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Green finance: The catalyst for artificial intelligence and energy efficiency in Chinese urban sustainable development

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  • Zeng, Ming
  • Zhang, Weike

Abstract

For a prolonged period, Chinese cities have been developing through high-investment and low-efficiency urbanization, posing a serious threat to urban sustainable development. In recent years, green finance (GF) has been recognized as a potential catalyst for fostering urban green economy and promoting urban sustainable development. This study thus explores whether GF stimulates artificial intelligence (AI) and energy efficiency (EE), thereby contributing to urban sustainable development. Leveraging 282 Chinese cities' datasets from 2014 to 2019, this study yields the following conclusions. This study evidences that GF contributes to the enhancement of the Chinese urban AI level. This conclusion holds true even when alternative measurements are considered, the instrumental variable (IV) method is employed, the interference of the GF policies is eliminated, the municipality samples are excluded, and doubly debiased Lasso regression is carried out. Additionally, the study highlights that the beneficial influence of GF on AI is notably significant in eastern and central cities, non-resource-dependent cities, cities with a population under 5 million, and low AI-level cities. Furthermore, this study demonstrates the positive effect of GF on EE and its spatial spillover effects. These findings help reinforce the role of GF in advancing urban sustainable development through enhancements in AI and EE.

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  • Zeng, Ming & Zhang, Weike, 2024. "Green finance: The catalyst for artificial intelligence and energy efficiency in Chinese urban sustainable development," Energy Economics, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:eneeco:v:139:y:2024:i:c:s0140988324005917
    DOI: 10.1016/j.eneco.2024.107883
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