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How to promote green innovation of high-pollution firms? A fuzzy-set QCA approach based on the TOE framework

Author

Listed:
  • Sha Lou

    (Harbin University of Commerce)

  • Chunqiong Yao

    (Harbin University of Commerce)

  • Dehua Zhang

    (Harbin University of Commerce)

Abstract

High-pollution firms’ green innovation is crucial for them to gain a competitive advantage in the green economy. However, scholars are limited to exploring the "net effects" of green innovation and rarely involve high-polluting industries. Based on the TOE framework theory and using fuzzy-set qualitative comparative analysis methodology, we explored the influence of antecedents upon high-pollution firms’ green innovation. These antecedents are: Green technology R&D investment, Green technology management capability, CEO’s green investment awareness, Green organizational structure, Government green subsidy and Environmental regulation. Data from the Science and Technology Innovation Board's survey of 62 Chinese enterprises in 2021. Our research findings are as follows: Firstly, multiple antecedents rather than a single condition drive high-pollution firms’ green innovation. Secondly, the government's green subsidy is the core indicator for highly polluting enterprises to achieve high-level green innovation. Thirdly, for enterprises with low-level green organizational structures and insufficient environmental regulations, it’s beneficial to focus on green technology management capabilities and government green subsidies. These discoveries enrich the study of green innovation and provide beneficial practical implications for high-pollution firms’ green innovation.

Suggested Citation

  • Sha Lou & Chunqiong Yao & Dehua Zhang, 2025. "How to promote green innovation of high-pollution firms? A fuzzy-set QCA approach based on the TOE framework," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(2), pages 4911-4935, February.
    • Handle: RePEc:spr:endesu:v:27:y:2025:i:2:d:10.1007_s10668-023-04107-x
    DOI: 10.1007/s10668-023-04107-x
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