| Author: | Jayasena, Nimesha Sahani |
| Title: | Best practice public-private partnership (PPP) models for delivering smart infrastructure development projects |
| Advisors: | Chan, W. M. Daniel (BRE) |
| Degree: | Ph.D. |
| Year: | 2022 |
| Subject: | Public-private sector cooperation City planning -- Technological innovations Communication in city planning Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Building and Real Estate |
| Pages: | xxxi, 393 pages : color illustrations |
| Language: | English |
| Abstract: | Rapid globalisation amidst climate change and other environmental pressures has aggravated the need for better-focused high-value sustainable development. Furthermore, with rapid urbanisation, cities face complex challenges in achieving such sustainability. Therefore, the growing demands for urbanisation and focused sustainable development need to be merged into a common goal for urban development activities throughout the world. In achieving sustainable urban development, smart cities are identified as an ideal solution. Many governments in both developed and developing countries are in dire need of long-term planning to meet rapid population growth, urbanisation and environmental degradation, thereby increasing the significance of smart cities. In developing smart cities, smart infrastructure development is inseparable as societies are built on infrastructure systems such as water distribution networks, electrical supply grids, communication networks, and transportation infrastructure. They serve as the foundation for our daily life, allowing to receive commodities, information, and services. Smart infrastructure requires special systems and features compared to conventional infrastructure to monitor its performance and algorithms to analyse data and manage operations and maintenance (e.g. with more advanced software programmes, technologies, components, and applications, usually also involving digitalisation, robotics and artificial intelligence, hence also involving multiple stakeholders). Therefore, the development of smart infrastructure incurs higher costs and is liable to higher risks than conventional infrastructure developments. Therefore, PPP is a potential solution, which could pool the resources of the public sector and private sector to deliver smart infrastructure more effectively and efficiently, enhancing the quality of life of the citizens. Therefore, the research aims to develop best practice models to study the use of PPP for optimising and accelerating the delivery of smart infrastructure in regions/ countries at different stages of development, based on their current scenarios and differing aspirations. The scope of the research study is limited to investigating the influence of the perceived benefits and enablers, barriers and challenges, and recommended strategies and enhancing factors in applying PPPs in smart infrastructure projects and proposing a useful model for predicting the success of PPPs in smart infrastructure development projects. Further, the success of PPP in smart infrastructure developments was measured in terms of public sector satisfaction (S1), private sector satisfaction (S2) and end-user satisfaction (S3). Six objectives were lined up in achieving the research aim of the study: 1. To review the goals and concepts of a smart city, the potential contributions of smart infrastructure for developing a smart city and the importance of smart infrastructure. 2. To review the main thrusts of PPP and popular PPP models used in smart infrastructure developments in general. 3. To develop a conceptual framework for illustrating the factors affecting the application of PPP to develop smart infrastructure. 4. To investigate the current best practices, plans and potential for leveraging PPPs to smart infrastructure developments. 5. To establish separate models to investigate the effect of the 'benefits and enablers', 'barriers' and 'recommended strategies and enhancing factors' influencing the success of PPP in smart infrastructure development projects in (i) developed; and (ii) developing regions/ countries. 6. To propose a model for predicting the success of PPP in smart infrastructure development projects in (i) developed; and (ii) developing regions/ countries. Objective 1 was achieved through a comprehensive literature review and expert interviews in both developed and developing countries. The importance and emergence of smart cities, definitions and models of smart cities, the importance of smart infrastructure for the development of smart cities and a definition for smart infrastructure were determined in achieving Objective 1. Further, the current status of smart infrastructure developments worldwide and the enablers and barriers to developing smart infrastructure were identified. Objective 2 was achieved through literature review, expert interviews and a questionnaire survey. The characteristics of PPPs and the importance of PPPs for smart infrastructure were further investigated and identified under this objective. Benefits and enablers, barriers and recommended strategies and enhancing factors affecting the success of PPP in smart infrastructure development projects were identified through literature review and expert interviews. Another set of expert interviews was conducted to investigate the factors affecting PPP in smart infrastructure developments in non-urban areas as well from which complementary findings were derived from this hitherto less-researched area, the focus having mostly been on smart cities before. Quantitative analysis of all factors identified through all the expert interviews, affecting the success of PPP in smart infrastructure development projects was also carried out by using the Interpretive Structural Modelling (ISM) technique, "Matrices d'Impacts Croises-Multiplication Applique a Classement (MICMAC) analysis and Analytic Hierarchy Process (AHP) method. Through the findings of the literature review, a conceptual framework was developed under Objective 3. Comparison of the factors identified through the literature review and the expert interviews were conducted in achieving Objective 4. Accordingly, a final list of the benefits and enablers, barriers and recommended strategies and enhancing factors were obtained and were categorised as per the expert opinions. A pilot survey was carried out to validate the identified factors and to improve the clarity and the readability of the factors. As a result, 23 benefits and enablers were identified and were categorised into four constructs 'political and legal', 'financial and economic', 'technical' and 'social'. Four constructs of barriers were also identified as 'financial and economic', 'legal and socio-political', 'social' and 'technical', which include 36 barriers. Another 25 recommended strategies and enhancing factors were identified and they were categorized into five constructs including 'stakeholder' related, 'legal and socio-political' related, 'technical' related, 'social' related, and 'financial' related. Separate models investigating the effect of the benefits and enablers, barriers, recommended strategies and enhancing factors on the success of PPP in delivering smart infrastructure were developed through the findings of an international questionnaire survey, which spanned across 10 developed countries and 7 developing countries. Models were developed by using the Partial Least Squares Structural Equation Modelling (PLS-SEM) method separately for developed countries and developing countries. Case studies, one each for developed and developing countries were also carried out to validate the models, achieving Objective 5 of the study. Finally, three Machine-Learning (ML) algorithms (Linear Regression, Decision Tree and Random Forest analysis) were evaluated to propose a customized model with the best prediction performance for predicting the success of PPP in delivering smart infrastructure for developed countries and developing countries as postulated in Objective 6. Accuracy was utilised as the metric to quantify the predictive performance of the models developed through ML algorithms. Accuracy is the percentage of model predictions that matched the true stakeholder satisfaction (i.e. correctly predicted by the model). It was identified that the overall best models came as outcomes of the random forest algorithm. Hence, random forest models could be used to predict the success of PPP in delivering smart infrastructure developments in developed and developing countries. The findings of the whole study have generated salient and significant contributions to both theory and practice. They have provided valuable insights into the causal relationships of the factors affecting the success of PPP in delivering smart infrastructure in both developed and developing countries in terms of public sector satisfaction (S1), private sector satisfaction (S2) and end-user satisfaction (S3). Further, a model was proposed to predict the success of PPP in delivering smart infrastructure. These insights, which were indicated and explained qualitatively and quantitatively, have contributed to the development of the theoretical concepts underlying the application of PPP in smart infrastructure and also to the existing literature base. Moreover, for construction industry policymakers and the project/ construction management sub-sector, these findings provide a solid basis to avoid potential complications in PPP procured smart infrastructure development projects and arm all the stakeholders with some useful pointers on how remedial actions may be planned, prioritized and executed. The models for recommended strategies and enhancing factors could be adopted after the project initiation, which would help to enhance the outcomes and increase the success rate of PPP in delivering smart infrastructure. Overall, the findings of this study have established a strong foundation for understanding how these diverse factors interact in these projects; in turn, helping to plan and deliver more successful PPP involved smart infrastructure projects in both developed and developing countries. |
| Rights: | All rights reserved |
| Access: | open access |
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