Coupling coordination between integrated transport–shipping system and nearshore marine ecology in China’s coastal cities: spatiotemporal evolution and XGBoost–SHAP-based mechanism identification

This recent study, published in a peer-reviewed journal, offers a valuable contribution to our understanding of the complex interplay between human activity and marine ecosystems, a topic of increasing urgency given the escalating pressures on coastal environments. The research, focusing on 53 Chinese coastal cities, employs a sophisticated methodology – the XGBoost–SHAP approach – to analyze the coupling coordination degree (CCD) between the integrated transport–shipping system (ITS) and nearshore marine ecology (NME). This mirrors the broader interest in leveraging machine learning for environmental analysis, as demonstrated in a recent study exploring Machine learning predictions for microbial eukaryotic plankton: implications from unevenly structured data, highlighting the potential of these tools to process complex, uneven data sets to reveal meaningful patterns. The findings underscore the importance of considering these systems as interconnected, rather than isolated entities, an understanding that is crucial for developing effective governance strategies. The need for robust data ecosystems to support these analyses is also increasingly evident, a perspective reinforced by the exploration of the digital economy’s role in marine productivity – a topic recently examined in The digital economy and marine new-quality productivity: unraveling the N-shaped relationship.

The observed steady increase in the ITS–NME CCD across Chinese coastal cities, despite persistent regional disparities, suggests a general trend towards improved coordination. Critically, the study identifies urbanization rate and trade dependence as key, non-linear factors influencing this coordination. The finding that urbanization acts as a "dominant interaction hub" is particularly insightful, highlighting the cascading effects of urban growth on both the transport system and the surrounding marine environment. The research also emphasizes the context-dependent nature of environmental pressure and governance support, suggesting that a one-size-fits-all approach to policy is unlikely to be effective. Furthermore, the distinction between high-coordination cities relying on balanced multi-factor support versus low-coordination cities facing multidimensional constraints provides a nuanced perspective on the challenges and opportunities for different urban typologies. This aligns with ongoing efforts to improve the sustainability of aquaculture, as explored in Research on China’s green total factor productivity of aquaculture industry and its influencing factors, demonstrating the importance of targeted interventions to address specific regional contexts.

The methodological rigor of this study, utilizing the XGBoost–SHAP approach, is particularly noteworthy. This allows for not only the identification of key influencing factors but also the interpretation of their effects, providing a level of transparency often lacking in complex machine learning models. The ability to understand *how* these factors interact—for example, how trade dependence can act as a bridge between economic openness and governance support—is vital for informed policy-making. The longitudinal data spanning 2013-2024 provides a valuable temporal perspective, allowing researchers to track trends and assess the effectiveness of implemented policies over time. The study’s emphasis on empirical evidence and its focus on providing interpretable results strengthen its contribution to the field and enhance its applicability to real-world challenges. The validated methodology also underscores the growing importance of calibrated data and integrated data ecosystems for informed decision-making in marine resource management.

Ultimately, this research highlights the critical need for land–sea integrated governance strategies tailored to the specific characteristics of each coastal city. While the overall trend towards improved coordination is encouraging, the persistent regional heterogeneity and the complex interplay of factors influencing CCD underscore the challenges ahead. As ocean intelligence continues to evolve, driven by technological innovation and a commitment to global collaboration, it will be essential to refine and adapt these strategies in real-time. A key question moving forward is how to effectively translate these findings into practical, scalable interventions that can not only improve coordination between transportation and marine ecology but also foster long-term resilience in the face of climate change and other environmental stressors.