Risk Assessment of Water Environment Treatment PPP Projects Based on a Cloud Model
Risk assessment of public-private partnership projects has been recently acknowledged as a crucial issue in infrastructure projects. Objective assessment of risk status is conducive to the establishment of scientific and reasonable management measures. The particularity of evaluating water environme...
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Hindawi Limited
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/b33ca13c05934f26a446f823bf2e35c8 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Risk assessment of public-private partnership projects has been recently acknowledged as a crucial issue in infrastructure projects. Objective assessment of risk status is conducive to the establishment of scientific and reasonable management measures. The particularity of evaluating water environment treatment PPP projects means that random errors in the evaluation index and the threshold fuzziness of evaluation degrees are issues that require attention. This paper uses the Pythagorean fuzzy cloud model to process the randomness and fuzziness of the indicators. This study assessed the risks of an iconic water environment treatment PPP project inn mid-China. The risk ranks were evaluated in terms of five dimensions: political, economic, construction completion, operational, and ecological. Moreover, the results of the evaluation were compared with results derived using a regular cloud model. It was found that the Pythagorean fuzzy cloud model produced results consistent with the regular method, while also having the advantage of reflecting the randomness and fuzziness of the evaluation indicators. According to the evaluation data in this case, the project risks were ranked as follows: political > construction completion > operational > ecological > economic. The overall project risk was medium. This study’s results could provide technical support for water treatment PPP project risk assessment, indicator measurement, and statistical error control. |
|---|