Climate mitigation scenarios with persistent COVID-19-related energy demand changes
Bibliographic record
Abstract
The COVID-19 pandemic caused radical temporary breaks with past energy use trends. How post-pandemic recovery will impact the longer-term energy transition is unclear. Here we present a set of global COVID-19 shock-and-recovery scenarios that systematically explore the effect of demand changes persisting. Our pathways project final energy demand reductions of 1–36 EJ yr−1 by 2025 and cumulative CO2 emission reductions of 14–45 GtCO2 by 2030. Uncertainty ranges depend on the depth and duration of the economic downturn and demand-side changes. Recovering from the pandemic with energy-efficient practices embedded in new patterns of travel, work, consumption and production reduces climate mitigation challenges. A low energy demand recovery reduces carbon prices for a 1.5 °C-consistent pathway by 19%, lowers energy supply investments until 2030 by US$1.8 trillion and softens the pressure to rapidly upscale renewable energy technologies. Major shifts in the structure, the levels and the locations of energy use were observed during COVID-19 lockdowns. However, uncertainty remains about the persistence and thus the long-term effects of these changes on the energy system. Kikstra et al. now present various energy scenarios that build on observed changes in energy use to achieve a low-emission global future.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".