Blue economy: labour automation as a driver of regional sustainability

Abstract

In the context of structural transformations in European regions, the Blue Economy is increasingly recognized as a potential driver of sustainable growth. The combination of global challenges — including the COVID-19 pandemic, the war in Ukraine that disrupted supply chains and destabilized commodity markets, rapid digitalization, and the adoption of artificial intelligence tools — has profoundly altered employment patterns, production structures, and regional resilience, intensifying the debate on balancing labour productivity with the need to preserve jobs. A key challenge in studying these processes at the regional level is the shortage of statistical data, which complicates a direct assessment of the impact of structural shifts on the socio-economic development of coastal areas where the Blue Economy is a significant component. A preliminary smart specialization analysis, conducted using aggregated data on employment and gross value added (GVA) in the Blue Economy for EU coastal countries over the period 2009–2022, revealed that countries with sustained growth in this sector in terms of value creation and job generation are characterized by low labour intensity and a diversified sectoral structure. In contrast, countries dominated by the coastal tourism sector, combined with high labour intensity, tend to face stagnation or decline in their Blue Economy. An in-depth analysis using quantile regression with country and year fixed effects demonstrated substantial heterogeneity in the impact of individual Blue Economy sectors on the development of coastal regions. The effect is sector-specific and depends on the initial development level, as defined by the quantile distribution of indicators. In the case of employment, higher quantiles (. = 0.75) correspond to the most developed coastal economies and exhibit a stable positive effect, particularly in the marine living resources and marine transport sectors. Lower quantiles (. = 0.25) display weaker or delayed effects over time. For unemployment, a higher quantile indicates a higher unemployment rate, and here the Blue Economy generally contributes to its reduction, with the most pronounced effect observed in the middle quantiles, while in countries with chronically high unemployment the impact is limited. Regarding regional GVA, the strongest positive effect of the Blue Economy is recorded in the upper quantile, reflecting the benefits of scale and developed infrastructure. Sectoral analysis shows that marine living resources and marine transport generally strengthen employment, reduce unemployment, and support regional GVA growth. Marine non-living resources show a positive effect mainly in more developed countries. In contrast, shipbuilding and repair, along with port activities sector, are often linked to declining employment levels despite increases in value added. This pattern reflects the impact of automation and the associated displacement of labour. Coastal tourism produces mixed results: in advanced regions, it has a moderately positive effect, while in regions with high unemployment its impact is weak or negative, consistent with earlier findings on stagnation in economies with a high share of labour-intensive tourism sectors. These results demonstrate that automation in the Blue Economy is not merely a technological trend, but one of the key factors of regional resilience. In sectors where it is combined with diversification and innovation, automation can enhance economic growth while maintaining or even increasing employment. In capital-intensive sectors without accompanying labour reskilling policies, however, it may deepen structural unemployment. This confirms that, in the context of “Blue Economy: Labour Automation as a Driver of Regional Sustainability,” targeted policies are needed to balance productivity gains with social integration, mitigate the risks of structural unemployment, and sustain the long-term growth of coastal regions.