English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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 &gt; construction completion &gt; operational &gt; ecological &gt; 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.

Risk Assessment of Water Environment Treatment PPP Projects Based on a Cloud Model