Green Finance and Regional Technological Innovation in China: The Mediating Role of R&D Investment

Abstract

In light of global initiatives aimed at promoting sustainability and low-carbon growth, this research investigates how green finance affects regional technological innovation in China, specifically highlighting the mediating effect of R&D investment. Utilizing panel data from 30 provinces in China from 2008 to 2021, we apply fixed-effects and mediation models to explore these relationships. The results indicate a strong positive link between green finance and regional technological innovation, with R&D investment acting as a partial mediator. Furthermore, the capabilities for regional innovation and entrepreneurship enhance the influence of green finance on R&D investment. However, in areas with greater innovation and entrepreneurship capabilities, the additional effect of R&D investment tends to decrease. Based on these results, the study proposes targeted policy recommendations, including enhancing green finance policies, improving financial institution services, promoting enterprise-led R&D activities, and fostering regional collaboration to achieve balanced innovation development. These insights provide both theoretical and practical significance for leveraging green finance to advance sustainable innovation.

1. Introduction

1.1. Research Background

Technological innovation is essential for driving high-quality economic growth, as it significantly boosts regional competitiveness and fosters sustainable development (H. Li et al., 2024). In recent years, Chinese regions have intensified their focus on technological innovation, strategically allocating resources to research and development (R&D) and systematically improving innovation frameworks. These efforts have yielded substantial improvements in both R&D funding and systemic innovation capacities, thereby strengthening regional innovative capabilities. Data from official statistics reveal remarkable growth in China’s technological innovation landscape. The aggregate expenditure on research and experimental development amounted to 3327.8 billion yuan in 2023, representing a 233-fold increase from 1991 and an average annual growth rate of 18.6% (National Bureau of Statistics of China, 2023). Moreover, the proportion of R&D investment as a percentage of GDP expanded from 0.6% in 1991 to 2.64% in 2023, positioning China 12th globally in this metric (Xinhua, 2024).

The pursuit of green economic transformation has become a global priority, with nations integrating green growth strategies and positioning regional technological innovation as a fundamental element of sustainable development (Shayegh et al., 2023). Green finance channels capital towards technological innovation, thereby enhancing resource allocation efficiency and strengthening regional innovative potential. Key indicators substantiate this trend. The China Regional Science and Technology Innovation Evaluation Report 2024 indicates that the national comprehensive science and technology innovation index reached 78.43 points, representing a 1.30-point year-on-year improvement (China Science and Technology Strategy Research Institute, 2024). The global green bond market expanded to $587.6 billion in 2023, with a 15% annual growth (Climate Bonds Initiative, 2023). In China, green credit increased from 7.59 trillion yuan in 2014 to 30.08 trillion yuan in 2023, demonstrating an average annual growth rate exceeding 29% (People’s Bank of China, 2024). Green finance demonstrates remarkable potential in propelling green economic transformation. It provides substantial impetus to regional technological innovation through strategic capital allocation and targeted investment mechanisms.

1.2. Research Gap and Contributions

As global sustainable development strategies progress, green finance has emerged as a key mechanism for driving economic transformation. Its impact on regional innovation mechanisms has attracted significant attention from scholars and policymakers. However, current research mainly emphasizes the environmental effects of green finance, with insufficient research on its role in fostering regional technological innovation. In particular, there is a lack of systematic exploration of how R&D investment mediates the relationship between green finance and regional innovation, as well as the moderating effect of regional innovation capacity on this process. The aim of this study is to develop a robust theoretical framework and perform an empirical analysis to explore how green finance enhances regional innovation capacity through R&D investment. Furthermore, this study investigates how regional innovation and entrepreneurship capacity moderate the connection between green finance and regional innovation. The findings provide specific theoretical insights and actionable policy recommendations to foster sustainable regional innovation development.

This study offers several key contributions in several key aspects. First, it expands the conceptual structure connecting green finance and regional innovation. Compared to X. Zhou et al. (2020), who focused primarily on the environmental effects of green finance, this study develops a more complex theoretical model. It systematically incorporates regional innovation and entrepreneurial capability as critical moderating variables, deepening the theoretical understanding of the intricate relationships among green finance, R&D investment, and regional innovation capacity. Second, this research thoroughly examines how R&D investment acts as a mediator. Unlike J. Y. Liu et al. (2017), who primarily examined the direct effects of green finance, this research rigorously tests the mediating function of R&D investment. It systematically uncovers the micro-level mechanisms through which green finance fosters regional innovation via R&D investment, significantly advancing empirical knowledge in this field.

Moreover, it offers innovative insights into regional heterogeneity. In contrast to the general perspective of Z. Huang et al. (2019), it thoroughly examines variations in the marginal effects of R&D investment among regions, considering their diverse levels of innovation capacity. This approach yields a more nuanced understanding of regional innovation dynamics and offers a fresh analytical perspective on regional disparities. Finally, the study proposes policy-oriented recommendations based on empirical analysis. These recommendations aim to optimize regional innovation ecosystems, improve green financing mechanisms, and facilitate sustainable economic transformation. They provide actionable guidance for policymakers, bridging the gap between academic research and practical policy implementation. These contributions offer critical theoretical support and practical insights for promoting sustainable regional innovation and development.

1.3. Research Structure

This study is divided into six sections. Section 2 includes a detailed literature review and theoretical framework, establishing the foundation for research hypotheses. Section 3 presents a detailed data analysis, including data sources, variable definitions, and descriptive statistics. Section 4 focuses on model construction and empirical investigation, analyzing how green finance influences the innovation capabilities of different regions and the mediating role of R&D inputs. Section 5 conducts a rigorous robustness check to validate the research findings. Section 6 synthesizes the key discoveries, offers policy recommendations, discusses research limitations, and proposes directions for further investigation.

2. Literature Review and Theoretical Examination

2.1. Literature Review

This research investigates how green finance influences regional innovation. Two primary areas of literature are pertinent to this study. The first addresses regional innovation, investigating its driving factors, dynamic trends, and contributions to economic development. The second explores green finance, analyzing its evolution, mechanisms, and role in promoting environmental sustainability and economic growth.

Studies on regional innovation primarily emphasize its economic effects and influencing factors. For example, regional innovation enhances innovation capacity and competitiveness by fostering collaboration among enterprises, universities, and research institutions. Through this collaboration, the sharing and combination of knowledge, technology, and resources at the regional level (Cooke et al., 1997). Research has shown that regional innovation capacity not only drives economic growth and industrial upgrading, but is essential for fostering environmentally friendly development, including ecological civilization (Xu et al., 2024). The development of regional innovation depends on various factors, including investments in R&D, levels of economic development, policy environments, and innovation ecosystems (Hu et al., 2023). Moreover, regional innovation interacts closely with the financial environment and industrial structure, where green finance functions as a key form of assistance. For instance, the synergy between innovation funding and technological advancements becomes more evident when supported by green finance (Lin & Zhang, 2024).

The second category of research examines the mechanisms of green finance. Theoretical studies explore the elements of green finance, such as green credit, securities, insurance, and investment. Ren et al. (2020) developed a green finance index that integrates these elements to support policy implementation and promote non-fossil energy adoption. Empirical research has also uncovered regional disparities in green finance development. Lv et al. (2021) used panel data to reveal these disparities and their consequences for the success of green finance policies. Green finance’s environmental benefits are particularly notable. Meo and Abd Karim (2022), using quantitative regression, demonstrated its role in promoting green energy and reducing carbon emissions. Similarly, Rasoulinezhad and Taghizadeh-Hesary (2022) applied the STIRPAT model to demonstrate the beneficial impact of green bonds on emission reduction and the advancement of green energy firms. Muganyi et al. (2021) found that China’s green finance policies led to a substantial decrease in industrial gas emissions, enhancing environmental protection. Esposito et al. (2021) linked green finance policies to improved environmental governance efficiency, particularly in waste management.

In addition to environmental benefits, green finance supports sustainable economic development (X. Zhou et al., 2020). G. Zhou et al. (2022) confirmed that green finance fosters green economic growth, with more pronounced effects in China’s eastern regions. Lee and Lee (2022) identified its considerable effect green total factor productivity, particularly in regions with challenging environmental conditions. S. Liu and Wang (2023) observed that green finance pilot zones have advanced regional green development by means of industrial upgrading and technological innovation, with noticeable regional differences. Bao and He (2022) emphasized the role of green credit in supporting sustainable economic development across all stages of the green transition.

Some studies also examine how green finance relates to regional innovation. Deng et al. (2024) constructed a comprehensive analytical framework linking green finance with technological innovation and green growth, while considering environmental factors and development quality. Through panel data analysis and mediating effect tests across multiple countries, they validated the important influence of green finance in driving technological innovation. Green financial instruments like green credit and bonds can ease enterprises’ financing constraints, thereby enhancing green technological innovation (S. Jiang et al., 2022). L. Li et al. (2024) noted that regional green innovation could create a siphoning effect on resources drawn from nearby locations, which impacts the process of innovation. This underscores the need for targeted government policies to optimize green financial tools.

While prior analyses have largely placed emphasis on the immediate influence of green finance on either technological advancements or environmental outcomes, a thorough exploration of the pathways through which green finance shapes regional innovation, particularly considering regional nuances, remains limited. To bridge this knowledge shortfall, the work presented here creates an integrated theoretical model to analyze the intermediary role of research and development (R&D) spending and the contingent effect of regional innovative and entrepreneurial dynamism. Utilizing a broad dataset of regional indicators, this investigation offers fresh perspectives and empirical support for understanding the contribution of green finance to enhanced regional innovation quality within China.

2.2. Theoretical Examination and Research Hypotheses

Green finance directly promotes green technological innovation and reduces environmental pollution, such as PM2.5 emissions. However, regional differences and enterprise characteristics may significantly influence these effects (Cui et al., 2023). Irfan et al. (2022), from the perspective of industrial structure and R&D investment, applied the DID method to confirm that inclusive green finance contributes positively to innovations that are environmentally sustainable. Industrial restructuring, economic expansion, and investment in research and development were found to be essential mechanisms through which green finance influences regional innovation.

R&D investment serves is a crucial means by which green finance fosters regional innovation (Sun et al., 2024). Specifically, green finance alleviates corporate financing constraints, thereby increasing R&D investment and enhancing regional innovation capabilities (C. Liu et al., 2023). Further research by Wang and Zhou (2023) highlights the regional and enterprise-level variations in this relationship, underscoring the critical and complex role of R&D investment in linking green finance to regional innovation.

From the preceding analysis, we put forth the following research hypotheses:

Hypothesis 1. 

There is a significant positive relationship between green finance (GF) and regional innovation capability (RIC).

Hypothesis 2. 

Green finance (GF) indirectly promotes regional innovation capability (RIC) is mediated by R&D investment (RD), such that green finance indirectly promotes regional innovation through increased R&D investment.

Moreover, Regional innovation is affected by green finance via a variety of mechanisms, rather than a singular process. In this process, regional innovation and entrepreneurial capabilities influence how efficiently R&D resources are allocated and how innovation outcomes are transformed, thereby highlighting the importance of green finance (L. Li et al., 2024). Moreover, Green finance tools, including green insurance and funds, offer risk mitigation to businesses, encouraging investment in green technology R&D (Y. Liu et al., 2024). Policy support and market incentives further facilitate the diffusion and application of green technologies (Y. Huang et al., 2022). Green finance also drives industrial upgrading and technological development, contributing to regional innovation capabilities (P. Jiang et al., 2024).

Drawing on this comprehensive framework, we further propose the following research hypotheses to be tested:

Hypothesis 3. 

Regional innovation and entrepreneurial capability (IU) play a moderating role in the connection between green finance (GF) and regional innovation capability (RIC) through R&D investment (RD). Enhanced regional innovation and entrepreneurial capabilities strengthen the indirect effects of green finance on regional innovation.

3. Methodological Approach

3.1. Acquisition of Data

The current analysis examines the implication of China’s green finance initiatives on regional innovation capacity over 14 years (2008–2021). We gathered data from key Chinese statistical sources, including the China Statistical Yearbook and other yearbooks related to regional innovation, finance, science & technology, and energy, along with the CSMAR and CCEER databases. The dataset comprises 420 observations across 30 provinces in mainland China, with the exclusion of Hong Kong, Macao, Taiwan, and Tibet. Data selection adhered to modeling requirements to ensure precision, representativeness, and reliability.

3.2. Introduction to Variables

3.2.1. Explained Variables

Regional Innovation Capability (RIC) is measured using the provincial “China Regional Innovation Capability Comprehensive Utility Value” from the “China Regional Innovation Capability Evaluation Report (2008–2021)”. This indicator is evaluated from five aspects:

Knowledge Creation (15%): Based on Schumpeter’s innovation theory (Schumpeter & Swedberg, 2021), it emphasizes original innovation capability and reflects the regional potential for fundamental scientific discoveries and technological breakthroughs.

Knowledge Acquisition (15%): According to the national innovation system theory (Funaba, 1988), it highlights inter-regional knowledge flow and embodies the dynamic process of technology diffusion and cross-regional innovation collaboration.

Enterprise Innovation (25%): Following the resource-based view (Barney, 1991), it considers enterprises as the micro-subjects of innovation, focusing on examining enterprise R&D investment, technological innovation capability, and new product development.

Innovation Environment (25%): Referring to the regional innovation system theory (Cooke, 1992), it emphasizes the support role of institutions and ecosystems for innovation, including innovation policy, science and technology finance, and talent environment.

Innovation Performance (20%): It reflects the ultimate economic and social value of innovation.

These weights are scientifically determined through expert Delphi method assessment, empirical analysis of historical data, reference to the international innovation index system, and are tailored to reflect the particularities of innovation across Chinese regions. The weight distribution not only reflects the contribution of each dimension to regional innovation capability but also balances theoretical insight and empirical evidence. It is a comprehensive measure of regional innovation capability and is suitable for in-depth empirical analysis (

Table 1. Regional innovation capacity measurement system.

Level 1 Indicators	Level 2 Indicators	Description of Indicators	Causality
Regional Innovation capacity (ric)	Knowledge creation 15%	Measuring a region’s ability to generate new knowledge.	Positive
	Knowledge acquisition 15%	Measurement of a region’s ability to utilise external knowledge and cooperation between industry, academia and research.	Positive
	Enterprise innovation 25%	Measures the ability of firms within a region to apply new knowledge, develop new technologies, utilize innovative processes, and manufacture new products.	Positive
	Innovation environment 25%	Measure the ability of a region to provide the appropriate environment for the generation, flow and application of technology.	Positive
	Innovation performance 20%	The ability to measure the benefits of innovation for the growth and advancement of a region’s economy and society.	Positive

).

3.2.2. Explanatory Variables

The GFDI, or Index of Green Finance Development, is created employing the entropy method. Its purpose is to assess the complex characteristics of green finance development in a way that is both comprehensive and objective. The GFDI uses four main areas-green credit, green securities, green insurance, and green investment-to show how far green finance has developed in total, in a systematic way. Specifically, the weight allocation of each dimension is scientifically justified: green credit is weighted at 50%, highlighting the strategic role of financial resource allocation in promoting green development (Volz, 2018); green securities account for 25%, reflecting institutional innovation in the green transformation of capital markets (Meng et al., 2024); green insurance accounts for 15%, emphasizing the critical role of risk management in environmental governance (Surminski & Eldridge, 2017); and green investment accounts for 10%, measuring resource inputs into actual environmental governance (Geddes et al., 2018).

This weighting structure is further supported by multiple methodological considerations. First, it draws upon existing academic studies that emphasize the differentiated roles and magnitudes of various green finance instruments in China’s financial system (Dikau & Volz, 2021). Second, it aligns with international practices in constructing composite green finance indices, ensuring comparability with global standards (Schoenmaker & Van Tilburg, 2016). Third, the Delphi method was applied to incorporate expert judgment on the relative importance and marginal contribution of each dimension to green development. Lastly, the weighting scheme corresponds to the actual distribution of green financial flows observed in national statistical yearbooks, enhancing the practical representativeness and credibility of the index framework.

The process of building the green finance indicator utilizing the entropy value method involves the following steps:

①

Normalization of Indicators: Raw data are standardized to mitigate the effects of differing units and scales. For positive indicators, the normalization formula is:

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

(1)

For negative indicators, the normalisation formula is adjusted to:

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

(2)

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

②

Calculation of Information Entropy: Based on the standardized values, the entropy for each indicator is computed, reflecting the degree of data variability.

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

(3)

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

(4)

③

Assignment of Indicator Weights: Using the calculated information entropy, the weight of each indicator ($W_i$) is determined:

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

(5)

④

Synthesis of the Green Financial Development Index: Finally, the composite index is derived by aggregating the weighted standardized values:

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

(6)

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

This method yields an objective and comprehensive green finance development index (detailed in

Table 2. Green finance development index indicator system.

L 1 Indicators	L 2 Indicators	L 3 Indicators	Description of Indicators	Causality
Green Finance Development Index (gf)	Green credit 50%	Proportion of interest costs within energy-intensive industrial sectors	Interest costs of the six major energy-consuming industrial Industries/Total interest expenditure of industrial industries	Negative
		Ratio of new bank lending to environmental firms with A-share listings	New bank credit by A-share listed environmental protection companies/Credit to banks by A-share listed companies	Positive
	Green securities 25%	Market capitalisation of A-share listed environmental enterprises	Market capitalisation of A-share listed environmental enterprises/Total market capitalisation of A-share listed enterprises	Positive
		Percentage of A-share value of A-share listed companies with high energy consumption	Market capitalisation of A-share listed energy-intensive enterprises/Total market capitalisation of A-share listed enterprises	Negative
	Green insurance 15%	Scale environmental pollution insurance	Income from agricultural insurance/property insurance	Positive
		Percentage of compensation from environmental pollution insurance	Agricultural insurance expenditure/Income from agricultural insurance	Positive
	Green investment 10%	Percentage of investment in environmental pollution regulation	Expenditure in environmental pollution control/GDP	Positive
		Percentage of fiscal spending on environmental conservation	Fiscal expenditure on environmental protection/Total fiscal expenditure	Positive

), which captures the multidimensional nature of green finance. This index forms a robust explanatory variable for subsequent empirical analysis.

3.2.3. Control Variables

To develop a rigorous methodology for quantifying the influence of green finance on regional innovation systems, the regression analysis incorporates the following covariates: ① Industrial structure (ind): Measured by the ratio of secondary industry value added to GDP, this value shows the effect of the high-tech industry’s percentage on innovation potential (Tu et al., 2023). ② Human capital (lnhes): Represented by the number of higher education institutions per province, it indicates the fundamental element driving regional innovation. ③ Urbanization level (ur): Measured by the urbanization rate, this shows that urbanization helps to bring innovative resources together. ④ Emphasis on technology (techi): Proxied by the proportion of fiscal spending allocated to science and technology, this measures the regional support for innovation. ⑤ Carbon emissions (lnco2): Measured using total emissions data, this indicates that environmental pressure is a factor in advancing green technology innovation (Shi et al., 2019). ⑥ Capital investment (capi): Calculated as fixed asset investment’s share of GDP, it measures the capacity of capital to support innovative activities. These variables, along with a fixed effects double-cluster model controlling for provincial and time effects, enhance the reliability of the results.

Table 3. Variable definitions and descriptions.

Name	Symbol	Definition
Regional innovation capacity	ric	Calculated by the weighted integrated evaluation method
Green finance development Index	gf	Entropy weighting
Industry Makeup	ind	Value added of secondary sector/GDP
Human capital	lnhes	Logarithmic number of general higher education institutions
Urbanisation level	ur	Urban/Resident population
Science and technology focus	techi	Local finance science and technology expenditure/Local finance general budget expenditure
Carbon footprint	lnco2	Logarithmic carbon dioxide emissions by province and region
Capital investment	capi	Investment in fixed assets/Gross regional product

provides detailed information on the variables.

3.2.4. Mediating and Moderating Variables

This study uses R&D investment (RD) acting as an intermediary variable to explore the mechanism of action of green finance on regional innovation. RD, defined as the ratio of R&D expenditure to GDP, signifies regional commitment to innovation-driven development. The innovation diffusion theory (Rogers, 1962) and resource-based theory (Wernerfelt, 1984) emphasize RD as a catalyst for technological advancement. Previous studies (Wernerfelt, 1984) confirm RD’s mediating role concerning green finance and innovation, especially in resource- and technology-intensive sectors. Therefore, incorporating RD into this study not only helps to clarify the pathways of green finance’s impact on innovation but also addresses a deficiency in the current body of literature.

Simultaneously, regional innovation and entrepreneurship capacity (IU) is selected as a moderating variable to analyze the varying impact of green finance on regional innovation. IU encompasses the capabilities of both enterprises and individuals in innovation, technology advancement, and entrepreneurial activities. According to the innovation diffusion theory, regions with strong innovation and entrepreneurship capacities are more active in technological innovation activities, and the promotional effect of green finance is more pronounced (Rogers, 1962). Therefore, IU oversees the nexus between green finance and regional innovation by enhancing the innovation environment, which work to improve the innovation environment.

3.3. Data Description

This study applies logarithmic transformation to the variables and removes extreme values from both ends of the continuous variables (top 1%) to reduce the impact of outliers. Based on 420 observations, we conducted descriptive statistical analysis, revealing the key characteristics of the dataset, which forms the foundation for subsequent empirical research.

Regarding regional innovation capacity, the mean logarithmic value (lnric) is 3.359. The standard deviation is 0.309, implying notable regional disparities in innovation capacity. The median (3.315) exhibits negative skewness relative to the mean (M = 3.359), suggesting that many regions have relatively weaker innovation capacities and require policy intervention to boost their innovation levels.

The mean value of the Green Finance Development Index (gf) is 0.152, and it has a standard deviation of 0.063, showing substantial regional variation. The index ranges from 0.072 to 0.450, with the median slightly below the average, suggesting that the level of green finance development is moderate in the majority of regions.

As for the control variables, indicators such as industrial development (ind), human capital (hes), urbanization rate (ur), and technological maturity (techi) display considerable variability, which may significantly affect regional innovation capacity. Environmental factors, particularly the fluctuations in CO₂ emissions, substantially elucidate the mechanisms through which green finance instruments foster regional innovation capabilities. Conversely, capital investment (capi) exhibits relatively little variation across regions.

These statistical findings provide valuable insights for the empirical analysis, revealing the distribution characteristics of each variable and their potential impact on the research outcomes. Detailed statistical results are presented in

Table 4. Descriptive statistics for all variables.

Variable	N	Mean	P50	Sd	Min	Max
lnric	420	3.359	3.315	0.309	2.820	4.197
gf	420	0.152	0.136	0.063	0.072	0.45
ind	420	0.418	0.427	0.083	0.16	0.62
hes	420	84.14	83.5	38.48	9	167
ur	420	0.575	0.557	0.131	0.291	0.896
techi	420	0.021	0.013	0.015	0.004	0.072
co2	420	362.3	265.9	305	32.12	2100
capi	420	0.138	0.128	0.057	0.0450	0.457

.

This study assesses multicollinearity by calculating the Variance Inflation Factor (VIF) and Tolerance. As shown in

Table 5. Multiple covariance test.

Variable	VIF	Tolerance
gf	1.30	0.770
ind	1.79	0.559
lnhes	2.19	0.456
ur	2.58	0.387
techi	2.93	0.341
lnco2	2.10	0.475
capi	1.18	0.849568
Mean VIF	2.01	/

, all variables have VIF values below 3, with the maximum value being 2.93 for the “techi” variable and an average value of 2.01, well below the commonly accepted threshold of 10. Tolerance values are all greater than 0.1, with a minimum of 0.341 for the “techi” variable. These results suggest that multicollinearity among the independent variables is not a concern.

3.4. Econometric Specification

To comprehensively investigate the multidimensional influence of green finance on regional innovation capacity, this study constructs fixed-effects models, mediation models, and moderated mediation models according to the suitability of the theoretical framework, aiming to systematically analyze their underlying mechanisms. Prior to model estimation, a series of preliminary tests were rigorously carried out to maintain methodological rigor in model construction and the consistency of data characteristics. First, the Hausman test was employed to determine the appropriate model form, with the results showing statistical significance (p = 0.001), lending substantial weight to the selection of fixed-effects models instead of random-effects models. Second, the Pesaran CD test results suggested that cross-sectional dependence was not a significant concern (p = 0.395), suggesting that the error terms across sectional units are independent, further validating the applicability of the fixed-effects model. Additionally, the Phillips-Perron unit root test results were significant (p = 0.0001), confirming that all variable series are stationary, effectively avoiding spurious regression issues. Finally, the Kao residual-based cointegration test statistically confirmed the existence of a stable long-run equilibrium relationship among the variables (p = 0.003), providing reliable support for long-term equilibrium relationships in subsequent analyses.

Table 6. Summary of Preliminary Test Results.

Test Method	Statistic	p-Value	corr	abs(corr)	Conclusion
Hausman Test	57.88	0.001	/	/	Fixed-effects model selected
Pesaran CD Test	0.83	0.395	0.012	0.045	Cross-sectional independence
Phillips-Perron Test	−3.7521	0.0001	/	/	Panel stationarity
Kao Test	2.677	0.003	/	/	Cointegration exists

presents a summary of the outcomes from these tests, which details the statistics, p-values, and conclusions for each test. These test results not only ensure the rationality of the model specification but also establish a solid data foundation for subsequent regression analyses.

3.4.1. Panel Data Fixed Effects Specification

This empirical analysis adopts a fixed-effects specification to control for the possible impact of regional heterogeneity and time-invariant factors on regional innovation. This model effectively accounts for individual and temporal fixed effects, mitigating their impact on the dependent variable. Consequently, it enables a more accurate estimation of the elasticity of innovation productivity with respect to green finance development. The model is formulated as follow:

$${RIC}_i={\alpha }_0+{\alpha }_{1}G{F}_{it}+{\alpha }_{2}contro{l}_{it}+{\lambda }_i+{\mu }_t+{ϵ}_{it}$$

(7)

which ${RIC}_i$ represents regional innovation capacity; $G{F}_{it}$ signifies green finance, $contro{l}_{it}$ are the control variables, the ${\lambda }_i$ and ${\mu }_t$ represent regional and year control variables, respectively; ${ϵ}_{it}$ represents the random error term.

3.4.2. Path Analysis Framework for Mediation Effects

This study the underlying mechanisms through which green finance instruments facilitate regional innovation ecosystems via research and development (RD) investment intensity, the study sets up a system of recursive equations, drawing on the methodological framework of Wen and Ye (2014a). This approach allows for an analysis of the internal process linking green finance to regional innovation capacity, by quantifying the elasticity of regional R&D investment with respect to green financial development (path a1) and the subsequent effect of RD on innovation capacity (path b1). The model is structured as follows:

Path a1: Effect of green finance ($GF$) on mediating variable ($RD$):

$${RD}_{it}={\mathsf{\delta }}_0+{\mathsf{\delta }}_{1}G{F}_i+{\mathsf{\delta }}_{2}Control{s}_{it}+{\lambda }_i+{\mu }_t+{ϵ}_{it}$$

(8)

Path b1: Effect of mediating variable ($RD$) on regional innovation capacity ($RIC$):

$${RIC}_i={\epsilon }_0+{\epsilon }_1{RD}_{it}+{\epsilon }_{2}G{F}_i+{\epsilon }_{3}Control{s}_{it}+{\lambda }_i+{\mu }_t+{ϵ}_{it}$$

(9)

where ${RD}_{it}$ is the mediating variable. Following the research line of Wen and Ye (2014a), the first stage requires verifying the coefficient ${\mathsf{\delta }}_1$ in Equation (8) and the coefficient ${\epsilon }_1$ in Equation (9). If both coefficients exist, the mediating effect is significant; The subsequent step entails verifying the coefficient ${\epsilon }_2$ in Equation (9), if it is significant, there is a partial mediation effect; if it is not significant, then a full mediation effect exists; In the third step, we compare ${\mathsf{\delta }}_1$ and ${\epsilon }_1{\epsilon }_2$ sign, if they share the same sign, it signifies a partial mediation effect, the strength of the mediation effect is ${\mathsf{\delta }}_1{\epsilon }_1$/${\alpha }_1$. The subsequent stage is to compare the signs of ${\mathsf{\delta }}_{1 }\mathrm{a}\mathrm{n}\mathrm{d} {\epsilon }_1{\epsilon }_2$. A masking effect occurs when the signs are different, the mediating effect accounts for |${\mathsf{\delta }}_1{\epsilon }_1$/${\alpha }_1$| of the total effect.

3.4.3. Moderated Mediation Model

To better understand how moderating variables influence the mediation process linking green finance to regional innovation capacity, building upon the work of Wen and Ye (2014b), this study presents a conditional process analysis framework to examine moderated mediation mechanisms. By introducing the moderating variable, Innovation and Entrepreneurship Ability (IU), the model quantifies the moderating effect on the green finance-R&D investment relationship (path a2, a3). As a result, this moderator significantly alters the transmission channels through which green finance affects innovation capacity of regions by way of research and development investments. (path b2, b3). The formal econometric specification takes the following functional form:

Path a2, a3: Effect of green finance ($GF$) and the moderating variable ($IU$) on $RD$ (mediating variable):

$${RD}_{it}=a_0+a_{1}G{F}_i+a_2{IU}_{it}+a_3(G{F}_{it}\times {IU}_{it})+a_{4}Control{s}_{it}+{\lambda }_i+{\mu }_t+{ϵ}_{it}$$

(10)

Path b2, b3: Effect of $RD$ (mediating variable) and $IU$ (moderating variable) on regional innovation capacity ($RIC$):

$${RIC}_i=b_0+b_{1}G{F}_i+b_2{RD}_{it}+b_3{IU}_{it}+b_4(G{F}_{it}\times {IU}_{it})+b_5({RD}_{it}\times {IU}_{it}){+b}_{6}Control{s}_{it}+{\lambda }_i+{\mu }_t+{ϵ}_{it}$$

(11)

Based on the test of Wen and Ye (2014b), we initially examine the value $a_3$ of the interaction term in Equation (4). If found significant, it signifies that the moderator variable (IU) exerts an influence on the causal effect of GF on ${RD}_{it}$. Following this, we proceed to verify the value $b_5$ of the interaction term in Equation (5). Significance in this context indicates that the moderating variable (IU) also affects the connection between the mediator variable (${RD}_{it}$) and the dependent variable (RIC), thereby confirming the existence of a regulatory mediation effect.

The precise mechanism by which this impact occurs is illustrated in Figure 1, which depicts the impact mechanism pathway diagram. Green finance (GF) indirectly influences regional innovation capability (RIC) through (R&D) investment (RD), with regional innovation and entrepreneurial capability (IU) moderating this mediating effect. The interplay between GF and IU highlights the heterogeneity across regions in terms of green finance’s impact.

4. Observed Findings

4.1. Benchmark Regression Analysis

The current research begins by evaluating the immediate effect of green finance (gf) on regional innovation capacity (lnric). Several fixed effects models are employed for this purpose.

Table 7. Results from benchmark regression analysis.

Variable	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
R2	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 significance at the 10 per cent, 5 per cent, and 1 per cent levels, respectively; Robust standard errors are in () in the table.

presents empirical results across the initial regression analysis.

Model 1 includes solely the primary explanatory variable: green finance (GF). The regression coefficient for GF is 1.315, a value that is statistically significant at the 1% significance level. These regression estimates demonstrate green finance has a notable positive impact on regional innovation capacity. However, the adjusted R-squared for Model 1 is 0.067, indicating a limited explanatory power when only green finance is considered.

Model 2 incorporates province and year-fixed effects, building upon Model 1. The coefficient for gf decreases to 0.214 but remains statistically significant at the 1 percent threshold. The adjusted R² increases substantially to 0.944, demonstrating that fixed effects significantly improve the model’s explanatory power.

Model 3 extends Model 1 by including control variables. With these additional factors, the coefficient for gf drops to 0.148 and loses significance. Nonetheless, variables such as industrial structure, science and technology expenditure, and capital investment show statistically significant positive effects on regional innovation capacity at the 1% level. In contrast, carbon emissions negatively affect innovation capacity at the 5% level. The adjusted R² rises to 0.771, reflecting an improvement in explanatory power.

Model 4 incorporates both fixed effects and control variables. The coefficient for gf is 0.207, remaining statistically meaningful, with a p-value lower than 0.01. This finding supports the conclusion that green finance positively affects regional innovation capacity, even when considering these additional factors. The adjusted R² further increases to 0.954, indicating the strength of the model.

In summary, the baseline regression analysis establishes a statistically strong positive correlation concerning green finance and regional innovation capacity. This relationship remains robust after controlling for additional variables and fixed effects. The findings highlight the essential contribution of green finance to promoting regional innovation.

4.2. Analysis of Mediating Effects

The study further explores R&D investment intensity (rd) as a critical transmission mechanism through which green finance (gf) enhances regional innovation capabilities (lnric). To test hypothesis 2, a mediation model (Model 7) is developed, as presented in

Table 8. Mediated Effects Test.

Variable	Model 7		Model 8
	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
Prov FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
R2	0.945	0.961	0.951	0.963
R2–a	0.938	0.956	0.944	0.957
Within– R²	0.371	0.238	0.438	0.261
F-statistic	188.17	62.52	216.49	70.57
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%		39.8%

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

.

In Model 7, our findings reveal that green finance positively affects R&D investment intensity, as evidenced by a coefficient of 0.004 (p < 0.1). This suggests that green finance provides an incentive for regions to expand their investments in research and development. Additionally, R&D investment positively impacts regional innovation capacity, as indicated by a coefficient of 9.464 (p < 0.01). The coefficient for gf remains significant at 0.168 (p < 0.05), confirming the existence of a partial mediation effect. The Sobel Z-value is 2.347 (p < 0.05), and the bootstrap Z-value is 2.13 (p < 0.05). The impact of mediation explains for 49% of the overall impact, highlighting the crucial role of research and development investment as an important intermediary.

This research further investigates the moderated mediation impact of regional innovation and entrepreneurial capabilities (lniu) on the link between green finance and the ability of a region to innovate. A moderating mediation model (Model 8) is constructed, incorporating interaction terms.

The interaction term (gf × lniu) in Model 8 exhibits a coefficient of 0.015 (p < 0.1), indicating that regional innovation and entrepreneurship capacity enhances the beneficial influence of green finance on R&D investment, consequently amplifying the mediation effect. R&D investment continues to have a significant influence on regional innovation capacity, with a coefficient of 11.469 (p < 0.01). The Sobel Z-value and bootstrap Z-value for the moderating mediation effect are 2.103 (p < 0.05) and −1.98 (p < 0.05), respectively. The moderating mediation effect represents 39.8% of the total mediation effect.

The analysis further investigates the quartile-based moderating mediation effect. As depicted in Figure 2, at lower quartiles of lniu, Research and development spending exerts a stronger impact on regional innovation. However, as the quartile level increases, the coefficient of R&D investment gradually decreases and becomes insignificant at the 90th percentile. This suggests that regions with higher innovation and entrepreneurship capabilities rely more on other innovation drivers, diminishing the relative importance of research and development spending.

In conclusion, the findings highlight the considerable influence of regional innovation and entrepreneurial potential in moderating the theoretical linkage between green finance and the ability of a region to innovate, as mediated by research and development spending. The intermediary role of R&D investment is more pronounced in regions with lower innovation capacity, offering valuable insights for policy design aimed at enhancing regional innovation through targeted support for green finance and R&D activities.

5. Robustness Tests

Table 9. Robustness tests.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
	lnric	lnci	lnric	lnric	lnric	lnric
gf	0.207 ***	0.684 ***	0.614 ***	0.573 ***	0.773 ***	0.601 ***
	(0.053)	(0.180)	(0.156)	(0.163)	(0.200)	(0.175)
ind	0.757 ***	1.562 ***		1.286 **	3.019 ***	1.467 **
	(0.135)	(0.500)		(0.519)	(0.590)	(0.568)
is			−1.705 ***
			(0.462)
lnhes	0.142 **	−0.213		−0.413	−0.552	−0.235
	(0.059)	(0.255)		(0.263)	(0.330)	(0.260)
hep			−35.255
			(26.260)
ur	0.252	0.636	0.294	2.822 **	2.742	1.460
	(0.301)	(0.760)	(0.842)	(1.094)	(1.694)	(1.027)
techi	2.097 ***	4.302 **	5.425 ***	2.040	4.526 *	4.803 **
	(0.475)	(1.762)	(1.483)	(1.358)	(2.284)	(1.918)
lnco2	−0.029 *	−0.028		−0.069	−0.095	0.001
	(0.015)	(0.049)		(0.048)	(0.058)	(0.056)
lnso2			0.086 *
			(0.042)
capi	0.500 ***	0.997 **	1.135 ***	0.844 **	0.730 *	1.046 **
	(0.088)	(0.354)	(0.348)	(0.339)	(0.374)	(0.350)
od				−0.521 **
				(0.176)
ep				−0.516
				(1.455)
fd				−0.323
				(1.347)
lnsize				0.887 *
				(0.449)
Constant	2.309 ***	3.076 **	3.524 ***	−3.983	3.066 ***	2.565 **
	(0.213)	(1.060)	(0.454)	(3.251)	(0.887)	(1.098)
N	420	420	420	420	300	392
Prov FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
R²	0.960	0.868	0.871	0.880	0.893	0.859
R²_a	0.960	0.851	0.853	0.863	0.874	0.840
Within_ R²	0.205	0.127	0.144	0.208	0.191	0.136
F-statistic	426.23	26.57	28.25	497.77	108.14	14.99

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

presents the findings from robustness tests evaluating the theoretical pathways through which green finance facilitates regional innovation capacity (lnric). The findings confirm the stability of green finance’s positive effect under different analytical approaches, including the Bootstrap Test and Random Shuffling, instrumental variable regression, variable substitution, a modified sample period, and exclusion of outlier samples. These results demonstrate the model’s robustness and reliability.

5.1. Bootstrap Test and Random Shuffling

To further verify the accuracy of the regression outcomes, the Bootstrap Test and Random Shuffling methods were both utilized. The Bootstrap method, as shown in column 1, is a nonparametric resampling technique that effectively reduces estimation errors and enhances the stability of the results. The regression coefficient for green finance (gf) is found to be 0.207 and is statistically significant at the 1% level (p < 0.01), suggesting a strong and notably beneficial impact of green finance on the capacity for regional innovation. Nevertheless, it should be noted that the effectiveness of the Bootstrap method depends on a sufficiently large sample size and may produce biased estimates in small samples.

Additionally, the Random Shuffling Test results (

Table 10. Random Shuffling Test Results.

Variable	Coefficient	Number of Permutations	p-Value	95% Confidence Interval
gf	0.207	1000	0.001	[0.0000253, 0.0055589]

) provide further support for the reliability of the findings. Based on 1000 permutations, the coefficient for green finance remains at 0.207, exhibiting a p-value of 0.001 along with a 95% confidence interval of [0.0000253, 0.0055589], reaffirming the statistical significance and stability of the relationship between green finance and regional innovation capacity.

5.2. Variable Substitution

Robustness is further tested by substituting variables, as shown in columns 2–4:

In column 2, firm-level innovation (lnci) replaces regional innovation capacity as the variable that depends on others. The estimated coefficient for green finance increases to 0.684 (p < 0.01), suggesting that its impact is more pronounced at the micro level.

Column 3, changes to the control variables yield a coefficient of 0.614 (p < 0.01), indicating that the effect of green finance remains stable despite adjustments in the model.

Column 4, additional control variables, including openness (od), environmental emphasis (ep), financial development (fd), and region size (lnsize), are introduced. The coefficient for green finance remains significant at 0.573 (p < 0.01), demonstrating the model’s robustness under an expanded framework.

5.3. Sample Period Adjustment

Another robustness test narrows the sample period to 2012–2021 (column 5). This period is chosen for two main reasons. First, 2012 signifies a pivotal shift in China’s progression towards green finance. That year’s introduction of the Green Credit Guidelines provided the basis for a more standardized and institutionalized green financial system. Meanwhile, China’s economy began to shift into a “new normal” phase, characterized by slower growth and a stronger emphasis on environmentally conscious and superior development—conditions under which the pathways linking green capital allocation with local inventive capabilities is more readily discernible. Second, from the perspective of data availability and accuracy, the data for 2012–2021 are more complete and representative, making the analysis more reflective of current policy conditions. Within this shorter timeframe, the regression coefficient increases to 0.773 (p < 0.01), suggesting that green finance has a stronger influence on regional innovation capacity within a shorter timeframe. However, given the potential limitations of a restricted period, these results should be interpreted alongside other robustness tests.

5.4. Exclusion of Outlier Samples

To strengthen the validation of the initial regression findings, we exclude removes Beijing and Shanghai from the sample and re-estimates the model (column 6). These two cities are excluded because they are China’s major financial hubs and may act as outliers due to their unique economic and financial structures, potentially biasing the overall results. After excluding them, estimated parameter for green finance (gf) remains markedly positive at 0.601 (p < 0.01), which is close to the original estimate, indicating that the beneficial influence of green finance on regional innovation capacity is not driven by these particular cities. These results further corroborate the model’s robustness across alternative specifications and samples.

5.5. Endogeneity Test

5.5.1. Endogeneity Tests for Relationships Among Variables

In order to mitigate concerns regarding potential endogeneity, we utilize the instrumental variable (IV) approach and conducts two-stage least squares (2SLS) estimation.

Table 11. 2SLS Regression results for endogeneity test.

Variable	GF & RIC		RD & RIC		GF & RD
	(1) lnric	(2) lnric	(3) lnric	(4) lnric	(5) lnric	(6) lnric
gfl1	0.129 ** (0.062)				0.175 ** (0.103)
gfl1, lngdp		0.118 ** (0.056)
rdl1			2.598 ** (1.019)
rdl1, hep				2.640 ** (1.018)
gfl1, fd						0.170 ** (0.154)
control variable	YES	YES	YES	YES	YES	YES
Constant	−0.022 (0.041)	−0.016 (0.045)	0.096 * (0.051)	0.097 * (0.051)	0.143 (0.041)	0.141 (0.045)
Prov FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
First-stage F statistic	939.513	469.257	743.148	829.754	939.513	474.851
Kleibergen-Paap rk LM statistic	11.934 ***	11.941 ***	12.463 ***	12.637 ***	11.934 ***	12.525 ***
Kleibergen-Paap Wald rk F statistic	1016.958 (16.38)	735.997 (19.93)	770.996 (16.38)	1133.605 (19.93)	1016.958 (16.38)	551.437 (19.93)
Hansen J p value	/	(0.270)	/	(0.331)	/	(0.148)
N	390	390	390	390	390	390
$R^2$	0.504	0.534	0.614	0.644	0.509	0.610

Note: Three-tier significance markers (* p < 0.1, ** p < 0.05, *** p < 0.01) accompany point estimates, while clustered standard errors (time dimension) appear in parentheses. For identification strength, the Kleibergen-Paap F-statistic’s reported [values] reflect Stock-Yogo’s 10% maximal relative bias cutoff.

provides a detailed overview of the regression outcomes, showcasing the results for three sets of variable relationships, each tested using two types of instrumental variables: a single instrument and a combination of two instruments.

Endogeneity Test of Green Finance (GF) and Regional Innovation (RIC):

In the first column (1), the one-period lag of the green finance index (gfl1) is selected as a single instrumental variable. The regression results indicate that the coefficient for green finance is 0.129, a value that is significant at the conventional 5% threshold. This suggests that green finance continues to have a notable positive effect on regional innovation capacity after accounting for endogeneity issues. To further confirm the reliability of these findings, column (2) incorporates both the lagged value of green finance (gfl1) and the level of regional economic development (lngdp) as instrumental variables. These findings demonstrate that the coefficient for green finance remains favorable and noteworthy at 0.118, also at the 5% level of significance, which further strengthens the evidence for the facilitative role of green financial instruments in enhancing the capacity for regional innovation.

Endogeneity Test of Investment in Research and Development (R&D) and Regional Innovation Capacity:

The column (3), utilizes the first-order lag of R&D investment (rdl1) as a single instrumental variable. Regression analysis reveals that the coefficient of R&D investment is 2.598 (p < 0.05), suggesting that investment in research and development exerts a substantial beneficial effect on local innovation capacity after controlling for endogeneity. To further bolster the dependability of these results, column (4) combines the first-order lag of R&D investment (rdl1) and the extent of human capital (hep) as instrumental variables. The coefficient remains significant at 2.640, further reinforcing the positive relationship between R&D investment and regional innovation capacity.

Endogeneity Test of Green Finance (GF) and R&D Investment (RD):

In column (5), the first-order lag of green finance (gfl1) is used as a single instrumental variable. The findings indicate that the coefficient for green finance is 0.175 (p < 0.05), suggesting green finance’s causal impact on R&D investment after addressing endogeneity issues. To ensure the reliability of our findings, column (6) combines the initial-order delay of green finance (gfl1) with contemporaneous financial development (fd) as instrumental variables. The coefficient remains significant at 0.170, also at the 5% level, further supporting the beneficial influence of green finance on R&D investment.

To ensure the validity of the instrumental variables, several statistical tests were conducted. First, the first-stage regression F-statistics in all models are well above 10, demonstrating the instrumental variables’ strong predictive power for the endogenous regressors, thus satisfying the relevance condition. Second, the Kleibergen-Paap statistics surpass the 10% Stock-Yogo benchmarks, confirming our instruments’ empirical relevance. Additionally, the Kleibergen-Paap LM test results are statistically significant, demonstrating the identification power of our instrument set. Finally, the Hansen J test p-values in columns (2), (4), and (6) are all greater than 0.1, failing to reject the null hypothesis of valid instruments, corroborating the exogeneity condition. In conclusion, the IV approach adopted in this analysis satisfies both relevance and exogeneity conditions, successfully mitigating endogeneity concerns.

5.5.2. Endogeneity Test for Sample Selection Bias

This study systematically examines the issue of endogeneity arising from sample selection bias using three methods—the Heckman two-step approach, propensity score matching (PSM), and weighted least squares (WLS).

Table 12. Endogeneity test of sample selection bias.

Variable	Heckman		PSM	WLS
	Selected	lnric	lnric	lnric
gf	0.498 ** (5.166)	0.320 * (0.158)
gfdummy			0.040 ** (0.017)	0.057 *** (0.015)
ind	10.353 * (5.593)	0.844 *** (0.158)	0.751 *** (0.138)	0.584 ** (0.208)
lnhes	2.164 (2.934)	0.070 (0.060)	0.126 ** (0.055)	0.106 (0.068)
ur	11.128 (11.064)	0.373 (0.297)	0.155 (0.312)	0.369 (0.397)
techi	−85.195 *** (31.270)	1.028 (0.928)	2.181 *** (0.475)	2.466 *** (0.583)
lnco2	0.206 (1.135)	−0.035 (0.020)	−0.035 ** (0.015)	−0.018 (0.019)
capi	−8.384 * (4.345)	0.575 *** (0.137)	0.514 *** (0.079)	0.508 *** (0.092)
mills		−0.007 (0.010)
ATT			0.090 *** (0.030)
Constant	−11.934 (9.463)	2.528 *** (0.355)	2.459 *** (0.222)	2.353 *** (0.263)
Observations	238	238	420	420
R-squared	0.534	0.934	0.960	0.960

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

comprehensively documents the diagnostic test outcomes, with all results substantiating the robustness of our primary findings

(1)

Heckman Two-Step Approach

The Heckman two-step approach first corrects for sample selection bias through a selection equation. In the selection equation, the variable selected is measured by interest expenditure shares in high-energy-consuming industrial sectors (ei). The estimated coefficient for the key explanatory variable gf (0.498, p < 0.01) demonstrates that gf significantly increases the probability of firms engaging in R&D innovation. Notably, two controls achieve statistical significance: ind with a coefficient of 10.353 (p < 0.01), and lnhes has a coefficient of 2.164 (p < 0.05), both showing significant effects. The inverse Mills ratio (mills) is −0.007 (p = 0.860), which is not significant, suggesting that sample selection bias has a limited impact on the outcome model.

In the outcome equation, the coefficient of gf decreases to 0.320 (p < 0.05), indicating that while selection bias correction reduces the magnitude of gf’s impact on lnric, the relationship retains statistical significance. The coefficient of the control variable ind is 0.844 (p < 0.01), continuing to show significance and highlighting the importance of the high-energy-consuming industry context. After selection bias correction, the model’s coefficient of determination rises substantially to R² = 0.934, reflecting markedly enhanced explanatory capacity.

(2)

Propensity Score-Based Matching Approach

The PSM approach mitigates selection effects by creating balanced samples through nearest-neighbor matching between treated and untreated observations. Before matching, the standardized bias of covariates (e.g., lnhes, ind, techi, etc.) is significant, with a mean exceeding 20% (as shown in Figure 3). After matching, the standardized bias of all covariates decreases to below 10%, indicating a significant improvement in the balance of the sample distribution.

The average treatment effect (ATT) of the core independent variable gfdummy (a binary variable based on the 75th percentile of rdp) on the dependent variable lnric is 0.090 (p < 0.01), showing that the effect of gfdummy on lnric remains robust after controlling for covariates. Other important variables, such as ind, lnhes, and ur, also exhibit significant effects, further supporting the reliability of the core findings. Sensitivity analysis reveals consistent ATT results across different matching methods, confirming the robustness of the results. However, the reduction in sample size after matching may lead to some loss of information, and extrapolation should be approached with caution.

(3)

Weighted Least Squares (WLS)

The WLS method constructs weights using the inverse of the selection probability estimated by a Probit model to correct for sample distribution bias. The results of the regression indicate that the coefficient for the core independent variable gfdummy is 0.057 (p < 0.01), indicating a significant positive effect on lnric and suggesting that sample selection bias has a limited impact on the OLS results. Other important variables, such as ind (coefficient 0.584, p < 0.05), techi (coefficient 2.466, p < 0.01), and capi (coefficient 0.508, p < 0.01), also show significant effects, supporting the rationality of the model.

The distribution of WLS weights is reasonable, concentrated between 0.8 and 1.2, with a significant correction effect. The R-squared value reaches 0.960, indicating a good model fit. The results are consistent with those of the Heckman two-step approach and PSM, further validating the strength of the WLS outcomes. However, the WLS method is sensitive to the accuracy of weights and sample size, necessitating a combination with other methods to comprehensively verify the research conclusions.

The results of the three methods consistently demonstrate that the core independent variable (gf or gfdummy) has a significant and robust effect on the dependent variable lnric, supporting the main conclusions of the study. The Heckman two-step approach corrects for selection bias through the inverse Mills ratio, PSM improves sample balance through matching, and WLS corrects sample distribution through weighting. These methods validate the limited impact of sample selection bias from different perspectives. Although PSM and WLS have certain limitations in terms of sample information loss and weight sensitivity, the consistency of the three methods enhances the credibility of the research findings. In conclusion, this study systematically addresses sample selection bias through multiple methods, providing strong support for the reliability of the core conclusions and offering methodological references for future research.

6. Conclusions, Innovations, and Policy Implications

6.1. Synthesis of Key Findings

Our empirical analysis utilizes a provincial-level panel dataset spanning 2008–2021, combining constructed Green Finance Development Index measurements with official regional innovation metrics across China’s 30 administrative regions. The primary research objective involves identifying and evaluating the operative channels through which green finance development impacts technological advancement at the regional level. The econometric analysis substantiates that regional green finance development significantly promotes regional technological innovation, further confirming its key role in driving the green economic transformation.

The mediation analysis establishes R&D investment as a critical transmission channel, mediating 49% of green finance’s total innovation impact. This operates through a dual mechanism where green finance alleviates corporate financing constraints and enhances capital allocation efficiency, collectively elevating regional R&D intensity and ultimately strengthening innovation capacity.

The moderated mediation analysis reveals that regional innovation ecosystems significantly enhance green finance’s transmission efficacy through R&D channels, amplifying its total innovation impact by 39.8%. Quantile regression nonetheless demonstrates diminishing R&D marginal returns across innovation quantiles, signaling a transition from factor-driven to knowledge-intensive innovation paradigms in advanced regions. This implies a shift from resource-driven to knowledge-driven and system-driven innovation in these regions.

In conclusion, this study verifies that green finance influences regional innovation through a mechanism of “capital input—R&D promotion—innovation output”. The effect varies across regions with different innovation capacities. These findings provide practical evidence for optimizing green finance policies to promote regional innovation tailored to local conditions.

6.2. Theoretical and Empirical Innovations

Regression results confirm that the proliferation of green financial instruments substantially enhances a region’s innovative capabilities. Our findings align with the research L. Li et al. (2024), who also emphasize that green finance enhances regional innovation efficiency by optimizing resource allocation and easing financing constraints. Unlike previous studies that focused primarily on firm-level dynamics, this research provides evidence at the regional level, showing that green finance functions not only as a financial tool supporting green development but also as a crucial mechanism for driving technological innovation. The analysis demonstrates green finance’s dual impact, significantly improving both environmental governance and regional innovation outputs.

6.2.1. Mediation Mechanism Breakthrough

The mediation analysis substantiates that green finance principally augments regional innovation capacity by stimulating R&D expenditure, revealing a significant transmission mechanism. This mechanism supports the conclusions of S. Liu and Wang (2023), who argued that green finance pilot policies improve corporate R&D efficiency and reduce resource waste by addressing economic and political constraints. Similarly, Sun et al. (2024) identified R&D expenditure as a vital driver of green innovation, and emphasized its importance in boosting regional innovation capability. However, systematic exploration of how green finance facilitates regional innovation through R&D investment remains scarce. By constructing a mediation effect model, this study advances the understanding of the mechanism through which green finance indirectly promotes technological innovation via R&D intensity. It provides both theoretical and empirical evidence for this process, offering a fresh perspective for further research.

6.2.2. Regional Heterogeneity Redefined

The analysis further uncovers that regional innovation capacity and entrepreneurial vitality collectively function as significant moderators, amplifying the mediated effects of green finance on innovation outcomes through R&D channels. In regions with stronger innovation capabilities, the influence of green finance on R&D investment is more pronounced, while the marginal effect of regional innovation tends to decline. This observation aligns with Cooke et al. (1997), who highlighted that regions with high innovation capability rely more on knowledge spillover and collaborative networks than on isolated capital investments. Our findings challenge conventional wisdom by clarifying the dual moderating role of regional capacity for innovation. On one side, regions with high innovation capability utilize green finance resources more effectively, amplifying their impact on research and development expenditure. Conversely, as ability to innovate improves, the marginal effect of R&D spending on technological advancements diminishes. This underscores the need for policy differentiation. In regions with high innovation capability, efforts should focus on strengthening knowledge networks and institutional support. In contrast, for innovation-lagging regions, targeted green finance interventions should prioritize bridging R&D funding gaps to optimize innovation efficiency.

6.2.3. Methodological Advancements

Additionally, from a methodological perspective, our empirical approach incorporates bidirectional fixed effects with temporal clustering of standard errors, enhancing estimation consistency and mitigating potential biases. The FE framework, though not eliminating all endogeneity sources, provides crucial protection against bias by absorbing unobserved regional fixed characteristics and temporal effects that could otherwise confound estimates. The time-clustering approach further mitigates concerns of serial correlation and model misspecification, making this approach a widely accepted method in regional panel data analysis. Therefore, the modeling framework adopted in this study is both theoretically grounded and empirically rigorous, reinforcing the reliability of the conclusions drawn.

6.3. Policy Implications

This study offers several policy recommendations to optimize green finance’s innovation impact. Governments should refine the policy framework for green finance by establishing standardized evaluation systems for green projects and strengthening support from policy banks. Tools like green bonds and green loans, and tax incentives should be further promoted to direct private capital towards sustainable sectors. Implementing these policy interventions can enhance green capital allocation efficiency and offer robust financial backing for innovation.

Financial organizations ought to develop diverse green financial offerings and enhance risk management capabilities. These efforts can lower financing costs for enterprises engaged in green innovation and ensure resources are allocated to high-potential projects.

Enterprises should strategically allocate 5–8% of annual revenues to build technological capacity. Partnerships with corporations and academic institutions should be encouraged to foster collaboration in industry-university-research initiatives. By leveraging green financial resources, firms can accelerate their transition toward sustainable practices.

Societal efforts are equally important. Local governments and communities should promote a culture of innovation through targeted programs and stronger intellectual property protection. These efforts can mobilize public creativity and attract social capital to green innovation projects.

Finally, policies should address regional disparities in innovation capacity. Regions with advanced ecosystems should focus on scaling up high-tech development, while weaker regions should receive targeted support, including additional R&D funding and technical assistance. This differentiated strategy ensures balanced regional development and maximizes the influence of environmentally sustainable financial practices

6.4. Constraint and Future Investigations

Although our analysis provides robust evidence of green finance’s innovation-enhancing mechanisms, several limitations warrant acknowledgement. First, the analysis is constrained by its reliance on provincial-level data, which may obscure micro-level heterogeneity, particularly variations across industries and firm types. A second constraint involves our analytical focus on R&D expenditure as the principal transmission channel, supplemented by regional innovation-entrepreneurship interactions as contextual moderators, potentially overlooking other salient factors such as green patents, environmental regulations, and industrial structure.

Future research could benefit from employing firm-level data to capture more granular dynamic changes and industry-specific responses to green finance policies. Furthermore, incorporating a broader spectrum of mediating and moderating variables, including green patents, environmental regulations, and industrial structure, would further refine the analytical framework. Although green patents and environmental compliance policies were considered as potential mediating variables, the absence of a statistically significant mediating effect in the current model suggests that these factors warrant further investigation in future research to better understand significant mediation pathways. In addition, the application of more advanced econometric techniques, such as Panel Autoregressive Distributed Lag (ARDL) models and Continuously Updated Fully Modified (CUP-FM) and Bias-Corrected (BC) estimators, could provide deeper insights into the extended duration relationships and temporary dynamics concerning green finance and regional innovation. These methods, which allow for more precise estimation of both short- and long-run effects, would complement the current analysis by offering a more robust understanding of the underlying mechanisms. Finally, multi-level modeling techniques could be applied to explore the complex and interactive the processes that support the relationship concerning green finance and innovation effectiveness, offering more nuanced perspectives for a comprehensive comprehension of its function in supporting sustainable economic development.