Sustainability of telework: Systematic quantification of the impact of teleworking on the energy use and emissions of offices and homes
Bibliographic record
Abstract
Previous research has explored the impact of telework on homes and offices separately, but it is necessary to examine these domains together, as energy savings in one may be offset by increased consumption in the other. Therefore, the present study aims to fill this gap by quantifying the impact of telework on the energy use of homes and offices simultaneously. Using a medium office building reference model and four home models, the present study simulates telework scenarios from 0% teleworking to 100% teleworking in 20% increments in six different Canadian climate zones in EnergyPlus. The results show homes and offices with technologies that adapt to occupancy levels (adaptable) consume less energy and produce less emissions compared to inadaptable ones. However, energy use associated with homes increases slightly due to longer hours of occupancy. Emissions associated with telework depend on the impact of telework on internal heat gains, climate zones, and sources of energy (emission factors). The results demonstrate that the increase in energy consumption associated with various teleworking scenarios ranges from approximately 0.6% to 6.1%. Similarly, the increase in emissions varies, ranging from nearly 0.5% to 7.6%. The present study is the first comprehensive study that considers the home type and size and other statistical data for different Canadian climate zones. The results have major implications for employers and policymakers aiming to adopt telework as a sustainable practice. The results of this study create the foundation for future comprehensive studies on teleworker behavior, transportation, and the internet use associated with telework.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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 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".