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<p>The agricultural sector is under increasing pressure to reduce greenhouse gas (GHG) emissions while sustaining crop productivity. Digital technologies such as smartphones may help reconcile these goals; yet their implications for environmental efficiency remain understudied. This study investigates whether smartphone use enhances environmental efficiency, defined as the practice of using fewer resources to produce goods and services while minimizing environmental impacts. Using cross-sectional data collected in 2018 from 324 maize farmers, we estimate environmental efficiency using a stochastic enhanced hyperbolic distance function. To address selection bias in both observable and unobservable factors, we combine propensity score matching with (Greene’s <CitationRef CitationID="CR21">2010</CitationRef>) selectivity bias-corrected stochastic frontier model. The results indicate an average environmental efficiency score of 0.95, with smartphone users achieving scores approximately 4% higher than nonusers. Smartphone adoption is more prevalent among younger farmers, those with off-farm work experience, and those in villages with better road infrastructure. Our findings suggest that promoting smartphone access and improving rural infrastructure can enhance environmental efficiency and support low-carbon transitions in China’s maize farming sector.</p>

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Can smartphone use promote environmental efficiency? A study of maize farmers in China

  • 期刊论文

  • 发表日期：

    2026年5月2日

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  • Gaofei Yang,
  • Maria Vrachioli,
  • Jianjun Tang,
  • Johannes Sauer

摘要

The agricultural sector is under increasing pressure to reduce greenhouse gas (GHG) emissions while sustaining crop productivity. Digital technologies such as smartphones may help reconcile these goals; yet their implications for environmental efficiency remain understudied. This study investigates whether smartphone use enhances environmental efficiency, defined as the practice of using fewer resources to produce goods and services while minimizing environmental impacts. Using cross-sectional data collected in 2018 from 324 maize farmers, we estimate environmental efficiency using a stochastic enhanced hyperbolic distance function. To address selection bias in both observable and unobservable factors, we combine propensity score matching with (Greene’s 2010) selectivity bias-corrected stochastic frontier model. The results indicate an average environmental efficiency score of 0.95, with smartphone users achieving scores approximately 4% higher than nonusers. Smartphone adoption is more prevalent among younger farmers, those with off-farm work experience, and those in villages with better road infrastructure. Our findings suggest that promoting smartphone access and improving rural infrastructure can enhance environmental efficiency and support low-carbon transitions in China’s maize farming sector.