Community Participation in Utilizing Livestock Waste Biogas to Support Sustainable Energy Development
Bibliographic record
Abstract
The development of livestock waste biogas technology as an alternative energy source can solve the problem of energy supply and solve the problem of livestock waste contamination to the environment. The utilization of biogas requires community participation, especially in filling and maintaining the digester. This study aims to analyze the level of community participation in biogas and analyze the factors that influence it. This research was conducted in Boyolali Regency, considering that the cattle population in this district is very high, namely 86,363 dairy cows and 86,988 beef cattle, so that the potential for biogas raw materials is high. Respondents of the study were 60 people who used livestock waste biogas. The research method used is descriptive quantitative, with survey data collection techniques, and the data were analyzed descriptively and multiple linear regression test. The results showed that community participation in biogas development began at the planning, implementation, or construction stages of biogas installations and the use of biogas as an energy source. The most significant community participation was at the consultation level (36%). The results of the multiple linear regression test obtained the value of the equation Y=1,835+0,092X1+0,083X2-0,006X3+0,078X4+0,024X5. The results of the F test show that the value of Fcount>Ftable (3.38 > 2.89) means that the variables of education, number of livestock, income, experience, and intensity of counseling together affect participation. In contrast, the results of the t-test show that the variable partially influences the participation number of livestock, income, and power of extension. This study concludes that the most significant community participation in the use of biogas is in consultation, and community participation can be increased through increasing the number of livestock as biogas raw material providers and assistance through counseling activities.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".