Impact of Risk, Subsidy, and Bid-Criteria on the Private Investment in Public–Private Partnerships in Infrastructure Projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Public–Private Partnerships (PPPs) are formed to finance and deliver large infrastructural projects that may not be entirely feasible by governments alone. This study investigates the intricate role of financial risks, subsidies, and bidding criteria in the context of PPPs in India, and their relationship to the amount and extent of investments made by private partners. Studies have claimed that the success of PPP projects is determined by the type of funding, the nature of risk undertaken by investors, and the bidding criteria used by a government to attract investors. However, there is sparse literature on these variables impacting the private investment in these projects. Thus, in an attempt to address this gap, we collated data from the World Bank for a ten-year period (i.e., 2009 to 2019) for the study variables, and used regression to analyze the hypotheses, while adopting both SPSS 24 and PROCESS Macro. This study disapproved some commonly held notions of risk relationships, such as the government using “viability gap” funding to attract private investment, and that “leverage” does not moderate the relationship between risk assumed and private investment, thereby contributing to the literature on private investment in PPPs as impacted by several factors. This study is among the first to recognize and elaborate on financial risk relationships, specifically in the context of Indian PPPs. These findings are significant for both private and public participants in terms of financial considerations in PPP projects, especially within the ambits of emerging markets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it