Assessing Greenhouse Gas Emission Impacts from Carsharing in North America: Theoretical and Methodological Design
Bibliographic record
Abstract
This paper presents a methodology for evaluating the carbon dioxide (CO2) emission reductions that result from individuals participating in a carsharing organization. Carsharing is most common in major urban areas where transportation alternatives are easily accessible. In carsharing, individuals typically access vehicles by joining an organization that maintains a fleet of cars and light trucks in a network of locations. Vehicles are most frequently deployed from lots located in neighborhoods, transit stations, employment centers, and colleges/universities. The authors contend that carsharing organizations provide numerous transportation, land use, social, and environmental benefits, including reduced vehicle miles/kilometers traveled and CO2 emissions. In this study, they outline the necessary factors that need to be considered in developing a robust estimate of greenhouse gas (GHG) emission impacts resulting from carsharing. A discussion of the theoretical foundations and methodological framework for calculating the impacts is presented. In additional studies conducted beginning in Fall 2008, the methodology will be applied to evaluate member reductions across numerous carsharing organizations in Canada and the United States, simultaneously. Results derived from this methodology will characterize emission impacts by member profile, business model, market segment, pricing plan, and the type of environment (e.g., urban) in which the member uses carsharing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".