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Green finance, green technology innovation, and wind power development in China: Evidence from spatial quantile model

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  • Xu, Bin
  • Lin, Boqiang

Abstract

Renewable energy is an inevitable choice to address global climate change and energy shortages. Wind power production has already ranked second in renewable energy production and has enormous development prospects. However, wind power companies generally face funding shortages. How to effectively leverage the positive role of the financial sector in wind power development is a topic worthy of research. Using a novel spatial quantile model, this article investigates the impact of green finance on wind power development and its impact mechanism. The results shows that green finance has a driving effect on the provinces with low- and middle-wind power production, such as Shanghai, Tianjin, Zhejiang, and Shaanxi. However, there is a negative spatial correlation between wind power development in the 10th–25th, and 25th–50th group provinces, such as Qinghai, Sichuan, and Henan. In addition, the impact of wind power prices on the wind power in all provinces is negative, since wind power prices are still on the high side. Green technology innovation and foreign direct investment are two important intermediate mechanisms that green finance affects wind power development. Mechanism analysis displays that foreign direct investment contributes the most to the wind power in the 25th–50th and 50th–75th group provinces, such as Jiangsu, Liaoning, and Guangdong. However, green technology innovation has the smallest contribution to the provinces with high-wind power production, such as Shandong, Shanxi, and Inner Mongolia. Therefore, policy makers should consider the spatial effects and inter- provincial differences in wind power development.

Suggested Citation

  • Xu, Bin & Lin, Boqiang, 2024. "Green finance, green technology innovation, and wind power development in China: Evidence from spatial quantile model," Energy Economics, Elsevier, vol. 132(C).
    • Handle: RePEc:eee:eneeco:v:132:y:2024:i:c:s0140988324001713
    DOI: 10.1016/j.eneco.2024.107463
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