Ship collision liability analysis is an important component of maritime safety governance. However, generating complete and rule-consistent liability analyses from unstructured accident narratives remains difficult because accident facts, navigational duties, causal chains, and the COLREGs must be considered together. This study proposes a rule-constrained large language model framework with lightweight knowledge graph refinement. Firstly, this study proposes a Maritime Collision Causal Distillation method that converts accident reports into structured causal representations and supervised fine-tuning samples while preserving rule-relevant evidence and liability logic. Then, the large language model is fine-tuned to generate structured liability analyses. A COLREGs-based lightweight knowledge graph is further introduced as a post-generation quality gate. It corrects missing or inappropriate rule citations, liability-label inconsistencies, and incomplete liability allocations. Experiments on ship collision cases demonstrate improved consistency, traceability, and rule relevance. The framework provides preliminary analytical support for maritime accident review and safety governance.
Coastal squeeze, the loss of intertidal habitat caused by sea level rise and constrained landward migration, occurs when human development restricts tidal marsh persistence in coastal environments. The resulting coastal squeeze pressure reflects the intensity of these coupled natural and human constraints across space and over time. However, few studies have examined this phenomenon using high-resolution temporal data and fine-scale spatial analyses at the municipal scale. To address this gap, we developed a new spatiotemporal analytical framework, the Municipality Coastal Squeeze Index (MCSI), and applied it to 17 watersheds in coastal Virginia, US. Annual MCSI values were calculated from 2020 to 2100 across 1-km2 hexagons by integrating built-environment constraints, marsh migration potential, and elevation thresholds. Time-series clustering identified four groups with distinct trajectories of increasing coastal squeeze pressure, characterized by similar growth patterns but differing onset times. These clusters range from areas with persistent high pressure to locations where exposure remains limited until mid- or late-century before rising rapidly. Spatial analysis shows that the earliest and most persistent high-pressure clusters are concentrated in low-lying and flood-vulnerable areas, while later-emerging pressures are more dispersed inland. Hot Spot Analysis further identifies statistically significant clusters where coastal squeeze pressures are expected to intensify first. By linking spatial concentration patterns with temporal trajectories, the MCSI framework provides a scalable tool for municipal-scale coastal management, helping planners identify where and when coastal squeeze will escalate and supporting phased adaptation strategies that incorporate intertidal marsh conservation as a nature-based approach to flood risk reduction.
Marine protected areas are increasingly used to reconcile biodiversity conservation with sustainable fisheries, yet long-term evaluations integrating ecological, social, economic, and governance dimensions remain scarce. This study assessed the sustainability of the artisanal Octopus mimus fishery in the Punta Coles Reserve (Peru) using the Drivers–Pressures–State–Impacts–Responses (DPSIR) framework and a 24-year time series (2000–2023). Relationships among DPSIR components were evaluated using Kendall's τ correlations, including lagged analyses, and compared between periods before (2000–2015) and after (2016–2023) management implementation. Economic drivers increased steadily over time, whereas fishing pressure and broad system-level indicators followed distinct trajectories. During the open-access period, lagged analyses revealed delayed relationships between market incentives and fishing activity, with increases in first-sale price preceding changes in vessel numbers by one to two years, while ecological and social responses unfolded across varying timeframes. During the managed period, fishing effort declined, exploitation rates remained below FMSY, and the highest fishers' incomes were achieved despite fewer fishing days, indicating improved economic performance without increasing extraction intensity. Environmental variables and benthic diversity indices showed limited direct associations with fishing pressure. These findings indicate that fishery performance emerged from complex interactions among governance processes, market dynamics, environmental variability, and fisher adaptation. The study highlights the value of integrating indicator-based DPSIR components to support adaptive fisheries management and provides insights into how marine protected areas can contribute to small-scale fisheries sustainability through coordinated governance and learning.
Coastal erosion worsened by climate change and human activities is a major threat to global shorelines. As sea levels rise and storms intensify, this rapid shoreline retreat endangers critical infrastructure and the economic security of coastal populations worldwide. However, traditional knowledge-driven frameworks for assessing these threats often rely on subjective, static rules that fail to capture complex, non-linear environmental interactions. To address this critical gap and demonstrate the value of advanced methods, this study compared the traditional Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model against machine learning algorithms, highlighting the potential for more accurate coastal erosion risk assessment. The study analyzed 500 km of the Egyptian Mediterranean coast over 40 years (1985-2025) using satellite imagery. The results mapped severe coastal changes, including 1.35 km of linear shoreline retreat at the Rosetta promontory due to trapped Nile sediment. The comparison showed that the traditional model struggles to identify local high-risk erosion areas, achieving only 33.2% accuracy. In contrast, the model based on the Extreme Gradient Boosting (XGBoost) algorithm, developed using 8946 spatial transects, achieved high accuracy on the blind validation dataset (R2 = 0.86, RMSE = 2.91, MAE = 0.86). By analyzing feature importance, the model showed that coastal erosion risk is mostly driven by ocean depth and human-made coastal defenses, rather than the physical type of the beach. This highly accurate framework addresses a critical planning gap for Egypt’s rapidly growing coastal cities. The transect-level vulnerability map provides a direct input to three existing management procedures: risk-tiered setbacks, vulnerability screening at the EIA scoping stage, and the sequencing of nourishment and nature-based protection, with the InVEST index retained for regional screening and stakeholder communication. Ultimately, this data-driven approach promotes the United Nations Sustainable Development Goals by supporting the design of resilient infrastructure, the protection of coastal cities, and the selection of effective climate action.
Aquaculture is a critical economic engine for coastal communities, powering blue economy, enhancing human well-being, and promoting the Sustainable Development Goals (SDGs). However, its multidimensional contributions at local scales remain inadequately assessed. To address this gap, we developed an integrated sustainability framework, linking 28 indicators across social, economic, environmental, and governance dimensions to 11 SDGs. By combining field sampling, data-driven modeling, and questionnaire-based surveys, we systematically evaluate multidimensional contributions of island-based marine aquaculture to local sustainability, using Changdao Island (China) as a case. Our analysis reveals diverse ecological impacts, particularly negative impacts on water and sediment quality alongside positive effects on local biodiversity, with variation across aquaculture types. The results indicate that while contributions to social and governance dimensions are significant, there is substantial untapped potential in the ecological and economic dimensions, particularly in mitigating marine pollution and stimulating economic development. Moreover, aquaculture sectors offer substantial contributions to SDG 3 (Good health and well-being) and SDG 11 (Sustainable cities and communities), both scoring above 4.0 (on a 5-point scale). In contrast, contributions for SDG 14 (Life below water) are highly variable across its specific targets. The results also show strong synergistic relationships among the SDGs and their associated targets, suggesting that sustainable mariculture practices can generate cascading positive impacts across multiple dimensions of sustainable development. This study provides an evidence-based framework to guide marine aquaculture practices towards a more synergistic alignment with environmental sustainability goals, blue economy development, and human well-being enhancement.
