Impacts of COVID-19 induced energy demand changes on emissions and mitigation challenges 
Bibliographic record
Abstract
The COVID-19 pandemic is causing radical temporary breaks with past energy use and GHG emissions trends. However, how a post-pandemic recovery will impact longer-term transformations to a low-carbon society is unclear. Here, we present different global COVID-19 shock-and-recovery scenarios that systematically explore economic uncertainty and the demand-side effect on emissions. We consider changes in the residential, industry and transport energy sub-sectors under diverging cases that might lead to a more carbon intensive and individualistic way of consumption, or to a policy-advised new future that supports the emission reduction opportunities seen during the pandemic. The resulting impact on cumulative CO2 emissions over the coming decade can range from 28 to 53 GtCO2 reduction depending on the depth and duration of the economic downturn and the extent and persistence of demand-side changes. Recovering from the pandemic with low energy demand practices - embedded in new patterns of travel, work, consumption, and production – reduces climate mitigation challenges in the long run. We show that a low energy demand recovery reduces carbon prices for a 1.5°C consistent pathway by 19%, saves energy supply investments until 2030 by 2.1 trillion USD, and lessens pressure on the upscaling of renewable energy technologies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".