Reduced health burden and economic benefits of cleaner fuel usage from household energy consumption across rural and urban China
Bibliographic record
Abstract
Abstract Energy consumption in the residential sector is increasing rapidly in China. This study applies an integrated assessment model to investigate the adverse impacts of household energy consumption by various fuel types across rural and urban areas on age- and sex-specific premature deaths associated with PM2.5 pollution at provincial levels for 2015. We further estimate the economic and health co-benefits of a switch from solid fuels to electricity within households. We find that energy consumed by Chinese urban households is nearly 1.6 times than that of rural households. However, premature deaths due to household energy usage is 1.1 times higher in rural areas compared to urban areas due to direct use of coal for heating in rural households. The majority of household consumption-related premature deaths are predominately in the Southern area of China due to the population size and aging population. By replacing coal and biomass with electricity, this paper estimates economic benefits equal to 0.09% (95% CI: 0.08%–0.1%) GDP for rural areas and 0.006% (0.005%–0.007%) of GDP for urban areas of China. The results suggest that mitigation measures such as the promotion and subsidization of cleaner fuels, modern stove within rural households would yield these potential significant economic benefits.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".