The Impacts on Greenhouse Gases Emission during the COVID-19 lockdown in the US: An Economic Input-Output Life Cycle Assessment
Bibliographic record
Abstract
The SARS-CoV-2 virus pandemic (COVID-19) is causing disruptions to energy, finance, tourism, and trade industries all around the world. These disruptions are the result of quarantining and lockdowns that cause reductions in production and consumptions. This change in production and consumption rates has environmental consequences. This study investigates the environmental effects of COVID-19 lockdown in the United States by Input-Output Life Cycle Assessment (IO-LCA) approach. The analysis is based on extraction of economic data in the US. The simulated results are based on different durations and strategies of lockdown measures. Among all industrial categories, utilities, which include power generation and supply, water supply, and natural gas supply sectors, saw the most significant reductions by approximately 110 kt CO2-eq in the first quarter and 265 kt CO2-eq in the second quarter of 2020. The assessed reductions were the results of both direct emission reductions caused by the shutdown of certain industries and also indirect emission reductions from upstream industries. The proposed methodology provides an effective guideline to predict the greenhouse gases emissions, which can be used as a prediction method for different regions in the world.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".