Reducing Greenhouse Gas Emissions from Long-Distance Business Travel: How Far Can We Go?
Bibliographic record
Abstract
Long-distance (LD) travel accounts for over 30% of person-trip miles, with important energy and emissions impact. LD business travel can often be replaced by remote participation, so targeting such trips for cost, time, and emissions savings may be a wise strategy for protection of the climate, budgets, and human health. To appreciate Americans’ LD travel choices better, a 73-question online survey was conducted in 2019 that captured 2,327 LD (over 100 mi each way) trips made by 929 respondents during the previous 12 months, of which 490 round trips were for business purposes. Predictive models for LD trips per adult per year, overnights, LD travel times, and willingness to participate remotely and/or purchase carbon offsets for those trips were developed using respondents in Austin only. As expected, those educated to degree level tend to travel more often, for both business and nonbusiness purposes; everything else is constant. People who undertake LD travel more frequently are more likely to spend less time in transit/en route. Single people or those from large households educated to degree level are more likely to be willing to pay for the carbon emissions produced by their flights. Out of the 298 LD business trips made by Austinites, remote participation is possible for approximately a quarter, and the respondents involved are willing to participate remotely in 44% of those trips. In other words, Austinites appeared willing to participate remotely in slightly over 10% of their business trips overall, at least before the COVID-19 pandemic. This is definitely not enough to address climate change concerns as a result of carbon emissions from LD travel.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".