Energy Utilization of Agricultural Waste: From Waste Management to Energy Production
Bibliographic record
Abstract
Agricultural waste management is a critical issue due to its environmental and economic implications. This study examines the transition from traditional waste management practices to innovative energy production technologies. Agricultural waste, including crop residues, animal manure, and agro-industrial by-products, varies in chemical and physical properties, and its production is influenced by seasonal and regional factors. Current waste management methods, such as burning and landfilling, have significant environmental and economic drawbacks, which are addressed by regulatory frameworks and policies. Advanced technologies like anaerobic digestion, pyrolysis, gasification, combustion, and biofuel production offer promising alternatives for converting waste into energy. Successful case studies from Europe, Asia, and North America demonstrate the practical implementation and benefits of these technologies. An economic analysis highlights the cost-effectiveness and market potential of energy products derived from agricultural waste, supported by government incentives. Environmental assessments reveal the sustainability and ecosystem benefits of these practices. Future research directions include emerging technologies, integration with other renewable sources, and policy recommendations to promote sustainable energy utilization of agricultural waste. This study underscores the importance of transitioning from waste management to energy production for enhanced environmental sustainability and economic viability.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".