Exploring the drivers of digital transformation in Chinese port and shipping enterprises: A machine learning approach
中国港湾・海運企業におけるデジタルトランスフォーメーションの推進要因の探求：機械学習アプローチ (AI 翻訳)
Jiahui Jin, Yongchun Guo
🤖 gxceed AI 要約
日本語
本研究は、中国の港湾・海運企業83社（2008～2023年）を対象に、機械学習（Ridge回帰、LightGBM、XGBoost）を用いてデジタルトランスフォーメーション（DX）の推進要因を分析。非線形モデルが線形モデルより優れており、グリーンファイナンスを支援する環境の重要性が示された。DXとグリーン発展の調和に向けた政策的示唆を提供する。
English
This study analyzes drivers of digital transformation (DX) in 83 Chinese port and shipping firms (2008–2023) using machine learning (Ridge, LightGBM, XGBoost). Nonlinear models outperform linear ones, highlighting the importance of a supportive environment for green finance. Provides policy insights for harmonizing digital innovation with green development.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
中国の港湾・海運企業のDXとグリーン化の関係を機械学習で分析した研究。日本のGX文脈では、港湾・海運の脱炭素化（e.g., 水素燃料、陸電供給）やDXとの連携に示唆を与えるが、中国特有の政策環境（グリーンファイナンス支援）に依存する点に注意。
In the global GX context
This paper applies machine learning to understand digital transformation drivers in Chinese port and shipping firms, linking to green finance. Globally, it contributes to the literature on digitalization for decarbonization in hard-to-abate sectors, though findings are China-specific. Offers methodological insights for similar studies in other regions.
👥 読者別の含意
🔬研究者:Demonstrates the application of machine learning (LightGBM, XGBoost) to analyze digital transformation drivers in a green context, useful for researchers studying technology adoption in shipping.
🏢実務担当者:Provides evidence that supportive green finance environments are key for digital transformation, guiding corporate strategy in port and shipping firms.
🏛政策担当者:Highlights the role of policy in fostering green finance to enable digital transformation, relevant for designing incentives in the shipping sector.
📄 Abstract（原文）
With the transition to a global green low‐carbon economy, the urgency for digital transformation in the port and shipping industry has become increasingly prominent in making enterprises more efficient and sustainable. This study focuses on how Chinese port and shipping enterprises, which are key carriers for global containerized trade, can attain digital transformation as a means to tackle environmental challenges and improve competitiveness. Using a representative sample of 83 A-share-listed companies (2008–2023) and employing several modeling techniques, such as Ridge regression, LightGBM, and XGBoost, we investigate a data-driven approach with the support of the Technology–Organization–Environment (TOE) framework. We find that nonlinear models (LightGBM, XGBoost) outperform linear models and emphasize the importance of a supportive environment for green finance. We further perform a number of sensitivity and robustness checks toensure the validity of our findings. These insights provide actionable guidance for policymakers and industry leaders seeking to harmonize digital innovations with green development.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.1371/journal.pone.0322872first seen 2026-05-05 19:07:29
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