The Impacts Of COVID-19 Pandemic on Greenhouse Gas Emissions and Climate Change
Bibliographic record
Abstract
The outbreak of COVID-19 in 2020 has brought enormous damage to human life and health and socioeconomic development. Yet, the influence of COVID-19 outbreak on the environment within the context of global warming has not been fully understood. Detailed and accurate explanation for the relationship between COVID-19 and economy, carbon emissions, and methane emissions remains a challenge. This study aims to highlight the significant impact of the COVID-19 pandemic on greenhouse gas emissions and climate change through a systematic literature review and comprehensive analysis of data from the U.S., China, Canada, and 27 European countries. To clarify the impact of COVID-19 on climate, the study outlines changes in carbon dioxide emissions by comparing data from pre-pandemic, during-pandemic, and post-pandemic (projected) scenarios. The correlation among carbon dioxide, temperature, GDP, and Population in countries is further examined with different levels of development using Pearson's Linear Correlation analysis and significance test. This study will potentially provide insights into future preparation and management of the impact of global emergency disaster emergencies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".