A CatBoost-Based Prediction Framework for Logistics Industry Prosperity Index to Support Sustainable Decision-Making: An Empirical Study from China

The logistics industry serves as a vital engine for economic growth, yet its prosperity is influenced by complex and dynamic factors. Accurate forecasting of the Logistics Industry Prosperity Index (LPI) is essential for optimizing resource allocation, enhancing operational efficiency, and mitigating potential risks, thereby supporting sustainable development and digital transformation. However, existing forecasting models often struggle with flexibility, interpretability, and handling complex nonlinear data. To address these challenges, this study proposes an innovative prediction framework based on the CatBoost algorithm and constructs an end-to-end prediction process integrating Bayesian optimization for hyperparameter tuning and a multidimensional evaluation system. The proposed framework is validated using a unique multidimensional dataset comprising 12 key indicators from Lanzhou City, China, spanning January 2022 to March 2025. Empirical results demonstrate that the CatBoost model significantly outperforms traditional and other machine learning approaches, including ARIMA, SVM, and XGBoost, achieving an R2 of 0.963 and a MAPE of 0.001%. From a theoretical perspective, this study enriches logistics prosperity forecasting and early-warning methodologies by introducing a highly accurate and robust learning-based framework. From a practical perspective, it provides governments and logistics enterprises with a reliable, data-driven tool for real-time decision support, strategic planning, and proactive risk management.