Off track to 2050?: a study of present and future interurban transportation emissions in British Columbia, Canada, relative to its Greenhouse Gas Reduction Targets Act of 2007
Bibliographic record
Abstract
Overall, two factors influence route-specific interurban passenger emissions from private vehicles in SMITE: distance and volume of vehicles.Emissions are a product of the distance of a route and the number of vehicles that travel it.Therefore, a long route with low traffic volume can have similar emissions to a short route with a high traffic volume.Determining route-specific emissions is essential for determining their geographic distribution, such as illustrated in Figure 4.4.This information can, in tum, be used by the public and policymakers to devise geography-specific strategies for reducing CO 2 emissions. Ferries'emissions-friendly' aircraft used in BC are Beech 1900 series planes, which have passengerkilometre EFs of up to 386 g C0 2 /pkm.By contrast, Dash 8-400 airplanes, Boeing 737 Next-Generationjets, and several small propeller airplanes have passenger-kilometre EFs between 75 g C0 2 /pkm and 85 g C0 2 /pkm, or only one-fifth those of the 'emissions-unfriendly' airplanes.4.2.4Long-distance bus BC's total interurban transportation emissions, generated mostly on the busy corridor east of Vancouver and several long routes in BC's interior.Despite the potential for a bus to be an efficient means of transport when it is fully or nearly fully occupied with a passengerkilometre EF of approximately 28 g C02/pkm (DEFRA 2011), it appears that low LFs (with estimates ranging between 21 % and 50%) mean that the bus is ultimately not as low emissions as it could be.4.2.5 Passenger trains Kamloops-Monte 30,393,054 31 ,184,899 2.6 Creek 38 Port Hardy-Campbell 15,280,506 15,598,056 2.1 River 39 N anaimo-Ladysmi th 19,226,886 19,393,983 0.9 40 Penticton--Osoyoos 19,048 ,138 19,107,925 0.3 41 Kitwanga-Meziadin 6,940,417 6,954,936 0.2 Junction 42 Cranbrook-Fairmont 36,733,133 36,784,379 0.1 Hot Springs 43 Vancouver-Squamish 21 ,382,926 21 ,382,926 0.0 44 Prince George-39,006,287 39,006,287 0.0 Vanderhoof 45 Bums Lake-Houston 17,894,344 17,894,344 0.0 46 Smithers-New 11,235,021 11 ,235,021 0.0 Hazelton 47 Golden-Radium Hot 41,845,491 41 ,845,491 0.0 Springs 48 Prince George-49,521 ,813 49,428,300 -0.2 Quesnel 49 Parksville-Campbell 10,353 ,882 10,301 ,760 -0.5 River 50 Parksville--N anaimo 103,021 ,922 102,181,677 -0.8 51 Buckinghorse River-13,018,061 12,899,407 -0.9 1 km north of Prophet River 52 Liard River-Lower 6,403,188 6,331,443 -1.1 Post 53 Williams Lake-14,821,482 14,583 ,473 -1.6 Alexis Creek 54 Gibsons-Sechelt 4,683,096 4,600,708 -1.8 55 Ucluelet Junction-7,516,080 7,352,093 -2.
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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 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".