A comparative analysis of air pollution levels during times of international uncertainty: The financial crisis of 2008 and the COVID-19 pandemic of 2020
Bibliographic record
Abstract
During a crisis, economies stagnate as uncertainty grows about the future state of the world. The financial crisis of 2008 led to a severe recession where the global economy halted for approximately two years, causing unemployment and poverty [1]. Coronavirus disease 2019 (COVID-19), which attacks the respiratory system [2], was first identified in Wuhan, China, in late December of 2019. Within a matter of months, it spread globally causing economies to shut down. As distinct as the financial crisis of 2008 may seem from the COVID-19 pandemic lockdowns, both have had devastating effects on national economies and industrial production, resulting in an overall decrease in air pollutant emissions such as carbon dioxide (CO 2 ) and nitrogen oxides (NO x ). Therefore, parallels can be made between air pollution levels during each crisis. Given air pollution rates increased after the financial crisis of 2008 [3], it is likely air pollution will also rise in the aftermath of the COVID-19 pandemic. This study aims to support this argument by analyzing air pollution trends outlined in the results of several published papers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".