Research on Predicting China’s Social Logistics Costs from an Industrial Demand Perspective

DOI

10.2991/978-94-6463-992-6_49How to use a DOI?

Keywords

Social Logistics Costs; Industrial Demand; Support Vector Regression; Grey Wolf Optimization; Scenario Simulation

Abstract

The abstract should summarize the contents of the paper in short terms, i.e. 150–250 words. Reducing social logistics costs is a key pathway to driving high-quality economic development. This study employs Bayesian networks to probabilistically infer industry development states under different economic scenarios from an industrial demand perspective. It generates simulated data for high-, medium-, and low-speed economic growth scenarios to characterize potential pathways of future economic development’s impact on logistics costs. A Support Vector Regression model optimized by Partial Least Squares dimension reduction and Grey Wolf Optimization (PLS-GWO-SVR) is proposed to simulate China’s social logistics cost evolution over the next 30 years. Results indicate that industrial structure upgrading, economic growth, and transportation efficiency improvements are key drivers of logistics cost reduction. The PLS-GWO-SVR model demonstrates optimal fitting performance. Projections suggest that under high-speed economic growth conditions, the ratio of logistics costs to GDP is projected to decline to 13.8% by 2027.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Mengyuan Zhang
AU  - Song Han
AU  - Weilu Sun
PY  - 2026
DA  - 2026/02/20
TI  - Research on Predicting China’s Social Logistics Costs from an Industrial Demand Perspective
BT  - Proceedings of the 2025 4th International Conference on Mathematical Statistics and Economic Analysis (MSEA 2025)
PB  - Atlantis Press
SP  - 525
EP  - 537
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-992-6_49
DO  - 10.2991/978-94-6463-992-6_49
ID  - Zhang2026
ER  -