Energy use per hour is key determinant of future transport energy consumption
Bibliographic record
Abstract
Abstract Transportation is a growing component of global energy consumption. Improvements in efficiency over time have reduced the energy used per kilometre travelled, but so far this has not reversed the increasing energy consumption per person. Instead, due to a complex interplay of factors—including changes in the built environment, shifts in transport modes, and human behaviour—average travel distances have increased, effectively negating efficiency gains. These adaptive dynamics have made it difficult to predict future energy consumption in travel. Here, we present data on remunerated and personal travel covering over half the global population, which supports a simple predictive heuristic based on energy use per unit of travel time, rather than distance. We find that total travel time among 43 countries converges to 1.3 ± 0.2 h d −1 and is invariant with per capita income across two orders of magnitude. This implies that psychological, social, and economic factors lead people to travel for similar daily durations, regardless of wealth, culture, geography, or transport technology, and that built environments and lifestyles co-evolve with economic and technological development to preserve stable travel times despite increasing travel speeds. Therefore, significant decreases in future energy consumption can only be achieved by reducing the average energy used per hour of human 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".