Saving the world from your couch: the heterogeneous medium-run benefits of COVID-19 lockdowns on air pollution
Bibliographic record
Abstract
Abstract In Spring 2020, COVID-19 led to an unprecedented halt in public and economic life across the globe. In an otherwise tragic time, this provides a unique natural experiment to investigate the environmental impact of such a (temporary) ‘de-globalization’. Here, we estimate the medium-run impact of a battery of COVID-19 related lockdown measures on air quality across 162 countries, going beyond the existing short-run estimates from a limited number of countries. In doing so, we leverage a new dataset categorizing lockdown measures and tracking their implementation and release, extending to 31 August 2020. We find that domestic and international lockdown measures overall led to a decline in PM2.5 pollution by 45% and 35%, respectively. This substantial impact persists in the medium-run, even as lockdowns are lifted, there is, however, substantial heterogeneity across different types of lockdown measures, different countries, and different sources of pollution. We show that some country trajectories are much more appealing (with fewer COVID-19 casualties, less economic downturn and bigger pollution reductions) than others. Our results have important policy implications and highlight the potential to ‘build back better’ a sustainable economy where pollution can be curbed in a less economically costly way than during the COVID-19 pandemic.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".