Factors influencing the adoption of biogas digesters in rural Ethiopia
Bibliographic record
Abstract
Increasing energy demands on farm households in Ethiopia have escalated challenges related to land degradation, indoor air quality, and rural economic development. Soil deterioration followed by reduced carbon sequestration compounds the adverse effects of environmental degradation and climate change. The Ethiopian government has disseminated thousands of bio-digesters across rural villages with the hope that introducing bio-digesters to rural farm households would address all of these issues. However, there is scant information about how households make energy choices and consequently how the introduction of biogas energy will affect income and the environment in these rural agricultural communities. Therefore, this study aims to verify how biogas energy adopters make decisions about their energy consumption and how biogas energy use compares to traditional alternatives such as firewood, charcoal, and dried animal dung. Quantitative data were gathered using semi-structured questionnaires of 300 farmers in the Tigray region of Ethiopia, following the collection of qualitative data obtained via focus groups. Using descriptive analysis, we quantified weekly consumption of traditional energy sources and major reasons why households choose each energy source. We estimated a multivariate probit model and conducted correlation tests to verify the use of biogas energy as a substitute or complement for traditional energy sources. Results show that a household’s choice for biogas energy was statistically and positively correlated to both firewood and charcoal use. Despite biogas digester adoption in several households, the majority continue to depend upon traditional energy sources. This suggests that overall household energy consumption increases with the availability of biogas digesters. The study reveals that the size of cattle holding, working age, gender, access to electricity, access to credit services, and livestock mobility influence household energy choices. The study concludes that household biogas energy use remains below expectations, even though subsidies make the units affordable for small farmers. We assert that households are more likely to adopt technologies that facilitate cooking food, baking injera, and preparing coffee. Biogas utilization might improve if farmers have access to improved stoves and credit services. However, policy makers also need to consider the possibility that providing access to biogas digesters may actually increase the use of traditional fuel sources and have the reverse effect than that intended.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| 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".