Role of green finance in regional heterogeneous green innovation: Evidence from China

China's economic development strategy has shifted from rapid growth to high-quality, green, and low-carbon development. This transition is crucial given China's significant carbon emissions and reliance on fossil fuels. Green innovation (GI) is key to this transformation, but it often faces financial constraints. Green finance (GF) aims to address these constraints by channeling capital towards environmentally friendly technologies and projects. While existing research shows GF's positive influence on GI, it lacks a comprehensive analysis of its diverse effects on various types of GI and its interaction with regional environmental regulations and spatial spillovers. This study addresses this gap by examining the heterogeneous effects of GF on five types of GI in China, considering the threshold effect of environmental supervision intensity and spatial spillover effects.

The literature highlights the importance of green innovation for sustainable development and its challenges, including high costs and long investment cycles. Studies show that green finance plays a vital role in mitigating financing constraints for green innovation, particularly for small and medium-sized enterprises (SMEs). However, the literature lacks a systematic examination of the heterogeneous impacts of green finance on different types of green innovation. Existing research also shows the importance of environmental supervision and its potential impact on innovation, with debate over whether it has a positive or negative impact. The impact of environmental supervision on green innovation also depends on factors like firm size, market competition, and financial strength. Finally, the spatial dimension of green innovation has been explored, acknowledging the potential for spatial spillover effects, but more research is needed to understand the heterogeneous impact of spatial spillover effects on different types of green innovation.

This study uses a balanced panel dataset of 30 Chinese provinces from 2010 to 2019. The dependent variable is the number of green invention patents, categorized into five types: fossil energy carbon reduction (GI_1), energy saving and recovery (GI_2), clean energy (GI_3), energy storage (GI_4), and greenhouse gas capture, utilization, and storage (GI_5). The independent variable is a composite GF index, constructed using a spatiotemporal entropy weighting method from multiple indicators of green financial development. The intensity of environmental supervision (ESI) is measured by the number of environment-related administrative penalties. Control variables include per capita GDP, foreign direct investment, financial development, population density, and industrial structure. Three econometric models are employed: a negative binomial model to analyze the impact of GF on heterogeneous GI, a threshold model to examine the threshold effect of ESI, and a spatial error model to investigate spatial spillover effects. Robustness tests are conducted by varying the lag period of the dependent variable, changing the econometric model (Poisson and OLS), and using alternative ESI measures.

The negative binomial model results show that GF has a significant positive impact on overall GI and specifically on GI_1, GI_2, and GI_3. The threshold model reveals a significant threshold effect of ESI on the GF-GI relationship. Below the threshold, GF only significantly impacts GI_2 (energy saving and recovery). Above the threshold, GF significantly impacts all five types of GI. The spatial error model indicates a significant negative spatial spillover effect of GI, suggesting that high GI in one region may negatively affect neighboring regions. Robustness tests confirm these findings.

The findings support the hypothesis that GF significantly promotes GI, but its effectiveness is heterogeneous across different types of GI and dependent on the intensity of environmental supervision. The negative spatial spillover effect suggests that resource competition and industrial relocation may occur. The positive relationship between GF and GI is stronger in regions with higher ESI, indicating that stricter environmental regulations create incentives for innovation and GF's ability to support those efforts. The negative spatial spillover effect points to a potential 'siphoning effect,' where high GI areas may draw resources away from neighboring areas. These findings have important implications for policymakers. They suggest that a ‘one size fits all’ approach to green finance policy is not optimal and that policies should be tailored to regional context.

This study demonstrates the heterogeneous effects of green finance on green innovation across different regions of China. The results highlight the importance of considering environmental supervision intensity and spatial spillovers in designing effective green finance policies. Future research could explore the mediating roles of other factors, such as technological diffusion and institutional arrangements, in the relationship between green finance, environmental supervision, and green innovation.

The study relies on patent data as a proxy for green innovation, which may not fully capture all aspects of green innovation activities. The composite GF index, while comprehensive, may not perfectly capture all relevant aspects of green finance development. The analysis focuses on China, limiting the generalizability of the findings to other countries with different institutional contexts.