Uncovering the role of financial technology for total factor energy efficiency in China: based on machine learning methods

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Abstract

In the context of China's long-standing extensive economic growth model and increasing environmental pressures, improving total factor energy efficiency (TFEE) has become crucial for environmental protection and sustainable development. Financial technology (FinTech), as an emerging technology-driven financial innovation model, offers potential solutions; however, empirical evidence on its environmental impact remains limited. Based on balanced panel data of 268 Chinese cities from 2011 to 2021, we employed a double machine learning approach and the SHAP value algorithm to assess the impact of FinTech on TFEE as well as its nonlinear effects, with Random Forest being the primary estimation model. The major findings were as follows: (1) FinTech has a significant promoting effect on improving TFEE, which has passed multiple robustness tests. (2) In terms of mediation mechanisms, FinTech exerts its positive influence on TFEE by promoting green finance development, facilitating industrial structure optimization, and driving green technological innovation. (3) Heterogeneity analysis indicates that the role of FinTech in enhancing TFEE is more pronounced in eastern China, non-old industrial bases, and large cities. (4) According to the distribution trend of SHAP value, the impact of FinTech on TFEE exhibits a nonlinear effect, and its positive driving force can be effectively exerted only after its development level exceeds a specific threshold around 4.2. This study provides empirical evidence on the role of FinTech in advancing TFEE, offering valuable insights for policymakers seeking to harness FinTech as an effective tool to advance energy efficiency and support sustainable economic transformation.

Keywords

financial technology (FinTech) / total factor energy efficiency (TFEE) / double machine learning / SHAP value / mediation mechanism / nonlinear effect / China

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