Jin Jiahui, Guo Yongchun
School of Maritime Economics and Management, Dalian Maritime University, Dalian, Liaoning, China.
School of Public Finance and Taxation, Dongbei University of Finance and Economics, Dalian, Liaoning, China.
PLoS One. 2025 May 5;20(5):e0322872. doi: 10.1371/journal.pone.0322872. eCollection 2025.
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.
随着向全球绿色低碳经济的转型，港口和航运业数字化转型的紧迫性在提高企业效率和可持续性方面日益凸显。本研究聚焦于作为全球集装箱贸易关键载体的中国港口和航运企业如何实现数字化转型，以此作为应对环境挑战和提升竞争力的手段。我们以83家A股上市公司（2008 - 2023年）为代表性样本，并运用岭回归、LightGBM和XGBoost等多种建模技术，在技术 - 组织 - 环境（TOE）框架的支持下研究一种数据驱动的方法。我们发现非线性模型（LightGBM、XGBoost）优于线性模型，并强调了支持性绿色金融环境的重要性。我们进一步进行了多项敏感性和稳健性检验以确保研究结果的有效性。这些见解为寻求将数字创新与绿色发展相协调的政策制定者和行业领导者提供了可操作的指导。
