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<p>Under China’s “dual carbon” goals, guiding agriculture toward a low-carbon transition has become a vital path to sustainable development. Green finance, a key policy tool for channeling resources into environmentally friendly activities, has attracted growing attention for its potential to support low-carbon agricultural development. Using provincial data from 2010 to 2022, this study measures carbon emission intensity of agricultural (CEIA) in 30 provinces. Treating the Green Finance Reform and Innovation Pilot Zones (GFPZ) initiative as a quasi-natural experiment, this study employs a double machine learning (DML) approach to examine how green finance influences agricultural carbon intensity and to identify the underlying mechanisms behind these effects. The analysis also investigates possible spatial spillover effects across regions. The findings show that: (1) Establishing GFPZ significantly lowers CEIA, and the effect is persistent. (2) The policy operates mainly by promoting technological progress that enhances agricultural productivity and energy efficiency; facilitating farmland transfers that stabilize land-use rights, and encouraging larger-scale, more efficient farming practices. Together, these factors support agriculture’s low-carbon transition. (3) GFPZ curb CEIA both within the pilot regions and in neighboring areas. Therefore, green finance policies should be further strengthened to support agricultural development. Efforts should focus on optimizing agricultural structures, advancing innovations in carbon-reducing and energy-saving techniques, boosting energy performance, and encouraging larger-scale farming operations. At the same time, policies should consider regional differences and promote locally tailored strategies and cross-regional cooperation to jointly advance low-carbon, sustainable agricultural development.</p>

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Can green finance policies drive the low-carbon transition of agriculture? — evidence from spatial difference-in-differences and double machine learning in China

  • 期刊论文

  • 发表日期：

    2026年4月14日

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  • Xuexue Wu,
  • Xiaoyu Chen,
  • Shengchao Ye,
  • Haohan Wang

摘要

Under China’s “dual carbon” goals, guiding agriculture toward a low-carbon transition has become a vital path to sustainable development. Green finance, a key policy tool for channeling resources into environmentally friendly activities, has attracted growing attention for its potential to support low-carbon agricultural development. Using provincial data from 2010 to 2022, this study measures carbon emission intensity of agricultural (CEIA) in 30 provinces. Treating the Green Finance Reform and Innovation Pilot Zones (GFPZ) initiative as a quasi-natural experiment, this study employs a double machine learning (DML) approach to examine how green finance influences agricultural carbon intensity and to identify the underlying mechanisms behind these effects. The analysis also investigates possible spatial spillover effects across regions. The findings show that: (1) Establishing GFPZ significantly lowers CEIA, and the effect is persistent. (2) The policy operates mainly by promoting technological progress that enhances agricultural productivity and energy efficiency; facilitating farmland transfers that stabilize land-use rights, and encouraging larger-scale, more efficient farming practices. Together, these factors support agriculture’s low-carbon transition. (3) GFPZ curb CEIA both within the pilot regions and in neighboring areas. Therefore, green finance policies should be further strengthened to support agricultural development. Efforts should focus on optimizing agricultural structures, advancing innovations in carbon-reducing and energy-saving techniques, boosting energy performance, and encouraging larger-scale farming operations. At the same time, policies should consider regional differences and promote locally tailored strategies and cross-regional cooperation to jointly advance low-carbon, sustainable agricultural development.