Radiative Effects of Residential Sector Emissions in China: Sensitivity to Uncertainty in Black Carbon Emissions
Bibliographic record
Abstract
Abstract Residential sector emissions of aerosols, primarily from solid fuels burned for cooking and heating purposes, are high in black carbon, a component that absorbs radiation efficiently across a wideband of wavelengths. Mitigation of residential sector emissions has been suggested as a method to rapidly reduce anthropogenic global warming. This study presents model results from a regional model with coupled chemistry, aerosols, and dynamics over an East Asian domain for January 2014 to investigate the radiative effects of residential sector emissions. Model results are evaluated against surface measurements of particulate matter and remote sensing products, comparing well but with a high aerosol optical depth bias over Sichuan and low single scattering albedo over many locations. We calculate effective radiative forcing of residential sector aerosols at the top of the atmosphere of +1.22 W/m2 over Eastern China, +1.04 W/m2 due to shortwave and +0.18 W/m2 due to longwave forcing. We decompose the shortwave forcing into component parts and find the direct radiative effect is the dominant component (+0.79 W/m2), with a smaller contribution from semidirect effects (+0.54 W/m2) partly countered by negative indirect effects (−0.29 W/m2). The effective radiative forcing varies from 0.20 to 1.97 W/m2 across a reasonable range of black carbon to total carbon emission ratios for the residential sector. Overall, this study shows that mitigation of the residential sector is likely a viable method to locally reduce short‐term atmospheric warming in China, but efforts are needed to reduce uncertainty in composition of residential sector emissions to be confident in this conclusion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".