Delivering Transportation Infrastructure Through Public-Private Partnerships: Planning Concerns
Bibliographic record
Abstract
Problem: The types of public-private partnerships (PPPs) in which private sector partners take on increased responsibility for project design, construction, financing, and operation have become popular in the United States and around the world as means of delivering large-scale transportation infrastructure. However, it is unclear what implications this model of project delivery has for planning theory and practice as distinct from public policy more generally. Purpose: I aim to develop a set of criteria on which to evaluate the procedural, spatial, design, public policy, and political implications of these PPPs from a planning perspective, and then to examine whether their proposed benefits are matched by real-world experiences. Methods: I examine in depth three case studies of transportation projects delivered through such PPPs: the Croydon Tramlink in London, UK; the State Route 91 toll lanes in Orange County, CA; and the Cross City Tunnel in Sydney, Australia. Results and conclusions: The short-term and long-term impacts of such PPPs on infrastructure project delivery were different. In the short term, the model was effective at raising funds for new facilities and transferring financial risks to the private sector, but its limited transparency minimized meaningful community engagement in project planning. Over the longer term, noncompetition clauses in the concession agreements restricted government flexibility to respond to changing conditions, lawsuits were common as relationships between the partners deteriorated, and all three concessions were ultimately sold under duress, two to public sector agencies. Takeaway for practice: These PPPs profoundly affected the planning processes for public infrastructure and the outcomes achieved once such facilities were operational. This research identifies the need for new ex ante evaluation tools, risk transfer mechanisms, community engagement processes, and data tracking and performance monitoring procedures, to prevent negative consequences from transportation PPPs. Research support: None.
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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 | 0.029 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".