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Record W3159357575 · doi:10.5194/egusphere-egu21-15574

Impacts of COVID-19 induced energy demand changes on emissions and mitigation challenges 

2021· article· en· W3159357575 on OpenAlexaff
Adriano Vinca, Jarmo Kikstra, Francesco Lovat, Benigna Boza‐Kiss, Bas van Ruijven, Charlie Wilson, Joeri Rogelj, Behnam Zakeri, Oliver Fricko, Keywan Riahi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicCOVID-19 impact on air quality
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGreenhouse gasNatural resource economicsConsumption (sociology)RecessionEconomicsClimate changeRenewable energyShock (circulatory)Energy demandEnergy consumptionEnvironmental scienceAgricultural economicsEngineering

Abstract

fetched live from OpenAlex

<p>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.  </p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.093
GPT teacher head0.352
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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