Public-Private Partnerships for Transport Infrastructure: Exploring Conditions and Links to Sustainability
Bibliographic record
Abstract
The provision of transport infrastructure and services creates fundamental value to society. With traditional sources of transport public funding running short, governments around the world are increasingly turning to public-private finance (PPPs) as a promising tool of public infrastructure project provision. PPPs have proven to be attractive to governments around the world, including Australia, Belgium, Canada, France, Spain, the UK and USA. Sustainability is a priority for many governments and a variety of tools and planning procedures exists for evaluating and assessing economic, environmental and social impacts related to regular transport public project procurement. The literature on transport infrastructure PPPs evaluations is extensive but lacks an explicit systematic reporting linking the PPP process and results to key sustainability principles. This paper contributes to filling this gap presenting the results of a literature study and exploratory review which offers an: 1) identification of key principles of transport sustainability from the literature, that have significance to infrastructure PPPs process and implementation assessment; 2) an overview of challenges that can be posed in infrastructure PPPs implementation concerning sustainability highlighted from the literature and, 3) suggest consideration of three steps: articulation, integration and evaluation and the suggestion that they can become framing elements to emphasize the correspondence between conditions for sustainability in the PPP lifecycle process. The review and analysis presented supports the following conclusions: Although flexibility needs to prevail in every PPP process, when a society’s objective is to achieve greater balance between PPP’s and sustainability, opportunities will be stronger when there is: a) the adoption of a systematic approach that is applied early in the process and runs throughout the entire PPP lifecycle process as exemplified here 2) the establishment of a strong publicly-accountable decision-making authority capable of following the approach through and 3) the commitment not only of the public but of the private actor as well, to act perhaps motivated by corporate social responsibility, committing to bringing innovation and transparency in their efforts for advancing sustainability.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".