ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Global Change and the Future Ocean
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1656454
Intelligent decision-making in smart port development in China through green finance instruments: a sustainable approach to the marine ecosystem
Provisionally accepted
Chen Ling1*
Thanh Le2- 1Nanjing Audit University Jinshen College, Nanjing, China
- 2Vietnam Maritime University, Haiphong, Vietnam
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Despite the benefits of smart ports development for productivity, energy saving, and environmental improvement, an intelligent investment strategy should consider potential adverse effects on marine ecosystems during the construction and operation processes. To address this issue, this study aims to examine the integration of green finance instruments with artificial intelligence (AI)- driven intelligent decision-making (IDM), utilizing data on 15 major Chinese ports. Employing machine learning (ML) models, alongside SHapley Additive exPlanations (SHAP) analysis, the research quantifies the impact of green finance on critical environmental metrics, including total organic carbon (TOC), carbon fluxes, carbon burial rate, pollution load index (PLI), flow velocity, and erosion/deposition rate (E/DR). First, ML models are employed to estimate these indicators based on historical data. Subsequently, SHAP is utilized to interpret the impact of financial instruments on ecological indicators. This enables the identification of financial instruments that positively influence ecological indicators in specific marine regions, thereby supporting IDM to prioritize those instruments in the corresponding areas. Findings highlight green bonds as the most influential, with SHAP values of 0.24–0.30 for carbon burial rate and 0.17–0.20 for PLI, particularly in advanced ports like Shanghai and Ningbo-Zhoushan, while eXtreme gradient boosting (XGBoost) achieves superior predictive accuracy. This study suggests that green bonds, green leasing, and green credit should be prioritized. Policymakers should establish a dedicated framework for green bonds and green leasing, specifically targeting ports with advanced smart infrastructure (L3–L4). Green credit schemes should be promoted to support infrastructure enhancement and renewable energy projects in L1–L2 ports.
Keywords: Intelligent decision system, Smart port, green finance, Marine ecosystem, Machinelearning, Port environmental performance, SHAP value, China
Received: 02 Jul 2025; Accepted: 27 Aug 2025.
Copyright: © 2025 Ling and Le. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chen Ling, Nanjing Audit University Jinshen College, Nanjing, China
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