Identifying Factors Affecting the Sustainability of Water Environment Treatment Public-Private Partnership Projects

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Abstract

Sustainability has recently been acknowledged as a crucial issue in infrastructure projects. Developing a model to evaluate project sustainability according to sustainability indicators plays a major role in promoting the sustainable development of water environment treatment public-private partnership (PPP) projects. Traditional sustainability assessments are mostly based on the triple bottom line (economic, social, and environmental) and lack a more integrated indicator system. To connect the research gap, this paper identifies 27 factors that affect the sustainability of water environment treatment PPP projects from five dimensions: economy, society, resources and environment, engineering, and project management using exploratory factor analysis. The fitting degree between the model and original data is verified by confirmatory factor analysis. The results showed that the fitting was successful. This paper makes two contributions: first, it provides a comprehensive sustainability evaluation indicator system from five aspects, laying a foundation for the evaluation of project sustainability. Second, this study defines a methodology to evaluate and rank factors, identifies the indicators that show the most significant impact on project sustainability in the five dimensions, which provide a reliable reference for the public and private sector to take appropriate measures to improve the sustainability level of water environment treatment public-private partnership projects.

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Reporting practices in confirmatory factor analysis: an overview and some recommendations.

Dennis Jackson, J Arthur Gillaspy, Rebecca Purc-Stephenson (2009)

Reporting practices in 194 confirmatory factor analysis studies (1,409 factor models) published in American Psychological Association journals from 1998 to 2006 were reviewed and compared with established reporting guidelines. Three research questions were addressed: (a) how do actual reporting practices compare with published guidelines? (b) how do researchers report model fit in light of divergent perspectives on the use of ancillary fit indices (e.g., L.-T. Hu & P. M. Bentler, 1999; H. W. Marsh, K.-T., Hau, & Z. Wen, 2004)? and (c) are fit measures that support hypothesized models reported more often than fit measures that are less favorable? Results indicate some positive findings with respect to reporting practices including proposing multiple models a priori and near universal reporting of the chi-square significance test. However, many deficiencies were found such as lack of information regarding missing data and assessment of normality. Additionally, the authors found increases in reported values of some incremental fit statistics and no statistically significant evidence that researchers selectively report measures of fit that support their preferred model. Recommendations for reporting are summarized and a checklist is provided to help editors, reviewers, and authors improve reporting practices.