their waste in order to produce energy in the form of heat and electricity. And they do it in a very successful and environmental friendly way, as they mainly utilize the waste fractions that cannot be recycled or reused, and they do not landfill these resources. This approach is proving that the sustainable waste management cannot be achieved without WTE facilities, since a fraction of wastes consists of non-recyclable and non-reusable materials, which present significant heating value that cannot be neglected as an energy source. Originality/value – This paper presents the WTE plants installed in ten European cities which have been selected among the most sustainable cities or among the best cities to live in. This work aims to present the strong and successful relation between WTE and sustainability in the modern complex urban environment.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".