Environmental payoffs of LPG cooking in India
Bibliographic record
Abstract
Over two-thirds of Indians use solid fuels to meet daily cooking energy needs, with associated negative environmental, social, and health impacts. Major national initiatives implemented by the Indian government over the last few decades have included subsidies for cleaner burning fuels like liquid petroleum gas (LPG) and kerosene to encourage a transition to these. However, the extent to which these programs have affected net emissions from the use of these improved fuels has not been adequately studied. Here, we estimate the amount of fuelwood displaced and its net emissions impact due to improved access to LPG for cooking in India between 2001 and 2011 using nationally representative household expenditure surveys and census datasets. We account for a suite of climate-relevant emissions (Kyoto gases and other short-lived climate pollutants) and biomass renewability scenarios (a fully renewable and a conservative non-renewable case). We estimate that the national fuelwood displaced due to increased LPG access between 2001 and 2011 was approximately 7.2 million tons. On aggregate, we estimate a net emissions reduction of 6.73 MtCO 2 e due to the fuelwood displaced from increased access to LPG, when both Kyoto and non-Kyoto climate-active emissions are accounted for and assuming 0.3 as the fraction of non-renewable biomass (fNRB) harvested. However, if only Kyoto gases are considered, we estimate a smaller net emissions decrease of 0.03 MtCO 2 e (assuming fully renewable biomass harvesting), or 3.05 MtCO 2 e (assuming 0.3 as the fNRB). We conclude that the transition to LPG cooking in India reduced pressures on forests and achieved modest climate benefits, though uncertainties regarding the extent of non-renewable biomass harvesting and suite of climate-active emissions included in such an estimation can significantly influence results in any given year and should be considered carefully in any analysis and policy-making.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".