FROM FREE TO PRICED INFRASTRUCTURE: U.S. ROADS AND THE INVESTMENT PUBLIC-PRIVATE PARTNERSHIP
Bibliographic record
Abstract
Much of microeconomics focuses on price system operation since prices are critical for allocating the demand for and the supply of goods and services. However, the use of major U.S. infrastructure assets remains un-priced (or “free”). Moving to priced provision has redistributive effects that can halt its implementation. Despite severe environmental harms from un-priced transportation infrastructure, economists have offered surprisingly few strategies for addressing such objections, even though pricing creates additional wealth that can help compensate potential losers. We describe a novel approach that relies on basic property laws to enhance the appeal of shifting from un-priced to priced road transportation services. Pricing of previously free road services allows value embedded in that infrastructure to be released. Value can be realized immediately through upfront concession lease payments offered by private operating companies in exchange for receiving the toll revenue from newly priced roads. We propose preserving a portion of the added wealth generated by pricing in a pubic permanent fund and distributing dividends from the fund’s investment income to the infrastructure’s citizen-owners. Dividends mitigate the redistributive effects of pricing and thus facilitate its adoption. Permanent funds are currently used in Alaska, Alberta, Texas, Norway and many other jurisdictions to preserve natural resource wealth. They can be innovatively applied to encourage road pricing, which mitigates a variety of environmental harms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".