A preliminary scenario analysis of the impacts of teleworking on energy consumption and greenhouse gas (GHG) emissions
Bibliographic record
Abstract
Abstract This paper uses scenario analysis to investigate the broader impact of teleworking in four scenarios including the COVID-19 pandemic, worst-, moderate-, and best-case scenarios on building-level energy use, energy consumption in transportation, and information and communication technology (ICT) usage by using the databases of the Government of Canada. The COVID-19 scenario relies on the available data for the pandemic period. The worst-case scenario is when telework has an adverse effect on energy use while the moderate-and best-case scenarios are when the minimum and maximum savings are achieved by telework. The data includes commuting distances, electricity and natural gas consumption for offices and residential buildings, and ICT usage. Then, the associated GHG emissions are calculated for transportation, residential and office buildings, and ICT and the analysis are carried out by applying a potential fraction of saving to the associated GHG emissions of each domain and scenario. This paper demonstrates the potential energy savings of teleworking significantly depends on teleworker behavior to a degree that in the worst-case scenario no potential saving is observed while the savings are significant in the best-case scenario. Therefore, the impact of telework is highly uncertain and complicated and current statistics are insufficient for accurate estimates.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".