Impact of Green Finance on Renewable Energy Technology Innovation: Empirical Evidence from China

Abstract

This work empirically analyzes the drivers of RETI from a financial perspective, using panel data from China’s provincial-level regions from 2013 to 2022. The results indicate that green financial development can significantly promote renewable energy innovation, which still holds after a series of robustness tests. Analysis of the mechanisms shows that green finance drives renewable energy technological innovation (RETI) by easing financing limitations and promoting the green transformation of industrial sectors. Furthermore, threshold effect analysis indicates a significant threshold effect regarding the influence of green finance on technological innovation within the renewable energy sector. Specifically, when the level of technological innovation in renewable energy surpasses a certain threshold value, the facilitating effect of green finance on this innovation becomes markedly stronger. Further analysis also reveals that technological innovation in renewable energy can significantly drive the low-carbon transformation of the energy consumption structure.

1. Introduction

In recent years, frequent droughts, high temperatures, and other extreme weather events around the globe have returned the issue of carbon emissions reduction back into the public spotlight. According to the United Nations, the past decade has been the hottest on record, whereas greenhouse gas emissions have reached a 3-million-year high. Extreme weather phenomena triggered by the intensification of the greenhouse effect pose a serious challenge to human health and economic development [1]. In particular, the persistent hot weather during the summer of 2022 in the Sichuan and Chongqing regions of China directly led to power shortages, signaling the urgent need for energy structure reform to safeguard both energy and economic security [2]. With the dual pressures of reducing carbon emissions and addressing the gap between energy supply and demand, the development of renewable energy has become crucial for mitigating global climate change and ensuring energy supply [3].

Since the dual-carbon target was proposed, China’s green and low-carbon industries have witnessed rapid growth, especially photovoltaic and wind power. As of the end of 2021, China’s newly installed renewable energy capacity had reached 134 million kilowatts, and annual renewable energy power generation amounted to 2.48 trillion kilowatt-hours, which has climbed to 14.2% of total primary energy consumption. Although China’s renewable energy sector has experienced robust growth, it is still necessary to elevate renewable energy development targets to achieve the carbon-neutral goal. The Green Finance Committee of the Chinese Society of Finance (2021) states that to meet the 2030 carbon peak target, wind power and photovoltaic installed capacity will need to exceed 1.2 billion kilowatts to achieve the 2030 carbon peak target, with an average of at least 0.7 billion kW of new installed capacity per year over the next 10 years. Achieving the more challenging “carbon neutral” target will require the annual addition of wind and solar power capacity to be doubled from this level [4]. While China’s renewable energy sector has made substantial strides, at the current development rate, additional efforts in technological innovation, policy support, and market mechanisms are essential to bridge the gap between current progress and the achievement of the dual-carbon objectives.

The theory of sustainable development emphasizes that innovation plays a pivotal role in advancing renewable energy. Breakthroughs in renewable energy technologies not only promote sustained growth in the energy sector but also reduce production costs and improve energy efficiency, leading to lower economic and environmental costs and enhanced energy accessibility [5]. Newell [6] proposed the theory of induced technological innovation, suggesting that adequate financial investment is a critical driver of technological advancement in renewable energy. However, the uncertainty of stability and economic returns associated with new technologies during industrialization and large-scale application, combined with the capital-heavy nature of the renewable energy sector, create significant capital demands that hinder technological innovation [7]. Therefore, financing constraints have long been the main obstacle facing renewable energy technological innovation (RETI) in China [8]. Numerous studies have shown that reducing financing costs can help reduce the economic burden of RETI, such as solar photovoltaic and wind energy [9]. In this context, the financial sector is increasingly looking into capital market instruments to resolve financing challenges in renewable energy technological innovation. Green finance, as an innovative financing model, is gradually becoming an important force in supporting technological innovation in renewable energy enterprises.

Marx’s concept of nature emphasizes the idea that man and nature should live in harmony, pointing out that, as an indispensable part of nature, mankind’s economic activities must follow the laws of nature to ensure sustainable development. He critically highlighted that during industrialization and marketization, enterprises and markets tend to make short-term economic interests the core driving force, ignoring long-term environmental costs and resource sustainability issues, leading to a serious ecological crisis [10]. Green finance has emerged in this context, aiming to integrate environmental protection and social responsibility into all levels of financial decision-making [11]. In contrast, the traditional financial model focuses on profit maximization and risk control, highlighting short-term economic results, and often fails to sufficiently consider environmental and social responsibility [12]. Green finance incorporates a deep consideration of environmental protection and social responsibility in every aspect of its business, demonstrating the financial sector’s commitment to and practice of sustainable development goals [13]. With increasing global awareness of environmental protection and sustainable development, green finance is poised to benefit from unprecedented opportunities, supported by favorable policies, growing market demand, and advancements in technology.

Green finance uses ESG criteria as the basis for investment evaluation and primarily offers funding and financial support for sectors such as new energy, energy conservation, emissions reduction, and other green technologies, products, equipment, and industries [14]. Since the issuance of the Green Credit Guidelines by the People’s Bank of China in 2012, green finance has seen rapid growth, driven by government support and a series of proactive policies. In particular, in 2017, China established green finance innovation pilot zones in eight areas (including Huzhou, Quzhou, Guangzhou Huadu, Ganjiang New District, Hami City, Changji Prefecture, Karamay City, and Guiyang Guian New District) in five provinces and regions of Zhejiang, Guangdong, Jiangxi, Xinjiang, and Guizhou. By successfully combining policy guidance with market incentives, these pilot zones not only broadened financing channels for green projects but also contributed significantly to advancing the ideas of green investment and sustainable development. During this period, green finance developed rapidly. A review and comparison of projects financed by green credit, green bonds, and green funds show that renewable energy projects are at the heart of green investment and financing efforts. Specifically, within the green bond rating framework, financing for renewable energy projects is classified as “deep green,” with the funds from green credit and green bonds primarily directed towards renewable energy innovation projects. Financial backing is provided for the research, development, construction, and operation of these projects. However, it remains to be seen whether the flow of green financial resources to renewable energy projects has truly fulfilled its intended purpose. How effective has it been? These questions warrant thorough investigation. This study empirically examines the impact and underlying mechanisms of green finance on RETI, utilizing provincial-level panel data from China spanning 2013 to 2022.

2. Literature Review and Hypothesis

2.1. Literature Review

In recent years, the growing urgency of climate change, combined with a global shift towards sustainable development, has pushed research in RETI to the forefront of scholarly attention [15]. A substantial body of literature has emerged, examining the key factors that influence RETI. These factors primarily encompass policy support, market demand, energy structure, resource endowment, and financial support.

Policy support is widely regarded as a core driver of RETI [16]. Numerous studies have demonstrated that governments can effectively stimulate the active participation of various stakeholders in RETI by formulating long-term or short-term action plans, setting clear targets, and implementing supportive policies to guide the behavior of market actors [17]. Moreover, public R&D funding and industry subsidies have been shown to exert a significant positive influence on RETI [18]. In addition, environmental regulations are essential in promoting RETI. Most studies agree that strict environmental regulations are highly effective in driving innovation within this field [19].

Market demand exerts a vital pull effect on RETI. Demand-pull instruments, like feed-in tariff subsidies, promote technological innovation by creating market demand and accelerating the diffusion of technologies [20]. In regions rich in renewable resources, the role of price-based demand-pull tools is particularly pronounced, effectively enhancing technological innovation capacity [21].

The influence of energy structure and resource endowment on the innovation of renewable energy technologies must not be underestimated. Researches suggest that a diversified energy structure significantly drives RETI, particularly as traditional fossil fuels face resource scarcity, intensifying the need for renewable alternatives [22]. Resource endowment also plays a crucial role in advancing RETI. In regions abundant in wind and solar resources, technological innovation is often higher. These regions’ resource endowment advantages not only facilitate the rapid diffusion of related technologies but also attract more R&D investments, thereby further promoting RETI [23]. Furthermore, the synergistic effect of energy structure adjustment and resource endowment optimization exerts a significant driving influence on RETI. By optimizing the energy structure and efficiently utilizing resource endowment, the cost of renewable energy can be lowered, and technological efficiency improved [24].

Financial support is a crucial driver in advancing RETI. Ghisetti et al. [25] argue that RETI is often marked by significant technological risks, extended payback periods, and unpredictable returns. These factors make financial limitations a major barrier to investment in renewable energy technologies. Such constraints limit the financial resources accessible to renewable energy companies for research and development, capacity growth, and commercial applications.

In this context, green finance, as an innovative financial instrument, has emerged as a key tool to mitigate these challenges. Green finance not only provides renewable energy enterprises with the necessary financial support to alleviate their financial pressures during the technological innovation process but also helps diversify investment risks, reduce uncertainties in R&D, and foster RETI [26]. For instance, green bonds and green funds channel specialized financing towards environmental protection and sustainable development projects, thereby promoting the R&D and RETI [27]. Venture capital further provides essential funding to renewable energy firms in the early phases of technology development, speeding up the incubation and implementation of new innovations [28]. According to Zheng et al. [29], green finance positively influences renewable energy innovation, based on large-scale data from 64 global economies and analyzed using a panel fixed-effects model. Nepal et al. [30], utilizing the 2017 Green Finance Reform and Innovation Pilot Zone policy in China as a natural experiment and employing a difference-in-differences model, confirmed the significant and positive effect of this policy on RETI. Nevertheless, some researchers have noted the occurrence of “green drift,” where companies may assert their commitment to environmental protection without implementing meaningful technological innovations or sustainable development initiatives. This phenomenon can result in the misallocation of resources and diminish the effectiveness of financial backing [31].

In summary, assessing the effectiveness of green finance to ensure that it genuinely promotes technological innovation and sustainable development has become a critical issue that warrants further exploration. Although there is an expanding body of research on the impact of green finance on RETI, a clear consensus has yet to be established. As the largest developing nation, China faces considerable challenges in reconciling its growing energy demands with the urgent need to reduce carbon emissions. To tackle these challenges, the Chinese government has introduced a range of policy measures, such as the creation of green finance pilot zones, the release of green credit guidelines, and the promotion of carbon emissions trading schemes. These measures collectively create a conducive policy environment for green finance to support RETI. However, existing studies remain inadequate in comprehensively assessing the effects of these green finance policies. For example, while Nepal et al. have analyzed the impact of the green finance reform and innovation pilot zone policy, their approach treats it as a singular policy variable [30]. This approach neglects the subtle variations in policy implementation across different regions and does not quantitatively evaluate the differing levels of green finance development within each area.

To fill these gaps, this study utilizes the entropy value method to develop a green financial development index for each region, using panel data from provincial-level regions across China. Additionally, we employ econometric models to examine how green financial development influences RETI and to uncover the mechanisms behind this relationship.

2.2. Theoretical Analysis and Hypothesis

2.2.1. Green Finance and Renewable Energy Technology Innovation

This section explores the role of green finance in promoting RETI, framed within the context of innovation theory and sustainable development theory. Innovation theory emphasizes the central role of innovation in fostering economic growth, with technological progress in renewable energy being essential for reaching sustainable development objectives.

Schumpeter’s innovation theory [32] emphasizes the role of innovation in economic development through the idea of “creative destruction.” This process, where enterprises introduce new products, technologies, and forms to disrupt traditional industries, drives dynamic economic development. The continuous breakthroughs in renewable energy technologies, such as solar and wind power, exemplify this theory by unprecedented changes in the traditional energy sector [33].

The theory of sustainable development provides a clear direction for renewable energy innovation. Initially outlined by the World Commission on Environment and Development in Our Common Future [34], sustainable development is defined as “development that satisfies the needs of the present without hindering the ability of future generations to fulfill their own needs”. This theory emphasizes the need for coordinated progress between economic, social, and environmental dimensions. In the realm of renewable energy, it requires that technological innovation not only pursues economic benefits but also prioritizes environmental protection and social equity. Advances in renewable energy technologies, like solar and wind power, decrease reliance on fossil fuels, reduce environmental pollution, and drive a shift toward a green economy.

Green finance serves as a bridge between financial capital and the green economy, supporting renewable energy innovation through multiple mechanisms. By leveraging innovative financial instruments, green finance reduces financing costs and facilitates green technological breakthroughs, aligning with Schumpeter’s “creative destruction” mechanism. Moreover, green finance aids the transition to a green and low-carbon economy, ensuring the harmonious development of economic, environmental, and social goals in line with the core objectives of sustainable development theory.

With the growing global focus on energy security and environmental protection, RETI has advanced significantly. Solar cell efficiency continues to improve, wind power costs keep decreasing, and other renewable energy technologies like biomass and geothermal energy are also progressing. Meanwhile, the fast-paced advancement of smart grids, energy storage technologies, and other complementary innovations has bolstered the broad adoption of renewable energy [35]. Although the remarkable progress in RETI, challenges persist, particularly in the area of financing [36].

Innovations in renewable energy technologies typically demand substantial investments in research and development (R&D), while also presenting several risk factors for investors. Firstly, renewable energy projects often necessitate large initial capital outlays coupled with extended payback periods, which can significantly amplify financial risks for investors. Secondly, uncertainties in technology and limited market acceptance can lead to returns that fall short of expectations [37]. Additionally, changes in policy can have a substantial impact on the viability and profitability of renewable energy projects. These multifaceted risks may cause investors to approach renewable energy projects with caution, resulting in financing constraints for related companies [38]. As a consequence, these companies often rely predominantly on government subsidies and bank loans to fund their operations and innovations. These financing channels have certain limitations in terms of the scale and stability of funds, resulting in many enterprises encountering difficulties in funding and sustaining R&D [39]. Therefore, financial constraints have been a major challenge for RETI.

In response to global climate change efforts and the push for energy structure transformation, green finance serves as a vital link between financial capital and the green economy, playing an essential role in supporting RETI [40]. First, green financial instruments offer a stable and cost-effective funding source for renewable energy projects, thereby minimizing the risks and costs associated with corporate technological innovation and project execution. Meanwhile, green finance also diversifies capital market development, attracts more social capital into the field of renewable energy, and further expands the scale of capital supply [41]. Second, green finance provides investors with effective risk diversification and management tools through innovative risk management mechanisms. For example, green insurance products can provide risk protection for renewable energy projects and reduce the potential losses faced by investors; green credit guarantee mechanisms can provide guarantees through the government or third-party institutions to enhance the credit rating and financing ability of projects. These measures help enhance investor confidence in RETI and promote the effective flow of capital [42]. Third, green finance accelerates the commercialization and application of technological advancements by fostering collaborative innovation across industry, academia, research institutions, and practical implementation. Green finance funds research institutions and enterprises to conduct cutting-edge technology research and development and demonstration project construction to expand technological innovation [43]. Finally, green finance helps create a favorable ecosystem for RETI by fostering the integration and collaboration of capital, technology, talent, and other innovative factors [44]. Based on this analysis, this paper introduces Hypothesis 1.

H1. 

Green finance can promote RETI.

2.2.2. Exploration of Impact Mechanisms

Explore the mechanisms by which green finance influences RETI at both the direct and indirect levels. Directly, green finance can offer long-term, low-interest loans for renewable energy projects, easing the financial burden on project developers and supporting the continuity of RETI [45]. Indirectly, green finance contributes to the growth of established green industries by steering funds towards environmentally friendly, low-carbon projects. As more capital is allocated to sectors with sustainable development potential, this facilitates the optimization and upgrading of industrial structures [46]. As industries undergo a green transformation and consumer demand for eco-friendly products grows, the market demand for renewable energy and other green technologies expands, serving as a significant driver for technological innovation. Based on the above analysis, this paper proposes Hypothesis 2.

H2. 

By easing financing limitations and expediting the green transformation of industrial structures, green finance can drive RETI.

2.2.3. Heterogeneity Analysis

In China, local network factors are critical in determining the effectiveness of green finance in supporting RETI. These factors primarily encompass local government policy support [47]. For instance, some local governments have vigorously promoted green financial innovation by establishing green finance pilot zones, which have attracted numerous venture capital firms to invest in renewable energy technology projects. These regions typically boast robust financial infrastructure and dynamic capital markets, providing abundant funding sources and effective risk management tools for RE technology innovation.

Moreover, local network factors are also evident in the cooperation and information sharing among financial institutions. Economically developed areas usually enjoy superior local network conditions. Leveraging their solid economic base, vibrant financial environment, and rich human capital, these regions can establish comprehensive green finance policy systems and meet market demands through policy innovation. Such policies effectively guide capital towards RETI, significantly reducing financing costs and accelerating the innovation process, thereby driving technological progress in the RE sector.

In contrast to high-carbon regions, low-carbon regions prioritize green and low-carbon development to a greater extent [48]. Financial institutions in these areas are more active in innovating low-carbon financial products, attracting a wide range of participants and fostering a virtuous cycle in the market ecosystem. Local governments in low-carbon regions also tend to implement strong and efficient green finance policies earlier, creating a favorable market environment that promotes the integration of RE technology innovation and green finance. This environment reduces financing costs and risks, enhances project feasibility and success rates, and significantly accelerates RETI. Based on the above analysis, this paper proposes Hypothesis 3.

H3. 

The impact of green finance on RETI varies across regions, depending on their levels of economic development and carbon emission intensity.

2.2.4. Threshold Effect Analysis

When the level of RETI is low, the technological risk and market risk of renewable energy projects are relatively high, and the support of green finance, although it helps to alleviate financial pressure, may not be able to fully stimulate the vitality of technological innovation [49]. In addition, the weak technological foundation may introduce many difficulties in the transformation and application of innovation results [50]. As a result, the impact of green finance on promoting RETI is limited at this stage. Once the level of RETI surpasses a certain threshold, technological risks in renewable energy projects decrease, and the core competitiveness and market potential of the technology become more apparent [51]. Green finance provides a continuous source of funding for enterprises at this stage by providing financial support, especially through green bonds, green funds, and other financing methods, which can accelerate the large-scale production of the technology, reduce production costs, and enhance the market competitiveness of technology. Meanwhile, high-level technological innovation can also provide better investment targets for green finance, forming a virtuous circle [52]. Based on this analysis, this paper introduces Hypothesis 4.

H4. 

The influence of green finance on RETI exhibits a threshold effect; as the level of RETI increases, the effectiveness of green finance in promoting it becomes more pronounced.

3. Empirical Research Design

3.1. Empirical Model Construction

To identify the causal effect of green finance on RETI, we constructed a panel data regression model for estimation. Because of the large amounts of missing data related to green finance before 2012 and considering that the examination cycle of invention patents is 2–3 years, which means that patents applied after 2022 are still under examination and the data are unreliable, we considered 30 provinces and regions in China from 2012 to 2022 as samples for empirical tests. The model is set up as follows:

$$RET{I}_{it}=\alpha +{\beta }_{i}GF{I}_{it}+\gamma X_{it}+{\eta }_i+{\lambda }_t+{\mu }_{it}$$

(1)

where $RET{I}_{it}$ denotes the level of RETI in the $\mathit{i}$ province in the t year; $GF{I}_{it}$ is the green finance development index of the first province in the t year measured in this paper; and $X_{it}$ is the control variables; ${\eta }_i$ indicates individual fixed effects; ${\lambda }_t$ indicates time-point fixed effects; ${\mu }_{it}$ is the residual term.

3.2. Description of Variables and Data Sources

3.2.1. Explained Variable: RETI

Previous studies have determined that the quantity of patents and inventions serves as a measure of technological development, while the number of citations these patents receive is a key indicator of their quality [53]. Therefore, drawing on the research of Zhang and Chen [54], this paper adopts the chain growth rate of renewable energy patents and invention applications with more than five citations to measure the level of RETI. The numbers of renewable energy patents and invention applications are searched on the patent search and analysis platform of the official website of the State Intellectual Property Office, with “applicant’s address” as the name of each province and keywords such as solar energy (“solar” or “photovoltaic”), wind energy (“wind” or “wind power”), biomass (“biomass” or “biofuels”), hydroelectricity (“hydro” “hydropower” or “hydropower generation” and “hydroelectricity”), geothermal energy (“geothermal energy” or “geothermal power”) and ocean energy (“ocean energy” “ocean power” and “ocean power generation”). “Ocean energy generation” “tidal energy” “tidal power” “wave energy” or “wave energy” “wave power” “wave energy” “wave power” “wave energy” or “wave power generation” were searched, and each of the searched patents and inventions was compared and examined. The patents obtained after de-weighting were compared and examined, and the patents obtained after de-weighting were compared and examined. The number of patents obtained after de-weighting was checked, the number of patents and inventions with more than five citations were selected, and the number of patents in these six categories was summed up to obtain the number of renewable energy patents.

3.2.2. Core Explanatory Variable: Green Finance Development Index

Based on the study by Shi and Shi [9], the Green Finance Development Index incorporates seven indicators for measurement: green credit (the percentage of loans allocated to environmental protection projects, defined as the ratio of regional loans for environmental protection projects to total regional credit), green investment (the ratio of regional investment in environmental governance to GDP), green insurance (the promotion level of environmental pollution liability insurance, measured by the ratio of regional revenue from environmental liability insurance to total premium revenue), green bonds (the share of regional green bonds in total bond issuances), green support (the proportion of regional fiscal spending on environmental protection relative to total budget expenditures), green funds (the proportion of regional green funds compared to total market capitalization of all funds), and the extent of green equity development (the ratio of regional carbon trading, energy use right trading, and sewage right trading to total equity market transactions). The green finance index was constructed using the entropy method.

3.2.3. Control Variables

Referring to the study of Su and Fan [55], control variables are selected from innovation system, policy instrument system, environmental system, energy system and economic environment. In the innovation system we select the intensity of public R&D investment ($PRD$). The policy instrument framework is grounded in the research of Mazzucato and Semieniuk [56], which identified the Renewable Energy Electricity Tariff Subsidy (REETS) as a key variable. The REETS is determined by multiplying the amount of renewable energy generated by the subsidy rate per kWh of renewable energy generation within each region. The Environmental Systems indicator is derived from Steffen’s study [57], which uses Carbon Dioxide Emission Intensity (CDEI) as a measure. CDEI is calculated as the ratio of carbon dioxide emissions to regional GDP. In alignment with the approach of Khan et al. [58], the energy system incorporates two pivotal indicators: Energy Consumption Intensity (ECI) and renewable energy endowment (RENEE). ECI is defined as the ratio of energy consumption in a region to its GDP. RENEE is a dummy variable. Based on the 2013 Notice by the National Development and Reform Commission on leveraging price mechanisms for the photovoltaic industry, photovoltaic power plants are categorized into Class I and Class II regions. We define these as regions rich in renewable energy resources, assigned a value of 1, which include Ningxia, Qinghai, Gansu, Xinjiang, Inner Mongolia, Beijing, Tianjin, Heilongjiang, Jilin, Liaoning, Sichuan, Yunnan, Hebei, Shanxi, and Shaanxi. All other regions, with fewer renewable energy resources, are assigned a value of 0. The economic environment is based on Dai et al.’s study [59], selecting indicators such as the degree of openness to external markets (FT), the level of human capital (HC), and the development of the technology market (TMD). FT is measured by the ratio of total imports and exports to GDP, HC is measured by the ratio of students enrolled in higher education to the total population, and TMD is quantified by the ratio of technology market participants to the total population. Human capital is assessed by the ratio of higher education enrollment to the total population, while the technology market development level is determined by the ratio of technology market turnover to GDP.

Definitions and data sources for all variables are in

Table 1. Descriptive statistics of the variable.

Variable Type	Symbol	Variables Definition	Data Sources
Explained variables	RETI	Level of innovation in renewable energy technologies	As measured in this paper
	IDECS	Decarbonization index of the energy mix	As measured in this paper
Core explanatory variables	GFI	Green Finance Development Index	As measured in this paper
Control Variables	PRD	Intensity of public R&D investment (public R&D expenditure/GDP)	China Statistical Yearbook
	REETS	Renewable energy tariff subsidies (calculated by multiplying the amount of renewable energy generation in each region by the amount of subsidy per kWh for renewable energy generation)	China Statistical Yearbook
	CDEI	Carbon intensity (carbon dioxide emissions/GDP)	CEADs
	RENEE	Renewable energy resource endowment (dummy variable)	Manually sorted
	ECI	Energy intensity (energy consumption/GDP)	China Statistical Yearbook
	FT	Degree of openness to the outside world (total exports and imports of goods/GDP)	China Statistical Yearbook
	HC	Level of human capital (number of students enrolled in higher education/total population)	China Statistical Yearbook
	TMD	Level of technology market development (technology market turnover/GDP)	Statistical Yearbook of the provinces
	IL	Level of industrialization (industrial value added/GDP)	Statistical Yearbook of the provinces
	PGDP	Level of economic development (GDP/total regional population)	Statistical Yearbook of the provinces
	GIIT	Government intervention (government expenditure on public services as a percentage of GDP)	Statistical Yearbook of the provinces
	UR	Level of urbanization (ratio of regional urban population to total population)	Statistical Yearbook of the provinces

. The results of the descriptive statistical analyses of all variables are presented in

Table 2. Descriptive statistics of the variables.

Variable	Observations	Mean	Media	Std. Dev.	Minimum	Maximum
RETI	300	1.09	1.12	0.67	0.15	7
IDECS	300	5.70	623	0.40	5.04	7.15
GFI	300	0.58	0.57	0.07	0.45	0.74
PRD	300	1.69	1.39	1.15	0.41	6.44
REETS	300	40.00	33.71	25.82	0.378	112.04
CDEI	300	0.02	0.04	0.01	0.00	0.07
RENEE	300	0.52	1.00	0.50	0.00	1.00
ECI	300	0.75	0.65	0.45	0.19	3.18
FT	300	0.30	0.28	0.63	0.02	9.84
HC	300	0.02	0.02	0.01	0.01	0.04
TMD	300	0.02	0.03	0.03	0.00	0.19
IL	300	0.33	0.38	0.08	0.10	0.54
PGDP	300	58,230.94	61,128.73	29,173.04	18,946.86	187,526
GIIT	300	0.25	0.19	0.10	0.11	0.64
UR	300	0.60	0.57	0.12	0.39	0.90

.

4. Analysis of Empirical Results

4.1. Green Finance Development and RETI

The results of Equation (1) are presented in

Table 3. Green finance and RETI.

Variant	RETI
	Model 1	Model 2	Model 3
GFI	2.72 ** (1.77)	2.57 ** (1.66)	2.72 ** (1.75)
PRD		0.35 ** (1.66)	0.32 * (1.53)
REETS		0.01 (1.00)	0.01 (1.22)
CDEI		9.42 (0.84)	7.97 (0.52)
RENEE		0.01 *** (2.97)	0.0127 * (1.73)
ECI			0.43 (0.87)
FT			0.09 (0.61)
HC			3.34 (0.09)
TMD			11.88 *** (2.46)
Individual fixed effect	YES	YES	YES
Year fixed effects	YES	YES	YES
R2	0.1334	0.1485	0.1709
N	300	300	300

Note: *, **, and *** indicate significant at the 10%, 5%, and 1% levels, respectively; data in parentheses in the table are t-values, as in the table below.

, in which model 1 is the regression result without introducing any control variables; model 2 is the estimation result of introducing $PRD$, $CDEI$, $REETS$, and $RENEE$; and model 3 is the regression result of adding $FT$, $ECI$, $HC$, and $TMD$ to model 2. The estimated coefficients of the core explanatory variables in all three columns are significantly positive, indicating that green finance significantly promotes RETI. Thus, H1 is validated.

4.2. Impact Mechanism Test

Drawing on the study of Lin and Chen [60], this paper incorporates cross-product terms of the core explanatory and mechanism variables in model (1) to examine how green finance promotes RETI through financing constraints ($CREDIT$) and green transformation of industrial structure ($GTIS$). Among them, the financing constraint refers to the study of Tabrizian [61], using the ratio of regional loan balances to deposit balances as the measurement. The green transformation of industrial structure draws on the research of Liu et al. [62] and establishes an indicator system from four dimensions: industrial development, industrial transformation, environmental quality, and government support, which is measured by the entropy value method.

The regression results in

Table 4. Mechanism exploration of green finance for RETI.

Variant	CREDIT	GTIS
GFI	3.22 ** (1.87)	2.49 *** (2.38)
GFI × CREDIT	−0.83 ** (−2.31) ***
GFI × GTIS		0.96 ** (1.71)
Control variable	YES	YES
Individual fixed effect	YES	YES
Year fixed effects	YES	YES
R2	0.1724	0.2135
N	300	300

Note: **, and *** indicate significant at the 5%, and 1% levels, respectively.

indicate that the coefficients for green finance remain significantly positive, even after including the interaction terms between green finance and regional financing constraints, as well as between green finance and the green transformation of industrial structure. Specifically, the cross term coefficient between green finance and regional financing constraints is significantly negative, indicating that green finance helps reduce regional financing constraints, thereby fostering regional RETI. The coefficient for the cross term between green finance and the green transformation of industrial structure is also significantly negative, suggesting that green finance accelerates regional RETI by driving the green transformation of the regional industrial structure. Hence, Hypothesis 2 is confirmed.

4.3. Analysis of the Results of Heterogeneity Test

4.3.1. Heterogeneity in Levels of Economic Development

The level of economic development is the basis for RETI in a region. Drawing on the study of Stevens et al. [63], the median per capita GDP of each region in 2022 was used as the criterion for dividing the sample into economically developed and less developed regions for regression, respectively. The results in

Table 5. Regression results for economic base heterogeneity.

Variant	RETI
	Economically Advanced Regions	Less Economically Developed Regions
GFI	2.09 ** (1.66)	1.96 (0.64)
Control variable	YES	YES
Individual fixed effect	YES	YES
Year fixed effects	YES	YES
Within-R2	0.2766	0.2253
N	150	150

Note: ** indicates significant at the 5% levels, respectively.

show that the impact of green finance on RETI is more pronounced in developed regions. In contrast, in less developed regions, the coefficient for green finance’s impact on RETI is smaller and statistically insignificant.

4.3.2. Heterogeneity of Carbon Emission Intensity

Due to the differences in policy support, market mechanism, financing environment and cost, and technological innovation ecology between high and low-carbon emission regions, there are differences in the effects of green finance on RETI. The median carbon emission intensity of regions in 2022 was used as the criterion to categorize the sample into high and low-carbon emission regions for the regression analysis. The estimation results in

Table 6. Carbon emission intensity heterogeneity regression results.

Variant	RETI
	High Carbon Emission Regions	Low Carbon Emission Regions
GFI	2.29 (0.94)	3.22 ** (1.64)
Control variable	YES	YES
Individual fixed effect	YES	YES
Year fixed effects	YES	YES
Within-R2	0.2764	0.1978
N	150	150

Note: ** indicates significant at the 5% levels, respectively.

indicate that the impact coefficient of green finance on RETI is both high and significant in low-carbon emission regions. In contrast, in high-carbon emission regions, the coefficient is smaller and statistically insignificant compared to low-carbon regions. As a result, Hypothesis 3 is confirmed.

4.4. Threshold Effect

RETI is a dynamic process of ongoing evolution, with varying levels across different contexts. Furthermore, the impact of green finance on RETI differs due to variations in the policy environment and market mechanisms. To account for these differences, we developed a threshold model to examine the nonlinear effect of green financial development on RETI levels. We employed the residual sum of squares minimization method suggested by Cancer and Hansen [64] to determine the number of thresholds. The results of the estimation are presented in

Table 7. Threshold number test.

Model	F-Statistic	p-Value	Bootstrap Times
Single threshold	304.39	0.0000	300
Double threshold	256.41	0.0000	300
Triple threshold	119.62	0.8767	300

.

Noticeably, the F-statistic of the three-gate test cannot reject the original hypothesis, and in the two-gate test, the F-statistic rejects the original hypothesis of the no-gate at the 1% significance level, indicating that a dual gate exists in the model, and therefore a two-gate model is established for the analysis. The form of the model is as follows:

RETI_it = α + β₁GFI_itI(RETI ≤ τ₁) + β₂GFI_itI(τ₁ < RETI ≤ τ₂) + β₃GFI_itI(RETI > τ₂) + γX_it + η_i + λ_t + μ_it

(2)

Once thresholds ${\widehat{\tau }}_1$ and ${\widehat{\tau }}_2$ are determined, the sample is divided into $RET{I}_{it}\le {\widehat{\mathit{\tau }}}_{\mathbf{1}}$, ${\widehat{\mathit{\tau }}}_{\mathbf{1}}<RET{I}_{it}\le {\widehat{\mathit{\tau }}}_{\mathbf{2}}$, and $RET{I}_{it}>{\widehat{\mathit{\tau }}}_{\mathbf{2}}$, and the panel data GMM are then applied to estimate the model coefficients.

The estimation results presented in

Table 8. Threshold effect test.

Variant	Estimated Coefficient
RETI ≤ 1.2848	0.07 (0.13)
1.2848 < RETI ≤ 2.7778	1.56 *** (2.57)
RETI ≥ 2.7778	6.87 *** (9.91)
Control variable	YES
Individual fixed effect	YES
Year fixed effects	YES
R2	0.7562
N	300

Note: *** indicates significant at the 1% levels, respectively.

demonstrate the existence of a threshold effect in how green finance influences RETI. When the RETI level is below 1.2848, the impact coefficient of green finance on RETI is merely 0.07, and the effect is statistically insignificant. For RETI levels between 1.2848 and 2.7778, the impact coefficient of green finance on RETI rises to 1.56, which is statistically significant at the 1% level. Lastly, when the RETI level exceeds 2.7778, the impact coefficient of green finance on RETI increases to 6.87, which is significant at the 1% level. Consequently, Hypothesis 4 is confirmed.

4.5. Further Analysis

In the context of global climate change and the growing challenges of resource and environmental limitations, RETI plays a pivotal role in driving the transition to low-carbon energy. Initially, RETI has diminished the dominance of fossil fuels in energy consumption by enhancing energy conversion efficiency and lowering production costs. The significant improvement in photovoltaic panel efficiency and rapid decline in wind power generation costs and other technological innovations mean that renewable energy in the economy is increasingly close to or it even supersedes traditional energy sources, with the energy consumption structure of the low-carbon transformation serving as the material basis. Second, RETI has facilitated the shift in energy consumption patterns. The wide application of smart grids, distributed energy systems, and other new technologies has made energy consumption more flexible, efficient, and convenient. Consumers can use renewable energy more conveniently and realize energy self-sufficiency or surplus sharing, thus reducing fossil energy consumption. This decentralized energy consumption model not only improves the efficiency of energy use but also promotes the low-carbon transformation of energy consumption. Finally, RETI also promotes the development of low-carbon technologies and low-carbon industries. Spurred by renewable energy technologies, innovations in low-carbon technologies, such as energy conservation, storage, and smart grids, have made significant strides and are widely applied, providing critical technical support for the low-carbon transformation of energy consumption. These technologies have promoted the development of low-carbon technologies and industries by enhancing energy conversion efficiency, reducing production costs, changing energy consumption patterns, and strengthening economic incentives and policy guidance, thus promoting the driving energy consumption toward a more low-carbon and cleaner direction. This transition not only helps to address the challenges of global climate change but also promotes sustainable economic and social development. Based on the above, this paper draws on the study of Sharma et al. [65] to construct the index of decarbonization of energy structure ($IDECS$) using the weighted multidimensional vector clamping method and establishes a panel data regression model to further measure the impact effect of renewable energy technologies on the decarbonization of energy consumption structure. Referring to the study of Hassan et al. [66], the control variables economic development level ($PGDP$), industrialization level ($IL$), government intervention ($GILT$), and urbanization level ($IDECS$). The regression results presented in

Table 9. Impact of RETI on the low-carbon transition of the energy mix.

Variant	IDECS
RETI	0.02 ** (1.67)
Control variable	YES
Individual fixed effect	YES
Year fixed effects	YES
R2	0.7034
N	300

Note: ** indicates significant at the 1% levels, respectively.

indicate that RETI significantly contributes to the low-carbon transformation of the energy consumption structure.

4.6. Robustness Tests

Three methods of robustness testing were performed in this paper. One is the method of gradually introducing control variables, the estimated results are shown in Table 3. First, without adding any control variables, then introducing the intensity of public R&D investment, carbon dioxide emission intensity, renewable energy tariff subsidy, and renewable energy endowment, before finally adding the degree of openness to the outside world the intensity of energy consumption, the level of human capital, and the level of development of the technology market reveals that the coefficients of green finance are all significantly positive. Second, by replacing the explained variable, existing studies studies have revealed a diffusion effect [8,14,20] among patents. Accordingly, we consider the depreciation rate and diffusion rate of patents and use the following formula to transform renewable energy patents into technological innovation indicators:

$${\mathit{ARETI}}_{\mathit{it}}={\displaystyle \sum _{j=0}^{t}RPA{T}_{ij}\exp [-{\beta }_1(t-j)]}\cdot \left\{1-\exp [-{\beta }_2(t-j)]\right\}$$

(3)

where $RPAT$ is the number of renewable energy patents granted and ${\beta }_1$ and ${\beta }_2$ are the depreciation and diffusion rates, respectively, which are taken as 0.36 and 0.3, based on the study of Batra [67]. Substituting the measured results into the model for re-estimation reveals that the results are consistent with the benchmark regression. Third, the core explanatory variables are replaced. For green finance measurement, some scholars have used comprehensive indicators for measurement, whereas other scholars have used a single indicator [68].

While the comprehensive indicator method provides a broad perspective on the development of green finance, it may lack precision in analyzing the transmission mechanisms and lacks established standards for determining the weights of secondary indicators. It can also be more effective in measuring the impact of specific green financial activities on economic development or other factors, yet it struggles to offer a comprehensive measure of the overall level of green financial development. In light of these considerations, this study re-estimated the model by using green credit (GC) as the primary explanatory variable, with results aligning with those from the benchmark regression. To mitigate potential endogeneity issues that could affect the results, we substituted the original explained variable RETI, with its one-period lagged value (LRETI) and re-estimated the model. The findings were consistent with the original regression results. The outcomes of the last three robustness tests are presented in

Table 10. Robustness check.

Variant	ARETI	LRETI	RETI
GFI	2.0452 *** (3.4900)	1.8843 *** (2.8500)
GC			1.3928 ** (2.1900)
Control variable	YES	YES	YES
Individual fixed effect	YES	YES	YES
Year fixed effects	YES	YES	YES
R2	0.2351	0.2529	0.2690
N	300	300	300

Note: **, and *** indicate significant at the 5%, and 1% levels, respectively.

.

5. Conclusions and Inspiration

The 20th Party Congress report presents a detailed strategy for advancing the energy revolution and securing energy supply, highlighting China’s deep commitment to energy transformation and sustainable development in the new stage of development. Within this framework, RETI plays a central role not only in optimizing the energy structure but also as the main driver in securing energy stability. Existing research, however, has primarily concentrated on the impact of RETI on reducing pollution and carbon emissions, upgrading industrial structures, and promoting high-quality economic development. Fewer studies have, however, examined the underlying drivers of RETI, particularly from the perspectives of renewable energy policy effectiveness, tariff subsidies, and environmental regulations. From a financial perspective, this paper examined the effect and mechanism of green finance on RETI. The main conclusions are as follows:

(1)

The development of green finance significantly fosters RETI, with this conclusion remaining consistent after various robustness tests.

(2)

Green finance encourages renewable energy technological innovation via two key channels: easing financing constraints and driving the green transformation of industrial structures.

(3)

Heterogeneity tests showed varying effects of green finance on RETI. Particularly, in more economically developed regions, the impact of green finance on RETI is more pronounced, and similarly, green finance has a greater promotional effect in low-carbon-emission regions.

(4)

The threshold effect test uncovered a significant threshold effect in the relationship between green finance development and RETI. Once RETI exceeds this threshold, the influence of green finance on RETI increases markedly.

(5)

The examination of RETI’s impact revealed that ongoing innovation and the application of renewable energy technologies significantly drive the transformation of energy consumption structures toward decarbonization and greater cleanliness.

This study offers the following policy insights: (1) Continue increasing green financial policy support. Local governments should continue to improve policies related to green finance to incentivize the flow of more funds toward renewable energy projects and technological innovation. Financial institutions should also scale up and diversify financial products such as green credit, bonds, and funds, thereby lowering the financing costs for green projects and increasing the financial market’s capacity to support green initiatives. (2) The government should focus on improving the financing environment, facilitating the green transformation of industrial structures, and driving technological innovation in renewable energy to support the achievement of sustainable development objectives. (3) Green finance policies should be tailored to local contexts. In advancing green finance, regional disparities must be taken into account, with policies designed to suit local conditions and needs. (4) Foster a supportive innovation environment by establishing platforms for industry-university-research cooperation to advance RETI.Given the threshold effect of green finance on RETI, it is crucial to monitor RETI status in real time across regions. For regions below the threshold, increased R&D funding, special funds, or subsidies should be allocated to address market funding gaps. Concurrently, efforts should be made to enhance the innovation and entrepreneurship ecosystem, streamline approval procedures, strengthen intellectual property protection, and optimize the overall innovation landscape. Deepened collaboration between universities, research institutions, and businesses should be encouraged to ensure the sustained research and development of renewable energy technologies. By bolstering green finance support and effectively leveraging its potential, continuous innovation and application of renewable energy technologies will be fostered.

Green finance is playing a crucial role in advancing the innovation and deployment of renewable energy technologies. Through concessional financing and focused investments, it fosters RETI and aids in reducing regional disparities in the development of sustainable technologies. This study explores the influence of green finance on renewable energy technological innovation in China using empirical analysis, contributing to the theoretical framework and offering insights for policy-making.

While China’s unique policy environment and market mechanisms provide a strong basis for this study, they also limit the generalizability of the results. China’s diverse regional policy implementation further complicates direct application to other regions. Nevertheless, green finance remains a globally relevant tool for supporting renewable energy innovation, particularly in developing countries, where it can address financing constraints and drive technological progress.