Global Economic Crisis, Energy Use, CO2 Emissions, and Policy Roadmap amid COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has emerged as one of the deadliest infectious diseases on the planet. Millions of people and businesses have been placed in lockdown where the main aim is to stop the spread of the virus. As an extreme phenomenon, the lockdown has triggered a global economic shock at an alarming pace, conveying sharp recessions for many countries. In the meantime, the lockdowns caused by the COVID-19 pandemic have drastically changed energy consumption patterns and reduced CO2 emissions throughout the world. Recent data released by the International Monetary Fund and International Energy Agency for 2020 further forecast that emissions will rebound in 2021. Still, the full impact of COVID-19 in terms of how long the crisis will be and how the consumption pattern of energy and the associated levels of CO2 emissions will be affected are unclear. This review aims to steer policymakers and governments of nations toward a better direction by providing a broad and convincing overview on the observed and likely impacts of the pandemic of COVID-19 on the world economy, world energy demand, and world energy-related CO2 emissions that may well emerge in the next few years. Indeed, given that immediate policy responses are required with equal urgency to address three things—pandemic, economic downturn, and climate crisis. This study outlines policy suggestions that can be used during these uncertain times as a guide.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".