Strategies to Reduce Energy Use for Commuting by Employees
Bibliographic record
Abstract
In the last decade, there has been a paradigm shift in the realisation of the environmental costs of transport, on a societal level and the failure of supply side infrastructure to reduce congestion and its negative impacts. Studies show urban areas with a population above 8 million, consume on an average 16,000 million litres of fuel daily. Cars and two-wheelers contribute to a majority of consumption, accounting for approximately 60 to 90 per cent of the total emissions produced by all modes of transport in various types of cities. This situation will be aggravated (more than twice the current fuel usage), if we continue the paradigm of satisfying increased travel demand with increased capacity. Travel Demand Management (TDM) strategies become imperative in this context. Work trips contribute to a major share of the trip profile in major cities and in Bangalore they constitute about 58 per cent. The paper summarizes the anticipated impact of work commute reduction strategies by a single organisation in Bangalore, in terms of vehicle kilometres travelled and energy use. The work trip profile of employees in an organisation is analysed, to understand the travel behaviour. This paper reviews existing research on policies and programs to reduce energy use and greenhouse gas emissions and discusses the possible impacts of various strategies based in the survey responses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".