After the pandemic: the global seafood trade market forecasts in 2030

The global seafood industry, one of the most heavily traded food commodities, has shown lower resilience than other agri-food sectors during the COVID-19 crisis. Domestic and international food supply chains were disrupted by closures of food services, reduced transport, and trade restrictions. Seafood trade value declined by 14.8% versus a 7.4% drop in global merchandise trade. Because seafood constitutes roughly 10% of all food trade by value and exceeds the combined value of sugar, maize, coffee, rice, and cocoa, understanding pandemic impacts on the seafood trade network and supply chain is critical. Ripple effects from disrupted seafood supply chains increase poverty risk, especially in developing nations where 97% of the global fishing workforce is employed and many value-chain participants are women. Developing countries in Asia account for 85% of nearly 38 million full-time aquaculture practitioners, and over 23 million low-income people rely on small-scale fisheries. Pandemic-related demand declines, transport cost increases, and market closures reduced incomes, created oversupply or wastage, and heightened vulnerability to poverty and malnutrition. With high uncertainty in pandemic impacts, the global seafood market needs stimulus and long-term planning. SDG14 (Life Below Water) connects with SDG2 (Zero Hunger) and SDG12 (Responsible Consumption and Production). Achieving trade-related targets requires policies to restore seafood market productivity and increase benefits to developing countries. Few integrated studies analyze response strategies to mitigate seafood market losses under lockdowns. This study responds with new analytical frameworks and scenarios to assess how lockdown strategies can keep the global seafood market near baseline by 2030 while supporting economic balance for developing countries. The authors optimized a Geographical Petrinet (Geo-PN) model to construct cascading relationships in the global seafood supply chain across 21 regions (per FAO classifications). They developed a Stringency Index (SI) based on OxCGRT to represent policy restrictions in 65 lockdown scenarios, and grouped regions into nine clusters (Top 5 import/export, other developing, other developed). The aim is to analyze trade damages in developing countries to inform international collaboration and stimulus amid ongoing shocks. The Geo-PN model offers two benefits: it incorporates historical knowledge of economic impacts within a self-adaptive global seafood trade system to more realistically represent shock propagation; and it represents spatial trajectories of cascading effects. The study’s goal is not precise cost prediction but improved analysis of cascading effects and identification of regional resilience to tailor trade stringency strategies for future crises.

Overview and model choice:

• Traditional empirical network analyses of food trade often use static snapshots and centrality metrics, missing cascading effects across regions. Macro-scale economic models (e.g., ARIO, CGE) simulate input–output relationships but are less flexible for multi-scale spatial cascading trajectories.
• The study employs an optimized Geographical Petri Net (Geo-PN) to simulate dynamic network transitions and cascading effects across the global seafood supply chain, capturing both micro-scale linkages and spatial trajectories.

Data and preprocessing:

• Constructed multi-year bilateral seafood trade matrices from FAO statistics for 21 regions, then aggregated into nine groups (Top 5 exporters, Top 5 importers, other developing, other developed, etc.).
• Estimated change rates of exports and imports by region and period, forming the basis for scenario simulations.

Stringency Index (SI) and scenarios:

• Developed a Stringency Index based on the Oxford COVID-19 Government Response Tracker (OxCGRT) to represent degrees of labor and transport availability under lockdowns.
• Designed 65 scenarios that vary by geography, duration, and strictness (20–80) to represent national and regional lockdown strategies and collaboration paths. Baseline 2020 SI forms a real-world anchor; adjusted scenario-sets use 2022 SI (new normal) as baseline for recovery cooperation analyses.
• Basic shock configurations include: S1 (export shock only), S2 (import shock only), S3 (both export and import shocks), and S4 (no shock). Combined scenarios assess cascading under different SI levels across developed/developing and top import/export groups (Tables 1–2 in the paper).

Trade interdependence and cascade threshold:

• Trade Integration Index (TII) measures bilateral trade interdependence between regions. TII greater than 1 indicates close trade links; a threshold of 1 determines whether cascade effects proceed to the next node.

Geo-PN formulation and cascade mechanics:

• Geo-PN is defined by: P (initial shock probabilities for regions), E (strength of inter-regional relations, here via TII), I (parent nodes), O (child nodes), and S (state of export and import shocks for regions). States Sexport and Simport represent fractional shock levels (e.g., 0.2 equals a 20% shock).
• Principle: An import shock in a parent node induces export shocks in child nodes, and vice versa, propagating via the interdependency network. Child nodes are selected probabilistically according to interdependence and current shock states.
• Successful transmission probability from region i to j depends on TIIij and the shock probability of i. Cascades proceed when the product of shock and TII exceeds the threshold; otherwise, hidden risk is recorded and may contribute in later steps.
• After each cascade, TII and trade matrices are updated; shocks accumulate as reductions in import/export values. Transmission paths are tracked, enabling spatial-temporal trajectory mapping.

Simulation design and computation:

• For each scenario, 500 random cascade events are simulated, each with over 450 time steps. Each sub-event’s next step depends on previous outcomes; affected regions cascade sequentially until iteration limits are reached.

Validation and prediction:

• Validated the model against the 2008–2011 financial crisis using moving windows (2010–2011, 2009–2011, 2008–2011) for TII computation. Evaluated accuracy via mean error (ME) and root mean square error (RMSE), selecting optimal windows and iteration counts for prediction.
• Used the validated setup to predict regional and global seafood trade values and network trajectories for 2030 under the 65 scenarios, comparing with- and without-COVID-19 impacts via SI.

Adjusted recovery scenario-sets (AS):

• Using 2022 SI as a new-normal benchmark, designed nine adjusted scenario families (AS1–AS9) varying strictness among top exporters/importers in developing regions, global top-10 exporters/importers, and under assumptions of SI decline to zero in developed regions. These assess cooperative pathways to accelerate recovery and reduce inequities.

Output metrics:

• Trade quantities (exports/imports) by region group, global totals, and uncertainty envelopes across scenarios.
• Resilience indicators: (1) number of cascading trajectories (responsiveness) over time by threshold levels; (2) trade uncertainty (ranges of potential losses/gains) across scenarios.
• Spatial cascade maps and Sankey diagrams visualize trajectory origins, propagation, and shifts in inter- and intra-regional trade patterns.

• Persistent pandemic impact: Even if strict restrictions were lifted globally by end-2022, pandemic-induced disruption persists to 2030. The global seafood market annual growth rate with pandemic impacts is about 1 percentage point lower than 2006–2019.
• Growth projections: With-pandemic scenario projects an average annual growth of 2.88%, implying ~24.91% cumulative increase over the next decade, versus ~28.94% in the previous decade. Without-pandemic simulations indicate higher growth; OECD-FAO projects ~5.3% increase for world food fish exports, though definitions differ.
• Shifting regional export potential: Future export growth potential concentrates in developing regions—especially South Asia, Southern Africa, Central America, and developing Oceania—while traditional leaders (China, East and Southeast Asia) face constraints on further expansion and productivity.
• Developed countries’ intra-regional reliance: By 2030 with COVID-19, developed countries import 29.75% of seafood from developed countries, exceeding 2019 levels, reflecting increased reliance on intra-regional trade amid rising post-pandemic demand and limited self-sufficiency.
• Developing countries’ trade role and intra-DEVs decline: Developing countries still account for over 58% of global seafood exports under with-pandemic scenarios, about 2% lower than without-pandemic. Intra-developing regional trade declines more sharply (−25.11%) compared to developing–developed flows.
• Trade uncertainty and asymmetry: Across 65 scenarios, global exports trend upward from 2019 but face potential reductions of 1.23% to 26.85%. Average export loss shares are higher in developing regions (14.05%) than developed (7.82%). Import loss shares in developing regions are about twice those of developed regions.
• Cascade dynamics and resilience timelines: Large shocks dampen within ~3 years (high threshold), median shocks may take up to ~8 years, while small cascading effects can persist until ~2028. Less-developed regions show longer persistence of propagation. EU shows rapid adaptation (high initial cascades but ~2-year recovery); Northern America, Western Europe, and Japan recover in ~4 years.
• High interdependence hubs: EU, Japan, and Northern America exhibit higher trade interdependence (network relationships >19), with diversified networks that mitigate external shocks and transportation cost impacts.
• Adjusted cooperation scenarios: Relaxing restrictions only in developed countries yields limited improvements (<2% for other developing) and can leave global exports ~1% lower. If China and East–Southeast Asia reduce SI below ~40 (e.g., AS2-20, AS10-20), other developing regions can achieve ~10% growth and global exports expand by ~1%. Coordinated reductions among Top-5 exporters in developing regions to SI ~20 in 2022 accelerate recovery and reduce inequalities.
• Food security risks: By 2030, 17–57 million people in developing countries may face seafood supply shortages due to lingering pandemic effects. South Asia is especially concerning; within a 1.7 billion population, 347–466 million may not meet seafood consumption needs. Eastern and Western Africa also face critical shortages (averages >158 million and >156 million people, respectively).

The study investigates how pandemic-related shocks propagate through the global seafood trade network and how resilience differs between developing and developed regions. By modeling cascading effects with a Geo-PN approach and embedding policy stringency via a Stringency Index, it demonstrates that COVID-19’s supply-chain disruptions have durable effects extending to 2030, lowering expected growth relative to pre-pandemic baselines. The simulations reveal a structural shift: developed countries, driven by higher standards, technology, and market access, increasingly rely on intra-regional trade, consistent with New Economic Geography explanations of North–North trade driven by economies of scale. Developing regions remain pivotal exporters but experience larger uncertainties and disproportionate trade losses, especially in intra-developing trade. Diversified trade networks in the EU, Japan, and Northern America help buffer shocks, whereas many developing regions, small island states, and transport-constrained economies have fewer alternatives and higher exposure to transport cost volatility. The adjusted cooperation scenarios indicate that unilateral easing in developed regions is insufficient to restore global trade; coordinated actions by major developing-country exporters—particularly China and East–Southeast Asia—are crucial. Such cooperation reduces cascading losses, accelerates recovery, and supports food security goals linked to SDGs. Overall, findings emphasize the need for multilateral strategies that account for supply-chain linkages and asymmetric vulnerabilities to build a more resilient global seafood system.

This work applies an optimized Geo-PN model to map how COVID-19 shocks cascade through the global seafood network and to explore recovery pathways to 2030.

• Main contributions: (1) Provides a dynamic, spatially explicit framework capturing cascading impacts and resilience; (2) Quantifies persistent pandemic effects on growth (with-pandemic ~2.88% annual growth vs pre-pandemic baselines), regional shifts in trade patterns, and asymmetric uncertainties between developed and developing regions; (3) Identifies the increasing intra-regional reliance among developed countries and highlights the role of diversified networks in resilience; (4) Demonstrates that multilateral cooperation—especially involving China and East–Southeast Asia—can materially improve outcomes for developing regions and global trade recovery.
• Policy implications: Coordinated reductions in policy stringency among top developing-country exporters (e.g., SI to ~20–40), targeted support for transport and cold-chain infrastructure, and knowledge-based aquaculture intensification can mitigate cascading losses and reduce inequalities. Free-trade agreements and resilience-oriented diversification can enhance adaptive capacity.
• Future research: Incorporate additional drivers (macroeconomic fluctuations, climate change), improve granularity with national-level trade and socio-economic data, and extend the model to quantify broader economic ripple effects (livelihoods and nutrition outcomes) across alternative seafood network configurations.

• The Stringency Index (OxCGRT-based) is used as a proxy for labor and transport constraints; while useful for relative shocks, it does not directly capture specific economic damages to livelihoods or nutrition security.
• The model currently omits explicit macroeconomic fluctuations and climate change impacts; these could be incorporated as additional interdependencies in future extensions.
• Data limitations persist: global, national-level datasets on geographic distribution of seafood trade actors and comprehensive socio-economic contributions are lacking; improved national-level trade data are needed.
• Transport infrastructure and route alternatives are simplified; real-world constraints (island states, limited providers) can exacerbate cost volatility beyond model scope.
• Despite validation against the 2008–2011 crisis, uncertainties remain regarding parameter choices (e.g., moving windows, thresholds) and structural differences between crises.