Risk Transmission and Resilience of China's Corn Import Trade Network

Abstract

The global corn trade is an important pillar of the agricultural economy, but its supply chain is vulnerable to geopolitical conflicts, climate change, and market volatility. As one of the major importers of corn in the world, China has long relied on the United States and Ukraine, and the risk of import concentration is prominent. The complicated international situation intensifies the external uncertainty of China's import supply chain. This study utilized bilateral trade data from 2010 to 2023 and employed advanced methodologies including complex network modeling, network index quantification, and simulation analysis to assess the impacts of external risks from major trading partners on China's corn import system and trace risk transmission pathways. The research objectives focused on examining the structural evolution of China's corn import trade network over the past decade, evaluating its resilience against external shocks, and identifying the critical roles played by key node countries in risk propagation mechanisms. The results showed that the resilience of China's corn import trade network had been enhanced in recent years and that the complementarity of planting cycles in the Northern and Southern Hemispheres and the adjustment of trade structure caused by the Russia-Ukraine conflict had improved its risk resistance. The United States, France, Romania, and Turkey were key intermediate nodes in risk transmission due to their geographical advantages and trade hub statuses. The risk transmission path presented regional heterogeneity. China should strengthen trade with countries in the Southern Hemisphere and built a more stable import system by taking advantage of complementary resource endowments and growth periods. Bilateral agreements with transit countries could ensure security of supply. Reserve centers and modern logistics infrastructure should be built in key areas. In addition, platforms such as the Regional Comprehensive Economic Partnership could promote harmonized standards and digital support for corn trade, and regional financial instruments and supply chain optimization could have balanced risks.

Keywords: corn trade; food security; network resilience; recovery capacity; resistance capacity; risk propagation.

Figure 1

An illustration of the cascading effect in a trade network. (a) The initial state when no external risks have occurred, where A to F represent six countries and the arrows indicate the direction of exports. (b–d) A situation in which countries are affected successively after the occurrence of external risks. Data source: Compiled by the author based on available information.

Figure 2

The evolution of the global corn trade network. (a) The global corn trade network for 2010, (b) the global corn trade network for 2021, (c) the global corn trade network for 2022, and (d) the global corn trade networks for 2023. Notes: (1) The red node represents China. (2) The top 10 countries in the global maize trade network with the highest weighted out-degree are labeled to identify the major exporters. Data source: Obtained from graphs created using Gephi 0.10.1 software.

Figure 3

The frequency distribution of the top ten countries with the highest out-degree from 2010 to 2023. Data source: Compiled by the author.

Figure 4

The frequency distribution of the top ten countries with the highest closeness centrality from 2010 to 2023. Data source: Compiled by the author.

Figure 5

The impact of reduced exports from the top ten countries by closeness centrality and weighted out-degree in certain years on China (tons). (a) The direct impact and indirect impact of export reductions in risk source countries selected based on weighted out-degree; (b) the direct impact and indirect impact of export reductions in risk source countries selected based on closeness centrality. Notes: (1) Because the effects vary widely from country to country, these values are logarithmic in order to spread the data more evenly across the chart. The value of the ordinate is the value after taking the logarithm. (2) In 2010, the risk source countries selected according to closeness centrality had neither direct nor indirect trade relations with China, so (b) only shows the simulation results for 2014, 2018, and 2023. Data source: Obtained by the author.

Figure 6

In the process of risk transmission, these are the most frequent countries and their frequencies of occurrence. Notes: (1) Risk source countries selected according to weighted out-degree in 2010: Argentina, Brazil, Paraguay, and the USA; risk source countries selected according to weighted out-degree in 2014: Argentina, Brazil, France, India, Paraguay, Romania, Russia, Switzerland, Ukraine, and the USA; risk source countries selected according to weighted out-degree in 2018: Argentina, Brazil, France, Hungary, Netherlands, Romania, Russia, South Africa, Ukraine, and the USA; risk source countries selected according to weighted out-degree in 2023: Argentina, Brazil, France, India, Paraguay, Poland, Romania, South Africa, Ukraine, and the USA. (2) Risk source countries selected according to closeness centrality in 2014: Austria, Denmark, France, Germany, Ireland, Italy, Netherlands, Philippines, Spain, and the UK; risk source countries selected according to closeness centrality in 2018: Belgium, Egypt, France, Germany, Greece, Italy, Netherlands, Poland, Portugal, Romania, Spain, and the UK; risk source countries selected according to closeness centrality in 2023: Austria, Germany, Italy, Kenya, Netherlands, Philippines, Romania, and Vietnam. (3) The numbers in the bubbles represent the number of times a country was present during the risk transmission process. Data source: Obtained by the author.

Figure 6

In the process of risk transmission, these are the most frequent countries and their frequencies of occurrence. Notes: (1) Risk source countries selected according to weighted out-degree in 2010: Argentina, Brazil, Paraguay, and the USA; risk source countries selected according to weighted out-degree in 2014: Argentina, Brazil, France, India, Paraguay, Romania, Russia, Switzerland, Ukraine, and the USA; risk source countries selected according to weighted out-degree in 2018: Argentina, Brazil, France, Hungary, Netherlands, Romania, Russia, South Africa, Ukraine, and the USA; risk source countries selected according to weighted out-degree in 2023: Argentina, Brazil, France, India, Paraguay, Poland, Romania, South Africa, Ukraine, and the USA. (2) Risk source countries selected according to closeness centrality in 2014: Austria, Denmark, France, Germany, Ireland, Italy, Netherlands, Philippines, Spain, and the UK; risk source countries selected according to closeness centrality in 2018: Belgium, Egypt, France, Germany, Greece, Italy, Netherlands, Poland, Portugal, Romania, Spain, and the UK; risk source countries selected according to closeness centrality in 2023: Austria, Germany, Italy, Kenya, Netherlands, Philippines, Romania, and Vietnam. (3) The numbers in the bubbles represent the number of times a country was present during the risk transmission process. Data source: Obtained by the author.