Predicted growth of world urban food waste and methane production
Bibliographic record
Abstract
Landfill gas emissions are one of the largest anthropogenic sources of methane especially because of food waste (FW). To prevent these emissions growing with world population, future FW best management practices need to be evaluated. The objective of this paper was therefore to predict FW production for 2025 if present management practices are maintained, and then, to compare the impact of scenario 1: encouraging people to stay in rural areas and composting 75% of their FW, and; of scenario 2, where in addition to scenario 1, composting or anaerobically digesting 75% of urban FW (UFW). A relationship was established between per capita gross domestic product (GDP) and the population percentage living in urban areas (%UP), as well as production of municipal solid waste (MSW) and UFW. With estimated GDP and population growth per country, %UP and production of MSW and UFW could be predicted for 2025. A relatively accurate (R(2) > 0.85) correlation was found between GDP and %UP, and between GDP and mass of MSW and FW produced. On a global scale, MSW and UFW productions were predicted to increase by 51 and 44%, respectively, from 2005 to 2025. During the same period, and because of its expected economic development, Asia was predicted to experience the largest increase in UFW production, of 278 to 416 Gkg. If present MSW management trends are maintained, landfilled UFW was predicted to increase world CH4 emissions from 34 to 48 Gkg and the landfill share of global anthropogenic emissions from 8 to 10%. In comparison with maintaining present FW management practices, scenario 1 can lower UFW production by 30% and maintain the landfill share of the global anthropogenic emissions at 8%. With scenario 2, the landfill share of global anthropogenic emissions could be further reduced from 8 to 6% and leachate production could be reduced by 40%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".