Predictive modeling for port retro-area management: future scenarios for strategic Brazilian ports

Brazilian ports are key gateways for national and international trade, but they continue to face logistical bottlenecks and high operational costs, known as the “Brazilian Logistic Cost”. In recent years, Brazil has experienced renewed economic growth driven by agribusiness exports, reindustrialization, energy transition investments, and stronger South–South trade relations. Over the past two decades, the country has invested billions of reais in transport infrastructure, including highways, railways, airports, and ports, with federal transport investments reaching approximately R$ 24 billion in 2024 and major concession programs expected to mobilize over R$ 160 billion. Additionally, port modernization programs have injected more than R$ 22 billion into port complexes in recent years. Despite these investments, port infrastructure expansion and retro-area planning remain insufficient compared to increasing trade demand, vessel upscaling, and hinterland integration needs, maintaining ports as persistent bottlenecks in Brazil’s logistics system. The expansion of port activities has increasingly extended beyond legal port boundaries into surrounding retro-areas, driven by the requirements of larger vessels, intermodal integration, logistics platforms, and industrial development. This process intensifies pressure on regional land-use and land-cover (LULC) dynamics, often converting natural and agricultural landscapes into industrial and logistics zones. Although these transformations have significant environmental and socio-economic implications, integrated studies combining high-resolution spatial data and socio-economic information remain limited in Brazil. Understanding these dynamics is essential to anticipate land-use conflicts and support sustainable territorial planning, climate resilience, and infrastructure development aligned with long-term economic growth. The methodology involved the selection of representative ports based on features such as size, cargo throughput, and economic relevance to ensure spatial representativeness across the Brazilian coastline. The selected hubs include Itaqui and Belém in the North; Açu, Pecém, Santos, and Suape in the Central and Northeast regions; and Paranaguá and Rio Grande in the South. These ports are critical logistics nodes for commodity exports, industrial complexes, and containerized cargo, and play a strategic role in supporting Brazil’s recent export growth and infrastructure expansion agenda. LULC data were obtained from the MapBiomas Collection and processed using Google Earth Engine (GEE) for the years 2000, 2010, and 2022. A 10-km buffer was established to define the port functional retro-area. Transition matrices and spatial weights of evidence were applied to simulate two future expansion scenarios: a trend-based scenario (without intervention) and a policy-oriented restricted scenario incorporating territorial and environmental constraints. These constraints include Indigenous Lands, Quilombola territories, speleological heritage areas (caves), and legally protected environmental areas such as Conservation Units. Restrictions were applied as either total or partial, depending on the level of legal protection and regulatory framework established under current Brazilian legislation.The modeling utilized land-use transition matrices and weights of evidence to simulate two distinct expansion paths. Scenario1 (Trend-based) simulates expansion between 2000 and 2022 based strictly on historical transitions without external intervention, while scenario2 simulates the expansion in the same period with considering environmental constrains reflecting an sustainable conservative standpoint. Model validation was performed by comparing simulated and observed LULC patterns for 2022, achieving high predictive performance, with accuracy values ranging from 0.61 to 0.96 and Kappa coefficients between 0.50 and 0.93 across the analyzed ports. Between 2000 and 2022, the most affected land-use classes were Urban and Farming areas. The most significant changes were observed in the retro-area of the Port of Suape, where farming land decreased by approximately 2570 ha, while urban areas increased by approximately 2200 ha, reflecting rapid industrial and logistics expansion. Water-related changes also showed contrasting patterns. In the Port of Santos, water surface decreased by approximately 360 ha during the same period. In contrast, the Port of Açu showed a water surface increase of approximately 390 ha, associated with port construction and infrastructure development initiated around 2014. Future scenario simulations for 2032 revealed that policy-oriented restrictions can play an essential role to urban and industrial expansion in most port retro-areas, particularly in rapidly expanding logistics hubs. The strongest avoided expansion was observed in urban and mining-related classes, with significant reductions detected in Itaqui (urban area = 1348 ha), Pecém (urban area = 805 ha), and Suape (urban avoided area = 266 ha). However, results indicate that policy interventions tend to redistribute land-use pressure rather than fully eliminate expansion trends, as evidenced by localized increases in agricultural, vegetated areas or mining in some regions. More mature port systems, such as Santos, Paranaguá, and Rio Grande, showed smaller differences between scenarios, suggesting higher territorial stability and consolidated land-use structures. Overall, the predictive framework successfully reproduced historical land-use dynamics and provided robust future projections under different planning scenarios. Port territorial management must be predictive rather than reactive, and spatial modeling tools are essential to strengthen societal capacity to anticipate and mitigate future land-use conflicts. The proposed approach supports proactive decision-making by identifying potential land-use conflicts and enabling the evaluation of policy effectiveness in port retro-area development, contributing to more efficient and sustainable port infrastructure planning, while aligning development strategies with the UN Agenda 2030, specifically Sustainable Development Goals #9 (Industry, Innovation and Infrastructure), #11 (Sustainable Cities and Communities), and #17 (Partnerships for the Goals). Furthermore, model performance and spatial realism can be improved by incorporating additional spatial drivers, such as trade volumes, infrastructure accessibility, urban expansion pressure, terrain suitability, and socioeconomic development indicators, which help refine the spatial allocation of future land-use transitions.