Forecasting organic waste and biomethane generation potential of a non industrial district of Eastern India: A data-driven approach to sustainable energy and waste management
Bibliographic record
Abstract
Waste management is a critical indicator, affecting public health, sanitation, and environmental sustainability, which can improve living standards in disadvantaged communities. The main objective of the study is to estimate population growth and organic waste production and assess the methane production until 2051 in 6 municipalities of Birbhum district. Arithmetic and Geometric Progression were used for population predictions, and Auto Regressive Integrated Moving Average (ARIMA) and Long Short Term Memory (LSTM) models for methane estimation. This study found that Birbhum municipal solid waste (MSW) contains 57–64 % organic material from markets, kitchens, and gardens, varying by municipalities. Results show that Class II municipalities like Bolpur generates 21 Metric Tonnes (MT) of organic waste everyday (364.86 gm per capita in 2021). With a 40 % organic waste collection efficiency, Bolpur can generate 7.67 Gg of methane yearly, expected to increase to 57 Gg/year by 2051, equivalent to 76 hm³ /year of Liquid Petroleum Gas (LPG). By 2051, other municipalities—Sainthia, Rampurhat, Nalhati, Suri, and Dubrajpur—shows high biomethane potential and the predicted LPG-equivalent methane production is 13.82, 19.92, 24.52, 22.95, and 1.78 hm³ respectively. This methane potential is due to exponential population increase and rising per capita MSW generation rates of 548.55, 609, 404.07, 645.38, and 501.45 gm per capita per day (g.p.c.d.) for Sainthia, Rampurhat, Nalhati, Suri and Dubrajpur municipality respectively. The results indicate that Birbhum's organic waste have high Biomethane Potential (BMP), highlighting the need to enhance waste collection and management. Effectively using this resource can boost district socio-economic growth, cleaner energy production, and environmental sustainability.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".