Forest, Farms and Fuelwood: Measuring changes in fuelwood collection and consumption behavior from a clean cooking intervention
Bibliographic record
Abstract
Abstract In many developing countries, fuelwood can contribute to 50–90% of all household energy, largely driven by the 2.6 billion individuals dependent on it to meet their daily household cooking energy requirements. This dependency can have negative impacts on forest stocks and climate change. Both, a transition to cleaner cooking and sustainable management of forest resources to ensure long-term supply, are essential for dependent communities. Understanding the forest impacts of fuelwood dependence and potential benefits of clean cooking interventions requires a careful analysis of local forest and agroforestry resources, the particular fuel collection habits of local populations, and the impact of clean cooking technologies on fuelwood consumption. The specific impacts of a cooking transition on fuelwood extraction from forests vs. other sources has been lacking in prior studies. We fill this gap by identifying household fuelwood consumption behavior change (farm vs. forest sources) due to clean cooking solutions implemented in two districts in rural India - Kullu (Himachal Pradesh) in northern India and Koppal (Karnataka) in southern India. To the best of our knowledge, this is the first intervention study that identifies fuelwood collection sources based on the species utilized by households. We conducted in-depth household surveys and physically measured household fuelwood consumption by source (forest or farm). Results indicate that households in Kullu reduced forest dependence, while households in Koppal reduced farm dependence due to clean cooking solutions. We find that a reduction from forests is dependent on a combination of demand (e.g., cooking vs. heating), substitutability of forest resources with farm resources (i.e., quality and availability), and the socio-economic characteristics (i.e., caste, wealth) of the household. Information on the variables impacting household reliance on forest sources would be important for future clean cooking interventions, and other forest resource policy decisions for the region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".