ORIGINAL RESEARCH article

Front. Sustain. Food Syst.

Sec. Agricultural and Food Economics

Can rural e-commerce policies enhance the total factor productivity of agricultural enterprises? Causal inference based on dual machine learning models

• LZ Luzhong Zhang ¹

• Xiang Li ¹

• YY Yunfang Yan ²

• 1. School of Business Administration, Guizhou University of Finance and Economics, Guiyang, China

• 2. School of Management, Zunyi Normal University, Zunyi, China

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Abstract

Rural e-commerce policies, as an important component of the digital economy, are profoundly reshaping the agricultural value chain and food supply chain, and are of great significance for enhancing the economic efficiency of the grain industry. Based on the panel data of 231 A-share agricultural listed enterprises in China from 2010 to 2024, this paper takes the comprehensive demonstration policy of e-commerce entering rural areas in China as the standard natural experiment, constructs A dual machine learning model, and systematically examines the impact of the rural e-commerce policy on the TFP of agricultural enterprises and its mechanism of action. The research findings are as follows: (1) RECP significantly enhanced the TFP of agricultural enterprises. After introducing high-dimensional fixed effects, replacing machine learning algorithms, and using instrumental variable methods to alleviate potential endogeneity, the conclusion remained robust. (2) Mechanism analysis indicates that RECP mainly promotes the improvement of TFP in agricultural enterprises by reducing information asymmetry, increasing R&D investment and optimizing the human capital structure. (3) Heterogeneity analysis shows that this promoting effect is more significant in enterprises in the growth stage, those with lower financing constraints, those engaged in agricultural product processing, and large listed agricultural enterprises. The research conclusion provides new empirical evidence for understanding the micro-mechanism by which the digital economy empowers the productivity improvement of listed agricultural enterprises, and offers policy implications for enhancing the supporting digital ecosystem of listed agricultural enterprises.

Summary

Keywords

Agricultural enterprises, agricultural productivity, dual machine learning model, e-commerce, total factor productivity

Received

28 July 2026

Accepted

28 October 2026

Copyright

© 2026 Zhang, Li and Yan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xiang Li

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