Unveiling the environmental impact of corn production in China: evidence from panel ARDL approach - Zhang, Ying; Ahmad, Muhammad Irshad; Shen, Qiong; Boota, Muhammad Waseem; Zhao, Cuiping; Ma, Hengyun (Frontiers in Sustainable Food Systems, 2025)
Understanding the cycle of carbon emissions resulting from agricultural practices is critical for evaluating their effect on environmental quality. This study investigates the influence of corn production on environmental quality across six major corn producing provinces in China: Hebei, Heilongjiang, Henan, Hubei, Shandong, and Sichuan, using panel datasets spanning from 1990 to 2022. Utilizing a robust methodological framework and advanced econometric techniques such as the Panel Mean Group Autoregressive Distributed Lag model (PMG-ARDL), Panel Quantile Regressions (PQR), Panel Least Square regression (PLSR), this study offers a comprehensive analysis of both short-term and long-term impacts of several agricultural inputs, agricultural GDP, and temperature on environmental quality. The findings reveal significant long-term contributions to carbon emissions from the use of agricultural water, agricultural credit, and fertilizers use, indicating the environmental costs associated with intensive agricultural practices. The study shows carbon emissions have a long-term negative relationship with corn production. The results from the PMG-ARDL model are consistent with those obtained from the PQR, and PLSQR analyses, demonstrating strong positive correlations between agricultural loans, fertilizer use, agricultural water usage, and carbon emissions. The Dumitrescu and Hurlin results show unidirectional causation of carbon emissions from pesticide use, temperature, and agricultural GDP, and bidirectional causal relationship between carbon emissions, corn production, fertilizer use, agricultural water usage, and agricultural credit. The study underscores the critical need for policies that balance agricultural productivity with environmental quality, suggesting directions for future research to explore diverse agricultural systems and incorporate more dynamic modeling approaches to better understand and mitigate the environmental impacts of agriculture.