Assessment of Environmental Impact and Economic Viability of Domestic Biogas Plant Technology in Bangladesh
Bibliographic record
Abstract
The study investigated the distinct environmental impacts and economic viability of domestic biogas technology in the countryside of Bangladesh. The study was carried out by a survey through personal interviews with biogas users. Seventy households were selected purposively and interviews were conducted through semi-structured questionnaires. The study mainly highlighted the potential reduction of greenhouse gas (GHG) emission and economic benefits of biogas utilization which were evaluated considering the substitution of traditional biomass fuels, by saving Liquefied Petroleum Gas (LPG) and cost of chemical fertilizer, and carbon trading. The economic benefits are addressed using some well-known economic indicators like Net Present Value (NPV), Payback Period (PBP), and Benefit-Cost Ratio (BCR). The results of the study revealed that a small-scale household anaerobic cow dung biogas digester not only exhibited the potential to cut carbon emissions on average by about 7.8 tons of CO2 equivalents yearly, but it also demonstrated the economic feasibility of doing so as the value of NPV and BCR was positive. This study recommends that the government approach, awareness program, and continuous and proper performing of the biogas technology are needed to intensify the multiples environmental benefits of the technology.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".