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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".