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<p>Digital and green transition (hereafter twin transition) has emerged as a new direction for firms to achieve sustainable development. However, whether and how green finance policies can facilitate this process remains unsolved. By constructing a multi-indicator evaluation framework, this study combines text analysis, Python crawler technique, and coupling coordination degree (CCD) model to measure the performance of corporate twin transition. We employ the establishment of Green Finance Reform and Innovation Pilot Zones (GFRIPZ) as a quasi-natural experiment, and investigate the impact of green finance policy on twin transition using the double machine learning (DML) technique. Using data of A-share listed firms in China spanning 2012–2022, we find that GFRIPZ significantly fosters the twin transition of enterprises by enhancing their green and digital technology innovation capabilities. In addition, the GFRIPZ demonstrates significant promotion effects on both state-owned enterprises (SOEs) and non-SOEs, but the impact is more pronounced for non-heavily polluting enterprises (non-HPEs), firms with a higher proportion of independent directors, and firms with lower original digital capabilities. Further analysis reveals that the deepening in twin transition positively impacts firms’ future financial performance. Our findings not only expand the understanding of the transformation effect of green finance reform, but also provide an empirical basis for deepening the twin transition in developing countries.</p>

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The Role of Green Finance Policy in Fostering Twin Transition: An Empirical Investigation using Machine Learning Techniques

  • 期刊论文

  • 发表日期：

    2026年5月25日

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  • Xiaoling Wang,
  • Xingyi Tao,
  • Min Bai,
  • Yiting Han,
  • Chao Feng

摘要

Digital and green transition (hereafter twin transition) has emerged as a new direction for firms to achieve sustainable development. However, whether and how green finance policies can facilitate this process remains unsolved. By constructing a multi-indicator evaluation framework, this study combines text analysis, Python crawler technique, and coupling coordination degree (CCD) model to measure the performance of corporate twin transition. We employ the establishment of Green Finance Reform and Innovation Pilot Zones (GFRIPZ) as a quasi-natural experiment, and investigate the impact of green finance policy on twin transition using the double machine learning (DML) technique. Using data of A-share listed firms in China spanning 2012–2022, we find that GFRIPZ significantly fosters the twin transition of enterprises by enhancing their green and digital technology innovation capabilities. In addition, the GFRIPZ demonstrates significant promotion effects on both state-owned enterprises (SOEs) and non-SOEs, but the impact is more pronounced for non-heavily polluting enterprises (non-HPEs), firms with a higher proportion of independent directors, and firms with lower original digital capabilities. Further analysis reveals that the deepening in twin transition positively impacts firms’ future financial performance. Our findings not only expand the understanding of the transformation effect of green finance reform, but also provide an empirical basis for deepening the twin transition in developing countries.