Global evidence of the exposure-lag-response associations between temperature anomalies and food markets

Recent years have witnessed a landmark shift in global food prices due to the frequency of extreme weather events caused by temperature anomalies as well as the overlapping risks of COVID-19. Notably, the threat posed by temperature anomalies has spread beyond agricultural production to all aspects across food supply and de-mand channels, further amplifying volatility in food markets. Exploring trends in global food prices will give nations early warning signs to ensure the stability of food market. Accordingly, we utilize the Distributed Lag Non-Linear Model (DLNM) to simultaneously establish the exposure-lag-response associations between global temperature anomalies and food price returns in two dimensions: "Anomaly Degree" and "Response Time". Meanwhile, we also examine the cumulative lagged effects of temperature anomalies in terms of different quantiles and lag times. Several conclusions have been drawn. First, global food price returns will continue to decrease when the average temperature drops or rises slightly. While it turns up once the average temperature rises more than 1.1 degrees C. Second, major food commodities are more sensitive to temperature changes, and their price returns may also trend in a directional shift at different lags, with the trend in meat price being more particular. Third, food markets are more strongly affected in the case of extreme temperature anomalies. Many uncertainties still exist regarding the impact of climate change on food markets, and our work serves as a valuable reference for international trade regulation as well as the creation of dynamic climate risk hedging strategies.