The heterogeneous effect of financial technology on green total factor productivity in China

Due to the unbalanced and inadequate development of fintech among various provincial regions, this paper proposes a novel sparse quantile model to explore the heterogeneous impacts of fintech on green total factor productivity (GTFP) in the provinces of China based on the annual data from 2011 to 2020. Quantile estimators of the proposed method are used as empirical "location" measures for the heterogeneous influence of fintech on GTFP. Two nonparallel twin functions at each quantile level capture the unbalanced information between fintech and GTFP. The empirical results show that fintech effectively promotes the quality of economic growth in every province, while the effects of fintech on GTFP are heterogeneous and unbalanced. Specifically, the effects in high-green-development regions are more powerful than those in low-green-development regions. Moreover, the influencing strength of fintech on GTFP among provincial regions has become weaker in recent years. The impact mechanism test indicates that fintech mainly promotes GTFP through the upgrading of industrial structure. Based on these results, this paper discusses some policy recommendations, such as optimizing the allocation of financial technology resources, improving the quality of innovation, promoting the upgrading of industrial structure, and strengthening foreign exchange and cooperation for the sustainable development. (c) 2023 The Authors. Published by Elsevier Espana, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY-NC-ND license (littp://cleativecommons.orgilicenses/by-nc-nd/4.0/)