UNCOVERING MULTIDIMENSIONAL DRIVERS OF GLOBAL FOOD EXPORTS: A NONLINEAR CAUSAL ANALYSIS USING CONVERGENT CROSS MAPPING

Amid escalating global food security challenges, global food trade has become increasingly pivotal in securing national food supplies and stabilizing nutritional availability. However, the identification of food export drivers remains hindered by fragmented indicator systems and the limitations of linear causality methods in capturing complex, nonlinear dynamics. In this study, we construct a comprehensive, multidimensional indicator framework encompassing economic, agricultural, and environmental factors, and apply the nonlinear Convergent Cross Mapping (CCM) method to identify robust causal relationships between these variables and global food exports. The results show that economic factors — particularly GDP, GDP per capita, and the Consumer Price Index (CPI) — are the most influential drivers of food exports, while renewable water resources show similarly strong effects and agricultural indicators such as arable land and food production exert moderate effects; in contrast, precipitation shows a weak causal signal. Furthermore, developed countries tend to rely more on economic efficiency and technological advancement, whereas developing countries are more dependent on agricultural production capacity and labor inputs, reflecting significant heterogeneity in export drivers across development levels. This research expands the methodological toolkit for international trade analysis, offers new insights into the causal mechanisms underlying food exports, and provides empirical guidance for tailoring food trade and agricultural policies to development contexts.