Mega Infrastructure Projects in China

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Jianwang Wang	--	1730	2023-11-09 03:37:53	\|
2	update references and layout	Rita Xu	Meta information modification	1730	2023-11-09 03:44:51	\|

This entry is adapted from the peer-reviewed paper 10.3390/su152115301

The “trillion-dollar era” of megaprojects has increased the demand for the scope of mega infrastructure. To address the requirement for high-quality “investment, construction, and operation” integration, the EPC and PPP models must be combined.

mega infrastructure projects PPP + EPC model decision-making risk identification

1. Introduction

Mega infrastructure projects are a crucial component of the support for social and economic growth. To maintain the necessary global economic growth, infrastructure costs will exceed USD 94 trillion in 2040, according to the report ^[1] released by the Global Infrastructure Hub. An additional USD 3.5 trillion will be required to achieve the Sustainable Development Goals (SDGs) aim of reducing global household water and electricity use by 2030. Hence, the cumulative global infrastructure requirements are projected to amount to the staggering sum of USD 97 trillion. Infrastructure projects have the duty and obligation to generate wider social value beyond the primary advantages derived from constructing assets ^[2]. Consequently, mega infrastructure projects will be designed with more emphasis on their convergence patterns ^[3]. Furthermore, scholars express apprehension regarding the sustainability of mega infrastructure projects, owing to their extensive scope, substantial financial commitment, intricate technical nature, and profound societal implications ^[4].

Currently, there is a pressing need for mega infrastructure projects to thoroughly examine the path towards value addition and to reconstruct the value associated with public goods. The Public–Private Partnership (PPP) model and the Engineering Procurement Construction (EPC) model individually fail to adequately address the requirements of achieving high-quality integration goals in “investment–build–operation” projects. Consequently, a pragmatic solution to this predicament lies in the integration of the EPC and PPP models. Their comprehensive incorporation in mega infrastructure endeavors aligns with the construction contracting market and has the potential to foster innovation in the management practices of the construction sector. The subject matter possesses prospective scholarly significance and practical applicability ^[5]. Nevertheless, the use of the EPC + PPP model has certain inherent risks regarding policy, contract, construction, and operation. The existing literature and empirical evidence indicate that megaprojects are characterized by their inherent complexity, making it challenging to establish clear decision-making objectives. Furthermore, these projects operate within dynamic and evolving implementation environments ^[6], which further complicates the decision-making process. Additionally, decisionmakers often possess limited knowledge and understanding of the project, further exacerbating the complexity of the decision-making process ^[7]. The numerous sources of risk variables and their potential for irreversible incidents, as exemplified by the Rakum Grand Canal project in the former Soviet Union and the overturning of the water stop during the installation of the Busan Geoje Immersed Tube Tunnel, contribute to the complexity of decision-making processes ^[8]^[9]. The identification of risk factors associated with decision making and the thorough examination of their underlying mechanisms are crucial requirements for enhancing the effectiveness of risk management and promoting evidence-based decision making in large-scale initiatives ^[10].

2. Risk Studies on Government-Funded Projects

The foundation of risk management is in the identification, recognition, and categorization of risks. According to Furlong et al., it is posited that the dependability of national government funding will progressively diminish in the forthcoming years. Consequently, it is suggested that risk management procedures should be further enhanced to ensure the protection of project construction financing by expanding the customer base ^[11]. According to Gebre et al., the government is currently facing financial limitations in allocating funds towards road projects, leading to inadequate investment in road infrastructure. The facilitation of the execution of the PPP model for the project will be significantly influenced by this element ^[12]. Liu et al. analyzed the economic aspect of the primary engineering risks associated with China’s outward investment. They highlighted that the limitations and diverse measures imposed by developed nations on China, such as taxation, industrial policies, and market regulations, can significantly impede the progress of construction projects ^[13]. Zayed et al. employed a hierarchical analysis approach to assess the risk and uncertainty associated with road developments in China. The risk elements associated with road projects were categorized into two main types: macrorisks and microrisks. To assess the current risk state of the project and aid the contractor in making prompt and suitable decisions, a risk index was computed ^[14].

3. Risk Management Methods for Megaprojects

At this stage, there is limited research from domestic and foreign scholars on the risk management of megaprojects under the combined EPC + PPP model. The previous studies can serve as a reference for future research directions. Carbonara et al. identified key risk factors in infrastructure projects and produced a list of significant risks in two broad categories: demand/usage risks and cost overrun/financial closure risks ^[15]. Ozdoganm and Birgonu proposed six main types of risks that need to be considered during the planning phase of a PPP project. These include political, market, financial, legal and regulatory, construction, and operational risks. To assist in decision making, the authors have developed a framework for supporting PPP project decision-making processes ^[16]. Iyer and Sagheer identified 17 risks, such as legalistic risk, cost overrun risk, schedule overrun risk, etc., by analyzing road PPP projects in India ^[17].

The potential hazards associated with megaprojects implemented under the EPC and PPP model permeate the entirety of the project’s lifespan, and the elements contributing to these risks are intricate and subject to change. Hence, scholars employ Monte Carlo simulation ^[18], Fuzzy Comprehensive Evaluation ^[19], and Gray Correlation ^[20] methodologies to evaluate the identified risk variables, aiming to integrate qualitative and quantitative evaluations and provide a more intuitive depiction of the level of risk in the projects. Based on this premise, some scholars suggest approaches to ascertain shared risk through the integration of hybrid fuzzy techniques and Cybernetic Analysis Network Process (CANP) models ^[21]. Pythagorean Fuzzy Sets (PFSs) are employed to examine the allocation of risk between the government and investors ^[22]. Furthermore, Kardes et al. proposed a classification of project risks, distinguishing between internal and external risks ^[23]. Internal risks encompass factors such as organizational, financial, and personnel-related issues, while external risks pertain to political, subcontractor-related, and legal factors ^[24]. Cifrian et al. proposed a categorization of the PPP model into five distinct stages. It has been noted that infrastructure projects encompass two distinct categories of hazards: internal system risks and external environment risks ^[25].

Within the realm of risk assessment in engineering projects, researchers have extensively examined many approaches for evaluating risk. In their study, Hong et al. developed a comprehensive network diagram to analyze the various aspects that impact the advancement of home-building projects ^[26]. Miller et al. employed a hierarchical analysis approach to examine the attributes of the project. They established a set of criteria for evaluating the documents provided by the general contractor and sought the input of domain experts to assess and appraise these criteria ^[27]. Gordon et al. conducted a comprehensive analysis of the causes of operational risks leading to the failure of metro projects under the PPP model. They specifically examined a case study of a metro system that was entirely separate from its design, construction, and operations ^[28]. Ameyaw employed the Fuzzy Comprehensive Evaluation Method (FCM) as a means of assessing the hazards associated with PPP projects in underdeveloped nations ^[29].

4. Bayesian Modeling Research Applicable to Risk Assessment

The intricate nature and inherent ambiguity of megaprojects give rise to a multitude of constituent elements, each of which engenders potential dangers. The aforementioned aspects hold significant importance in the process of identifying risk interrelationships and transmission mechanisms, as well as critical risk variables and linkages. Nevertheless, the majority of the aforementioned approaches fail to consider the analysis of risk interdependencies and solely focus on evaluating the influence of individual risk variables on the project as a whole. Currently, there exists a limited number of scholarly investigations about the assessment of risk associated with decision making in megaprojects within the unique framework of EPC combined with PPP. The evaluation methodologies utilized in previous studies predominantly consist of explanatory structural models, social network analysis, and other similar approaches. While these methodologies examine the interconnectedness of risks, they do not possess the capability to thoroughly assess the qualitative and quantitative aspects of all risk factors. Hence, it is imperative to integrate qualitative and quantitative assessments to conduct risk identification in a more extensive and scientifically rigorous manner. Simultaneously, the utilization of the Bayesian network approach in simulation allows for a more intuitive and scientifically grounded identification of the pivotal aspects that influence decision-making risk. This method offers valuable insights and guidance for the development of risk management strategies.

The introduction of Bayesian network analysis can be attributed to Thomas Bayes during the 18th century. As the field of statistics progressed, Bayesian approaches gained recognition and were highly regarded ^[30]. Pearl (1986) introduced a graphical network that relies on probabilistic inference and provided a precise elucidation of Bayesian networks ^[31]. A Bayesian network is a graphical model that is commonly employed to conduct probabilistic inference ^[32]. The conventional Bayesian model is limited to representing a singular time slice structure, and when confronted with very intricate situations, it fails to account for the temporal impact on the entire process. A dynamic Bayesian network (DBN) is a type of Bayesian network that integrates temporal information to effectively describe time series data and capture the temporal dependencies among nodes ^[33]. The DBN is presently employed in a diverse range of applications, such as crisis warning ^[34], defect identification and route analysis ^[35], and data mining ^[36]. A DBN is a preferable choice when it comes to monitoring and predicting the values of random variables. Additionally, a DBN can effectively reflect the state of the system at any given point in time ^[37]^[38]. The predominant area of investigation within Bayesian research is the examination of logical reasoning in conjunction with empirical data. Conversely, there exists a dearth of scholarly inquiries that specifically address the use of Bayesian methods in practical engineering contexts ^[39]^[40].

In conclusion, the extant literature has examined the hazards associated with megaprojects; however, these studies have not adequately addressed the unique circumstances surrounding the integration of EPC with the PPP model. Furthermore, previous research has neglected to examine the interplay and evolving nature of risk factors. Hence, it is imperative to undertake comprehensive investigation, examination, and research regarding the hazards associated with megaprojects in China. The utilization of a DBN is employed to assess the risks associated with megaprojects, considering both dynamic factors and correlated aspects. In conclusion, the integration of game theory and mathematical modeling is utilized to develop a risk response decision model that takes into account the varying objectives of decisionmakers. This model facilitates a comprehensive study of significant risks and aids in making informed response decisions.

References

Global Infrastructure Hub. Available online: https://www.gihub.org/resources/publications/global-infrastructure-investment-index/ (accessed on 9 June 2023).
Freelove, S.; Gramatki, I. Creating long-term social value on major infrastructure projects: A case study. Eng. Sustain. 2022, 175, 186–193.
Floricel, S.; Abdallah, S.; Hudon, P.-A.; Petit, M.-C.; Brunet, M. Exploring the patterns of convergence and divergence in the development of major infrastructure projects. Int. J. Proj. Manag. 2022, 44, 102433.
Hosny, H.E.; Ibrahim, A.H.; Eldars, E.A. Development of infrastructure projects sustainability assessment model. Environ. Dev. Sustain. 2021, 24, 7493–7531.
Guixia, G.; Fang, Z.; Rui, Z. A study on the bilateral moral hazard of PPP projects in China. Ind. Econ. Rev. 2022, 13, 147–160.
Goodenough, R.A.; Page, S.J. Evaluating the environmental impact of a major transport infrastructure project: The Channel Tunnel high-speed rail link. Appl. Geogr. 1994, 14, 26–50.
Hyun, S.; Park, D.; Tian, S. Infrastructure Bond Markets Development in Asia: Challenges and Solutions. Glob. Econ. Rev. 2017, 46, 351–371.
Hillier, J. Politics of The Ring: Limits to Public Participation in Engineering Practice. Int. J. Urban Reg. Res. 2018, 42, 334–356.
Zhang, Q. Risk Management in Offshore Towing and Installation of Immersed Tunnel Tubes. Tunn. Constr. 2015, 35, 1150–1156.
Han, S.Z.; Long, X.X.; Shi, A. Constructing Theoretical System and Discourse System of Mega Infrastructure Construction Management with Chinese Characteristics. J. Manag. World 2019, 35, 2–16.
Furlong, C.; De Silva, S.; Gan, K.; Guthrie, L.; Considine, R. Risk management, financial evaluation and funding for wastewater and stormwater reuse projects. J. Environ. Manag. 2017, 191, 83–95.
Gebre, Y.K.; Demsis, B.A. Reasons for the Potential Implementation of Public-Private Partnerships in Ethiopian Road Infrastructure Provision. Adv. Civ. Eng. 2022, 2022, 4863210.
Liu, H.Y.; Tang, Y.K.; Chen, X.L.; Poznanska, J. The Determinants of Chinese Outward FDI in Countries Along “One Belt One Road”. Emerg. Mark. Financ. Trade 2017, 53, 1374–1387.
Zayed, T.; Amer, M.; Pan, J.Y. Assessing risk and uncertainty inherent in Chinese highway projects using AHP. Int. J. Proj. Manag. 2008, 26, 408–419.
Carbonara, N.; Costantino, N.; Gunnigan, L.; Pellegrino, R. Risk Management in Motorway PPP Projects: Empirical-based Guidelines. Transp. Rev. 2014, 35, 162–182.
Ozdoganm, I.D.; Birgonul, M.T. A decision support framework for project sponsors in the planning stage of build-operate-transfer (BOT) projects. Constr. Manag. Econ. 2010, 18, 343–353.
Iyer, K.C.; Sagheer, M. Hierarchical Structuring of PPP Risks Using Interpretative Structural Modeling. J. Constr. Eng. Manag. 2010, 136, 151–159.
Koulinas, G.K.; Demesouka, O.E.; Sidas, K.A.; Koulouriotis, D.E. A TOPSIS—Risk Matrix and Monte Carlo Expert System for Risk Assessment in Engineering Projects. Sustainability 2021, 13, 11277.
Zhang, Y.; Wang, R.; Huang, P.; Wang, X.; Wang, S. Risk evaluation of large-scale seawater desalination projects based on an integrated fuzzy comprehensive evaluation and analytic hierarchy process method. Desalination 2020, 478, 114286.
Zavadskas, E.K.; Vilutienė, T.; Turskis, Z.; Tamosaitienė, J. Contractor Selection For Construction Works By Applying SAW-G And Topsis Grey Techniques. J. Bus. Econ. Manag. 2010, 11, 34–55.
Valipour, A.; Yahaya, N.; Noor, N.M.; Mardani, A.; Antuchevičienė, J. A new hybrid fuzzy cybernetic analytic network process model to identify shared risks in PPP projects. Int. J. Strateg. Prop. Manag. 2016, 20, 409–426.
Dorfeshan, Y.; Taleizadeh, A.A.; Toloo, M. Assessment of risk-sharing ratio with considering budget constraint and distruption risk under a triangular Pythagorean fuzzy environment in public-private partnership projects. Expert Syst. Appl. 2022, 203, 117245.
Kardes, I.; Ozturk, A.; Cavusgil, S.T.; Cavusgil, E. Managing global megaprojects: Complexity and risk management. Int. Bus. Rev. 2013, 22, 905–917.
Sanchez, A.; Sanchez, J.; Cardona, R. Phylogenetic relationship according allergen sensitization pattern between 10 mites in a tropical area. Allergy 2017, 72, 545.
Cifrian, E.; Andrés, A.; Galán, B.; Viguri, J.R. Integration of different assessment approaches: Application to a project based learning engineering course. Educ. Chem. Eng. 2020, 31, 62–75.
Li, C.Z.; Hong, J.; Xue, F.; Shen, G.Q.; Xu, X.X.; Mok, M.K. Schedule risks in prefabrication housing production in Hong Kong: A social network analysis. J. Clean. Prod. 2016, 134, 482–494.
Miller, K.D. A framework for integrated risk management in international business. J. Int. Bus. Stud. 1992, 23, 311–331.
Gordon, C.; Mulley, C.; Stevens, N.; Daniels, R. How optimal was the sydney metro contract?: Comparison with international best practice. Res. Transp. Econ. 2013, 39, 239–246.
Ameyaw, E.E.; Chan, A.P. Evaluation and ranking of risk factors in public–private partnership water supply projects in developing countries using fuzzy synthetic evaluation approach. Expert Syst. Appl. 2015, 42, 5102–5116.
Spooner, S. An essay towards solving a problem in the doctrine of chances. Resonance 2003, 8, 80–88.
Pearl, J. Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning. In Proceedings of the 7th Conference of the Cognitive Science Society, Irvine, CA, USA, 15–17 August 1985.
Jose, S.; Louis, S.; Dascalu, S.; Liu, S. Transfer Learning-based Hybrid Approach for Bayesian Network Structure Learning. Int. J. Artif. Intell. Tools 2022, 31, 2260003.
Shuo, C.; Pazilai, M. Research on Reliability of Inverter System Based on Bond Graph and Dynamic Bayesian Network. J. China Three Gorges Univ. (Nat. Sci.) 2022, 44, 101–107.
Dabrowski, J.J.; Beyers, C.; de Villiers, J.P. Systemic banking crisis early warning systems using dynamic Bayesian networks. Expert Syst. Appl. 2016, 62, 225–242.
Amin, M.T.; Khan, F.; Imtiaz, S. Fault Detection and Pathway Analysis using a Dynamic Bayesian Network. Chem. Eng. Sci. 2019, 195, 777–790.
Sheidaei, A.; Foroushani, A.R.; Gohari, K.; Zeraati, H. A novel dynamic Bayesian network approach for data mining and survival data analysis. BMC Med. Inform. Decis. Mak. 2022, 22, 251.
Cuaya, G.; Munoz-Meléndez, A.; Carrera, L.N.; Morales, E.F.; Quinones, I.; Pérez, A.I.; Alessi, A. A dynamic Bayesian network for estimating the risk of falls from real gait data. Med. Biol. Eng. Comput. 2013, 51, 29–37.
Wu, X.; Liu, H.; Zhang, L.; Skibniewski, M.J.; Deng, Q.; Teng, J. A dynamic Bayesian network based approach to safety decision support in tunnel construction. Reliab. Eng. Syst. Saf. 2015, 134, 157–168.
Rizzi, F.; Khalil, M.; Jones, R.; Templeton, J.; Ostien, J.; Boyce, B. Bayesian modeling of inconsistent plastic response due to material variability. Comput. Methods Appl. Mech. Eng. 2019, 353, 183–200.
Duhr, C.; Huss, A.; Mazeliauskas, A.; Szafron, R. An analysis of Bayesian estimates for missing higher orders in perturbative calculations. J. High Energy Phys. 2021, 2021, 122.

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