Abstract
Public-Private Partnership (PPP) projects represent a pivotal strategy in addressing infrastructure funding shortages. Originating in the 1980s, the PPP model gained traction globally, with China entering its standardization phase in 2014. However, the extensive scale, prolonged construction periods, and diverse stakeholders involved in PPP projects introduce multifaceted risks. To navigate these challenges, the Ministry of Finance emphasized the need for accurate risk identification in 2019. This paper integrates the "accurate identification" theory with big data technology, providing a theoretical foundation and technical support for precise risk assessment in PPP projects. The 5V characteristics of big datascale, diversity, low-value density, data validity, and rapid processingenable cost-effective and efficient risk analysis. Key risks, including financial, government credit, pricing mechanism, force majeure, and approval delays, are identified. The "accurate identification" theory and big data facilitate not only risk identification but also preventive measures. This comprehensive approach aims to enhance decision-making, reduce conflicts, attract social capital, and improve the overall efficiency of PPP projects.
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