Article Preview
TopIntroduction
In recent years, with a series of technological breakthroughs such as higher computational intelligence, faster speed, lower energy consumption, and greater comfort, the intelligent subway has become a collection of “intelligent manufacturing,” “intelligent transportation,” and “smart city” technology, comprehensively demonstrating the strength and style of China’s intelligent manufacturing in urban rail transit. With the steady development of China’s economy and society and the acceleration of the country’s urbanization process, the urban railways have ushered in a golden period of rapid expansion (Tay & Mourad, 2020; Dhaini & Mansour, 2021; Tout et al., 2021). The introduction of competitive market mechanisms for investment and financing of urban railways has led to the emergence of diversified investors, varied financing modes, and complex financing structures (Chehab & Mourad, 2020; Peñalvo et al., 2022). These features have, to some extent, solved the difficulty in financing urban railway projects; but the long construction periods, large investment scale, extensive coverage, and technical complexity of urban railway projects also bring greater risks for rail transit construction (Stergiou et al., 2021; Al-Qerem et al., 2020; Ramaru et al., 2022). Therefore, a correct understanding and analysis of the investment and financing risks of urban railways on the one hand, and an accurate assessment of these projects on the other, are important means of effectively controlling the investment and financing risks of urban railways, as well as achieving the sustainable development of urban railways (Alakbarov, 2022; Lu et al., 2021; Lin, 2021).
Considerable research on urban railway investment and financing risk assessment methods has been conducted, yielding notable results. By identifying the risk factors at all stages of the urban rail transit financing process, a risk evaluation model for urban rail transit project financing has been established based on the extension theory of Liu et al. (2019). However, this method takes each financing risk as an independent factor, ignoring the dynamic correlation among them. Wong et al. (2022) explored the efficacy of fiscal policy in addressing the financing challenges of high-speed rail construction, and conducted a study on the economic and environmental benefits of high-speed rail construction investment for urban development, using the statistics of high-speed rail construction in Chinese cities from 2003 to 2018. However, this study relies too much on the knowledge and experience of experts, which is subjective. Lee et al. (2021) provided a comparative analysis of different assessment criteria and tools used by the private sector, financial institutions, and government contracting agencies to assess the feasibility of urban rail projects and to transfer risks to the party best suited to handle them by optimizing the allocation of risks to minimize the cost. However, this approach only transfers the risks and does not fundamentally offer a solution corresponding to the financing risk assessment. Lv et al. (2020) observed that partners involved in PPPs(Public-Private Partnership) share common interests but come into conflict regarding the value of government subsidies. These authors proposed a method to address this problem by calculating the equitable subsidy ratio favored by all participants, taking into account the uncertain nature of PPPs and the incomplete information used in the decision-making process. However, this method becomes less effective with an excess of sample data, as it is susceptible to interference from redundant information, which reduces learning efficiency.