Factors Affecting Fuelwood Consumption and CO2 Emissions: An Example from a Community-Managed Forest of Nepal
Bibliographic record
Abstract
Fuelwood is the primary source of energy in Nepal, where 87.1% of the total energy is derived from wood, making it the major source for carbon emissions. This study explores the factors affecting the fuelwood consumption, the amount of carbon emissions including the potential for carbon sequestration in community forests, taking a case study of Kankali Community Forest Users Group (CFUG) of Chitwan district of Nepal. Interviews with 217 households revealed that 60% of the households still depend on fuelwood for cooking, which apparently emits approximately 13.68 tons of carbon dioxide annually. The emission, however, varies with the economic status of the households; poor households rely exclusively on fuelwood for cooking and therefore emit greater amount of carbon. Similarly, the carbon emission was also found to be directly proportional to the family size and livestock holding, and inversely proportional to landholding and per capita income. A more conservation-oriented forest management along with activities to support livelihood has contributed to lower carbon emissions. Interestingly, the poverty-energy trap seemed to have a distinct gender dimension. We argue that CFUGs need to invest in income-generating activities for local users, and especially for women of low-income households, in order to reduce current carbon emission.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".