Journal of Applied Science and Engineering

Published by Tamkang University Press

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Application of Data-Driven Block Chain Technology in International Trade Industry Value Chain Division

 2026

 Volume 33

Lin Yang

School of International Trade and Economics, Shandong University of Finance and Economics, Jinan 250014, China

Received: April 7, 2026
Accepted: May 21, 2026
Publication Date: June 27, 2026

上傳圖片

Trade efficiency before and after blockchain adoption

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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This study investigates the application of data-driven blockchain technology in the international trade industry value chain. To address issues of lag, fragmentation, and low credibility in traditional multi-agent data systems, we construct a unified sample integrating trade data, on-chain records, and enterprise operational metrics. Core indices measuring blockchain adoption and value chain division are established. Employing regression analysis,
machine learning models, and moderation effect models, we quantify the impact of on-chain activities. Results demonstrate that blockchain’s transparent and immutable data structure enhances inter-firm collaborative stability, leading to a sustained rise in the value chain division index. Furthermore, synchronized on-chain recording significantly improves trade efficiency, reducing customs clearance time, reconciliation cycles, and
logistics node delays by 15% to 48%. The effect is more pronounced in high-tech industries. This work elucidates the functional pathway of blockchain within international trade systems, providing a verifiable, repeatable, and extensible analytical framework to inform trade governance and corporate digital strategy.

Keywords: Statistics; Block chain; International trade; Value chain division of labor; Trade efficiency; Data-driven model

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