Science AI Journal
Environmental Science · Research topic

Open research questions in Coastal and Marine Management

63 unresolved questions extracted from the limitations and future-work sections of 1,447 Coastal and Marine Management papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • It uses previously derived values, functions or models to approximate benefits for ecosystems that have not been directly studied. Although the European Commission has issued directives on the monitoring and management of non-indigenous species, limited data and knowledge hinder practical implementation on the ground. RESULTS AND IMPACT Due to limited data availability, the results of the economic valuation were associated with a high level of uncertainty.

    Deliverable D5.2 - Experiences from marine protected area governance and management in action; case studies from the Blue4All tool testing and validation process. · 2026 · DOI

  • aspect, decision-making. On networks such as DOSI and many others mentioned in this study can play an important role. Such initiatives can contribute toward safe spaces dedicated to ECOPs where activities such as mentoring, collaboration with peers, learning about opportunities to access funds and job opportunities, interacting with more as well as preparing for and contributing to policy processes, can take place. experienced professionals, that of kin in accordance with the national institutional requirements.

    Deep-sea early career ocean professionals (ECOPs) at the science-to-policy interface: needs, challenges, and opportunities against country economic status · 2026 · DOI

  • 703. I recommend that: - THE WALES ROD AND LINE (SALMON AND SEA TROUT) BYELAWS as set out in Core Document Ref. APP/53, as modified by Inquiry Document Ref. NRW/INQ/17, be confirmed; and, - THE WALES NET FISHING (SALMON AND SEA TROUT) BYELAWS as set out in Core Document Ref. APP/52, be confirmed.

    Taking Spatial Justice to the Coast: The Curious Case of Welsh Fishing Byelaws and the Battle for Defining the Value of the Tidal Wye and Usk Rivers · 2026 · DOI

  • This study highlights several critical measures necessary for achieving a sustainable blue economy and enhancing maritime security across the Great Lakes region. First and foremost, it is essential to harmonize transport, fisheries, and security laws across the region. This alignment will ensure consistency in legal systems and promote the effective domestication of international instruments, such as the FAO Code of Conduct for Responsible Fisheries, to facilitate their practical application to inland water bodies. Furthermore, creating a robust regional legal framework tailored to the unique context of inland lakes drawing from existing oceanic regimes but adapted to local needs is vital for governing these resources effectively. 157 There is a pressing need for stakeholders to invest in joint patrols and shared surveillance technologies, including low-cost community-based mobile reporting platforms. It is imperative to clarify the roles and mandates of national ministries, lake commissions, and security agencies to minimize duplication and conflicts of jurisdiction. Additionally, expanding training programs for coast guard units, fisheries inspectors, and judicial officers will enhance enforcement capabilities and support effective dispute resolution. Establishing a regional incident data-sharing platform will also allow stakeholders to monitor illegal, unreported, and unregulated (IUU) fishing, security incidents, and smuggling, fostering a more coordinated response among affected parties. To alleviate the pressure on already exploited fisheries, alternative livelihoods such as sustainable agriculture, aquaculture, and eco-tourism should be promoted. Equitable benefit- sharing mechanisms must be put in place to ensure that shoreline communities and small-scale fishers are not marginalized by large-scale blue economy projects. Integrating climate-smart measures, such as pollution control, ecosystem restoration, and invasive species management into all blue economy activities is also crucial for maintaining the health of these vital resources. Leveraging regional and international partnerships will be key to enhancing governance in the Great Lakes region. Collaborations with organizations like the United Nations Development Program (UNDP), United Nations Environment Program (UNEP), Food and Agriculture Organization (FAO), and the World Bank (WB) can provide essential financial and technical support. Engaging private sectors and civil society in co-management of fisheries, environmental protection, and transport safety will further strengthen efforts toward a sustainable blue economy. Ultimately, the strategic implication is clear: reframing inland waters as integral components of national security strategies will elevate freshwater marine security and embed sustainable blue economy objectives within the broader agenda for regional integration.

    Legal Pluralism, Maritime Security, and the Blue Economy · 2026 · DOI

  • The robustness checks demonstrate stability across alternative AI measurements (entropy-weighted TOPSIS, patent counts) and alternative Marine Green Development Index constructions (Table 12), but dynamic lagged effects are tested only at one-period lag. Investigation of multi-period lagged effects and autoregressive specifications would clarify whether AI's impact on marine green development exhibits delayed or cumulative temporal patterns.

    Artificial intelligence, capability transformation, and marine green development: empirical evidence from Coastal China · 2026 · DOI

  • While the instrumental variable approach (AI search attention as an instrument for AI index) successfully mitigates endogeneity concerns, the exclusion restriction is validated only through a single placebo test using 'number of travel agencies per 10,000 people.' Testing the instrument's validity against additional placebo outcomes in the marine and environmental sectors (e.g., coastal tourism, fishing vessel registrations) would strengthen causal inference.

    Artificial intelligence, capability transformation, and marine green development: empirical evidence from Coastal China · 2026 · DOI

  • The moderation effect of marine green finance (MGF) is tested at the aggregate level (coefficient 0.461**, Table 10), but the paper does not examine how specific types of green financing mechanisms (green bonds, green credit, green insurance) differentially moderate AI's impact on marine green development across various industrial subsectors.

    Artificial intelligence, capability transformation, and marine green development: empirical evidence from Coastal China · 2026 · DOI

  • The mediation mechanisms (Marine Resource Index and Digital Governance Capacity) are tested only for coastal Chinese provinces from 2011-2023. The generalizability of these capability transformation pathways to non-coastal regions, other national contexts with different industrial structures, or different time periods remains unvalidated.

    Artificial intelligence, capability transformation, and marine green development: empirical evidence from Coastal China · 2026 · DOI

  • The heterogeneity analysis reveals threshold effects associated with embedding AI into different industrial structures, but the paper does not quantify the critical industrial structure thresholds at which AI's green benefits transition from insignificant to significant effects. Future work should employ threshold regression or breakpoint analysis to identify the precise secondary industry output share breakpoints that determine AI effectiveness in marine green development.

    Artificial intelligence, capability transformation, and marine green development: empirical evidence from Coastal China · 2026 · DOI

  • The study demonstrates that AI's positive effect on marine green development is unstable and not yet fully materialized in regions with higher shares of marine secondary industry output (coefficient of 0.108, statistically insignificant). Future research should investigate the specific technological and institutional barriers preventing AI adoption in traditional marine manufacturing sectors and identify policy interventions to overcome these adoption constraints.

    Artificial intelligence, capability transformation, and marine green development: empirical evidence from Coastal China · 2026 · DOI

  • Environmental justice concerns related to AI implementation in the Blue Circular Economy are mentioned as cross-cutting challenges but lack specific examination of how AI-driven resource allocation decisions in fisheries management and coastal tourism affect indigenous communities, small-scale fishers, and marginalized coastal populations differently.

    Artificial intelligence and blue circular economy in relationship with UN sustainable development goals (SDGs) and vision 2030: Systematic review of literature · 2026 · DOI

  • The paper identifies AI-enabled marine pollution control and environmental monitoring as key Blue Circular Economy applications but does not address interoperability standards, data integration protocols, or real-time coordination mechanisms required when multiple AI systems operate simultaneously across ports, fisheries, and waste management sectors in integrated marine governance.

    Artificial intelligence and blue circular economy in relationship with UN sustainable development goals (SDGs) and vision 2030: Systematic review of literature · 2026 · DOI

  • The literature review examined AI applications in marine waste management and fisheries across multiple geographic contexts but did not conduct a systematic analysis of performance differences across varying regulatory environments, resource availability levels, or technological infrastructure maturity in developed versus developing coastal economies.

    Artificial intelligence and blue circular economy in relationship with UN sustainable development goals (SDGs) and vision 2030: Systematic review of literature · 2026 · DOI

  • While the paper maps AI applications to SDGs 2, 7, 12, 13, and 14, it does not quantify the contribution magnitude of specific AI technologies (predictive sensors for plastic accumulation, chatbots for marine tourism, satellite detection for illegal fishing) to measurable SDG indicators or Vision 2030 economic growth targets in Kenya's blue economy.

    Artificial intelligence and blue circular economy in relationship with UN sustainable development goals (SDGs) and vision 2030: Systematic review of literature · 2026 · DOI

  • The review acknowledges labor displacement as a potential consequence of AI implementation in maritime sectors (ports, fisheries, aquaculture) but provides no analysis of retraining requirements, workforce transition mechanisms, or social impact assessment methodologies specific to Blue Circular Economy transitions in coastal communities.

    Artificial intelligence and blue circular economy in relationship with UN sustainable development goals (SDGs) and vision 2030: Systematic review of literature · 2026 · DOI

  • The paper identifies data ownership, equitable access, and transparency as cross-cutting ethical challenges for AI in the Blue Circular Economy but does not specify governance frameworks needed to address these issues. Future work must develop context-specific governance models for AI data ownership in marine resource management, particularly for developing economies like Kenya, with documented protocols for stakeholder inclusion.

    Artificial intelligence and blue circular economy in relationship with UN sustainable development goals (SDGs) and vision 2030: Systematic review of literature · 2026 · DOI

  • The systematic review identified AI applications across six Blue Circular Economy areas (ports, marine governance, renewable energy, fisheries, marine pollution, coastal tourism) but did not conduct empirical validation of AI implementation effectiveness in these domains. Quantitative measurement of AI's actual impact on waste reduction, resource recycling efficiency, and logistics optimization in maritime contexts requires primary data collection and comparative case studies.

    Artificial intelligence and blue circular economy in relationship with UN sustainable development goals (SDGs) and vision 2030: Systematic review of literature · 2026 · DOI

  • Development of globally harmonized data standards and open-source sensor platforms for marine monitoring remains incomplete; establishing standardized classification schemes and annotation protocols across international collaborations is critical for enabling transferable AI models across ocean regions, ecosystems, and sensor modalities used in biodiversity monitoring and pollution detection.

    Leveraging artificial intelligence (AI) techniques for sustainable marine resources · 2026 · DOI

  • AI tools for marine governance often remain underutilized due to lack of user-friendliness, transparency, or operational compatibility; integration of explainable AI (XAI) techniques with marine stakeholder workflows and investigation of human-AI interaction design for fisheries management and pollution surveillance decision-support systems requires interdisciplinary collaboration between AI developers and marine operational practitioners.

    Leveraging artificial intelligence (AI) techniques for sustainable marine resources · 2026 · DOI

  • Many coastal and island communities dependent on marine resources lack digital infrastructure (electricity, internet, computing facilities) required for AI deployment; decentralized solutions such as offline inference models, low-power sensors, AIoT systems, and LEO satellite network integration require targeted development and validation to ensure inclusive technology access for remote marine governance contexts.

    Leveraging artificial intelligence (AI) techniques for sustainable marine resources · 2026 · DOI

  • Deep learning models for marine monitoring demand high computational resources that raise barriers to equitable access; institutions in resource-limited settings lack infrastructure to train or deploy AI models at scale, concentrating benefits in high-income countries, necessitating research on green AI strategies, lightweight model architectures, edge computing deployment, and open-access cloud infrastructure specifically designed for marine conservation applications.

    Leveraging artificial intelligence (AI) techniques for sustainable marine resources · 2026 · DOI

  • High-quality, standardized, and interoperable marine datasets remain fragmented with inconsistencies in data formats, classification schemes, and annotation practices that hinder cross-platform integration; ocean regions in the Global South and deep-sea environments are critically undersampled, and the lack of long-term time series datasets limits capacity to model temporal dynamics and climate-driven ecosystem changes in AI models.

    Leveraging artificial intelligence (AI) techniques for sustainable marine resources · 2026 · DOI

  • Marine AI models trained in specific ecological or geographic contexts fail to generalize across new domains; the environmental heterogeneity of ocean systems—variations in salinity, light, turbidity, and species composition—significantly alters ML algorithm features, requiring broader representative datasets and investigation of transfer learning, data augmentation, and hybrid ecological-knowledge modeling approaches specifically for cross-regional marine ecosystem transfer.

    Leveraging artificial intelligence (AI) techniques for sustainable marine resources · 2026 · DOI

  • Despite recent attention to the drivers of this emerging flood hazard, the scope and extent of socio-economic impacts of HTF impacts are not well understood.

    A global systematic review of socio-economic impact assessments of high-tide flooding · 2025 · DOI

  • Still, we know little about ES supply, flow and demand and their spatio-temporal variability, whilst integrated approaches that consider ES cross-island realms (terrestrial, marine and their interface) remain scarce.

    Enhancing Small-Medium IsLands resilience by securing the sustainability of Ecosystem Services: the SMILES Cost Action · 2023 · DOI

Most-cited papers in Coastal and Marine Management

Most recent work

Find a gap in your own Coastal and Marine Management sub-topic

This page shows what the Coastal and Marine Management literature already flags as unresolved. To narrow it to your specific question, run the guided finder — it searches the gap library on demand and checks candidates against 250M+ OpenAlex works.

Open the Research Gap Finder →

Related topics in Environmental Science

Environmental Education and Sustainability
249 open questions
Sustainability and Climate Change Governance
174 open questions
Microplastics and Plastic Pollution
146 open questions
Species Distribution and Climate Change
122 open questions
Wildlife Ecology and Conservation
117 open questions
Air Quality and Health Impacts
117 open questions
Climate variability and models
117 open questions
Urban Green Space and Health
107 open questions
63 open questions have been extracted from the limitations and future-work passages of 1,447 Coastal and Marine Management papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

Tools for your next paper

Gaps & Literature SearchFind open questions in any field — 120,000+ open gaps mined from our own library.Pre-CheckIs your paper ready? A Tier 1–5 acceptance probability in seconds.Journal FinderA ranked shortlist of target journals from a 17,500-venue index.Duplicate Publication CheckerCheck prior-publication & salami-slicing risk across 8 sources.Citation GeneratorFreeA DOI or title → APA, MLA, Vancouver, Chicago, BibTeX, RIS. No signup.Graphical Abstract MakerFreeTurn your findings into a colorblind-safe graphical abstract.

Compare the category — Honest roundups of the AI research tools, ours listed alongside the alternatives.

Command palette

Jump anywhere, run any action.