Research on External Risk Prediction of Belt and Road Initiative Major Projects Based on Machine Learning

Siyao Liu and Changfeng Wang ()
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Siyao Liu: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
Changfeng Wang: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China

Sustainability, 2025, vol. 17, issue 20, 1-20

Abstract: The Belt and Road Initiative (BRI) represents one of the world’s most ambitious transnational infrastructure and investment programs, but its implementation faces considerable external risks. Specifically, these risks include geopolitical instability, regulatory disparities, socio-cultural conflicts, and economic volatility, which threaten project continuity, economic viability, and sustainability of the BRI framework. Consequently, effective risk recognition and prediction has become crucial for mitigating disruptions and supporting evidence-based policy formulation. What should be noticed is that existing risk management frameworks lack specialized, dynamically adaptive indicator systems capable of forecasting external risks specific to international engineering projects under the BRI. They tend to rely on static and traditional methods, which are ill-equipped to handle the dynamic and nonlinear nature of these transnational challenges. To address this gap, we have developed a machine learning-based early warning system. Drawing on a comprehensive dataset of 31 risk indicators across 155 BRI countries from 2013 to 2022, we constructed a stacked ensemble model optimized via Grid Search. The resulting ensemble model demonstrated exceptional predictive performance, achieving an R 2 value of 0.966 and outperforming all baseline methods significantly. By introducing a data-driven early-warning framework, our study contributes to more resilient infrastructure planning and improved risk governance mechanisms in the context of transnational cooperation initiatives.

Keywords: BRI; machine learning; risk prediction; stacking ensemble model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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