Performance monitoring and evaluation of water environment treatment PPP projects with multi-source heterogeneous information

Su, Limin; Cao, Yongchao

doi:10.3389/fenvs.2022.1024701

Cited by 7 publications

(2 citation statements)

References 60 publications

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“…Sun et al (2019) construct an operational performance measurement system for waste treatment projects. Su and Cao (2022) develop a performance monitoring and measurement model for water environment treatment public-private partnership projects. The above performance management research can provide reference for the performance measurement at the transfer phase, and make the performance management research in the whole-of-life cycle of PPP continuous and complete.…”

Section: Ppp Project Performance Measurement Research At the Transfer...mentioning

confidence: 99%

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Structural Equation Model-Based Performance Measurement System for Transferring PPP Water Projects: A China Perspective

Chen,

Liu,

Zhang

et al. 2024

Journal of Civil Engineering and Management

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A series of performance measurement need to be carried out throughout the project life cycle to ensure the successful transfer of public-private partnership (PPP) assets and meet stakeholder needs. More and more water projects are stepping into the transfer phase, but less studies carry out in-depth and systematic discussion on the performance measurement of PPP water projects at the transfer phase. Hence, to fill this gap, this research establishes a new performance measurement system (PMS) for evaluating the performance of the PPP projects stepping into the transfer phase based on the key performance indicators (KPIs). Through case study, expert interview and questionnaire, this paper formulates the logical basis behind PPP water projects at the transfer phase and subsequently constructs the transfer performance measurement system (TPMS) of these projects. Then, it conducts a confirmatory analysis of the impact relationship between the indicators based on structural equation modeling (SEM). Findings of expert interview and questionnaire indicate that there are 7 primary and 26 secondary indicators, and the model has a good fit. The TPMS will provide governments, operators and other stakeholders with a comprehensive and complete understanding as to indicators required of an effective performance measurement of PPP water projects.

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Section: Ppp Project Performance Measurement Research At the Transfer...mentioning

confidence: 99%

“…a few studies focus on transfer phase, most researchers carry out research on construction phase (Xu et al, 2022), operation phase (Sun et al, 2019;Ming et al, 2021;Su & Cao, 2022) and whole life circle of PPP projects (Yao & Chen, 2012;Ismail et al, 2021).…”

mentioning

confidence: 99%

Structural Equation Model-Based Performance Measurement System for Transferring PPP Water Projects: A China Perspective

Chen,

Liu,

Zhang

et al. 2024

Journal of Civil Engineering and Management

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Principles and Elements for Creating and Sustaining Successful PPP for Environmental Community Monitoring Programs: Results from a Scoping Review and Interviews

Rule,

Wagner,

Negi

et al. 2024

Environmental Justice

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Construction and Classification Prediction of Risk Assessment Iindicators for Water Environment Treatment PPP Projects

Yang

Feng

Sun

2023

Preprint

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Water pollution control is crucial for ecological environmental safety and sustainable socio-economic development. Public Private Partnership (PPP) collaboration is an important approach for water pollution control, but it faces numerous risks. Accurately assessing and predicting these risks is essential for ensuring effective water pollution management. This study aims to develop an effective risk classification prediction model for water environment treatment PPP projects, addressing the limitations of traditional methods. First, based on the relevant research on the risk assessment system for water environment treatment PPP projects, a risk data feature set of water environment treatment PPP projects consisting of four subsystems, namely, natural environment, ecological environment, socio-economic, and engineering entity, is proposed. Second, the association between different feature indicators and project risk levels is analyzed from a statistical perspective, and the contribution value of risk features is obtained. Then, an ensemble learning model based on Stack-ing is established to predict the risks of water environment treatment PPP projects. To improve the model's performance, a weighted voting mechanism is designed by introducing weight factors to adjust the relative importance of base learners during the voting process, allowing the model to better exploit the differences between base learners and improve prediction accuracy. Finally, an empirical analysis is conducted on the Phase I project of the comprehensive management of the water environment system in the central urban area of Jiujiang City, China, verifying the effectiveness and accuracy of the risk assessment system and evaluation model constructed in this study. Experimental results show that the constructed Water Environment Treatment Project Risk Support Vector Machine (WETPR-SVM) model outper-forms other traditional single machine learning classification models in terms of accuracy, macro-average precision, macro-average recall, and macro-average value, providing an effective method for risk classification prediction of water environment treatment PPP projects.

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Performance monitoring and evaluation of water environment treatment PPP projects with multi-source heterogeneous information

Cited by 7 publications

References 60 publications

Structural Equation Model-Based Performance Measurement System for Transferring PPP Water Projects: A China Perspective

Structural Equation Model-Based Performance Measurement System for Transferring PPP Water Projects: A China Perspective

Principles and Elements for Creating and Sustaining Successful PPP for Environmental Community Monitoring Programs: Results from a Scoping Review and Interviews

Construction and Classification Prediction of Risk Assessment Iindicators for Water Environment Treatment PPP Projects

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