Implementing the Blue Economy: Analysis of indicator interrelationships across countries and over time

The Blue Economy aims to foster equitable and sustainable economic development by balancing ecological, governance, and economic factors. Tracking progress relies on a set of indicators, with the assumption that improvements in one area lead to progress in others. However, the empirical correlations among these indicators are often overlooked or untested, and this can contribute to inefficient or conflicting policies. This study examines the empirical statistical relationships among 21 datasets of indicators related to the Blue Economy, both across countries (cross-sectional), and within countries over time (longitudinal). We classify relationships as direct (positive correlation), inverse (negative correlation), or neutral. Results suggest that, across countries, there is statistical evidence of direct correlations in ecological, economic, and governance indicators (52% direct, 48% neutral), indicating that improvements in one area might generally support progress in others. However, when analysed over time (e.g., 2000–2019), correlations between indicators within each country become predominantly neutral, although slightly more diverse (8% direct, 86% neutral, 6% inverse). This means that common assumptions on co-benefits of development progress may not hold over time due to more nuanced and dynamic interactions within individual countries. As the first study analysing the empirical relationships of indicators commonly used in the Blue Economy, we discuss how selecting analytical approaches can yield distinct insights. By incorporating both cross-sectional and longitudinal perspectives, future research could provide a more holistic framework for implementing policies and decision-making strategies that effectively address the social, environmental, and economic dimensions of the Blue Economy. • Analysed 21 datasets to explore Blue Economy indicator relationships. • Cross-sectional analysis revealed 52% direct correlations among indicators across countries. • Longitudinal analysis showed 86% neutral relationships within countries over time. • Interactions vary significantly depending on the analytical approach. • Calls for integrating diverse methods for holistic Blue Economy policy insights.