Impact of surface temperature change on food production: Evidence from PLFC and MMQR models

Type of the article: Research Article AbstractThe analysis of the mechanisms through which temperature variations affect food production has become a paramount concern for sustainable development and policy formulation, as global food security faces unprecedented challenges from accelerating climate change. Temperature anomalies are threatening agricultural systems that sustain billions of people worldwide. Using panel data from 40 countries from 1980 to 2021, this study investigates the influence of annual surface temperature fluctuations on food production. The Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) model is estimated to analyze this correlation in the mixed evidence on the integration levels of the variables. The results corroborate the cointegration among the variables. Temperature change has a significant negative effect on food production in both the short and long run. Food production is positively influenced by economic growth and renewable energy consumption. The study also considers potential nonlinearity by utilizing the Partially Linear Functional Coefficient (PLFC) and the Method of Moments Quantile Regression (MMQR) model. The PLFC estimates imply that economies with lower GDP levels are more adversely influenced by temperature change, emphasizing the crucial role of economic growth in mitigating climate change. Significant negative effects of temperature change are also corroborated by the MMQR estimates in all quantiles, with the largest effects obtained at the higher quantiles. The variation in the impact of renewable energy consumption over quantiles implies that energy policies should be modified according to the developmental phases of countries. The empirical findings have significant implications for formulating sustainable agricultural policies and climate adaptation strategies.