Digital Finance and Green Technology Innovation: A Dual-Layer Analysis of Financing and Governance Mechanisms in China

Abstract

As China advances its green transition, digital finance broadens firms’ access to external financing; however, whether improved access enhances financial allocation efficiency or drives green technology innovation remains unclear. This study addresses this through a dual-layer framework examining financing mechanisms and governance conditions. Using panel data from 2165 Chinese A-share firms (2011–2022) with two-way fixed-effects models, the analysis yields three key findings: First, digital finance significantly enhances green technology innovation. Second, financial mismatch partially mediates this relationship. Third, governance moderates these effects. Equity incentives exhibit threshold effects, where positive impacts emerge only above certain levels. Cash incentives, however, vary by period: they are positive in 2011–2018 and negative in 2019–2022. These results underscore that financial reform must accompany governance improvements, especially equity incentives. The Chinese experience also provides insights for emerging economies navigating digital finance expansion and green transition.

1. Introduction

Green technology innovation (GTI) has become a critical way for firms to pursue environmental sustainability while preserving competitiveness. International carbon neutrality goals and China’s dual-carbon strategy have heightened the importance of corporate innovation in emission reduction [1]. By combining technological development with organizational restructuring, green technology innovation is believed to achieve both environmental improvements and superior long-term financial performance through cost reductions and differentiated market positioning [2,3]. However, these projects typically involve high costs, extended timelines, and significant uncertainty, and these characteristics are likely to discourage conventional financial institutions from providing adequate financing [4]. Consequently, the design of financial and governance structures becomes critical for enabling sustained corporate investment in green R&D.

Digital finance (DF) offers a potential solution to these challenges. As data-driven technologies reshape financial services, they enhance access, reduce transaction costs, and strengthen risk assessment capabilities [4,5]. Evidence suggests that digital financing platforms alleviate capital constraints and reduce borrowing costs, which allow for firms to pursue higher-risk innovation projects that mainstream credit markets typically reject [6,7]. There are also studies showing that digital finance enhances green credit accessibility and resource allocation efficiency, and this promote corporate green technology innovation [8,9,10].

What remains less clear is how and under what conditions digital finance drives green technology innovation. Two issues stand out: First, while the positive association between digital finance and green technology innovation has been well established, the underlying mechanisms remain systematically unexplored. Financing inefficiencies, including financial mismatch and elevated debt costs, have been widely recognized as obstacles to firm innovation, but their mediating role in the digital finance–green technology innovation relationship is poorly understood [11,12,13]. Second, governance arrangements influence innovation resource allocation, yet whether executive incentives condition digital finance effectiveness remains unclear. For example, research on Chinese firms indicates that equity-based compensation can lengthen managerial horizons and encourage exploratory green R&D [14,15]. Whether these arrangements amplify or mute the effects of digital finance, however, remains largely unexplored.

To address these gaps, this study develops a dual-layer framework, drawing on insights from resource allocation theory and agency theory, and captures both the financing capacity dimension and the governance willingness dimension of green innovation. This approach enables a more comprehensive understanding of the relationship by uncovering not only the direct effect, but also the transmission channels and boundary conditions that shape it.

Specifically, this study addresses three key research questions: RQ1: Does digital finance significantly enhance green technology innovation in Chinese firms? RQ2: Does financial mismatch serve as a mediating mechanism in this relationship? RQ3: Do executive incentive structures moderate the effectiveness of digital finance in promoting green innovation?

The remainder of the paper is organized as follows: Section 2 reviews the literature and develops the hypotheses. Section 3 describes the research design. Section 4 presents the empirical results. Section 5 discusses theoretical and practical implications. Section 6 concludes.

2. Literature Review and Hypothesis Development

2.1. Green Technology Innovation

Green technology innovation has emerged as a critical pathway for enterprises to achieve environmental sustainability while maintaining competitive advantage, particularly under global carbon-neutrality commitments and China’s “dual carbon” strategy [1]. Unlike conventional innovation, green technology innovation spans technological and organizational changes that reduce environmental impacts while improving resource efficiency [16]. Recent studies highlight dual benefits: green technology innovation improves environmental outcomes and supports longer-term firm performance through efficiency gains and market positioning [2,3]. Complementary evidence from policy and manufacturing contexts further reinforces this dual role [17,18].

The measurement of green technology innovation typically relies on the count of green patent applications due to their standardized definition, objectivity, and broad availability across firms and time periods [19,20]. While this approach ensures comparability and consistency, it does not fully capture dimensions such as implementation effectiveness or environmental benefits [16,21]. For instance, many patents are merely technical solutions that may never translate into actual production or market application. In addition, some firms may apply for nominal “green patents” primarily to enhance their image, yet their genuine environmental contribution remains limited. Nevertheless, patent applications remain the most widely adopted and comparable measure for cross-firm innovation research.

Previous research has shown that green technology innovation is influenced by multiple factors, including policy and institutional drivers [22,23], corporate governance and stakeholder pressures [24,25], financial and capital market conditions [26,27], technological capabilities and knowledge resources [28,29], and globalization and external linkages [30]. While governance factors are recognized as relevant influences, their role remains underexplored in the digital finance context.

Among these factors, financing conditions are particularly vital for green technology innovation because green projects typically involve substantial upfront costs and uncertain payoffs. Extensive empirical evidence demonstrates the positive link between external finance dependence and innovative output, and green technology innovation-specific studies show that credit availability and the broader financial structure influence the intensity of green innovation [26,31]. In this context, digital finance has emerged as a significant driver of green innovation.

2.2. Digital Finance

Digital finance refers to the application of data-driven technologies, such as big data analytics, artificial intelligence, blockchain, and cloud computing, to provide financial services with greater accessibility, efficiency, and precision [5,32]. Unlike conventional intermediation, it relies on real-time information processing and algorithmic decision-making that reduce information asymmetry and transaction costs [33,34].

Research demonstrates that digital finance promotes green technology innovation through multiple mechanisms. Studies show that it alleviates financing constraints and reduces debt costs, enabling firms to pursue environmentally uncertain R&D projects [6,7]. Digital platforms also improve credit screening and environmental risk assessment, which can direct capital toward green investments [10,35]. Additionally, enhanced access to green credit strengthens resource allocation efficiency, promoting substantive rather than symbolic green transformation [10,36]. However, existing research primarily examines direct relationships, with limited systematic analysis of underlying mechanisms and boundary conditions.

2.3. Theoretical Background

2.3.1. Resource Allocation Theory

Resource allocation theory emphasizes that financial markets often fail to channel resources to their most productive uses when information is imperfect. Seminal research demonstrates that asymmetric information gives rise to credit rationing and resource misallocation [37]. Subsequent work in the economics of information offered a foundational reinterpretation of market failures, showing how such frictions systematically undermine efficient allocation [38]. Such distortions are particularly problematic for green innovation because these projects typically involve long horizons, high uncertainty, and significant upfront costs. Digital finance, with its capacity to process real-time information and expand credit access, can mitigate these frictions by improving credit screening, broadening the reach of financial services, and enhancing the efficiency of capital allocation [6,10,39]. This perspective provides the financing rationale for analyzing the contribution of digital finance to green technology innovation.

2.3.2. Agency Theory

Agency theory highlights the divergence between managers and shareholders, particularly in their preferences for risk and investment horizons [40]. While shareholders are inclined to support long-term innovation, managers tend to prefer short-term outcomes to ensure immediate performance. Incentive structures are therefore critical in aligning managerial interests with those of shareholders. Evidence indicates that firms with stronger governance generate more green patents [24], and higher ESG ratings are associated with more efficient green innovation [41]. Moreover, executive equity incentives directly stimulate green R&D [42], while external monitoring, such as media attention, reinforces managerial accountability for environmental performance [25]. These findings suggest that governance structures influence whether managers are willing to translate financial opportunities made possible by digital finance into substantive innovation efforts. Accordingly, agency theory provides the governance rationale for examining how executive incentives condition the impact of digital finance on green technology innovation.

Together, these perspectives establish a dual-layer framework in which digital finance promotes green technology innovation through a financing channel and governance mechanism. The financing channel concerns firms’ ability to access and allocate resources efficiently, while the governance contingency reflects whether managerial incentives, support the commitment of those resources to long-term green innovation.

2.4. Hypothesis Development

Green technology innovation requires substantial upfront investment and long development horizons, and entails considerable technological and market uncertainty [4,16]. Conventional financial systems are often reluctant to support such projects because their extended payback periods and complex risk profiles exceed conventional risk assessment capabilities [43,44]. Digital finance constitutes a new external resource environment that reshapes how firms access and utilize financial capital. By leveraging big data analytics, artificial intelligence, and algorithmic credit screening, digital finance enhances the precision of risk assessment, broadens the pool of available financing channels, and lowers transaction costs [22,23]. These advantages are particularly valuable for green technology innovation, where accurate evaluation of uncertain projects and flexible capital allocation are essential.

Recent studies demonstrate that digital finance promotes green innovation by enhancing resource allocation efficiency and facilitating collaborative innovation networks [20,45]. Based on this reasoning, this study proposes the following hypothesis:

Hypothesis 1 (H1).

Digital finance positively influences green technology innovation.

Financial mismatch arises when financial resources are not allocated to the most productive or environmentally beneficial firms and projects, leading to inefficient investment and distorted innovation outcomes [11,12]. Such distortions constrain firms’ ability to undertake long-term and uncertain projects, thereby hampering innovation efficiency. Research shows that financial misallocation suppresses technological and green innovation by limiting access to credit and discouraging risky but socially valuable projects [16,46]. These effects are especially challenging for green technology innovation, where high investment requirements and long development cycles make efficient capital allocation a critical precondition for success.

Digital finance offers mechanisms to alleviate these inefficiencies. By improving credit screening, expanding financing channels, and reducing information asymmetry, digital finance mitigates financial mismatches and enables firms to pursue innovation activities more effectively [13,47,48]. Consistent with resource allocation theory, this suggests that financial mismatch can function as a mediating channel through which digital finance influences green technology innovation [37,38].

Although recent studies provide evidence that digital finance corrects misallocation and supports firm innovation, most analyses focus on general innovation or small- and medium-sized enterprises, while systematic evidence remains limited. Therefore, it is necessary to investigate whether financial mismatch serves as a mediating pathway through which digital finance influences green technology innovation. Based on this reason, this study proposes the following hypothesis:

Hypothesis 2 (H2).

Financial mismatch mediates the relationship between digital finance and green technology innovation.

Executive equity incentives (EEI) refer to stock-based compensation arrangements that tie executives’ wealth to long-term firm value and are distinct from cash-based incentives [15]. However, the impact of digital finance on green technology innovation may be contingent on executive incentive structures. While digital finance increases resource availability for green innovation, whether these resources yield substantive green outcomes depends on managers’ willingness to undertake long-term, uncertain projects. Agency theory suggests that, without proper incentive alignment, enhanced financing access may not translate into innovation investments [40].

Equity incentives address this agency problem by aligning managerial interests with long-term value creation, extending decision horizons and increasing tolerance for uncertainty inherent in green innovation projects [15,49]. Such incentive alignment may enhance the effectiveness of digital finance. When managers possess equity stakes, they are more likely to allocate digital finance resources toward substantive innovation rather than conservative, short-term projects [50]. In addition, empirical evidence from China indicates that executive equity incentive plans raise firms’ green technology innovation [14]. Conversely, without proper incentive alignment, the innovation benefits of digital finance may be diminished [24].

Although equity incentives are generally associated with stronger innovation performance, their moderating role in the conversion of digital finance into green technology innovation has received limited attention. Accordingly, the following hypothesis is proposed:

Hypothesis 3 (H3).

The positive effect of digital finance on green technology innovation is stronger for firms with executive equity incentives than for those without such incentives.

In contrast, executive cash incentives may weaken the DF–GTI relationship. Cash bonuses typically tie to short-term performance metrics [51], which may create pressure for immediate and measurable results [52]. This short-term orientation conflicts with green innovation’s characteristics of extended development cycles and delayed returns [16,53].

When digital finance expands resource availability, managers incentivized by cash face a critical allocation choice. The immediacy of cash compensation creates incentives to direct resources toward projects that generate quick returns within evaluation periods, even when long-term green innovation opportunities exist [54]. Unlike equity holders, who capture future value appreciation, cash-compensated executives bear immediate opportunity costs, but may not personally capture the eventual benefits of green innovation.

Empirical evident supports this argument. Prior research shows that cash-based compensation discourages long-term innovation by shortening managerial horizons and increasing short-term performance pressure [55]. Recent evidence further demonstrates that executive compensation incentives can weaken digital transformation effects on green technology innovation [56]. However, existing studies have rarely examined how cash and equity mechanisms differentially condition the digital finance–green innovation relationship. To address this gap, we propose the following hypothesis:

Hypothesis 4 (H4).

Executive cash incentive levels negatively moderate the relationship between digital finance and green technology innovation.

Based on these hypotheses, a conceptual framework is proposed, as shown in Figure 1.

3. Research Design

3.1. Sample and Data Sources

This study uses all Chinese A-share listed firms from 2011 to 2022 as the initial sample. We apply the following sample selection criteria: (1) exclude ST and *ST firms; (2) exclude firms in the financial and real estate sectors; and (3) remove observations with missing values for key variables used in this study. After this process, the final sample comprises 2165 firms, yielding 23,486 firm-year observations. To reduce the influence of outliers, all continuous variables are winsorized at the 1st and 99th percentiles.

Data are obtained from three main sources: the Peking University Digital Finance Index (PKU-DFI) for digital finance development, the China Research Data Services (CNRDS) for green patent information, and the China Stock Market & Accounting Research (CSMAR) database for firm-level financial indicators.

These data sources provide authoritative and reliable foundations for comparable measurement. The Peking University Digital Financial Inclusion Index (PKU-DFI) covers 337 prefecture-level cities and is constructed from transaction-level data provided by Ant Financial Services (now Ant Group), making it the most comprehensive assessment of regional digital finance development in China [57]. CNRDS provides standardized green patent data, classified according to the WIPO IPC Green Inventory, enabling systematic tracking of firms’ environmental innovation across technology domains. Firm-level financial indicators are obtained from the CSMAR database, the most widely used source for research on Chinese listed firms.

3.2. Variable Measurement

3.2.1. Dependent Variable: Green Technology Innovation (GTI)

Green technology innovation serves as the dependent variable in this study. Following the established literature, we measure green technology innovation using patent application counts, which provide objective and comparable indicators of innovation output across firms and time periods [19,20]. Patent applications are preferred over granted patents, as they better capture the timing of innovation activities and avoid potential delays in the patent approval process. Consistent with prior studies, we take the natural logarithm of patent counts after adding one. This adjustment ensures that firms with zero patent applications are retained in the sample, as log(0) is undefined, and it also mitigates the skewness of the distribution [45], where Green Patent Applications represents the annual count of green technology patent applications filed by each firm:

$$GTI=ln(\mathrm{Green}\mathrm{Patent}\mathrm{Applications}+1)$$

(1)

To ensure measurement consistency and reproducibility, the patent-counting methodology in this study is specified as follows: Green patents are identified based on the WIPO IPC Green Inventory, which provides a formal classification framework for technologies with environmental benefits. Green Patent Applications represents the sum of invention and utility model patent applications filed by the firm in a given year, based on application dates. To enhance measurement precision, the sample includes only patents filed by a single applicant, thereby excluding joint applications to avoid ambiguous attribution. Furthermore, patents are matched to firms using the standardized company identifiers (stock codes) in the CSMAR database, which ensures accurate linkage between the CNRDS patent records and firm-level observations throughout the sample period.

3.2.2. Independent Variable: Digital Finance (DF)

Digital finance serves as the primary explanatory variable in this study. We employ the city-level Digital Financial Inclusion Index developed by Peking University’s Institute of Digital Finance, which represents a widely recognized and extensively validated measure for digital finance development in Chinese cities [57]. The index is constructed from comprehensive Ant Financial Services transaction data and provides a composite measure of regional digital finance development.

The city-level index provides sufficient variation for empirical analysis while matching our firm-location identification. The index is matched to individual firms based on their registered headquarters location. Following [19], we standardize the index by dividing by 100 and take the natural logarithm to improve distributional properties and ensure scale compatibility with other variables in our models:

$$DF=ln\left({\displaystyle \frac{\mathrm{PKU-DFI}}{100}}\right)$$

(2)

3.2.3. Mediating Variables: Financial Mismatch (FM)

Financial mismatch functions as a mediating variable in this study. It captures the extent to which firms’ financing costs deviate from industry norms due to credit market inefficiencies and financial resource misallocation. Consistent with the existing literature, this study employs the relative deviation method to measure financial mismatch [47,58], an approach widely adopted to capture distortions in credit allocation. This method calculates mismatch as the proportional difference between firm-specific financing costs and industry averages, enabling standardized comparisons across heterogeneous firms and sectors. Financial mismatch is calculated as

$$FM={\displaystyle \frac{\mathrm{Firm}\mathrm{level}\mathrm{Interest}\mathrm{Rate}-\mathrm{Industry}\mathrm{Average}\mathrm{Interest}\mathrm{Rate}}{\mathrm{Industry}\mathrm{Average}\mathrm{Interest}\mathrm{Rate}}}$$

(3)

where firm-level interest rate equals interest expenses divided by interest-bearing debt, and industry average represents the mean interest rate across all firms within the same industry classification during the observation period. Greater proportional deviations from industry norms indicate a more severe financial mismatch.

3.2.4. Moderating Variable: Executive Equity Incentive (EEI)

This study adopts the compensation ratio approach to measure executive equity incentive, consistent with [59], which captures the relative prominence of equity-based compensation within the overall executive compensation structure. This choice is based on several methodological considerations: the ratio approach provides cross-firm comparability by reducing scale effects associated with firm size and industry differences, offers appropriate statistical properties for empirical analysis, and captures the relative importance of equity-based compensation within the overall compensation structure. Following [59], the executive equity incentive is calculated as

$$EEI={\displaystyle \frac{\mathrm{Equity}\mathrm{Value}}{\mathrm{Equity}\mathrm{Value}+\mathrm{Cash}\mathrm{Compensation}}}$$

(4)

3.2.5. Moderating Variable: Executive Cash Incentive (ECI)

Executive cash incentives function as a pivotal moderating variable in examining how digital finance affects green technology innovation outcomes. ECI captures the intensity of monetary-based incentives, including base salary, cash bonuses, and allowances, that shape managerial willingness to pursue innovative strategies, particularly those requiring substantial upfront investments with uncertain returns. The measurement of executive cash incentive intensity demands methodological approaches that accommodate the inherent distributional properties of compensation data while preserving analytical utility for interaction effect detection. Following established practices in executive compensation research, this study employs the natural logarithm of total executive cash compensation to measure cash incentive intensity [60,61]. The calculation is as follows:

$$ECI=ln\left(\mathrm{Total}\mathrm{Executive}\mathrm{Cash}\mathrm{Compensation}\right)$$

(5)

3.2.6. Control Variables

To ensure robust empirical identification, this study incorporates a comprehensive set of control variables following established practices in digital finance and green innovation research [8,9,45]. The control variables are organized into three categories, addressing different sources of potential omitted variable bias. Firm financial characteristics include Leverage Ratio (LEV), Return on Assets (ROA), Tobin’s Q (TQ), and Revenue Growth Rate (GROWTH), which control for capital structure, profitability, market valuation, and growth dynamics, which may influence innovation investment decisions. Organizational characteristics comprise Company Size (SIZE), Company Age (AGE), and State-Owned Enterprise (SOE), accounting for scale effects, organizational maturity, and institutional differences in the Chinese context. Corporate governance characteristics include Board Size (BOARD) and Independent Board Ratio (INDBOARD), controlling for board structure effects on strategic decision-making regarding technology adoption and environmental investments. Detailed definitions and measurement methods for all variables are provided in

Table 1. Summary of variables and measurement.

Variable Type	Variable Name	Initials	Measurement	Refs.
Dependent variable	Green technology innovation	GTI	ln(Green Patent Applications + 1)	[19,20]
Independent variable	Digital finance	DF	ln(Digital Financial Inclusion Index/100)	[19,57]
Mediating variable	Financialmismatch	FM	(Firm level Interest Rate − Industry Average Interest Rate)/Industry Average Interest Rate	[47,58]
Moderating variable	Executive equity incentive	EEI	Equity Value/(Equity Value + Cash Compensation)	[59]
	Executive cashincentive	ECI	ln (Total Executive Cash Compensation)	[60,61]
Control variable	Leverage ratio	LEV	Total Liabilities/Total Assets	[8,9,45]
	Return on assets	ROA	Net Income/Total Assets
	Tobin’s Q	TQ	Market Value/Book Value of Assets
	Revenue growth rate	GROWTH	Year-over-year change in operating revenue
	Company size	SIZE	ln (Total Assets)
	Company age	AGE	Number of years since establishment
	State-owned enterprise	SOE	Binary indicator (1 if state-controlled, 0 if privately-owned)
	Board size	BOARD	ln (Total number of directors)
	Independent board ratio	INDBOARD	Proportion of independent directors relative to total board members

.

3.3. Empirical Models

This study employs four empirical specifications to examine the relationship between digital finance and green technology innovation. All models utilize panel data regression with two-way fixed effects, incorporating industry fixed effects to control for time-invariant sectoral characteristics and year-fixed effects to absorb macroeconomic shocks and policy changes.

The appropriateness of the fixed effects specification is assessed using the Hausman test. The test strongly rejects the random effects model (${\chi }^2$(19) = 86.16, p < 0.001), indicating that the fixed effects specification is more appropriate for this analysis.

To address potential reverse causality concerns, digital finance variables are lagged by one period, consistent with recent innovation studies [8,45]. Considering that firms within the same city may face similar digital finance development environments and policy backgrounds, leading to within-group correlation in error terms, this study employs city-level clustered robust standard errors for statistical inference [62].

• Model 1: Direct Effect

$$GT{I}_{i,t}=\alpha +{\beta }_{1}D{F}_{i,t-1}+{\beta }_{x}Control{s}_{i,t}+{\delta }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(6)

This baseline model examines the direct relationship between digital finance and green technology innovation.

• Model 2: Mediation Analysis

Following [63], mediation effects are tested using bootstrap procedures to examine indirect effects through financial mismatch.

First stage:

$$F{M}_{i,t}={\alpha }_1+{\beta }_{1}D{F}_{i,t-1}+{\gamma }_{1}Control{s}_{i,t}+{\delta }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(7)

Second stage:

$$GT{I}_{i,t}={\alpha }_2+{\beta }_{2}D{F}_{i,t-1}+{\beta }_{3}F{M}_{i,t}+{\gamma }_{2}Control{s}_{i,t}+{\delta }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(8)

Equation (7) estimates the impact of digital finance on financial mismatch. Equation (8) then includes both digital finance and the observed value of financial mismatch simultaneously to examine whether FM mediates the DF–GTI relationship. To test the significance of the indirect effect, we apply bootstrap procedures with 5000 replications.

• Model 3: Moderation by Executive Equity Incentive

Given that the sample contains firms without equity incentives (N = 6556) and with equity incentives (N = 14,453), this study employs separate regressions for the two groups. This approach captures potential discontinuities in the moderating relationship that may not be evident in linear interaction models. The empirical models are specified as follows:

Firms without executive equity incentives:

$$GT{I}_{i,t}={\alpha }_1+{\beta }_1·D{F}_{i,t-1}+{\gamma }_1·Control{s}_{i,t}+{\delta }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(9)

Firms with executive equity incentives:

$$GT{I}_{i,t}={\alpha }_2+{\beta }_2·D{F}_{i,t-1}+{\gamma }_2·Control{s}_{i,t}+{\delta }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(10)

• Model 4: Moderation by Executive Cash Incentive

The analysis employs interaction term regression to capture the moderating effects of incentive magnitude. The empirical model is specified as follows:

$$\begin{array}{c}GT{I}_{i,t}=\alpha +{\beta }_{1}D{F}_{i,t-1}+{\beta }_{2}EC{I}_{i,t-1}+{\beta }_3(D{F}_{i,t-1}\times EC{I}_{i,t-1})\hfill \\ \hfill +\gamma ·Control{s}_{i,t}+{\delta }_i+{\lambda }_t+{\epsilon }_{i,t}\end{array}$$

(11)

where $D{F}_{i,t-1}\times EC{I}_{i,t-1}$ represents the interaction term between digital finance and executive cash incentive.

4. Empirical Results

4.1. Descriptive Statistics

Table 2. Descriptive statistics of main variables.

Variable	N	Mean	SD	Min	P25	Median	P75	Max
GTI	23,486	0.329	0.745	0	0	0	0	3.611
DF	23,486	1.169	0.261	0.442	1.026	1.233	1.368	1.507
COST	23,038	0.02	0.015	0	0.008	0.018	0.029	0.062
FM	23,486	−0.335	1.201	−13.154	−0.751	−0.04	0.403	2.682
IC	22,531	6.475	0.164	5.424	6.436	6.504	6.554	6.733
EEI	23,486	0.228	0.326	0	0	0.015	0.444	0.962
ECI	23,486	14.992	0.774	13.170	14.478	14.959	15.478	17.150
LEV	23,486	0.407	0.195	0.007	0.251	0.401	0.552	1.501
ROA	23,486	0.041	0.055	−0.191	0.016	0.039	0.068	0.193
AGE	23,486	18.252	5.979	1	14	18	22	55
SIZE	23,486	22.289	1.296	20.05	21.356	22.108	23.036	26.477
TQ	23,022	0.568	0.48	−0.471	0.211	0.477	0.838	3.447
SOE	23,486	0.37	0.483	0	0	0	1	1
BOARD	23,485	2.131	0.197	1.099	1.946	2.197	2.197	2.89
INDBOARD	23,485	0.376	0.056	0.167	0.333	0.353	0.429	0.8
GROWTH	22,689	0.143	0.281	−0.392	−0.017	0.103	0.247	1.175

Note: This table reports descriptive statistics for all variables employed in the analysis.

reports descriptive statistics for the main variables. GTI shows notable variation (mean = 0.329, SD = 0.745) with a right-skewed distribution: 75% of observations are 0, while the maximum reaches 3.611, indicating that green innovation is concentrated among a subset of firms. DF averages 1.169 (SD = 0.261), showing moderate variation that reflects heterogeneity in local digital financial development.

The mediating variable, FM, shows wide heterogeneity (mean = −0.335, SD = 1.201). Regarding executive incentives, EEI are absent in a substantial share of firms, while those adopting them display large differences in intensity. By contrast, ECI follow a more standardized pattern (mean = 14.992, SD = 0.774). Control variables display variation consistent with listed firm characteristics, including moderate leverage (LEV = 0.407), average board size of about nine members, and 37% of firms being state-owned enterprises.

4.2. Baseline Results

Table 3. Main effect of digital finance on green technology innovation.

Variables	GTI	GTI
	(1)	(2)
DF	0.7455 ***	0.7842 ***
	(0.2540)	(0.2292)
LEV		0.2661 ***
		(0.0723)
ROA		0.1496
		(0.1443)
AGE		−0.0070 *
		(0.0037)
SIZE		0.1316 ***
		(0.0123)
TQ		0.0424 *
		(0.0253)
SOE		0.0271
		(0.0439)
BOARD		0.0928
		(0.0814)
INDBOARD		0.0653
		(0.2398)
GROWTH		−0.0304
		(0.0222)
Constant	−0.5150 *	−3.7402 ***
	(0.2847)	(0.3246)
Industry FE	Yes	Yes
Year FE	Yes	Yes
Observations	21,009	21,009
R-squared	0.0653	0.1234
Adjusted R-squared	0.0641	0.1219
Within R-squared	0.0042	0.0662
Number of clusters	309	309

Notes: All regressions employ cluster-robust standard errors at the city level, reported in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

reports the baseline estimates of the direct effect of digital finance on green technology innovation. Column (1) presents the specification including only the key explanatory variable with firm- and year-fixed effects, whereas column (2) adds the full set of controls.

The findings consistently support Hypothesis 1, showing that digital finance exerts a significant positive influence on firms’ green innovation. In the baseline model (column 1), the coefficient of digital finance is 0.746 and significant at the 1% level. After introducing control variables (column 2), the coefficient increases slightly to 0.784, with stronger statistical significance. These results confirm that the positive impact of digital finance on green technology innovation is robust across model specifications, suggesting that higher levels of digital finance development encourage firms to undertake more green innovation activities.

4.3. Mediation Analysis

Table 4. Mediation effect of financial mismatch (FM) between digital finance and green technology innovation.

Variables	(1)	(2)
	FM	GTI
DF	−1.1131 ***	0.7424 ***
	(0.2658)	(0.2341)
FM	–	−0.0376 ***
		(0.0082)
LEV	1.8936 ***	0.3372 ***
	(0.1308)	(0.0788)
ROA	−3.0630 ***	0.0346
	(0.3049)	(0.1405)
AGE	0.0084 ***	−0.0067 *
	(0.0029)	(0.0037)
SIZE	−0.0108	0.1312 ***
	(0.0198)	(0.0123)
TQ	0.1318 ***	0.0375
	(0.0491)	(0.0262)
SOE	−0.3202 ***	0.0150
	(0.0365)	(0.0430)
BOARD	−0.0532	0.0908
	(0.1224)	(0.0799)
INDBOARD	0.0086	0.0656
	(0.3542)	(0.2411)
GROWTH	0.0108	−0.0300
	(0.0326)	(0.0221)
Constant	0.6544	−3.7156 ***
	(0.5504)	(0.3261)
Industry FE	Yes	Yes
Year FE	Yes	Yes
Observations	21,009	21,009
R-squared	0.1456	0.1265
Clusters (city level)	309	309

Notes: All regressions employ cluster-robust standard errors at the city level, reported in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

presents the mediation analysis of financial mismatch (FM) in the relationship between digital finance (DF) and green technology innovation (GTI). Column (1) shows that DF significantly reduces FM, with a coefficient of −1.113 (p < 0.01). Column (2) indicates that FM has a negative effect on GTI (−0.0376, p < 0.01), while DF remains positive and significant (0.742, p < 0.01) after controlling for FM. These results provide initial evidence of a partial mediation effect.

To formally assess the mediating role of FM, both Bootstrap and Sobel tests are conducted. The Bootstrap procedure, based on 5000 replications, reveals a statistically significant indirect effect of 0.0418 (p < 0.01), with a 95% confidence interval of [0.0287, 0.0549]. As a complementary check, the Sobel test yields consistent evidence, with a z-statistic of 3.097 (p = 0.002). Collectively, these results confirm the presence of a partial mediation effect, in line with the mediation testing procedure proposed by [63], thereby supporting H2.

4.4. Moderation Analysis

4.4.1. Executive Equity Incentives (EEI)

Table 5. Group regression results for executive equity incentives moderation.

Variables	(1)	(2)
	Without EEI	With EEI
DF	0.1810	1.0360 ***
	(0.2871)	(0.3195)
LEV	0.0400	0.3637 ***
	(0.1123)	(0.0728)
ROA	−0.5484 **	0.3820 **
	(0.2491)	(0.1703)
AGE	−0.0072	−0.0063
	(0.0048)	(0.0040)
SIZE	0.1486 ***	0.1240 ***
	(0.0249)	(0.0167)
TQ	0.0195	0.0581 *
	(0.0403)	(0.0311)
SOE	0.0707	0.0407
	(0.0437)	(0.0494)
BOARD	0.1323	0.0525
	(0.1524)	(0.1004)
INDBOARD	0.4824	−0.1686
	(0.4378)	(0.3185)
GROWTH	−0.0467	−0.029
	(0.0376)	(0.0266)
Constant	−3.6464 ***	−3.7375 ***
	(0.5548)	(0.4969)
Industry FE	Yes	Yes
Year FE	Yes	Yes
Observations	6556	14,453
R-squared	0.151	0.127
Clusters (city level)	243	282
Coefficient Difference (${\beta }_2-{\beta }_1$)	0.855
Bootstrap 95% CI	[0.5348, 1.1751]

Notes: All regressions employ cluster-robust standard errors at the city level, reported in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

reports the moderating role of executive equity incentives (EEI) in the relationship between DF and GTI. Column (1) shows that for firms without EEI, the coefficient of DF is small and statistically insignificant (0.181, p > 0.1). By contrast, column (2) indicates that for firms with EEI, DF has a positive and highly significant effect on GTI (1.036, p < 0.01).

Figure 2 further visualizes this moderating role: digital finance significantly promotes green technology innovation only in firms with equity incentives, whereas the effect is insignificant for firms without such incentives. The coefficient difference between the two groups is 0.855. To formally test its significance, a Bootstrap analysis is conducted, and the 95% confidence interval [0.535, 1.176] excludes zero. This confirms the statistical significance of the moderating effect. Overall, the results indicate that EEI strengthens the positive impact of DF on GTI, supporting H3.

4.4.2. Executive Cash Incentives (ECI)

The moderating role of executive cash incentives (ECI) is examined with an interaction model, as reported in

Table 6. Interaction term regression results for executive cash incentives moderation.

Variables	GTI
DF	1.1979
	(0.4050)
ECI	0.1322 ***
	(0.0408)
DF × ECI	−0.0381
	(0.0323)
LEV	0.2730 ***
	(0.0757)
ROA	0.0261
	(0.1510)
AGE	−0.0073 **
	(0.0035)
SIZE	0.1021 ***
	(0.0140)
TQ	0.0234
	(0.0264)
SOE	0.0296
	(0.0437)
BOARD	0.0632
	(0.0799)
INDBOARD	0.0422
	(0.2448)
GROWTH	−0.0085
	(0.0251)
Constant	−4.7918 ***
	(0.6845)
Industry FE	Yes
Year FE	Yes
Observations	20,860
R-squared	0.128
Clusters (city level)	309

Notes: All regressions employ cluster-robust standard errors at the city level, reported in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

. The coefficient on DF is positive, but not statistically significant (1.198, p > 0.10). ECI itself is positively associated with GTI (0.132, p < 0.01). The interaction term DF × ECI is negative (−0.038), but statistically indistinguishable from zero (p > 0.10), indicating that higher cash incentives do not weaken the DF–GTI relationship within our sample. Taken together, these results do not provide evidence for the predicted negative moderation, so H4 is not supported.

Given the insignificant interaction effect in the full sample period (2011–2022), we conducted temporal subsample analyses to explore potential heterogeneity in the moderating role of executive cash incentives. The detailed progressive results are reported in Supplementary Materials Table S1, and the two representative periods are summarized in

Table 7. Executive cash incentives: subsample analysis.

Variables	(1)	(2)
	2011–2018	2019–2022
DF	−1.0119 *	7.5528 ***
	(0.5816)	(2.5704)
ECI	0.0003	0.6732 ***
	(0.0445)	(0.2430)
DF × ECI	0.1082 **	−0.4505 **
	(0.0455)	(0.1754)
LEV	0.2656 ***	0.2908 ***
	(0.0924)	(0.0709)
ROA	0.0617	0.0708
	(0.2173)	(0.1436)
AGE	−0.0086 **	−0.0057 **
	(0.0044)	(0.0027)
SIZE	0.1161 ***	0.0891 ***
	(0.0177)	(0.0122)
TQ	0.0416	0.0174
	(0.0307)	(0.0283)
SOE	0.0121	0.0521
	(0.0504)	(0.0381)
BOARD	0.0767	0.0350
	(0.1008)	(0.0801)
INDBOARD	0.0473	0.0090
	(0.2887)	(0.2788)
GROWTH	−0.0076	−0.0107
	(0.0349)	(0.0267)
Constant	−2.9991 ***	−12.9912 ***
	(0.7607)	(3.6654)
Industry FE	Yes	Yes
Year FE	Yes	Yes
Observations	12,346	8514
R-squared	0.1213	0.1464
Clusters (city level)	308	304

Notes: All regressions employ cluster-robust standard errors at the city level, reported in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

. The evidence shows that 2018 is the last year in which the moderating effect remains significant at the conventional 5% level, while significance deteriorates to marginal levels once 2019 observations are added and vanishes thereafter. This statistical break motivates us to partition the sample into 2011–2018 and 2019–2022.

During 2011–2018, the interaction term is positive and statistically significant ($\beta$ = 0.108, p < 0.05), indicating that higher cash incentives amplified the positive effect of digital finance on green innovation. This positive moderation persists through 2019 ($\beta$ = 0.077, p < 0.10), but becomes statistically insignificant once 2020 data are included. Notably, during 2019–2022, the interaction term turns significantly negative ($\beta$ = –0.450, p < 0.05), suggesting that cash incentives began to attenuate rather than strengthen the DF–GTI relationship.

To illustrate this temporal shift, Figure 3 presents interaction plots for the two distinct periods. Panel A (2011–2018) shows diverging slopes across ECI levels, with firms offering higher cash incentives exhibiting stronger responses to digital finance. In contrast, Panel B (2019–2022) displays converging slopes, where the digital finance effect diminishes at higher incentive levels. The timing of this shift, which began around 2019, coincides with major institutional changes, including escalating trade tensions, progressive reductions in green energy subsidies, and the onset of the COVID-19 pandemic in 2020. These contextual dynamics are elaborated further in Section 5.

4.5. Robustness and Endogeneity Tests

4.5.1. Robustness Checks

To ensure the validity and reliability of the baseline findings, this study conducts four robustness checks, reported in

Table 8. Robustness checks.

Variables	Province	Province FE	Patent	Pre-Pandemic
	Clustering		Grants
	(1)	(2)	(3)	(4)
DF	0.7842 ***	1.1119 ***	0.7478 ***	0.6182 ***
	(0.2485)	(0.2451)	(0.2272)	(0.2275)
LEV	0.2661 ***	0.2540 ***	0.2031 ***	0.2699 ***
	(0.0647)	(0.0707)	(0.0676)	(0.0924)
ROA	0.1496	0.0818	0.0565	0.2382
	(0.1670)	(0.1374)	(0.1383)	(0.1954)
AGE	−0.0070 *	−0.0071 **	−0.0071 **	−0.0078 *
	(0.0036)	(0.0036)	(0.0036)	(0.0045)
SIZE	0.1316 ***	0.1345 ***	0.1250 ***	0.1517 ***
	(0.0126)	(0.0120)	(0.0112)	(0.0153)
TQ	0.0424 *	0.0545 **	0.0168	0.0607 **
	(0.0242)	(0.0244)	(0.0232)	(0.0298)
SOE	0.0271	0.0355	0.0006	0.0219
	(0.0539)	(0.0449)	(0.0376)	(0.0495)
BOARD	0.0928	0.0865	0.0917	0.1062
	(0.0739)	(0.0820)	(0.0689)	(0.0942)
INDBOARD	0.0653	0.0425	0.1335	0.0814
	(0.2503)	(0.2585)	(0.2176)	(0.2756)
GROWTH	−0.0304	−0.0326	−0.0268	−0.0292
	(0.0230)	(0.0235)	(0.0216)	(0.0285)
Constant	−3.7402 ***	−4.1575 ***	−3.5520 ***	−3.9116 ***
	(0.3447)	(0.4299)	(0.2994)	(0.3677)
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Province FE	No	Yes	No	No
Observations	21,009	21,009	21,009	14,587
R-squared	0.1234	0.1388	0.1148	0.1130
Number of clusters	31	309	309	309

Notes: Robust standard errors are reported in parentheses. Column (1) applies province-level clustering, while Columns (2)–(4) cluster at the city level. *, **, and *** denote significance at the 10%, 5%, and 1% levels.

. First, the standard errors are clustered at the province level instead of the city level, and the coefficient on DF remains positive and significant (0.784, p < 0.01). Second, introducing province fixed effects as a robustness check to mitigate potential unobserved regional differences yields a coefficient of 1.112 (p < 0.01). Third, replacing green patent applications with patent grants as the dependent variable confirms that the results extend to the quality dimension of innovation (0.748, p < 0.01). Finally, restricting the sample to the pre-pandemic period (2011–2019) yields a consistent result (0.618, p < 0.01). Across all specifications, the coefficient on DF remains positive and significant, providing consistent evidence for the robustness of the main result.

4.5.2. Endogeneity Test

To address endogeneity concerns, a two-stage least squares (2SLS) estimation is employed, using lagged internet penetration at the city level as an instrument for digital finance (DF). Following prior studies [8], internet infrastructure is considered a relevant predictor of digital financial development, but unlikely to directly affect firm-level green innovation once controls and fixed effects are included.

The first-stage regression confirms instrument strength: lagged internet penetration is positively associated with DF (coefficient = 0.089, p < 0.01), with a Kleibergen–Paap Wald F-statistic of 41.99, well above the conventional threshold of 10. In the second stage, the coefficient of DF on GTI is 0.981 (t = 2.63, p < 0.01), exceeding the corresponding OLS estimate (0.784). Instrument validity tests support these findings, with the Kleibergen–Paap LM statistic (29.40, p < 0.001) rejecting underidentification and the Stock–Yogo critical values confirming instrument relevance.

Overall, the 2SLS estimates corroborate the baseline results and suggest that the positive impact of DF on GTI is unlikely to be driven by endogeneity, lending further support to H1. The detailed results are reported in

Table 9. Instrumental variable analysis.

Variables	First Stage	Second Stage
	(1)	(2)
Internet Penetration (L1)	0.0890 ***
	(0.0137)
DF		0.9808 ***
		(0.3729)
LEV	0.0156	0.2840 ***
	(0.0101)	(0.0770)
ROA	0.0542	0.1575
	(0.0421)	(0.1551)
AGE	−0.0008	−0.0065
	(0.0011)	(0.0041)
SIZE	0.0124 ***	0.1321 ***
	(0.0034)	(0.0133)
TQ	0.0098	0.0404
	(0.0068)	(0.0268)
SOE	−0.0037	0.0157
	(0.0121)	(0.0475)
BOARD	0.0435 *	0.1084
	(0.0221)	(0.0848)
INDBOARD	0.0077	0.1132
	(0.0659)	(0.2675)
GROWTH	−0.0056	−0.0307
	(0.0061)	(0.0236)
Constant	0.7123 ***	Absorbed by FE
	(0.0893)
Industry FE	Yes	Yes
Year FE	Yes	Yes
Observations	19,010	19,010
R-squared	0.4062	0.0653
Number of clusters	216	216
Kleibergen–Paap rk LM		29.395 ***
Kleibergen–Paap rk Wald F		41.9940
Stock-Yogo 10% critical value		16.3800

Notes: All regressions employ cluster-robust standard errors at the city level, reported in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

.

5. Discussion and Implications

5.1. Discussion

Digital finance demonstrates a positive and significant influence on green technology innovation, with effects remaining robust across alternative specifications and IV estimation. These results align with earlier studies documenting digital finance’s role in supporting innovation and sustainability initiatives [36,45]. The magnitude of the effect suggests that the relationship is economically meaningful for firms’ innovation capacities. While prior research has established this general relationship, this study extends the evidence using a comprehensive sample period (2011–2022) and an IV strategy that enhances causal identification. This baseline finding provides the foundation for analyzing how financing channels and governance incentives condition digital finance effectiveness.

The analysis further reveals that financial mismatch partially mediates the relationship between digital finance and green technology innovation, with both bootstrap and Sobel tests confirming significance. This finding supports resource allocation theory, which emphasizes that imperfect information and credit rationing distort capital allocation and discourage firms from pursuing risky, long-term projects [37]. The results also extend prior evidence on financial misallocation and innovation [12,46] by demonstrating that this mechanism specifically applies to green technology contexts. By identifying financial mismatch as a mediating channel, this study provides empirical confirmation that digital finance promotes green technology innovation not only through direct effects, but also by correcting resource allocation distortions that hinder environmentally oriented innovation.

The results indicate that digital finance promotes green technology innovation only in firms that adopt executive equity incentives (EEI), whereas the effect is insignificant in firms without such incentives. This pattern is consistent with agency theory, which emphasizes that managers are often reluctant to commit to long-term high-risk projects unless their personal wealth is tied to the firm’s future value [40]. By extending managerial horizons and increasing tolerance for interim volatility, equity incentives align executives’ interests with shareholders and make them more willing to allocate financial resources derived from digital finance toward uncertain but transformative green projects. This finding also extends research demonstrating that equity incentives enhance innovation outcomes [14,15] by identifying executive equity incentives as a critical boundary condition that determines whether digital finance advantages translate into sustainable innovation.

H4 proposed that cash incentives would weaken the DF–GTI relationship, as short-term compensation pressures would steer resources away from uncertain green projects.

However, subsample analyses reveal a more complex picture, with a clear temporal shift in their moderating role. During 2011–2018, the interaction between digital finance and executive cash incentives is positive and statistically significant, indicating that cash incentives enhanced the positive DF–GTI relationship. This finding is contrary to H4, and several factors may account for it.

First, extensive government subsidies substantially reshaped the risk–return profile of green innovation. China’s 12th and 13th Five-Year Plans designated green development as a national priority, backed by feed-in tariffs, tax incentives, and preferential credit policies. These subsidies compressed the payback period of green projects, transforming them into shorter-term profitable opportunities [64]. Executives rewarded through cash incentives could thus pursue green innovation without jeopardizing immediate performance metrics.

Second, policy-driven demand reduced investment uncertainty. Public procurement expenditure expanded substantially over the past two decades, reached approximately 2.9% of GDP by 2022. Government procurement programs, renewable energy targets, and environmental regulations created guaranteed markets for green technologies, which lowered the risk premium traditionally associated with innovation [65]. This institutional scaffolding made green projects compatible with the short-term orientation typically induced by cash incentives.

Third, this pattern may reflect the signaling value of green innovation during the early period, where environmental initiatives generated immediate reputational benefits and government recognition [66]. These short-term visible outcomes aligned with cash-incentivized managers’ performance goals, potentially explaining why cash incentives initially reinforced green innovation investment.

In contrast, the 2019–2022 period shows a significantly negative interaction, and this confirms H4. Three institutional changes may explain this shift. First, escalating trade tensions since 2019 imposed cash flow pressures, forcing executives to prioritize liquidity over long-term investments [67,68]. Second, progressive subsidy reductions altered project economics. Wang et al. (2022) [69] reported significant cuts in new energy subsidies, with similar transitions across other renewable sectors. Without subsidies, green projects reverted to long-horizon commitments that are less attractive to cash-incentivized executives. Third, the COVID-19 pandemic intensified uncertainty and liquidity pressures. Firms adjusted compensation structures and reduced innovation spending [70,71], with cash incentives reinforcing managerial short-termism and crowding out green innovation. These institutional shifts explains why the positive moderation observed in the earlier period has shifted toward a negative effect in more recent years, restoring the theoretical mechanism underlying H4.

5.2. Implications

This study offers several theoretical and practical contributions. Theoretically, this study advances our understanding of digital finance and green innovation through three key findings: First, robust empirical analysis confirms the positive relationship between digital finance and green technology innovation across multiple specifications and identification strategies. Second, the identification of financial mismatch as a mediating mechanism demonstrates how digital finance alleviates resource allocation inefficiencies to promote green innovation, and it also enriches resource allocation theory. Third, the differential moderating effects of equity versus cash incentives extend agency theory by showing that governance effectiveness depends on incentive alignment rather than compensation levels. Together, these findings establish a dual-layer theoretical framework integrating financing capacity and governance willingness dimensions of green innovation.

The findings also provide practical implications for multiple stakeholders. For policymakers, the positive effect of digital finance on green innovation suggests that expanding access to digital financial services should be a policy priority for the green transition. Several concrete actions can enhance effectiveness. Authorities should integrate environmental performance metrics into digital credit scoring systems, creating financing advantages for firms with strong green innovation records. Regulators should also establish standardized green project certification to reduce information asymmetry between digital platforms and borrowing firms. Critically, because digital finance effectively fosters green innovation when governance mechanisms are in place, policymakers should consider governance standards as prerequisites for accessing preferential green financing programs.

For corporate managers, equity incentives emerge as essential for leveraging digital finance effectively. Firms seeking to translate improved financing access into innovation outcomes should set equity compensation at a certain level. Based on the sample distribution (mean EEI = 0.228) and regression results by group (Table 5), the moderating effect of equity incentives is observed when equity compensation exceeds the threshold of approximately 20–30% of total compensation. While this range should be interpreted as an empirical benchmark rather than a universal rule, it nevertheless provides a useful guideline for equity compensation design. Equity grants should feature multi-year vesting periods explicitly tied to green innovation milestones, aligning incentives with the long development cycles of environmental projects. The temporal analysis also reveals that cash incentives are context-dependent, supporting green innovation during subsidy-rich years, but becoming ineffective amid uncertainty and subsidy retrenchment. Hence, hybrid designs that combine equity incentives with performance-contingent cash bonuses can balance long-term orientation with short-term motivation.

However, a critical risk remains that the expansion of digital finance without corresponding governance reforms may fail to deliver sustainable green innovation outcomes. Firms lacking incentive alignment show no innovation response to improved financing access, suggesting three interrelated risks. First, rapid digital finance growth without appropriate incentive mechanisms may create green investment bubbles, characterized by financing abundance without substantive output. Second, governance mechanisms effective under certain conditions, such as cash incentives during subsidy-intensive years, may become counterproductive when institutional contexts shift, such as during subsidy retrenchment or economic crises, requiring adaptive frameworks responsive to changing environments. Third, without proper alignment between financing access and governance incentives. These risks underscore the need for synchronized interventions that expand digital finance access and strengthen corporate governance capacity in tandem.

6. Limitations and Future Research Directions

This study has several limitations that suggest potential directions for future research. First, the analysis focuses on Chinese listed companies, and these findings may not generalize to other institutional contexts. Comparative studies across countries with different financial systems and regulatory environments can be conducted to assess the broader applicability of these results. Second, the measurement of green technology innovation relies primarily on patent application data. Although this measurement provides a standard measure of innovation, it may not fully capture other forms of environmental innovation such as process improvements, management practices, or product redesigns. Future research using multiple indicators could provide a more comprehensive view of firms’ innovation activities.

Third, this study focuses on financial mismatch as a mediating channel, but digital finance may operate through additional mechanisms such as network facilitation and enhanced monitoring. Examining these pathways would clarify digital finance’s broader sustainability impact. Finally, the null cash incentive findings suggest complex relationships between compensation and innovation. Future work is expected to explore how different incentive designs interact with institutional environments.