Introduction

2. Methodology

2.2. Introduction to Variables

2.2.1. Explained Variables

Regional Innovation Capacity (RIC). As a proxy variable for the explanatory variable regional innovation level, the composite score of the provincial-level ‘Comprehensive utility value of China's regional innovation capacity’ in the Report on the Evaluation of China's Regional Innovation Capacity (2008-2021), jointly published by the China Science and Technology Development Strategy Group and the Chinese Academy of Sciences, was used. Its measurements include the dimensions of provincial knowledge creation, knowledge acquisition, enterprise innovation, innovation environment and innovation performance, which can objectively evaluate the regional innovation capacity (see Table 1 for details).

Specifically, knowledge creation (15 per cent) is the cornerstone of innovation activities; knowledge acquisition (15 per cent) reflects the importance of open innovation; enterprise innovation (25 per cent), as the core driving force, directly reflects the degree of activity and effectiveness of regional innovation activities; innovation environment (25 per cent), as a supportive indicator, is the necessary guarantee for the continuation of innovation activities; and finally, innovation performance (20 per cent), as an outcome indicator, is the key basis for assessing the effectiveness of regional innovation capacity enhancement.

In summary, by adopting this comprehensive utility value as a proxy variable for regional innovation level and combining it with its multi-dimensional measurement indicators, we are able to understand and evaluate the regional innovation capacity in a more comprehensive and in-depth way, which provides a precise quantitative tool for empirical analyses, and helps to explore in-depth the internal mechanisms and external factors affecting the regional innovation capacity.

Table 1. Regional innovation capacity indicator system.

Table 1. Regional innovation capacity indicator system.

Level 1 indicators	Secondary indicators	Description of indicators	causality
Regional Innovation Capacity (ric)	Knowledge creation 15 per cent	Measuring a region's ability to generate new knowledge.	positive
	Knowledge acquisition 15 per cent	Measurement of a region's ability to utilise external knowledge and cooperation between industry, academia and research.	positive
	Enterprise innovation 25 per cent	Measures the ability of firms within a region to apply new knowledge, develop new technologies, utilise new processes, and manufacture new products.	positive
	Innovation environment 25 per cent	Measure the ability of a region to provide the appropriate environment for the generation, flow and application of technology.	positive
	Innovation performance 20 per cent	The ability to measure the benefits of innovation for the economic and social development of a region.	positive

2.2.2. Explanatory Variables

Green Finance Development Index (GF), the index is constructed based on the entropy method, aiming to objectively reflect the level of green finance development. Green finance covers green loans, green securities, green insurance and green investment. In order to construct the green financial development index, this paper designs a set of index system, and objectively assigns weights to each index by entropy value method, in order to avoid the subjectivity of traditional expert scoring method. The steps of constructing green financial index by entropy value method are as follows:

①Indicator standardisation: The raw data are standardised to eliminate the effect of the scale. For positive indicators, the following formula is used for standardisation:

$$Y_{ij}={\displaystyle \frac{max\left(X_{ij}\right)-X_{ij}}{max\left(X_{ij}\right)-min\left(X_{ij}\right)}}$$

For negative indicators, the normalisation formula is adjusted to:

$$Y_{ij}={\displaystyle \frac{X_{ij}-min\left(X_{ij}\right)}{max\left(X_{ij}\right)-min\left(X_{ij}\right)}}$$

where$X_{ij}$ represents the observed value of the ith indicator in the jth region;$Y_{ij}$ is the standardised value.

②Information Entropy Calculation: The information entropy value calculates the information entropy value of each indicator using the standardized indicator value, which reflects the degree of dispersion of the indicator:

$$z_{ij}={\displaystyle \frac{Y_{ij}}{\sum _{j=1}^n Y_{ij}}}$$

$$E_i=-\mathrm{l}\mathrm{n}(n{)}^{-1}\sum _{j=1}^n z_{ij}\mathrm{l}\mathrm{n}{z}_{ij}$$

③Determination of indicator weights: The weights of the indicators are determined by the information entropy value $W_i$ ;

$$W_i={\displaystyle \frac{1-E_i}{\sum _{i=1}^n {(1-E}_i)}}$$

④Synthesis of green financial development index: Finally, the green financial development composite index is calculated by combining the weights and standardized values of each indicator G:

$$G=a_{ni}{W}_i$$

where$a_{ni}$ is the standardised value of the ith indicator.

Through the above steps, we obtained an objective quantitative green finance development index, which is a detailed and specific index system (as shown in Table 2), comprehensively covers multiple dimensions of green finance, accurately reflects its level of development, and lays a solid foundation of explanatory variables for the subsequent empirical analyses.

2.2.3. Control Variables

In order to ensure the accuracy of the estimation of the impact of green finance on regional innovation, the selection of control variables in this paper takes full account of the region's basic innovation environment, actual resource inputs and external environment. Specifically, a series of control variables including industrial structure (ind), human capital (lnhes), urbanisation level (ur), science and technology emphasis (techi), carbon emissions (lnco2) and capital investment (capi) are introduced, and the following theoretical and empirical bases support the choice of these variables.

Firstly, industrial structure plays a key role in technological innovation. According to the theory of industrial economics, regions with a higher share of service and high-technology industries usually have stronger innovation capacity (Ge et al. 2023) [41]. Therefore, incorporating industrial structure as a control variable can more accurately measure the innovation potential of the region. Human capital directly affects the quality of regional human capital, which is the basic element to promote technological innovation. The higher the human capital, the stronger the region's innovation and technology absorption capacity, so the article needs to use it as a control variable. The level of urbanisation is also an important factor affecting regional innovation, as the urbanisation process is able to gather innovative resources and promote the diffusion and application of knowledge and technology, and controlling for this variable is crucial to accurately assessing innovation capacity.

In addition, the level of emphasis on science and technology is an important indicator of the level of support for innovation activities in a region. Higher S&T investment usually implies a stronger incentive to innovate, so this variable is crucial for an accurate assessment of regional innovation capacity. Carbon emissions, as a control variable for environmental factors, influence the demand and direction of green technology innovation, and controlling for this variable helps to exclude the external effects of environmental pressures on innovation activities. Finally, capital investment is a key resource for firms to carry out R&D and innovation activities, and regions with sufficient capital are more likely to support large-scale innovation activities, so controlling for capital investment helps to avoid overestimation or underestimation of innovation capacity due to regional capital differences.

By introducing these control variables, the analyses in this paper can reflect the actual impact of green finance on regional innovation more comprehensively and accurately, ensuring the robustness and reliability of the research results. In addition, this paper adopts a fixed-effects double clustering (Cluster) regression model to control for the invariant effects of province and time, thus further enhancing the robustness of the research results. Details of each variable are detailed in Table 3.

2.2.4. Mediating and Moderating Variables

In this study, the mediating variable is the intensity of R&D investment (RD), which serves as a key indicator of regional innovation capacity and reflects the level of regional financial investment in S&T innovation. This variable is quantified by the ratio of R&D expenditure to regional gross domestic product (GDP), which reveals the level of regional emphasis on S&T R&D activities and its support for the innovation-driven development strategy. Theoretical analyses anticipate that the development of green finance may indirectly promote regional technological innovation capacity by enhancing the intensity of R&D investment, thus establish an intrinsic link between green finance and regional technological innovation.

Research and development (R&D) investment intensity (RD) is selected as a mediator variable, aiming to explore how green finance can enhance regional innovation capacity by promoting R&D activities. The importance of R&D investment in promoting technological innovation has been widely recognised, and both the Diffusion of Innovation Theory (ROGERS 1962) [42]and the Resource Base Theory (Wernerfelt 1984)[43]emphasise that R&D investment is the core driver of technological innovation and maintaining competitive advantage for enterprises. By increasing R&D investment, firms can develop new technologies and improve products more effectively, thus enhancing their overall innovation capability. Therefore, RD as a mediating variable is of great significance in this study. In addition, existing empirical studies have shown that RD investment has a significant mediating role between green finance and technological innovation, especially in resource-intensive and technology-intensive industries, and this mechanism is particularly obvious (Tang et al. 2023) [44]. The introduction of RD as a mediating variable not only helps to reveal the path of green finance affecting regional innovation, but also fills the research gap in the existing literature in exploring the specific mechanism between green finance and innovation.

In this paper, the regional innovation and entrepreneurship capacity (IU) was selected as a moderating variable, aiming to deeply explore the impact of green finance on regional innovation. As an intrinsic factor, regional innovation and entrepreneurship capacity (IU) reflects the ability of enterprises and individuals in the region in innovative ideas, technological development and entrepreneurial activities. According to the theory of diffusion of innovation, regions with stronger innovation and entrepreneurship capabilities are more active in technological innovation activities, and green finance is more likely to play a significant facilitating role in such regions. Therefore, IU can moderate the relationship between green finance and regional innovation, and further promote the research, development and application of green technologies by enhancing the innovation atmosphere.

Combining these mediating and moderating variables, this study aims to reveal the complex interaction mechanism between green finance and regional technological innovation. Through rigorous empirical analyses, this study can not only provide a supplement to existing theories, but also provide policymakers with strong decision support in promoting green transformation and technological innovation in regional economies.

3. Empirical Results

3.1. Analysis of Baseline Regression

This study first tests the direct effect of green finance (gf) on regional innovation capacity (lnric), hypothesising that green finance can promote regional innovation capacity. To this end, this paper constructs a variety of fixed effects models to test the hypothesis. The following are the results of the baseline regression analyses of the impact of green finance on regional innovation capacity, which are detailed in Table 6.

In Model 1, only the explanatory variable green finance (gf) is included in this paper. The results show that the regression coefficient of gf is 1.315 and significant at the 1% statistical level, indicating that the development of green finance has a significant positive contribution to regional innovation capacity. However, the goodness of fit of model 1 is low, with an adjusted R² of 0.067, indicating that the explanatory power of the model is more limited when only green finance is considered.

Model 2 adds province fixed effects and year fixed effects to model 1. The results show that the regression coefficient of green finance decreases to 0.214, but is still significant at the 1% statistical level. The adjusted R² of Model 2 significantly increases to 0.944, showing that the introduction of province and year fixed effects significantly enhances the explanatory power of the model.

Model 3 further introduces control variables based on model 1. After adding these control variables, the regression coefficient of green finance is 0.148, although no longer significant, but other control variables such as industrial structure, science and technology inputs and capital inputs have a significant positive impact on regional innovation capacity at the 1% statistical level, while carbon emissions have a significant negative impact on regional innovation capacity at the 5% level. The adjusted R² of model 3 is 0.771, indicating that the control variables have a certain enhancement on the explanatory power of the model.

Model 4 further adds control variables to model 2, and the results show that the regression coefficient of gf is 0.207 and is significant at the 1 per cent statistical level. This indicates that green finance still has a significant positive effect on regional innovation capacity even when controlling for both province and year fixed effects and multiple control variables. The adjusted R² of model 4 further increases to 0.954, indicating that the model has stronger explanatory power and robustness.

Overall, the results of the baseline regression analyses indicate that green finance has a significant positive contribution to regional innovation capacity, and the finding remains robust after the inclusion of control variables and fixed effects. This suggests that green finance is an important factor in promoting regional innovation.

Table 6. Baseline regression results.

Table 6. Baseline regression results.

	Model 1	Model 2	Model 3	Model 4
	lnric	lnric	lnric	lnric
gf	1.315*** (0.235)	0.214*** (0.052)	0.148 (0.133)	0.207*** (0.052)
ind			0.742*** (0.118)	0.757*** (0.133)
lnhes			0.174*** (0.017)	0.142** (0.057)
ur			0.337*** (0.089)	0.252 (0.300)
techi			13.187*** (0.850)	2.097*** (0.477)
lnco2			-0.070*** (0.013)	-0.029* (0.015)
capi			0.427*** (0.138)	0.500*** (0.082)
Constant	3.159*** (0.039)	3.326*** (0.008)	2.142*** (0.095)	2.309*** (0.211)
N	420	420	420	420
R^2	0.069	0.950	0.775	0.960
Prov FE	NO	YES	NO	YES
Year FE	NO	YES	NO	YES
r2_a	0.067	0.944	0.771	0.954

Note: *, **, *** indicate significant at the 10 per cent, 5 per cent, and 1 per cent levels, respectively; standard errors are in () in the table.

3.2. Analysis of Mediating Effects

This study further explores the mediating role of R&D investment intensity (rd) in the impact of green finance (gf) on regional innovation capacity (lnric). We hypothesise that green finance enhances regional innovation capacity by promoting an increase in R&D investment. To test this hypothesis, we constructed a regression model (Model 5) with mediating variables.

In Model 5, we find that green finance has a significant positive effect on R&D investment intensity (coefficient of 0.004, p < 0.1), indicating that green finance can effectively incentivise regions to increase R&D investment. In addition, the positive effect of R&D investment intensity on regional innovation capability (lnric) is also confirmed (coefficient of 9.464, p < 0.01), suggesting that R&D investment is an important driver of regional innovation capability. Nevertheless, the coefficient of green finance is still significant (coefficient of 0.168, p < 0.05), suggesting that there is a partial mediating effect. The Sobel Z-value of mediating effect is 2.347 (p < 0.05), the bootstrap Z-value is 2.13 (p < 0.05), and the mediating effect accounts for 49% of the total, and all these results indicate that R&D investment intensity plays a partially mediating role in the impact of green finance on regional innovation capacity. In short, green finance not only directly promotes the enhancement of regional innovation capacity, but also indirectly promotes it by increasing the intensity of R&D investment, where the intensity of R&D investment plays a significant mediating role.

In addition, this study explores the moderating mediating effect of regional innovation and entrepreneurship (lniu) in the mechanism of green finance affecting regional innovation capacity through R&D investment intensity. We hypothesise that regional innovation and entrepreneurship capacity may influence the impact of green finance on R&D inputs, which in turn changes the mediating effect of R&D inputs on regional innovation capacity. To test this hypothesis, we constructed a moderated mediation effect model (Model 6) containing interaction terms based on the mediation effect model.

In Model 6, the coefficient of the interaction term (gf × lniu) for regional innovation and entrepreneurship capacity (lniu) is 0.015, which is significant at the 10% significance level (p < 0.10), suggesting that regional innovation and entrepreneurship capacity significantly enhances the positive impact of green finance on R&D inputs, which may further enhance the mediating effect of green finance on regional innovation capacity through R&D inputs. In addition, the positive effect of R&D investment on regional innovation capacity is also significant (coefficient of 11.469, p < 0.01).The results of both the Sobel test and the bootstrap test indicate that the mediation effect of regulation is significant (Sobel Z value of 2.103, p < 0.05; bootstrap Z value of -1.98, p < 0.05 ), and the mediation effect under moderation is 39.8%. All these results indicate that regional innovation and entrepreneurship capacity plays a moderating role in the mechanism of green finance's influence on regional innovation capacity, and this moderating role is realised through the mediating variable R&D input.

Table 7. Mediated effects test.

Table 7. Mediated effects test.

	Model 5		Model 6
	rd	lnric	rd	lnric
gf	0.004* (0.002)	0.168** (0.068)	-0.063* (0.031)	-1.609* (0.891)
rd		9.464*** (2.781)		11.469*** (3.368)
lniu			-0.006*** (0.001)	0.051 (0.046)
gf×lniu			0.015* (0.007)	0.412* (0.197)
control variable	YES	YES	YES	YES
Constant	0.015*** (0.004)	2.171*** (0.203)	0.026*** (0.005)	2.299*** (0.293)
N	420	420	420	420
R^2	0.945	0.961	0.951	0.963
Prov FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
r2_a	0.938	0.956	0.944	0.957
Sobel Z	2.347		2.103
Sobel Z-p value	0.019		0.035
bootstrap Z	2.13		1.98
bootstrap Z-p value	0.033		0.048
Percentage of intermediary effects	49 per cent		39.8 per cent

Note: *, **, *** indicate significant at the 10 per cent, 5 per cent, and 1 per cent levels, respectively; standard errors are in () in the table.

The quantile moderated mediation effect analysis further reveals the moderating role of regional innovation and entrepreneurship capability (lniu) on the mediation effect of research and development (R&D) investment (RD) at different quantile levels. Figure 2 shows that the coefficient of RD investment is higher at lower quartile levels of lniu, indicating that RD investment plays a more significant role in promoting regional innovation in regions with lower regional innovation and entrepreneurship capabilities. However, this impact coefficient shows a gradual decline as the lniu quartile increases and is no longer significant at the 90 per cent quartile. This finding suggests that the marginal promotional effect of R&D investment is weakened in regions with higher regional innovation and entrepreneurship capabilities, probably because other innovation drivers are more prominent in a high level of lniu, which relatively reduces the strength of the impact of R&D investment.

In summary, the results of the mediation effect analysis and quartile moderated mediation effect analysis in this study indicate that regional innovation and entrepreneurship (lniu) plays a significant moderating role in the process of green finance affecting regional innovation capacity through R&D investment intensity. The mediating effect of R&D inputs is more significant at lower quartile levels of lniu, while it is weakened at higher quartile levels. These findings provide insights into understanding the mechanism of green finance's effect on regional innovation and provide important references for the formulation of more targeted policies.

Figure 2. Quantile Moderation Effect Plot.

Figure 2. Quantile Moderation Effect Plot.

4. Robustness Tests

Table 8. Robustness tests.

5. Conclusions

References

1. Li, H.; Liu, J.; Wang, H. Impact of green technology innovation on the quality of regional economic development. Int. Rev. Econ. Finance 2024, 93, 463–476. [Google Scholar] [CrossRef]
2. Yin Hejun. China’s R&D expenditure exceeds 3.3 trln yuan in 2023: minister. Xinhua, 5 Mar 2024. Available online: https://english.news.cn/ 20240305/d3d97f55bdf44d40a49d9ae2224ce0dc/c.html (accessed on 26 Setember 2024).
3. Shayegh, S.; Reissl, S.; Roshan, E.; Calcaterra, M. An assessment of different transition pathways to a green global economy. Commun. Earth Environ. 2023, 4, 1–12. [Google Scholar] [CrossRef]
4. China Science and Technology Strategy Research Institute. (2024). China Regional Science and Technology Innovation Evaluation Report 2024[M]. Beijing: China Science and Technology Strategy Research Institute. (In Chinese).
5. Climate Bonds Initiative. (2024). Global State of the Market Report 2023. Available online: https://www.climatebonds.net/resources/reports/global-state-market-report-2023 (accessed on 26 Setember 2024).
6. People's Bank of China. (2024). China's green loan balance exceeds 30 trillion yuan. People's Daily Overseas Edition. Available online: https://www.gov.cn/lianbo/bumen/202401/content_6928561.htm (accessed on 26 Setember 2024).
7. Cooke, P.; Uranga, M.; Etxebarria, G. Regional innovation systems: Institutional and organisational dimensions. Res. Policy 1997, 26, 475–491. [Google Scholar] [CrossRef]
8. Xu, L.; Shu, H.; Lu, X.; Li, T. Regional technological innovation and industrial upgrading in China: An analysis using interprovincial panel data from 2008 to 2020. Finance Res. Lett. 2024, 66. [Google Scholar] [CrossRef]
9. Yan, X.; Sun, Q. How to Evaluate Ecological Civilization Construction and Its Regional Differences: Evidence from China. Sustainability 2023, 15, 12543. [Google Scholar] [CrossRef]
10. Hu, B.; Guo, P.; Gao, M. Enhancing high-quality development in regional innovation ecosystems. Phys. Chem. Earth, Parts A/B/C 2023, 132. [Google Scholar] [CrossRef]
11. Zhang, Y. The Sustainability of Regional Innovation in China: Insights from Regional Innovation Values and Their Spatial Distribution. Sustainability 2023, 15, 10398. [Google Scholar] [CrossRef]
12. Lin, B.; Zhang, A. Digital finance, regional innovation environment and renewable energy technology innovation: Threshold effects. Renew. Energy 2024, 223. [Google Scholar] [CrossRef]
13. . Nexus between green finance, non-fossil energy use, and carbon intensity: Empirical evidence from China based on a vector. J. Clean. Prod. 2020, 277, 122844. [CrossRef]
14. Zhou, X.; Tang, X.; Zhang, R. Impact of green finance on economic development and environmental quality: a study based on provincial panel data from China. Environ. Sci. Pollut. Res. 2020, 27, 19915–19932. [Google Scholar] [CrossRef]
15. Meo, M.S.; Abd Karim, M.Z. The role of green finance in reducing CO2 emissions: an overview. finance in reducing CO2 emissions: an empirical analysis. Borsa Istanb. Rev. 2022, 22, 169–178. [Google Scholar]
16. Muganyi, T.; Yan, L.; Sun, H.-P. Green finance, fintech and environmental protection: Evidence from China. Environ. Sci. Ecotechnology 2021, 7, 100107. [Google Scholar] [CrossRef]
17. Rasoulinezhad, E.; Taghizadeh-Hesary, F. Role of green finance in improving energy efficiency and renewable energy development. Energy Effic. 2022, 15, 1–12. [Google Scholar] [CrossRef]
18. Lv, C.; Bian, B.; Lee, C.-C.; He, Z. Regional gap and the trend of green finance development in China. Energy Econ. 2021, 102, 105476. [Google Scholar] [CrossRef]
19. Lee, C.C.; Lee, C.C. How does green finance affect green total factor productivity? Evidence from China. Energy Econ. 2022, 107, 105863. [Google Scholar] [CrossRef]
20. Zhou, G.; Zhu, J.; Luo, S. The impact of fintech innovation on green growth in China: Mediating effect of green finance. Ecol. Econ. 2022, 193. [Google Scholar] [CrossRef]
21. Irfan, M.; Razzaq, A.; Sharif, A.; Yang, X. Influence mechanism between green finance and green innovation: Exploring regional policy intervention effects in China. Technol. Forecast. Soc. Chang. 2022, 182. [Google Scholar] [CrossRef]
22. Wang, Y.; Zhao, N.; Lei, X.; Long, R. Green Finance Innovation and Regional Green Development. Sustainability 2021, 13, 8230. [Google Scholar] [CrossRef]
23. Liu, Y.; Lei, J.; Zhang, Y. A Study on the Sustainable Relationship among the Green Finance, Environment Regulation and Green-Total-Factor Productivity in China. Sustainability 2021, 13, 11926. [Google Scholar] [CrossRef]
24. Cao, S.; Nie, L.; Sun, H.; Sun, W.; Taghizadeh-Hesary, F. Digital finance, green technological innovation and energy-environmental performance: evidence from China's regional economies. J. Clean. Prod. 2021, 327, 129458. [Google Scholar] [CrossRef]
25. Zhang, W.; Hong, M.; Li, J.; Li, F. An Examination of Green Credit Promoting Carbon Dioxide Emissions Reduction: A Provincial Panel Analysis of China. Sustainability 2021, 13, 7148. [Google Scholar] [CrossRef]
26. Ma, R.; Li, F.; Du, M. How Does Environmental Regulation and Digital Finance Affect Green Technological Innovation: Evidence From China. Front. Environ. Sci. 2022, 10. [Google Scholar] [CrossRef]
27. Bao, J.; He, M. Does green credit promote green sustainable development in regional economies?-Empirical evidence from 280 cities in China. PLOS ONE 2022, 17, e0277569. [Google Scholar] [CrossRef]
28. Chang, Y.; Wang, S. China's pilot free trade zone and green high-quality development: An empirical study from China. development: An empirical study from the perspective of green finance. Environ. Sci. Pollut. Res. 2023, 30, 88918–35. [Google Scholar] [CrossRef]
29. Zhitao, Z.; Khan, A.A.; Khan, S.U.; Ali, M.A.S.; Zonglin, W.; Luo, J. Untangling the causal mechanisms and spatial dynamics of digital financial development’s impact on energy intensity: insights from panel data of Chinese provinces. Environ. Sci. Pollut. Res. 2023, 30, 96147–96162. [Google Scholar] [CrossRef]
30. Jiang, P.; Xu, C.; Chen, Y. Can green finance reduce carbon emission? A theoretical analysis and empirical evidence from China. Environ. Sci. Pollut. Res. 2024, 31, 35396–35411. [Google Scholar] [CrossRef]
31. Jiang, S.; Liu, X.; Liu, Z.; Shi, H.; Xu, H. Does green finance promote enterprises' green technology innovation in China? Front. Environ. Sci. 2022, 10, 981013. [Google Scholar] [CrossRef]
32. Liu, F.; Xia, Z.; Lee, C.C. Does green credit benefit the clean energy technological innovation and how? The policy catering behaviour of enterprises. J. Clean. Prod. 2024, 444, 141256. [Google Scholar] [CrossRef]
33. Hu, Y.; Du, S.; Wang, Y.; Yang, X. How Does Green Insurance Affect Green Innovation? Evidence from China. Sustainability 2023, 15, 12194. [Google Scholar] [CrossRef]
34. Huang, Y.; Chen, C.; Lei, L.; Zhang, Y. Impacts of green finance on green innovation: A spatial and nonlinear perspective. J. Clean. Prod. 2022, 365. [Google Scholar] [CrossRef]
35. Cui, Y.; Zhong, C.; Cao, J.; Guo, M. Can green finance effectively mitigate PM2. 5 pollution? What role will green technological innovation play? Energy Environ. 2023, 0958305X231204030. [Google Scholar] [CrossRef]
36. Sun, H.; Luo, Y.; Liu, J.; Bhuiyan, M.A. Digital inclusive finance, R&D investment, and green technology innovation nexus. PLOS ONE 2024, 19, e0297264. [Google Scholar] [CrossRef]
37. Xiao, Y.; Shi, X.; Kong, L. From green finance to sustainable innovation: how to unleash the potential of China’s high-tech industry. Environ. Sci. Pollut. Res. 2023, 30, 123368–123382. [Google Scholar] [CrossRef] [PubMed]
38. Liu, C.; Dai, C.; Chen, S.; Zhong, J. How does green finance affect the innovation performance of enterprises? Evidence from China. Environ. Sci. Pollut. Res. 2023, 30, 84516–84536. [Google Scholar] [CrossRef]
39. Yulin, W.; Yahong, Z. Green Finance Development and Enterprise Innovation Journal of Finance and Economics. 2023, 49, 49–62. Available online: https://link.oversea.cnki.net/doi/10.16538/j.cnki.jfe.20220615.101 (accessed on 1 Oct 2024).
40. Li, L.; Ma, X.; Ma, S.; Gao, F. Role of green finance in regional heterogeneous green innovation: Evidence from China. Humanit. Soc. Sci. Commun. 2024, 11, 1–13. [Google Scholar] [CrossRef]
41. Ge, L.; Li, C.; Sun, L.; Hu, W.; Ban, Q. The Relationship between High-Tech Industrial Agglomeration and Regional Innovation: A Meta-Analysis Investigation in China. Sustainability 2023, 15, 16545. [Google Scholar] [CrossRef]
42. ROGERS E M. Diffusion of Innovations [M]. New York: Free Press, 1962.
43. Wernerfelt, B. A resource-based view of the firm. Strateg. Manag. J. 1984, 5, 171–180. [Google Scholar] [CrossRef]
44. Tang, D.; Yan, J.; Sheng, X.; Hai, Y.; Boamah, V. Research on Green Finance, Technological Innovation, and Industrial Structure Upgrading in the Yangtze River Economic Belt. Sustainability 2023, 15, 13831. [Google Scholar] [CrossRef]
45. Wen, Z.; Ye, B. Analyses of Mediating Effects: The Development of Methods and Models. Adv. Psychol. Sci. 2014, 22. [Google Scholar] [CrossRef]
46. Wen, Z.; Ye, B. Different methods for testing moderated mediation models: competitors or backups? Sin. 2014. [CrossRef]