Climate risk and private participation projects in infrastructure
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the impact of climate risk on the success vs failure of foreign direct investments (FDIs) in private participation infrastructure (PPI) projects. The authors also consider the extent to which project-level characteristics mitigate such risks. Design/methodology/approach The authors study a sample from the World Bank covering 18,846 projects in 111 countries from 2004 to 2013. The authors apply logistic regressions to determine the impact of climate risk and mitigating project characteristics on project failure. Findings The authors find that higher levels of climate risk at the host country level are associated with higher risk of project failure. The authors also find that the disadvantage of higher climate risk is weakened by two project-level characteristics, namely, the inclusion of host government ownership in the project consortium and the size of the project. Originality/value The research contributes to the current debate about the impact of climate risks on international business ventures. The authors demonstrate that climate risk is a locational disadvantage for FDI in PPI projects. The authors establish that the “fittest” projects in locations characterized by higher climate risk tend to be those that involve host government participation in their ownership structure as well as those of larger sizes.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".