Methods for assessing the impact of PM2.5 concentration on mortality while controlling for socio-economic factors
Bibliographic record
Abstract
Even though industrial development has brought vast improvements to our daily lives, it carries with it negative effects such as adverse health outcomes caused by PM 2.5 and other pollutants. The negative externalities and external costs might occur when property rights are not properly defined, which means that if no one holds a property right on the atmosphere and the quality of air, there is no appropriate mechanism to prevent a further expansion of negative effects. An economic burden of pollution related to premature morbidity and mortality in individual countries can account for 5–14% of GDP (World Bank, 2021). In 2019, the worldwide health cost of mortality and morbidity caused by exposure to PM 2.5 concentration was $8.1 trillion, which is equivalent to 6.1 percent of the global gross domestic product (GDP) (World Bank estimate). Policymakers require evidence-based results that clearly show the impact that air pollution has on the economy and society, in order to be able to establish the proper regulations and ensure their successful implementation. The purpose of this long term study is to provide methods for assessing the negative effects of PM 2.5 concentration on PM 2.5 -related mortality using panel data structure and demonstrate how socio-economic factors affect this relation. This study employed advanced econometric techniques to analyse the long-term impact of PM 2.5 on human health, while controlling for socio economic indicators. This study has demonstrated significant effects of socio-economic, health risk and system and governance variables on the relation between PM 2.5 concentration and PM 2.5 -related mortality.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".