Evaluation of Direct and Indirect Effects of Teleworking on Mobility: The Case of Paris
Bibliographic record
Abstract
This paper investigates how and to what extent changes in user behavior may mitigate the benefits of teleworking on commuting distance and time, a phenomenon often referred to as a “rebound effect.” The direct effect of teleworking is to reduce the number of commuting trips ( work travel effect). This may trigger behavioral changes among transport users: teleworkers may carry out additional trips for other purposes ( non-work travel effect), and may change their residential/job location, leading to longer commuting distances ( residential location effect). In addition, the improvement in travel conditions consequent to the work travel effect might result in greater mobility by non-teleworkers ( induced demand). Considering the Paris region as a case study, this paper applies a four-step travel demand model to evaluate several teleworking scenarios and quantify the rebound effects. We complete the analysis with an economic evaluation of the scenarios, focusing on mobility effects. The overall rebound effect is found to be substantial, cancelling out 62%–68% of the gains in travel distance and 74%–85% of travel time savings. Nonetheless, the social benefits of teleworking remain significant, amounting to 1.4% of the social cost of road transport in the region. This suggests that teleworking may be able to contribute significantly to a policy mix aimed at reducing travel demand.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".