The potential of waste-to-energy in reducing GHG emissions
Bibliographic record
Abstract
Background: The combustion of municipal solid waste (MSW) to generate heat or electricity (waste-to-energy [WTE]) could reduce net GHG emissions in the USA compared with combusting methane from landfills. Moreover, negative CO2 emissions could be achieved with CCS because 66% of the carbon in MSW is typically biogenic. Results and conclusion: For the five largest landfill sites in each state, we estimate that at least 58 and 11 sites have enough MSW to fuel WTE plants of >50 MWe and >100 MWe, respectively. Furthermore, half of these sites lie within 20 km of potential underground saline and other CO2 storage reservoirs. We estimate that the levelized electricity cost for WTE without CO2 capture is US$94/MWh and is $285/MWh with amine-based post-combustion capture technology. The cost of CO2 capture is $58/Mg CO2, resulting in a cost for carbon negative emissions of $93/Mg CO2; substantially lower than for some geoengineering methods, including capturing CO2 from air.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".