The Effects of the Sci-Tech Finance Policy on Urban Green Technology Innovation: Evidence from 283 Cities in China

Abstract

To balance economic development with environmental sustainability and address the challenges posed by the new wave of technological innovation, China has focused on leveraging the synergistic effects of technology and finance. This approach aims to promote urban green technology innovation (UGTI), which is critical in achieving innovation-driven, high-quality development. This study draws on two phases of China’s “Promoting the Integration of Technology and Finance Pilot” policy, implemented in 2011 and 2016. It utilizes data from 283 cities from 2007 to 2021 and employs a multiple-period difference-in-differences (DID) model to examine the effect, mechanisms, and heterogeneity of science and technology (sci-tech) finance policies on UGTI. The results indicate that (1) sci-tech finance policies significantly foster UGTI. (2) The mechanism analysis reveals that sci-tech finance policies stimulate UGTI by enhancing the agglomeration of scientific and technological talent and factor allocation. (3) The heterogeneity analysis shows that sci-tech finance policies have a considerably greater effect on UGTI in eastern and non-resource-based cities than in western and resource-based cities. Furthermore, strengthening intellectual property protection, advancing digitalization, and implementing suitable financial regulations amplify the green innovation effects of the sci-tech finance policies.

1. Introduction

China, the most populous country and the world’s second-largest economy, faces significant sustainable development challenges, including resource shortages, environmental pollution, and climate change (Fan et al., 2021; Khan et al., 2021) [1,2]. In recent decades, China’s economy has grown rapidly, with its gross domestic product (GDP) increasing from CNY 367.9 billion in 1978 to CNY 11.4367 trillion in 2021, reflecting an average annual growth rate of 14.62%. However, this rapid growth has resulted in severe environmental challenges, such as air pollution, water scarcity, and ecosystem degradation, which now present major barriers to sustainable development. In response, the Chinese government has prioritized green development and incorporated it into the national strategy (Zhou et al., 2020) [3]. Consequently, several policy objectives have been introduced, including “green development”, “innovation-driven growth”, and “carbon peak and carbon neutrality”, to promote the balanced growth of the economy and the environment.

Green innovation offers dual benefits for the economy and the environment (You and Zhang, 2024) [4]. It not only drives green development but also acts as a key enabler of the green transformation of the economy (Du et al., 2021) [5]. Green technology innovation is distinct from traditional forms of innovation. It is grounded in ecological economics and promotes technological progress through resource conservation to reduce environmental pollution (Huang and Li, 2017) [6]. Many scholars argue that green technology innovation fosters environmental sustainability by optimizing energy systems (Mahmood et al., 2022) [7], enhancing resource efficiency (Miao et al., 2017) [8], and cutting greenhouse gas emissions (Abbasi and Zhang, 2024) [9]. As such, it plays an increasingly pivotal role in global climate governance. In particular, green technology innovation not only creates new economic growth opportunities for China during industrial upgrading and manufacturing transformation but also substantially boosts its international competitiveness (Hu et al., 2021) [10]. However, some studies indicate that China’s green technology innovation level remains comparatively low, with marked regional disparities (Tan et al., 2022) [11].

The urban innovation system is a crucial component in the national innovation system. To strengthen China’s green technology innovation, focusing on improving urban green technology innovation is essential (Dian et al., 2024) [12]. The improvement of urban green technology innovation (UGTI) is a complex systemic endeavor, with the provision of adequate financial support to innovation entities being a key prerequisite. Compared with general technological innovation, UGTI involves higher R&D costs and greater market uncertainty, making it more vulnerable to financial market frictions, thereby facing challenges in securing financing (Nie et al., 2024) [13]. The structural imbalance in the traditional financial system often leads to innovation-driven small and medium-sized enterprises encountering the so-called “Macmillan gap” in green technology innovation, preventing them from securing sufficient funding (Belas et al., 2024) [14]. This gap not only weakens the innovation drive of enterprises but also hinders the advancement of UGTI. In response to this issue, the “Recommendations of the Central Committee of the Communist Party of China on Formulating the 14th Five-Year Plan for National Economic and Social Development and Long-Term Goals for 2035” explicitly call for the improvement of the financial support innovation system and encourage financial institutions to develop financial products like intellectual property pledge financing and technology insurance. Therefore, promoting the deep integration of technology and finance is likely to be a critical breakthrough in addressing the low levels of UGTI in China.

Science and technology (sci-tech) finance refers to a collection of financial instruments specifically created to foster technological innovation (Sheng et al., 2024) [15]. It channels financial resources into the technology sector, supporting the entire process of technological development, from inception to commercialization (Zou et al., 2022) [16]. The ongoing advancement of sci-tech finance has been pivotal in China’s shift from a factor-driven to an innovation-driven growth model. To promote the implementation of the innovation-driven development strategy and effectively direct financial resources toward the technological innovation sector, the Ministry of Science and Technology, the People’s Bank of China, and five other departments organized expert reviews and issued a notice titled “Determination of the First Batch of Pilot Regions for Technology–Finance Integration”, designating 41 cities, including Tianjin and Shanghai, as the first pilot areas. The second batch of pilot cities was announced in 2016, with nine cities selected, including Zhengzhou, Xiamen, Ningbo, Jinan, and Nanchang. Fifty pilot cities promoting technology–finance integration had been established nationwide by 2021. We use the ArcMap 10.4v software to illustrated the pilot cities of sci-tech financial policies, as shown in Figure 1.

Existing research on sci-tech finance primarily examines its effects on industrial upgrading (Ren et al., 2023) [17], productivity (Gao et al., 2023) [18], and regional innovation (George and Ganesh, 2003) [19]. Additionally, some scholars assess the role of fintech in driving technological innovation (Li et al., 2023) [20]. They emphasize that these policies can effectively boost innovation by alleviating corporate financing constraints (Chen and Yoon, 2022) [21], improving the local financial efficiency, and increasing the government expenditure on technology (Fan et al., 2024) [22]. In recent years, the focus of research has shifted toward the green effect of technology (tech) finance. Scholars have particularly explored how fintech contributes to reducing carbon emissions (Lu et al., 2022) [23], enhancing energy efficiency (Teng and Shen, 2023) [24], promoting green economic growth (Zhou et al., 2023) [25], and improving corporate environmental disclosures (Zhao et al., 2024) [26].

Fintech is widely recognized as a critical tool in driving traditional innovation, and an increasing number of scholars are exploring its role in green efficacy. Unlike traditional innovation, green technology innovation demands a fundamental shift in existing models, involves longer return cycles, and presents greater uncertainty. Thus, it requires higher risk tolerance from investors. Although sci-tech finance has been successful in traditional sectors, its role in advancing UGTI remains underexplored, offering a unique opportunity for this study. This study seeks to find answers to several key questions: Have the sci-tech finance pilot policies integrating tech finance effectively improved the level of green technology innovation in the pilot cities? Specifically, through which mechanisms have these policies fostered UGTI? Selecting 283 cities across China as the sample, we applied a multiple-period difference-in-differences (DID) model to assess the impact of the sci-tech finance pilot policies on UGTI, while exploring the underlying mechanisms through a mediation effect model. This study also examines the policies’ heterogeneous effects across various contexts. Understanding the impact and mechanisms of the sci-tech finance pilot policies on UGTI in China would not only offer theoretical support for the government to develop more targeted sci-tech finance tools for sustainable development, but could boost the comprehension of the relationship between sci-tech finance policies and UGTI in developing countries, while providing valuable insights for other nations in implementing similar policies.

The marginal contributions of this study are demonstrated in the following aspects. First, this study provides new empirical evidence of the effectiveness of sci-tech finance integration policies in promoting UGTI, further expanding the application of finance in sustainable development research. Second, this study investigates the specific mechanisms through which sci-tech finance policies affect UGTI. It focuses on two dimensions, technological talent concentration and innovation resource allocation, thereby clarifying the key role of finance in driving sustainable development. Finally, this study analyzes the differentiated effects of sci-tech finance policies across cities from perspectives such as the spatial location, urban attributes, intellectual property protection, financial regulation intensity, and digitalization levels. The analysis offers an accurate depiction of the policy mechanisms and provides precise recommendations for the future development of sci-tech finance.

The remainder of this paper is structured as follows. Section 2 presents the theoretical analysis and research hypotheses. Section 3 describes the methods and data sources. Section 4 presents and discusses our empirical results. Finally, Section 5 draws some conclusions and implications from this study.

2. Theoretical Analysis and Hypothesis Formulation

2.1. Sci-Tech Finance Policy and UGTI

Innovation systems theory proposes that innovation is a result of the collective efforts and cooperation of certain key entities, including businesses, research institutions, government agencies, and financial organizations. Groundbreaking research by Freeman (1987) [27] emphasized the central role of national institutions in driving technological innovation. In addition, a fundamental aspect of this theory maintains that the effectiveness of innovation policies hinges on their ability to facilitate and enhance collaboration among diverse stakeholders within an innovation ecosystem. Sci-tech finance refers to the use of various financial instruments, policies, and services to support technological R&D, the commercialization of innovation, and the growth of high-tech industries (Zhang et al., 2018; Zhao et al., 2023) [28,29]. It is an essential part of the national innovation and financial systems. Financial capital plays a crucial role by providing the necessary resources for sustainable UGTI (Tian et al., 2021) [30].

By offering various financing options, sci-tech finance lowers the funding barrier for environmental technology R&D, easing the limitations faced by green innovation enterprises under traditional financing methods. In the early stages of innovation, green technology development faces challenges, including long research cycles, high risks, and capital intensity, which make financing particularly challenging (Wu and Kung, 2020) [31]. By using innovative financial instruments like green loans, equity investments, and green bonds, sci-tech finance offers customized financial services to enterprises, thereby fostering the growth and sustainability of green technology innovation (Wang et al., 2022; Irfan et al., 2022) [32,33].

Green technology innovation frequently encounters various risks, including technical, market, and policy challenges (Roper and Tapinos, 2016) [34]. Sci-tech finance policies implement several risk mitigation strategies, such as government subsidies, loan guarantees, and venture capital, to help enterprises and investors to manage these risks and reduce the chances of failure during the innovation process (Shao and Wang, 2023) [35]. These measures not only increase enterprise confidence but also motivate more investors and companies to actively engage in the innovation and implementation of green technologies.

Moreover, marketization is crucial for the widespread adoption and promotion of green technologies (Zeng et al., 2021) [36]. Only with market support can green technologies be widely disseminated, thus facilitating the transition to a green, low-carbon economy. Sci-tech finance policies offer market incentives to encourage enterprises to incorporate green technologies into production and operations. Additionally, the government accelerates the commercialization of green technologies by establishing green innovation awards, technology transfer funds, and other initiatives, thereby facilitating their swift implementation in the market. To summarize, through the application of varied financial tools and policy frameworks, sci-tech finance ensures vital financial support for green technology innovation. It also advances the research, commercialization, and adoption of green technologies by addressing risks and offering market incentives, thus strengthening UGTI. Based on the analysis above, we present the following assumption.

Hypothesis 1.

Sci-tech finance policy will promote UGTI.

2.2. Mechanism of Sci-Tech Finance Policy in UGTI

2.2.1. Agglomeration of Scientific and Technological Talent

Based on endogenous growth theory, human capital is a key driver of technological progress. Technological progress plays a significant role in achieving energy saving and environmental protection objectives. Scientific and technological talent plays a critical role in driving knowledge creation and technological innovation (Liu et al., 2024) [37]. Sustainable urban innovation depends not only on financial backing but also on attracting specialized talent, integrating advanced technologies, and expanding the market reach. Sci-tech finance policies are intricately linked with talent recruitment strategies. In policy formulation, the government prioritizes addressing the demand for high-end talent from technological entities by implementing various measures to attract and retain top talent. These measures encompass talent recruitment incentives, innovation project support, and tax breaks, which are designed to foster a supportive innovation environment and encourage fruitful collaboration between tech enterprises and scientific talent (Chen et al., 2023) [38]. Additionally, sci-tech finance fosters the seamless integration of the innovation funding and financial capital chains and provides comprehensive financial support to technology enterprises at every stage—from product and technology incubation to the commercialization of the results. These strategies effectively draw tech talent into entrepreneurial ventures (Shi et al., 2023) [39].

The concentration of tech talent improves a company’s production technology and management methods, reducing the environmental impact of resource consumption throughout the production and sales process (Zhang et al., 2024) [40]. Consequently, it promotes the greening of the entire production-to-sales chain and advances green innovation. Furthermore, a company’s high-tech talent pool directly influences its capacity for green innovation. A robust talent reserve not only enhances a company’s R&D and innovation capabilities (Yan and Fan, 2024) [41] but also mitigates the potential risks in green technology development. It increases the company’s willingness to adopt green innovation (Yang et al., 2022) [42] and further elevates the regional green innovation levels. Based on the analysis above, sci-tech finance policies attract and develop top-tier technological talent, stimulate technological innovation, and foster market growth, thereby offering robust support for green innovation. We present the following assumption.

Hypothesis 2.

The agglomeration of scientific and technological talent is an effective intermediary mechanism by which the sci-tech finance policy improves UGTI.

2.2.2. Factor Allocation

Sci-tech finance, through the establishment of a robust review mechanism, enhances information disclosure by tech enterprises, reducing information asymmetry between external investors and innovative companies. As a result, it improves the resource allocation efficiency (Luo et al., 2022) [43], allowing more green innovation projects to secure financial backing. Additionally, the development of sci-tech finance directs funds toward environmentally conscious enterprises, fostering the research, development, and commercialization of low-carbon technologies (Qian et al., 2017) [44]. This supports cities under pressure due to economic development and industrial transformation by providing technological and industrial backing, helping them to achieve their “dual carbon” goals.

Microbanking theory suggests that information asymmetry between banks and enterprises leads to moral hazards and adverse selection, resulting in credit rationing by banks (Moro et al., 2015) [45]. Sci-tech finance enhances the transparency and credibility of tech enterprises by establishing service platforms and improving their credit systems, thereby reducing information asymmetry (Huang, 2022) [46]. Consequently, sci-tech finance offers accurate risk assessments, fosters trust between enterprises and investors, and promotes optimal resource allocation. Moreover, the expert database established by sci-tech finance provides professional assessments in areas like technology, market potential, and feasibility for enterprises, while offering banks objective, scientific risk evaluation criteria to help identify and select high-quality green innovation projects.

Previous studies indicate that resource misallocation hampers innovation productivity in China (Zhang et al., 2020) [47]. By contrast, optimizing resource allocation enhances enterprise value chains and fosters high-quality economic development. Resource allocation promotes regional specialization and collaboration, driving the development of new clean energy and low-carbon technologies, which in turn generates positive external effects on the green innovation efficiency. Specifically, resource allocation reduces regional innovation disparities, fosters connections among green innovation entities, facilitates the precise matching of technological elements, and improves the research and decision-making efficiency, thereby driving the upgrading and transformation of the green innovation chain (Che et al., 2024) [48]. To summarize, sci-tech finance, through the implementation of information review systems and service platforms, successfully mitigates information asymmetry, improves resource allocation, and facilitates financing for green innovation projects and low-carbon technology development, thus fostering UGTI. Based on the analysis above, we present the following assumption.

Hypothesis 3.

Improving factor allocation is an effective intermediary mechanism by which the sci-tech finance policy improves UGTI.

3. Methods and Data Sources

3.1. Model Construction

To test the research topic, this study adopts the multiple-period difference-in-differences (DID) model, a method widely used to evaluate policy effectiveness (Kesidou and Wu, 2020) [49]. Specifically, the DID model assesses the net effect of policy shocks by comparing the differences between the treatment and control groups. Cities that implemented the pilot program for sci-tech finance policies are classified as the treatment group, and those that did not are classified as the control group. Given that China launched the “Pilot Program for Promoting the Integration of Science, Technology, and Finance” in 2011 and 2016, this study uses the method of Beck et al. (2010) [50] to develop a multi-period DID model for policy evaluation:

$$UGT{I}_{it}={\alpha }_0+{\alpha }_{1}ST\_financ{e}_{it}+{\alpha }_i\ln X_{it}+{\delta }_i+{\theta }_t+{\epsilon }_{it}$$

(1)

Here, i denotes the city and t denotes the year. UGTI is denoted by urban green technology innovation, and ST_finance is a binary indicator for the sci-tech finance policy. Specifically, if city i implements the policy in year t, ST_finance takes the value of 1; otherwise, it is 0. X represents a series of control variables, including the level of economic development (PGDP), the degree of openness (OPEN), environmental regulation (ER), the education level (EDU), and government support (GOV). We used the Box–Cox test to determine whether logarithmic transformation was needed. Based on the Box–Cox test, we identified the optimal transformation parameter (λ) as 0.059. This value, being close to 0, suggests that a logarithmic transformation is the most suitable option. Therefore, the variables were log-transformed to reduce the interference caused by dispersion and heteroscedasticity. ${\delta }_i$ and ${\theta }_t$ denote the time and city fixed effects, respectively, and ${\epsilon }_{it}$ represents the random error term.

Building on Jiang’s (2022) [51] recommendations for the identification of mechanism variables, this study explores the effect of sci-tech finance policies on UGTI, while considering the effects of talent aggregation and resource allocation. The analysis focuses solely on the direct influence of these policies on the mechanism variables, which enhances the reliability of the model’s identification. The mechanism model is presented in Equation (2).

$$MI{D}_{it}={\alpha }_0+{\alpha }_{1}ST\_financ{e}_{it}+{\alpha }_i\ln X_{it}+{\delta }_i+{\theta }_t+{\epsilon }_{it}$$

(2)

Here, MID represents the mediating variables, including the aggregation of scientific talent and factor allocation. Other variables are consistent with those in Equation (1).

3.2. Variable Definition

3.2.1. Independent Variable

We define ST_finance as the key explanatory variable that captures the implementation of sci-tech finance policies. Specifically, if city i implements the sci-tech finance policy in year t, ST_finance is set to 1; otherwise, it is 0. The sci-tech finance pilot program is implemented in two phases, with 50 cities selected in total. The list of the 41 cities, including Beijing and Tianjin, selected for the first phase of the pilot program was announced in 2011. The list of the nine cities, including Zhengzhou and Ningbo, for the second phase of the pilot program was announced in 2016.

3.2.2. Dependent Variable

In green innovation research, scholars typically assess the level of green innovation at the city scale through the green innovation efficiency (Liao and Li, 2023) [52] and the number of green patents (Li et al., 2022) [53]. Compared with the green innovation efficiency, the number of green patents provides a more direct reflection of a city’s innovation capacity. Furthermore, when classified according to the patent application process, the number of green patents can be divided into green patent applications and green patent grants. Given that patent grants typically involve a time lag of one to two years, they are ineffective in reflecting the current innovation capacity of the innovators. Additionally, patents granted may have already influenced the economy during the application process. Therefore, the number of green patent applications is more suitable for practical evaluation. However, relying solely on patent application numbers to measure innovation levels fails to account for variations in human resources among cities, which introduces limitations. To address this issue, this study employs the indicator of green invention patent applications per 10,000 people to more accurately assess the UGTI.

3.2.3. Mediating Variable

The agglomeration of scientific and technological talent (AST) is measured by the proportion of the workforce engaged in sectors such as scientific research, technical services, geological exploration, information transmission, and computer services and software industries, relative to the total population of each city.

“Factor allocation” refers to the adjustment and distribution of production factors within an industrial economy. Drawing on the methods of Liu and Xia (2023) [54], we use the resource allocation efficiency to assess the factor allocation efficiency of each city. The calculation procedure is as follows.

First, the production function approach is employed to assess the extent of factor market distortion in each city. The procedure is as follows.

The Cobb–Douglas production function is formulated, and the relevant variables are then subjected to a logarithmic transformation, yielding the following expression:

$$\ln Y_{it}=c+\alpha \ln K_{it}+\beta \ln L_{it}+{\epsilon }_{it}$$

(3)

where Y denotes the output, measured by the regional GDP; K represents the capital stock, derived from fixed asset investments using the perpetual inventory method with a 9.6% depreciation rate; and L indicates labor input, represented by the number of employed individuals in each city.

The marginal products of labor (MP_L) and capital (MP_K) are calculated, yielding the following results:

$$M{P}_L={\displaystyle \frac{\alpha Y_{it}}{L_{it}}};M{P}_K={\displaystyle \frac{\beta Y_{it}}{K_{it}}}$$

(4)

The absolute distortion coefficients of capital (distK) and labor (distL) are calculated based on the deviation between their marginal products and corresponding prices, yielding the following results:

$$dist{K}_{it}=|{\displaystyle \frac{\alpha Y_{it}}{r_{it}{K}_{it}-1}}|;dist{L}_{it}=|{\displaystyle \frac{\beta Y_{it}}{w_{it}{L}_{it}-1}}|$$

(5)

The overall distortion coefficient (dist) is derived by combining the degrees of capital and labor distortion, as shown in the following formula:

$$dis{t}_{it}={{\mathit{distK}}_{\mathit{it}}}^{\frac{\alpha }{\alpha +\beta }}{{\mathit{distL}}_{\mathit{it}}}^{\frac{\beta }{\alpha +\beta }}$$

(6)

Second, the degree of factor allocation (FA) in each city is measured by calculating the ratio of each city’s dist_it value to the maximum dist_it value among all cities for that year.

$$F{A}_{it}={\displaystyle \frac{dis{t}_{it}}{Max(dis{t}_{it})}}$$

(7)

3.2.4. Control Variables

This study integrates existing research findings and introduces five control variables into the econometric model to address the endogeneity issue caused by the omission of key explanatory variables, thereby improving the estimation accuracy. The environmental Kuznets curve posits a strong relationship between economic development and pollutant emissions. In this study, the GDP per capita (PGDP) serves as an indicator of economic development. The ratio of foreign direct investment to the regional GDP is used to assess the level of openness (OPEN) (Wang et al., 2021) [55]. Moderate environmental regulations can encourage firms to invest more in environmental technology R&D. Therefore, environmental regulation (ER) is quantified by the ratio of environmental governance investment to the GDP (Zhao and Sun, 2016) [56]. Furthermore, as educational levels increase, environmental awareness improves, resulting in higher demands for energy conservation and emission reduction. This, in turn, pressures the government to enhance its regulations and motivates firms to invest in green technology. To assess regional education levels, this study employs the average number of college students per 100,000 people (EDU). Moreover, the government can provide subsidies for clean energy research and promote innovation in low-carbon technologies to encourage environmental investments. The level of government support for green innovation is quantified by the ratio of fiscal expenditure to total government spending (GOV).

3.3. Data Source and Sample Selection

Given the data availability, this study analyzes a sample of 283 cities from 2007 to 2021. The city-level green patent data are obtained from the Green Innovation Patent Database of the China National Research Data Service. Information on pilot cities for sci-tech finance policies is derived from a review of relevant policy documents. The data involved in the calculation of control and mediating variables are derived from the China Urban Statistical Yearbook, China Regional Economic Statistical Yearbook, and annual statistical yearbooks and bulletins at the provincial and municipal levels. Missing values are handled through linear interpolation. The descriptive statistics of the variables are shown in

Table 1. Statistical descriptions of variables.

Variable	Measurement	Obs.	Mean	S.D.	Min	Max
UGTI	Urban green technology innovation	4245	4.489	1.749	0	10.372
ST_finance	Sci-tech finance pilot policies	4245	0.086	0.280	0	1
AST	Agglomeration of scientific and technological talent	4245	0.028	0.019	0.005	0.213
FA	Factor allocation	4245	0.329	0.154	0.004	1
PGDP	Level of economic development	4245	10.535	0.679	4.595	13.055
ER	Environmental regulation	4245	−5.004	1.430	−14.944	−1.822
OPEN	Level of opening up	4245	−0.213	0.510	−2.586	2.008
EDU	Level of education	4245	5.809	1.035	−4.605	9.359
GOV	Government support	4245	−4.527	0.850	−7.472	−1.575

.

4. Empirical Results

4.1. Baseline Regression

Table 2. Baseline regression results.

Variable	UGTI	UGTI	UGTI	UGTI
	(1)	(2)	(3)	(4)
ST_finance	2.053 *** (24.23)	0.411 *** (4.93)	0.164 ** (2.06)	0.145 ** (2.04)
PGDP		1.663 *** (17.60)		0.386 *** (5.21)
ER		0.078 *** (3.36)		0.036 ** (1.99)
OPEN		1.008 *** (9.99)		0.117 ** (2.31)
EDU		0.030 (1.54)		0.006 (0.37)
GOV		0.103 *** (2.97)		0.191 *** (7.54)
_cons	4.312 *** (590.65)	−12.172 *** (−10.86)	2.783 *** (80.87)	0.102 (0.13)
Year FE	No	No	Yes	Yes
Region FE	No	No	Yes	Yes
R2	0.093	0.808	0.861	0.872
Observations	4245	4245	4245	4245

Note: ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively, and T-values are reported in parentheses.

presents the regression results regarding the effect of sci-tech finance pilot policies on UGTI. Column (1) displays the univariate regression for the policy variable ST_finance, with an estimated coefficient of 2.053, significant at the 1% level. In addition, Columns (2) and (3) report the regression results with control variables and fixed effects, showing estimated coefficients of 0.411 and 0.164, respectively, both significant at the 5% level. Column (4) reports a correlation coefficient of 0.145 between ST_finance and UGTI, significant at the 5% level. In comparison to non-ST_finance pilot areas, the sci-tech finance policy significantly boosts UGTI in the pilot areas. Hypothesis 1 is validated.

Among the control variables, PGDP significantly and positively affects UGTI, with this effect validated at the 1% significance level. Higher economic development levels typically mean the greater input of resources and funds for technological innovation, which can also foster the development and deployment of green technologies, thereby enhancing UGTI. Similarly, OPEN significantly and positively affects UGTI by facilitating the spillover of advanced technologies and innovative ideas from foreign businesses. Higher openness can attract foreign investment, foster the development of the modern industrial system, and increase the likelihood of the adoption of foreign technology, finally driving green transformation in the manufacturing industry and promoting UGTI growth (Jiang et al., 2024) [57]. The positive coefficient of ER, significant at the 5% level, suggests that moderate regulation could drive companies to implement more environmentally friendly production methods and encourage more investment in green technology development (Wang et al., 2024) [58]. GOV fosters UGTI by funding the development of new green technologies, improving the existing ones, and facilitating their commercialization, thus accelerating UGTI’s growth. EDU correlates positively with UGTI, but this relationship is not statistically significant. Although improvements in education may produce highly skilled talent, it takes time to translate this talent advantage into green innovation.

4.2. Robustness Test

4.2.1. Parallel Trend Test

To accurately reflect the effect of policy shocks on UGTI, the baseline regression model does not require the strict exogeneity of policy shocks. However, the treatment and control groups must show similar trends in UGTI prior to the policy’s implementation. Drawing on the study by Lyu et al. (2023) [59], we incorporate the interaction between the policy implementation time dummy and the policy dummy in the model. Additionally, we use the year prior to the policy’s implementation as the reference period to avoid multicollinearity issues. The model is specified as follows:

$$UGT{I}_{it}={\beta }_0+{\displaystyle \sum _{k=-4,k\ne -1}^{k=3}{\beta }_k}ST\_financ{e}_{it}^l+\gamma \ln X_{it}+{\lambda }_i+{\theta }_t+{\epsilon }_{it}$$

(8)

where k represents the k-th year after the implementation of the sci-tech finance pilot policy. To address the potential multicollinearity in the model, we set the year before the policy’s implementation (k = −1) as the baseline but exclude its dummy variable from the regression analysis. β_k is a key parameter to indicate the difference in green technological innovation between the treatment and control groups in the k-th year after the policy’s implementation. When k < 0, if β_k is not significantly different from zero, it would support the common trend assumption; otherwise (i.e., β_k differs significantly from zero), it suggests that the assumption is violated.

The results of the parallel trend test are shown in Figure 2. Prior to the implementation of the sci-tech finance policy, the coefficient of the binary variable is zero within the 95% confidence interval and is not statistically significant. This indicates that, before the policy was implemented, the treatment and control groups followed common trends, with no systematic differences between them. After the policy’s implementation, the dynamic effects show a clear upward trend, suggesting that the sci-tech finance policy has a positive impact on UGTI, thus confirming the robustness of the baseline results.

4.2.2. Placebo Test

Considering the potential influence of unobservable variables on the model, we randomly assign 50 cities to the treatment group for the sci-tech finance pilot policy, with the remaining cities serving as the control group (Zeldow and Hatfield, 2021) [60]. We randomly re-assign the treatment and control groups 1000 times, creating 1000 policy intervention combinations to construct counterfactual virtual policy variables for a placebo test. We then create a kernel density plot of the coefficients from 1000 baseline regressions (Figure 3). The plot indicates that most regression coefficients cluster around 0, with none exceeding 0.1, which contrasts sharply with the coefficient of ST_finance (0.145) in the baseline regression. This finding suggests that random factors minimally influence the policy’s effect on UGTI, further validating the robustness of the baseline results.

4.2.3. Endogeneity Treatment: Propensity Score Matching (PSM)–DID

To address potential selection bias, we conduct a robustness check using PSM-DID (Ma et al., 2024) [61]. Specifically, we first use PSM to match the treatment group with appropriate control group individuals. We use all control variables as matching criteria and apply nearest-neighbor matching. Figure 4 illustrates the changes in the standardized bias for each covariate before and after matching. As can be seen, significant differences exist between the covariates before matching; however, the standardized bias decreases significantly and approaches zero after matching. This suggests that the matching process has effectively reduced the characteristic differences between the two groups. In sum, favorable matching results are acquired, indicating that the PSM-DID estimation method is highly applicable.

Following the matching, we re-estimate the model, yielding 2560 valid observations. The relevant results are presented in Column (1) of

Table 3. Robustness tests.

Variable	PSM-DID	Changing the Dependent Variable	Excluding Policy Interference			Excluding Municipalities and Provincial Capitals
	(1)	(2)	(3)	(4)	(5)	(6)
ST_finance	0.150 *** (3.54)	0.209 *** (4.81)	0.163 *** (3.59)	0.149 *** (3.43)	0.148 *** (3.44)	0.201 *** (3.80)
Policy 1			−0.043 (−1.24)
Policy 2				−0.027 (−1.02)
Policy 3					0.065 ** (2.18)
PGDP	0.386 *** (9.38)	0.315 *** (7.67)	0.385 *** (9.41)	0.387 *** (9.47)	0.401 *** (9.68)	0.385 *** (8.80)
ER	0.031 *** (2.87)	0.016 (1.47)	0.035 *** (3.30)	0.035 *** (3.26)	0.035 *** (3.23)	0.036 *** (3.08)
OPEN	0.096 *** (2.77)	0.082 ** (2.40)	0.120 *** (3.50)	0.118 *** (3.44)	0.114 *** (3.34)	0.119 *** (3.12)
EDU	0.003 (0.31)	0.013 (1.18)	0.006 (0.55)	0.006 (0.62)	0.007 (0.65)	0.006 (0.57)
GOV	0.185 *** (10.87)	0.154 *** (9.64)	0.192 *** (12.05)	0.191 *** (12.03)	0.191 *** (12.02)	0.197 *** (11.67)
_cons	0.091 (0.21)	0.066 (0.16)	0.120 (0.28)	0.089 (0.21)	−0.056 (−0.13)	−0.054 (−0.12)
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Region FE	Yes	Yes	Yes	Yes	Yes	Yes
R2	0.875	0.874	0.872	0.872	0.873	0.866
N	2560	4245	4245	4245	4245	3795

Note: ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively, and T-values are reported in parentheses.

. The results indicate a significant and positive coefficient for the sci-tech finance policy, further validating the robustness of our conclusions.

4.2.4. Changing the Dependent Variable

We use the number of granted green patents as a substitute for the dependent variable and re-estimate the regression model. The corresponding results are provided in Column (2) of Table 3. The results indicate that the coefficient for ST_finance is 0.209, which is statistically significant at the 1% level. This finding further strengthens the robustness of the baseline regression results.

4.2.5. Excluding Policy Interference

UGTI may be influenced not only by sci-tech finance policies but also by other concurrent policies (Li et al., 2022) [53]. To accurately evaluate the effect of sci-tech finance policies on UGTI, we account for and control the effects of other concurrent policies, such as the “Innovative City Pilot”, “Smart City”, and “Broadband China Pilot” policies introduced around 2010. The regression results, accounting for the “Innovative City”, “Smart City”, and “Broadband China” policies, are presented in Columns (3), (4), and (5) of Table 3. The results further confirm the robustness of the initial model results, because they indicate that, even after the effects of these policies are measured, the coefficient of ST_finance still shows little change and remains significant.

4.2.6. Excluding Municipalities and Provincial Capitals

We removed municipalities directly under the central government and provincial capital cities from the sample and reran the regression, and the results are presented in Column (6) of Table 3. These cities tend to outperform others in resource endowment, economic development, and technological advancement and inherently attract innovation resources, which may result in the overestimation of the green innovation effects of regional sci-tech finance policies. Consequently, these samples were excluded from the analysis. The results indicate that, at the 1% significance level, the coefficient for the science and technology finance policy is significant and positive, further supporting the robustness of the research conclusions.

4.3. Mechanism Analysis

This study adopts the approach of Jiang (2022) [51] to address potential endogeneity bias, introducing two key mediators: the aggregation of technological talent and resource allocation. This study clarifies the causal links between the core explanatory variables and the mediators and explores the pathways through which the sci-tech finance pilot policy drives UGTI. The results are presented in

Table 4. Mechanism analysis.

Variable	AST	FA
	(1)	(2)
ST_finance	0.008 *** (9.78)	0.504 *** (7.68)
PGDP	−0.005 *** (−5.73)	−0.135 * (−1.88)
ER	−0.000 (−1.44)	−0.018 (−1.12)
OPEN	−0.001 ** (−2.48)	−0.147 *** (−2.71)
EDU	−0.004 (−0.42)	0.046 *** (2.69)
GOV	0.017 (1.05)	0.088 *** (3.59)
_cons	0.076 *** (8.20)	1.870 ** (2.52)
Year FE	Yes	Yes
Region FE	Yes	Yes
R2	0.087	0.382
Observations	4245	4245

Note: ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively, and T-values are reported in parentheses.

.

According to the regression results in Column (1) of Table 4, the coefficient for ST_finance on the AST is 0.008, significant at the 1% level, showing that the sci-tech finance policy positively affects the concentration of technological talent. The sci-tech finance policy facilitates high-quality innovation finance services for technology-based small and medium-sized enterprises, stimulating regional technological innovation and attracting a large flow of talent to the pilot regions (Xie, 2022) [62]. The influx of skilled talent enables companies to improve their production technologies and management practices, while also mitigating the risks associated with the development of green technologies. This confirms Hypothesis 2.

According to the regression results in Column (2) of Table 4, the coefficient for ST_finance on FA is 0.504, significant at the 1% level, indicating that the sci-tech finance policy optimizes factor allocation. The sci-tech finance policy supports technology-based enterprises through direct or indirect financing, credit, and risk-sharing, while directing resources to industries, livelihoods, green environmental protection, and clean energy, effectively reducing resource misallocation (Xie and Zhu, 2022; Ang et al., 2024) [63,64]. Hypothesis 3 is verified. By aligning technological components effectively, sci-tech finance policies improve the R&D and decision-making efficiency, thereby driving the upgrading and transformation of the green innovation chain (Huang et al., 2023) [65].

4.4. Heterogeneity Analysis

4.4.1. Heterogeneity of City Locations

The effect of ST_finance policies on UGTI varies across regions due to differences in their economic development levels and resource endowment. To examine these regional differences, this study divides the sample into three regions, namely eastern, central, and western, and conducts heterogeneity tests. The results are shown in

Table 5. Heterogeneity regression results for city locations.

Variable	Eastern	Central	Western
	(1)	(2)	(3)
ST_finance	0.215 *** (4.28)	0.399 *** (4.39)	−0.097 (−0.82)
PGDP	0.457 *** (6.64)	0.607 *** (6.38)	−0.173 (−1.57)
ER	0.052 *** (3.60)	−0.013 (−0.72)	0.012 (0.49)
OPEN	0.116 ** (2.32)	0.106 * (1.67)	0.006 (0.08)
EDU	−0.009 (−0.57)	−0.023 (−0.97)	0.031 (1.57)
GOV	0.184 *** (7.58)	0.302 *** (10.25)	0.091 ** (2.55)
_cons	−0.058 (−0.08)	−1.581 (−1.61)	3.945 *** (3.74)
Year FE	Yes	Yes	Yes
Region FE	Yes	Yes	Yes
R2	0.879	0.905	0.863
Observations	1785	1200	1260

Note: ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively, and T-values are reported in parentheses.

. In the eastern and central regions, the coefficients of ST_finance are 0.215 and 0.399, respectively, both passing the significance test at the 1% level. By contrast, the coefficient of ST_finance in the western region is −0.097, which fails the significance test. This finding suggests that the sci-tech finance policies in the eastern and central regions have a stronger stimulating effect on UGTI than those in the western region.

The reason is that the eastern and central regions have more developed economies, higher local fiscal capacities, and better allocation efficiency, which provide more favorable conditions for the implementation of sci-tech finance policies. Conversely, the western region’s economic development is relatively underdeveloped, characterized by simple industrial structures and limited high-tech industry foundations; this pattern undermines the effectiveness of technology finance policies, which typically depend on a well-established capital market as well as venture capital mechanisms. In addition, in the western region, the capital market is underdeveloped: limited financing channels have hindered the financial support for and risk-bearing capacity of green innovation projects.

4.4.2. Heterogeneity of City Endowment

The innovation environment in a region is closely tied to its natural resource endowment, and the level of reliance on these resources can influence the effectiveness of pilot policies. In this context, this study examines whether a city is classified as a resource-based city. Based on the list of resource-based cities in the “National Sustainable Development Plan for Resource-Based Cities” issued by the State Council, this study classifies 283 cities into 113 resource-based cities and 170 non-resource-based cities for analysis. The regression results are summarized in

Table 6. Heterogeneity regression results for city endowment.

Variable	Resource	Non-Resource
	(1)	(2)
ST_finance	0.127 (1.06)	0.110 ** (2.43)
PGDP	0.579 *** (7.23)	0.291 *** (4.82)
ER	0.056 *** (3.09)	0.025 * (1.86)
OPEN	0.235 *** (3.75)	0.073 (1.65)
EDU	0.010 (0.45)	0.007 (0.56)
GOV	0.183 *** (6.80)	0.185 *** (8.88)
_cons	−2.084 ** (−2.53)	1.186 * (1.90)
Year FE	Yes	Yes
Region FE	Yes	Yes
R2	0.846	0.891
Observations	1695	2550

Note: ***, **, and * indicate significance levels of 1%, 5%, and 10% respectively, and T-values are reported in parentheses.

. The results indicate that the coefficient of ST_finance is positive in the non-resource-based city group and is statistically significant at the 5% level, but it fails to meet the significance threshold in the resource-based city group.

“Resource curse” theory suggests that resource-based cities’ economies heavily rely on resource-intensive industries, which can lead to the “Dutch disease” phenomenon, crowding out the demand for human capital and innovation. Specifically, resource-based industries often show diminishing or constant returns to scale, with a low demand for new technologies and skilled human capital, leading to limited innovation. This phenomenon results in insufficient investment in the sci-tech finance sector, ultimately limiting the effectiveness of policies aimed at promoting green innovation.

The primary reason is that resource-based cities’ economies are heavily dependent on natural resources, with their industrial structures dominated by resource-intensive industries that are energy intensive and highly polluting. Abundant natural resources provide these cities with a stable income, often diverting talent and capital from green technology innovation to resource extraction, thus crowding out innovation resources. Resource-based cities often have policy systems and local government management structures that rely heavily on the development of resource industries. This institutional inertia could retard policy adjustments and the adoption of innovation. Moreover, resource-based cities have fewer technology-driven enterprises and limited technological resources, with dominant industries typically lacking the drive for innovation, resulting in a weak demand for technological advancement (Zhang and Cui, 2020) [66]. This creates significant challenges for resource-based cities in promoting green innovation and implementing sci-tech finance policies (Xie et al., 2020) [67], hindering the full realization of the policy effects. By contrast, non-resource-based cities are better equipped to meet the development demands of the “dual carbon” goal, leveraging industrial advantages and the support of sci-tech finance policies to promote renewable energy use and accelerate green technology innovation.

4.4.3. Heterogeneity of Intellectual Property Protection Intensity

Intellectual property protection plays a vital role in creating an environment conducive to innovation and driving technological progress (Luo and Zhao, 2024) [68]. Different levels of intellectual property protection can affect how effectively sci-tech finance policies promote green technological innovation. This study follows the method suggested by Shen et al. (2019) [69] and uses the indicator “(Number of intellectual property case closures in the city/City GDP)/(National number of intellectual property case closures/National GDP)” to assess the intellectual property protection levels across cities. We classify the sample cities into two groups—strong and weak protection—based on their intellectual property protection levels and analyze how sci-tech finance policies affect UGTI in each group. The regression results are shown in

Table 7. Heterogeneity regression results for intellectual property protection intensity.

Variable	Strong	Weak
	(1)	(2)
ST_finance	0.167 *** (2.65)	0.103 (1.62)
PGDP	0.421 *** (6.29)	0.411 *** (7.32)
ER	0.171 *** (4.08)	0.012 (0.94)
OPEN	0.096 ** (2.26)	0.093 (1.61)
EDU	0.004 (0.33)	−0.003 (−0.22)
GOV	0.173 *** (7.98)	0.231 *** (9.81)
_cons	0.172 (0.26)	0.021 (0.04)
Year FE	Yes	Yes
Region FE	Yes	Yes
R2	0.871	0.857
Observations	2228	2017

Note: ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively, and T-values are reported in parentheses.

. In cities with stronger intellectual property protection, the regression coefficient for ST_finance is 0.167, significant at the 1% level. By contrast, in cities with weaker intellectual property protection, the ST_finance coefficient is not statistically significant. This finding suggests that stronger intellectual property protection enhances the effectiveness of sci-tech finance policies in promoting UGTI.

A strong intellectual property protection system facilitates the transfer and sharing of green technologies across enterprises, speeding up their application and market adoption. Technology transfer typically requires financial support and policy incentives, and it can proceed more smoothly in an environment with strong intellectual property protection. However, in regions with weaker intellectual property protection, green technology inventions and innovations may fail to receive adequate legal protection. Innovators may fear that their technologies will be quickly imitated or stolen, preventing them from receiving the returns that they deserve. This concern reduces innovators’ motivation, especially in capital-intensive green technology sectors (He et al., 2023) [70], where high R&D costs and uncertain returns lead to diminished investment incentives.

4.4.4. Heterogeneity of Financial Supervision Intensity

Considering that differences in financial regulatory systems across countries and regions may affect the implementation of sci-tech finance policies, we further examine how these policies influence UGTI under varying intensities of financial regulation. Current research on financial regulation predominantly focuses on the provincial level. To assess the financial regulation level in each city, we weigh its relative financial development level. Specifically, the city’s financial regulation level is calculated as follows: “City Financial Regulation Level = Provincial Financial Regulation/(City Financial Development/Provincial Financial Development)”. Provincial financial regulation is based on the methodology of Tang (2020) [71], which uses the ratio of provincial financial regulatory expenditure to the added value of the financial sector. The financial development levels of the city and province are assessed using the ratio of year-end financial institution deposits and loans to the GDP.

We divide the total sample into three groups based on the 25th and 75th percentiles—low, medium, and high financial regulation—and conduct a regression analysis for each group. The regression results are shown in

Table 8. Heterogeneity regression results for financial supervision intensity.

Variable	Strong	Medium	Weak
	(1)	(2)	(3)
ST_finance	0.015 (0.13)	0.150 ** (2.32)	−0.065 (−0.56)
PGDP	0.411 *** (5.22)	0.389 *** (5.26)	0.410 *** (4.74)
ER	0.036 (1.55)	0.032 ** (1.98)	−0.018 (−0.68)
OPEN	0.106 (1.44)	0.099 * (1.91)	0.140 * (1.85)
EDU	0.021 (0.69)	−0.006 (−0.36)	−0.009 (−0.59)
GOV	0.187 *** (4.65)	0.192 *** (8.35)	0.106 *** (3.43)
_cons	−0.457 (−0.55)	0.180 (0.24)	−0.535 (−0.59)
Year FE	Yes	Yes	Yes
Region FE	Yes	Yes	Yes
R2	0.878	0.849	0.851
Observations	1056	2132	1057

Note: ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively, and T-values are reported in parentheses.

. When financial regulation is at a moderate level, the coefficient of ST_finance is 0.150, significant at the 5% level. However, at the low and high levels of financial regulation, the coefficient of ST_finance is not statistically significant. The effectiveness of financial regulation is preliminarily confirmed (Wang et al., 2024) [72], as it promotes corporate innovation through the optimal allocation of financial capital while ensuring financial stability. Insufficient financial regulation can result in the inadequate risk assessment and control of green innovation projects by financial institutions. As green innovation typically involves high uncertainty and long-term returns, the lack of an effective regulatory framework may cause financial institutions to be overly conservative when selecting projects. This may lead to a preference for traditional, low-risk, high-return sectors, neglecting the long-term investment and innovative risks crucial for green innovation.

Excessive financial regulation can negatively affect financing channels, structures, and costs (Mertzanis, 2020) [73]. Green innovation projects, especially early-stage entrepreneurial ventures or high-risk innovations, often require flexible and diverse funding. If the financial regulation is overly stringent, the approval procedures, process complexity, and capital requirements can become cumbersome or costly, limiting businesses’ ability to access the necessary funds. In such cases, the role of financial technology policies may be undermined, hindering the effective advancement of green innovation. Effective financial regulation enhances market transparency, optimizes the capital flow, and improves the capital allocation efficiency, thus providing stronger financial support and policy protection for green innovation.

4.4.5. Heterogeneity of Digital Infrastructure Level

A robust digital environment fosters collaboration among stakeholders, creating an ecosystem that integrates technological R&D, financial support, and market promotion, thereby accelerating green innovation (Nie et al., 2023) [74]. This study explores whether the incentive effects of sci-tech finance policies on UGTI differ in environments with varying levels of digitalization. The digitalization level of cities is measured by the ratio of Internet users at the year’s end to the total population of the region. The sample cities are classified into high- and low-digitalization groups based on the median level, with the regression results shown in

Table 9. Heterogeneity regression results for digital infrastructure level.

Variable	Strong	Weak
	(1)	(2)
ST_finance	0.252 ** (2.41)	0.056 (1.10)
PGDP	0.212 *** (3.03)	0.078 (1.27)
ER	0.024 (1.36)	0.039 *** (2.71)
FD	0.184 *** (3.18)	0.014 (0.30)
EDU	0.018 (1.09)	0.025 (1.58)
GOV	0.232 *** (7.94)	0.118 *** (6.05)
_cons	1.585 ** (2.24)	2.934 *** (4.55)
Year FE	Yes	Yes
Region FE	Yes	Yes
R2	0.821	0.851
Observations	2145	2100

Note: ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively, and T-values are reported in parentheses.

. The findings show that, in regions with higher digitalization (Column (1)), the regression coefficient of ST_finance is 0.252, significant at the 5% level. By contrast, in regions with lower digitalization, the coefficient of ST_finance is not statistically significant. Thus, higher digitalization levels enhance the incentive effects of sci-tech finance policies on UGTI.

In a digital environment, green innovation projects can more effectively access market information, technological expertise, and policy support. Digital infrastructure enhances capital market activity and liquidity while reducing the financing costs and improving the capital flow, particularly through digital currencies, online financing platforms, and blockchain technology. Additionally, leveraging big data and AI technologies enables banks, investors, and other financial institutions to assess the potential and risks of green innovation projects more accurately, improving the capital allocation efficiency (Fu and Guo, 2025) [75]. This allows financial institutions to offer more flexible, customized financial products for green innovation projects and support high-risk, high-return projects through intelligent risk-sharing mechanisms.

5. Conclusions and Implications

This study uses data from 283 cities in China between 2007 and 2021, employing tech-finance pilot policies as a quasi-experiment and applying the DID method to analyze the causal effects of sci-tech finance on UGTI. The main findings are as follows: (1) sci-tech finance pilot policies significantly advance UGTI; (2) sci-tech finance policies also boost UGTI by fostering scientific talent aggregation and optimizing resource allocation; and (3) the positive effects of such policies vary significantly across different spatial locations, city types, levels of intellectual property protection, financial regulations, and digitalization levels. Specifically, non-resource-based cities in eastern and central regions, with strong intellectual property protection, moderate financial regulation, and higher digitalization, are better positioned to leverage tech-finance policies to promote UGTI.

To further promote sci-tech finance policies and improve UGTI, the following policy recommendations are proposed.

First, the effect of sci-tech finance policies must be maximized to advance UGTI. The government should direct sci-tech finance funds to prioritize green R&D initiatives, such as the establishment of green technology special funds or offering guarantees for technology projects, to accelerate enterprises’ green transformation. Simultaneously, the government should continue to develop innovative financial service models and products to ensure that financial resources are strategically aligned with the green technology and clean energy sectors, thereby reinforcing green innovation’s role in urban sustainable development.

Second, the mechanism analysis indicates that sci-tech finance policies significantly drive UGTI through two primary channels: the concentration of scientific talent and resource allocation. On the one hand, the government should enhance the recruitment and support of scientific talent by implementing targeted policies, such as offering housing guarantees and social services like school enrollment for children, to foster UGTI. On the other hand, in order to reduce information asymmetry between financial institutions and enterprises, the government should further enhance technology information service platforms and expert databases, while encouraging financial institutions to actively identify and select promising green innovation projects.

Third, the government should adopt differentiated sci-tech finance support policies based on the resource types and regional characteristics of cities. In developed cities in the eastern and central regions, the government should encourage the further exploration of innovations in sci-tech finance products and services to support the development of advanced green technologies. In the western region of China or in certain less developed countries, the government should bolster its support for the development of sci-tech finance through policy adjustments and fiscal subsidies, so as to boost the sci-tech finance service system and foster the incubation and growth of green innovation projects. In resource-based cities, the government should boost the attraction of technology talent and enterprises by offering such favorable policies as tax incentives and financing support, so as to encourage more high-end tech talent and innovative enterprises to operate. Establishing a robust sci-tech finance ecosystem can promote the research, development, and application of green technologies more effectively, so as to provide crucial technical support to meet the carbon neutrality targets of China.

Fourth, the heterogeneity analysis reveals that the effect of sci-tech finance policies on the promotion of UGTI differs across cities with varying levels of intellectual property protection, financial regulation intensity, and digitalization. In cities with weaker intellectual property protection, the government should reinforce the institutional framework and improve the intellectual property safeguards to protect carbon-neutral technology innovations created by enterprises under sci-tech finance policy support. In so doing, the innovation efficiency fostered by sci-tech finance can be enhanced. Meanwhile, financial regulators should align the focus and intensity of regulation with the development needs of green technologies, implementing flexible regulatory measures to avoid excessive market interference and prevent detrimental competition resulting from resource price reductions, which can lead to excessive competition for land, talent, and other factors. Furthermore, the government should enhance the development of regional digital platforms to foster better coordination between the government, financial institutions, and enterprises. Such development can improve the efficiency of sci-tech finance policy implementation and promote the formation and upgrading of cross-sectoral, cross-industry green innovation networks.

This study acknowledges certain limitations and highlights the need for further in-depth exploration. First, this paper examines the impact of sci-tech finance pilot policies on UGTI in China. Future research could expand this analysis to include other countries and regions, with a particular emphasis on comparing the effects on developed versus developing nations. This would offer valuable insights for the development of a global strategy for sci-tech finance, fostering the advancement of sustainable innovation. Furthermore, future research might incorporate synthetic control methods alongside other analytical techniques to conduct in-depth case studies of policy implementation in specific regions, facilitating the more flexible identification and assessment of policy impacts.