Digitalization’s Role in Shaping Sustainable Agriculture—Evidence from Chinese Provincial Panel Data Using the Baidu Index

Qirui Zhang, Xinhui Feng, Wangfang Xu and Longbao Wei ()
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Qirui Zhang: China Academy for Rural Development, Zhejiang University, Hangzhou 310058, China
Xinhui Feng: School of Public Affairs, Zhejiang University, Hangzhou 310058, China
Wangfang Xu: School of Accounting, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Longbao Wei: China Academy for Rural Development, Zhejiang University, Hangzhou 310058, China

Agriculture, 2025, vol. 15, issue 12, 1-23

Abstract: The impact of digital transformation on agricultural sustainability has attracted significant attention, and empirical methods are widely being used to provide a scientific framework for research in this field. However, commonly used digitalization indicators based on the entropy method are prone to distortion due to outliers and the influence of selected evaluation factors. Yet, empirical studies often overlook the heterogeneity in the measurement of explanatory variables, which potentially produces biased estimates. This study addresses these gaps by constructing a digitalization index based on text recognition named the Baidu Index and by employing a dynamic panel model to systematically analyze the intertemporal effects of digitalization on agricultural sustainability across 31 Chinese provinces. The key findings reveal that digitalization not only directly enhances agricultural sustainability but also exerts positive moderating effects through agricultural production, industrial structure, and technological progress. Critically, the results are slightly different when the choices are between absolute and relative units for agricultural carbon emissions and green total factor productivity, highlighting the necessity for standardized measurement frameworks in future research. Practically, policymakers should prioritize rural digital infrastructure investment and narrow the digital divide caused by institutional and technological factors. This study provides both a novel analytical framework and actionable insights for advancing sustainable agriculture in the digital era.

Keywords: agricultural carbon emissions; agricultural green total factor productivity; ordinary least squares (OLS); moderating effect; measurement heterogeneity (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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