Substantial Differences in Source Contributions to Carbon Emissions and Health Damage Revealed by Adjoint Modeling
Bibliographic record
Abstract
China's dual strategy to mitigate climate change and air pollution is constrained by insufficient data on the distinct sources of carbon emissions and associated health damages. This study utilizes adjoint emission sensitivity modeling with the CMAQ Adjoint model, alongside an exposure-response model and a multiregional input-output model, to perform high-resolution source attribution across 53 production sectors and fuel/process combinations, as well as 42 consumption economic sectors. Our analysis uncovers significant discrepancies between sources of CO2 emissions and PM2.5-related premature mortality, with monetized health damages surpassing climate impacts in over half of the subsectors examined. Additionally, more than one third of the CO2 emissions and health damages are outsourced from well-developed coastal provinces to less-developed inland provinces, though the regions absorbing these burdens differ geographically. CO2 emissions are primarily shifted to the northwestern region, which relies heavily on coal as an energy source, while PM2.5-related deaths are concentrated in the central region, the heavy industrial hub of China with high population densities. These findings demonstrate that high population densities and lower adoption of control technologies exacerbate health damages, particularly in downstream provinces that bear the majority of emission leakages. The CMAQ Adjoint model’s capability to evaluate the marginal benefits of emission reductions at a granular level enabled precise source attribution by incorporating emission profiles, population density, and atmospheric conditions. This research underscores the critical advantage of adjoint modeling in integrating health and climate impacts, advocating for tailored mitigation strategies that address emissions from both production and consumption sides to achieve balanced decarbonization and effective health risk mitigation in China.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".