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Performance Evaluation of Water Environment Treatment PPP Projects Based on Multisource Spatiotemporal Data Fusion.
- Source :
- Journal of Infrastructure Systems; Mar2024, Vol. 30 Issue 1, p1-12, 12p
- Publication Year :
- 2024
-
Abstract
- The performance data of water environment treatment public–private partnership (PPP) projects have the characteristics of multiple data sources, diverse types, and difficulties in integration. Based on existing literature and the characteristics of water environment treatment projects, this study establishes a performance evaluation index system for water environment treatment PPP projects. To address the multitemporal performance data, the data similarity method is employed to integrate multiple time data, while an ordered weighted averaging (OWA) operator is used to integrate multiple spatial location data. For indicator weighting, the OWA operator and overweighted weighting method are applied to allocate weights. Lastly, the Dempster–Shafer (D-S) evidence theory is utilized to evaluate project performance and determine the level of project performance. This model is implemented in the Pingyu County water environment treatment PPP project, yielding an overall performance level of level II (better). Additionally, the model offers distinct performance levels for each indicator, with special purpose vehicle (SPV) company governance B1 , water conservancy facilities B3 , garden plants B6 , and bridges B7 all achieving level III (general). Future managers can concentrate on enhancing these indicators to improve project performance. The incorporation of this approach for integrating multiple spatiotemporal data in performance evaluation enhances the reliability of results and carries substantial practical implications for advancing the sustainable development of water environment governance PPP projects. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10760342
- Volume :
- 30
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Journal of Infrastructure Systems
- Publication Type :
- Academic Journal
- Accession number :
- 174815152
- Full Text :
- https://doi.org/10.1061/JITSE4.ISENG-2278