Modeling the Ningbo Container Freight Index Through Deep Learning: Toward Sustainable Shipping and Regional Economic Resilience

Haochuan Wu () and Chi Gong
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Haochuan Wu: Department of Global Business, Kyonggi University, Suwon 16227, Republic of Korea
Chi Gong: Department of Global Business, Kyonggi University, Suwon 16227, Republic of Korea

Sustainability, 2025, vol. 17, issue 10, 1-21

Abstract: With the expansion of global trade, China’s commodity futures market has become increasingly intertwined with regional maritime logistics. The Ningbo Containerized Freight Index (NCFI), as a key regional indicator, reflects freight rate fluctuations and logistics efficiency in real time. However, limited research has explored how commodity futures data can enhance NCFI forecasting accuracy. This study aims to bridge that gap by proposing a hybrid deep learning model that combines recurrent neural networks (RNNs) and gated recurrent units (GRUs) to predict NCFI trends. A comprehensive dataset comprising 28,830 daily observations from March 2017 to August 2022 is constructed, incorporating the futures prices of key commodities (e.g., rebar, copper, gold, and soybeans) and market indices, alongside Clarksons containership earnings. The data undergo standardized preprocessing, feature selection via Pearson correlation analysis, and temporal partitioning into training (80%) and testing (20%) sets. The model is evaluated using multiple metrics—mean absolute Error (MAE), mean squared error (MSE), root mean square error (RMSE), and R 2 —on both sets. The results show that the RNN–GRU model outperforms standalone RNN and GRU architectures, achieving an R 2 of 0.9518 on the test set with low MAE and RMSE values. These findings confirm that integrating cross-market financial indicators with deep sequential modeling enhances the interpretability and accuracy of regional freight forecasting. This study contributes to sustainable shipping strategies and provides decision-making tools for logistics firms, port operators, and policymakers seeking to improve resilience and data-driven planning in maritime transport.

Keywords: NCFI; futures market; sustainable shipping; RNN–GRU; machine learning (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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