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<p>The convergence of digital technologies and intelligent systems (Digital-Intelligence Integration, DII) in agricultural production, is increasingly acknowledged for its transformative potential in enhancing carbon efficiency and ecological sustainability. This study develops a comprehensive framework to evaluate Agricultural Ecological Total Factor Carbon Productivity (AETFCP), an integrated metric capturing carbon emissions, ecological inputs, and agricultural outputs, and empirically examines how DII shapes agricultural carbon performance. Drawing on provincial panel data from 30 Chinese provinces spanning 2012 to 2022, we employ a double machine learning approach to address potential endogeneity and identify causal effects. The findings indicate that DII is positively associated with improvements in AETFCP, operating primarily through two channels: the advancement of agricultural technology and the optimization of industrial structures. Furthermore, this effect is more pronounced in regions with lower fiscal decentralization and higher marketization, highlighting the moderating role of institutional and policy environments. These findings contribute to the literature on digital transitions in agriculture and offer actionable implications for policymakers in both developing and developed economies seeking to integrate digital innovations into low-carbon agricultural systems.</p>

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The role of digital-intelligence integration in agricultural carbon productivity in China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月3日

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  • Xiran Ke,
  • Qiaoling Huang,
  • LuBin Ke,
  • Dajing Hu,
  • Bingrui Dong

摘要

The convergence of digital technologies and intelligent systems (Digital-Intelligence Integration, DII) in agricultural production, is increasingly acknowledged for its transformative potential in enhancing carbon efficiency and ecological sustainability. This study develops a comprehensive framework to evaluate Agricultural Ecological Total Factor Carbon Productivity (AETFCP), an integrated metric capturing carbon emissions, ecological inputs, and agricultural outputs, and empirically examines how DII shapes agricultural carbon performance. Drawing on provincial panel data from 30 Chinese provinces spanning 2012 to 2022, we employ a double machine learning approach to address potential endogeneity and identify causal effects. The findings indicate that DII is positively associated with improvements in AETFCP, operating primarily through two channels: the advancement of agricultural technology and the optimization of industrial structures. Furthermore, this effect is more pronounced in regions with lower fiscal decentralization and higher marketization, highlighting the moderating role of institutional and policy environments. These findings contribute to the literature on digital transitions in agriculture and offer actionable implications for policymakers in both developing and developed economies seeking to integrate digital innovations into low-carbon agricultural systems.