Optimizing Organic Waste to Energy Operations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A waste-to-energy firm that recycles organic waste with energy recovery performs two environmentally beneficial functions: it diverts waste from landfills and it produces renewable energy. At the same time, the waste-to-energy firm serves and collects revenue from two types of customers: waste generators who pay for waste disposal service and electricity consumers who buy energy. Given the process characteristics of the waste-to-energy operation, the market characteristics for waste disposal and energy, and the mechanisms regulators use to encourage production of renewable energy, we determine the profit-maximizing operating strategy of the firm. We also show how regulatory mechanisms affect the operating decisions of the waste-to-energy firm. Our analyses suggest that if the social planner's objective is to maximize landfill diversion, offering a subsidy as a per kilowatt-hour for electricity is more cost effective, whereas if the objective is to maximize renewable energy generation, giving a subsidy as a lump sum to offset capital costs is more effective. This has different regulatory implications for urban and rural settings where the environmental objectives may differ.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 it