Pilot study to measure the energy and carbon impacts of teleworking
Bibliographic record
Abstract
Teleworking offers various socio-economic benefits to the workforce, especially during major disasters. However, the holistic net energy and greenhouse gas (GHG) emissions impacts of telework remain poorly understood. This paper develops and tests a longitudinal mixed-methods approach to estimate energy and emissions in three domains: home office, transportation, and information and communications technology (ICT). A pilot study of 11 participants from Ottawa, Canada, is used to evaluate the method, while generating a rich dataset and new insights. The results show transportation, home heating and cooling account for > 94% of telework-related energy, while home office equipment, lighting and ICT account for the remaining 6% (and < 2% of GHG emissions). Not including employer offices, teleworking will likely yield a net reduction in energy and GHG emissions compared with conventional working arrangements, but this result is dependent on personal choices, routines, purchasing decisions and household structure. The paper concludes with a discussion and future recommendations for the developed method based on the lessons learned. Practice relevance A new mixed-methods approach was developed and piloted to study the holistic energy impacts of teleworking. This demonstrates measurement tools, data analysis measures and scenario modelling. It provides lessons learned and acknowledges limitations. It is a major step forward in setting the stage for larger scale studies. The specific results showed that compared with conventional working arrangements, nine of the 11 participants are likely to consume less energy and produce fewer GHG emissions when teleworking based on a scenario-based analytical approach. However, if workers use sustainable transportation, teleworking may not yield any energy savings as increases in the home domain are expected. Future studies should include the employer offices.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.006 | 0.001 |
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".