The Impacts on Greenhouse Gases Emission during the COVID-19 lockdown in the US: An Economic Input-Output Life Cycle Assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 it