A benefit/cost analysis of reconstructing the Alaska highway to R.A.U. 100 status
Bibliographic record
Abstract
The Alaska Highway, conceived and constructed in the early 1940's as a strategic military link and supply route, now serves primarily as a tourism and commercial highway. Alignments and surface quality today are dramatically superior relative to those characteristic of the original military highway. However, vast distances of surface treated roadbed remain which can only be described as substandard in comparison to most other rural Canadian highways. Low grade surfaces and roadbeds not only act as a deterrent to traffic but add to vehicle operating costs, user time consumption, the level of occupant discomfort, etc., and result in high levels of highway maintenance efforts being incurred. This thesis analyses the impact on Canadians of reconstructing that section of the Alaska Highway which lies within Canada's political boundaries to R.A.U. 100 status (Rural Arterial Undivided - 100 kph design speed; as defined by the American Association of State Highway Officials). Three traffic growth scenarios are developed, disaggregated by traffic category (tourism, commercial, local) and by whether it is normal, diverted, or newly generated. Volume projections, particularly those associated with generated tourism traffic, are subject to fairly high levels of uncertainty. However, the three growth scenarios are sufficiently wide in range that it should be considered highly improbable if actual volumes realized throughout the 20 year study time horizon should fall outside this range. Reconstruction of the Alaska Highway will generated a variety of indirect costs and benefits, only some of which can be reasonably quantified. Attempts are made to attach a dollar value to those considered quantifiable while others, such as impacts on native culture and the environment are merely listed and their valuation left to the discretion of the decisionmaker. Reconstruction costs and incremental highway maintenance savings are determined and then modified to reflect the high levels of unemployed and underemployed resources present in Canada's currently depressed economy. Savings to highway users accrue in eight distinct resource categories. Prices in some of these categories, such as comfort, non-commercial time, and accident costs, are not easily established. However estimates are derived and the inputs then analysed with respect to: - the impact on NPV's and B/C ratios if input values are changed by an arbitrary 10% - the relative uncertainty inherent in each of the user resource categories Similarly, estimates are derived for the per vehicle-kilometer consumption of each resource, before and after highway reconstruction. These are also examined for their potential impact on quantitative results. Inputs with which NPV's and B/C ratios are very sensitive are highlighted. A fortran program is developed for the purpose of calculating NPV's and B/C ratios for each of the growth rate scenarios and for discount rates ranging between 5% and 15% (1% intervals). In addition, the output lists user savings, disaggregated by resource category and by vehicle type for a discount rate of 10%. Quantitative results are calculated and discussed for each of 5 scenarios. These scenarios differ in their assumptions related to: - the approach to shadow pricing reconstruction costs, highway maintenance, and commercial time - the extent of funding support from the United States - inclusion or exclusion of user benefits which accrue to non-Canadians Results indicate that 84% of total benefits are in the form of reduced vehicle operating costs, time savings, and enhanced comfort. More than one-half of these savings accrue to the tourism traffic categegory. Time and fuel savings are the most significant user resource categories, together representing 40% of total user savings. In four of five scenarios considered, results indicate that the project is definitely quantifiably beneficial to Canada. One scenario, in which reconstruction costs and commercial time are not shadow-priced, and benefits to non-Canadians are excluded, indicates that the project is probably beneficial. However, in this case, further research into relative time and fuel consumption, before and after reconstruction, is warranted prior to the project's acceptance or rejection. In each case, the decision-maker must consider indirect impacts in conjunction with quantitative results prior to a final decision being made.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".