How Fintech Impacts Enterprises’ Digital–Green Synergy

Abstract

Based on a sample of A-share companies listed on the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2011 to 2022, this paper measures and analyzes the degree of enterprises’ digital–green synergy and further tests the influence mechanism of fintech on enterprises’ digital–green synergistic development. It is found that fintech has a significant positive effect on enterprises’ digitization, enterprises’ greening, and their digital–green synergistic development, and the conclusion still holds after robustness and endogeneity tests. A heterogeneity analysis shows that the heterogeneity of enterprises’ size and the degree of industry emissions affects the promotional effect of fintech on the synergy. Fintech effectively promotes enterprises’ digital–green synergistic development through the three channels of green innovation, efficiency enhancement, and environmental information disclosure, and the heterogeneity of the executive team’s ages and the heterogeneity of their occupational backgrounds have a positive moderating effect on the promotional effect of fintech. The findings provide a conceptual framework and policy formulation guidelines for fintech to support the promotion of enterprises’ digital–green synergy and the improvement of new-quality productivity.

1. Introduction

Amidst the dual imperatives of the global digital economy and carbon neutrality targets, digital–green synergy has become a core engine to promote high-quality economic growth. Through technology integration and resource optimization, digital–green synergy can achieve the carbon peak targets and neutrality targets of improved production efficiency, as well as environmental benefits, but it faces significant challenges in its implementation: contemporary businesses face the dual challenge of shouldering technological expenditures for digital overhaul and regulatory compliance expenses for ecological transitions simultaneously. Meanwhile, conventional financial mechanisms encounter difficulties in precisely aligning with multifaceted funding requirements, constrained by informational disparities and outdated risk evaluation methodologies. On the other hand, cross-domain technology integration is characterized by data silos and standard barriers, leading to inefficient synergies. Although the existing literature discusses digital financial tools [1] and green capital allocation [2], it focuses on a single dimension and has not yet formed a systematic solution.

As an outcome stemming from the profound convergence of innovative technologies and conventional financial systems, fintech harnesses next-generation tools, including artificial intelligence, big data analytics, and distributed ledger technologies. By maximizing the potential of technological empowerment and model innovation, it provides a new path for cracking problems of digital–green synergy. From one perspective, this field diminishes informational asymmetries between financial institutions and enterprises through real-time risk evaluation frameworks and blockchain-powered traceability systems, thereby enhancing the precision in eco-friendly credit distribution and mitigating the funding barriers encountered during enterprises’ dual transformation processes. Additionally, fintech steers investment toward sustainable technology development and environmentally conscious manufacturing practices by pioneering emission quantification instruments and climate-aligned financial instruments, effectively bridging digital advancement with ecological modernization. Nevertheless, prior investigations have predominantly concentrated on the macro-inclusive effect of fintech, while the exploration of its micro-enabling mechanism and heterogeneous adjustment path needs to be deepened.

This research employs data from A-share companies listed in Shanghai and Shenzhen from 2011 to 2022 for analysis, developing an integrated coupling coordination model to assess the enterprises’ digital–green synergy levels while systematically exploring fintech’s catalytic mechanisms. The scholarly contributions emerge through three distinct dimensions: First, by diverging from conventional single-dimensional analyses of digital or environmental transitions, this paper measures the interactive synergy between enterprises’ digital transformation and green development initiatives through the establishment of a coupled coordination degree model and demonstrates fintech’s critical bridging function in advancing this synergy, which furnishes novel theoretical frameworks for enterprises’ strategic choices regarding digital–green synergy. Second, the pathways through which fintech facilitates the development of enterprises’ digital–green synergy is systematically tested through three mechanisms: efficiency improvement, green innovation, and information disclosure. In addition, this paper adopts multidimensional indicators, such as patent data and environmental information disclosure, to innovatively quantify the empowering effect of fintech, and this innovation in research methodology provides an empirical framework that can be drawn upon for subsequent related studies. Third, this research expands the analytical framework by integrating the age variations in and occupational backgrounds of the executive teams of the enterprises and reveals how executive team diversity, in regard to career specialization and generational characteristics, significantly amplifies the beneficial impact of fintech on the growth of enterprises’ digital–green synergy. It not only expands the micro-theoretical framework of the fintech-enabled dual transformation of firms but also provides empirical evidence for policymakers to design differentiated technology inclusion and regulatory programs to help the sustainable improvement of new-quality productivity.

2. Literature Review

2.1. Research Related to Digital–Green Synergy

Combing through the existing literature, the relevant research on digital–green synergy focuses on the following aspects: First, the concepts, characteristics, and realization pathways of digital–green synergy. As far as the concept is concerned, digitalization enables greening, while greening propels digitalization. Through the deep integration of these dual drivers, a mutually reinforcing effect is formed, helping to realize high-quality social development [3]. When it comes to characteristics, digital transformation, with the application of digital technology as the core feature, effectively promotes national economic growth and finds a new balance between sustainable development and economic growth by utilizing the greening development strategy [4]. In terms of realization paths, it is crucial to broaden the reach of digital infrastructure and ensure the accessibility of digital technologies [5,6], utilize the resources of energy-rich countries to enhance the inclusiveness of factor costs, and lower the threshold of the dual transformation of enterprises [7]. Second, the influencing factors that drive the development of digital–green synergy. At the individual level, CEOs’ multi-career background [8], managerial myopia [9], and senior management team stability [10] all affect corporate digitization. CEOs with green experience tend to choose green innovation for sustainable advancement strategies [11]. At the firm level, internal control critically influences the formation of the strategic behavior of enterprises’ digitalization [12]. With strategic change and comprehensive innovation as catalysts, long-term perspectives take precedence over short-term gains, thereby strengthening firms’ internal green transformation capabilities [13]. At the level of environmental regulation, the regional policy environment influences the digitalization of enterprises through a series of incentives and subsidies, on the one hand, and raises the awareness of market competition on the other hand [14]. ESG ratings can rapidly communicate enterprises’ risk signals to stakeholders, and they play the role of supervisory and constraining mechanisms to promote green development [15]. The green signal of environmental information disclosure guides enterprises to improve their energy structure to realize the green goal of emission reduction [16]. Third, the economic consequences of digital–green synergistic development. Digital–green synergy not only accelerates innovation at the technological level but also stabilizes the technical talent system, optimizes the employment environment [6], innovates the direction of the carbon emission reduction process in traditional industries [17], enables the maximum allocation and utilization of enterprises’ resources, raises total factor productivity, and ultimately boosts high-quality and sustainable economic expansion [18].

2.2. Research Related to Fintech

Fintech is the application of cutting-edge digital technologies, including cloud computing, distributed ledgers, and machine learning, to disrupt and optimize traditional financial systems, fostering inclusive finance, automation, and risk management. The established literature on fintech discussions can be sorted into the following six categories. Based on technological drivers, fintech’s cross-periodic influence and technological diffusion capabilities can significantly alleviate information disparities before and after investment. By precisely identifying green technology innovation initiatives, fintech acts as a catalyst, motivating enterprises to strengthen eco-friendly innovation efforts [19]. Consumers in India enjoy the convenience of technology and select from competitive goods and brands quickly in technology-intensive industries, demonstrating a high level of fintech acceptance, at 87% [20]. Based on the economic market, after a specific inflection point value, fintech has the capacity to alleviate information disparity and cut down on operational expenses. Leveraging the transformative impact of technological advancements, it streamlines the process of resource distribution, thereby enhancing the overall economic efficiency. By harnessing the innovative prowess of technology, fintech minimizes the gap in information availability, reduces the burden of transactional expenditures, and optimizes the allocation of resources, leading to a more efficient and seamless financial ecosystem [2]. Based on risk management, fintech can effectively curb the likelihood of corporate debt defaults arising from principal–agent conflicts by functioning as an external oversight mechanism. Through its monitoring capabilities, fintech mitigates credit risks and enhances corporate creditworthiness, strengthening financial stability [21]. Based on institutional regulation, with the application of innovative tools like big data, fintech adjusts the current regulatory system according to the scale of the market economy and risk management requirements and improves the efficiency of financial regulation [22]. Based on social behavior, from the perspective of happiness economics, fintech mitigates the liquidity constraints and risks of rural households and indirectly improves happiness by enhancing financial inclusion and optimizing resource allocation [23]. Driven by the continuous development of fintech, disembodied and embodied agents are capable of realistically emulating the presence of social interlocutors, which not only enriches the user experience but also improves the effectiveness of communication [24]. Based on sustainable development, by leveraging technologies like mobile payments and digital banking, fintech optimizes the loan process to reach the underserved, finally facilitating poverty alleviation and promoting economic equality [25]; meanwhile, data-driven fintech tools facilitate sustainable investment decisions, directing capital to environmentally and socially beneficial projects [26]. Thus, fintech reduces the possibility of resource mismatches to foster both inclusive finance and green finance, which validates the enabling role of fintech in driving social sustainable development and achieving shared prosperity [27].

3. Research Hypothesis

Emerging from the convergence of innovative technologies and conventional financial systems, fintech leverages advanced technological tools to bridge information asymmetries and deliver sustainable capital solutions for businesses [28]. Within today’s rapidly evolving commercial landscape marked by accelerated technological disruption, evolving consumer preferences, and unprecedented market volatility, enterprises’ organizations are compelled to strategically prioritize holistic digital transformation as a fundamental catalyst for attaining value-oriented developmental objectives [29]. However, the substantial financial commitments to technological innovation, digital infrastructure modernization, and workforce upskilling initiatives frequently impose significant constraints on organizational resource allocation capabilities. Through the strategic aggregation of big data across financial ecosystems, the methodical mapping of technological–industrial value creation networks, and the multidimensional expansion of financial service accessibility channels, this paradigm-shifting innovation effectively reduces structural barriers to credit market participation [30]. Such transformations empower enterprises to circumvent traditional funding obstacles, thereby catalyzing the full-spectrum implementation of enterprise-wide digital transformation initiatives across operational, strategic, and governance dimensions.

In the realm of the eco-friendly transition, fintech emerges as a critical enabler of targeted environmental empowerment through the strategic development of sustainable financial infrastructure. For instance, blockchain-enabled systems ensure the immutable tracking and verification of carbon footprint records, while IoT-enabled devices embedded across production chains enable the continuous monitoring of energy utilization and pollutant discharge. This integration generates comprehensive environmental profiles that evolve in real time. Leveraging these advancements, financial entities can design sophisticated evaluation systems for ecological creditworthiness, utilizing analytics-powered risk profiling to diminish financing hurdles for sustainability initiatives [31]. Furthermore, by enhancing the reliability and auditability of enterprises’ environmental disclosures, fintech mitigates the fraudulent manipulation of ecological metrics, thereby curbing deceptive “greenwashing” practices [32], maximizing profits [33], and promoting corporate green transformation.

As a potent catalytic force in organizational modernization, fintech propels the simultaneous advancement of enterprises’ digitalization and ecological sustainability. The shift towards digital operations demands substantial computational power and expansive data infrastructure. However, this technological progression inevitably generates intensive energy demands [34], which, to a certain extent, are potentially in conflict with the goal of green transformation. Nevertheless, by embedding environmental, social, and governance (ESG) metrics within investment evaluation systems, fintech effectively resolves this dichotomy, incentivizing businesses to prioritize eco-conscious technological upgrades [35]. This strategic alignment not only strengthens institutional environmental stewardship but also fosters self-driven commitments to sustainable innovation. Consequently, organizations are increasingly pursuing harmonized digital–ecological strategies, enhancing operational productivity while mitigating ecological footprints to achieve the dual optimization of economic and environmental benefits. Fintech not only provides digital tools to optimize resource management and process efficiency but also promotes greening transformation by directing capital flow to environmental projects through green financial products. In addition, fintech also promotes information sharing and cooperation among enterprises, enabling them to learn from each other and make progress together on the road of “digital–green synergy”, forming a virtuous cycle. In light of the above analysis, the following research hypothesis is formulated:

H1. 

Fintech significantly enhances the digital–green synergy of enterprises by facilitating their dual transformation in digitalization and greening.

The theoretical research framework of this paper is shown in Figure 1.

By harnessing the synergistic potential of advanced technological ecosystems, fintech reshapes conventional financial paradigms through big data analytics, artificial intelligence, and algorithmic innovations, enhancing capital distribution effectiveness via transparent information flows, precise risk evaluation frameworks, and automated transactional architectures. This digital transformation enhances capital allocation efficiency by establishing multidimensional information networks that aggregate both structured transactional data and unstructured behavioral patterns [36], thereby addressing systemic information asymmetries through enhanced data granularity. By generating comprehensive informational networks, it mitigates distortions caused by data imbalance and realigns financial capital with industrial requirements [2]. On the one hand, geography-agnostic capital mobilization directs societal surplus funds toward sustainable technology sectors, thereby lowering fiscal barriers to eco-friendly transitions. On the other hand, its algorithm-driven resource-matching mechanism can dynamically identify the synergistic needs of enterprises’ digitization and decarbonization development and promote the coupled allocation of factors such as arithmetic power, data, and clean energy. Drawing upon resource dependency frameworks and adaptive capability theories, the enhanced allocation efficiency empowers enterprises to rapidly acquire specialized assets for ecological innovation through digital marketplaces. Blockchain-enabled digital interfaces allow organizations to achieve the holistic governance of energy consumption analytics, carbon emission mapping through IoT sensors, and sustainable supply chain optimization via digital twins. These technological enablers establish mutually reinforcing cycles between digital transformation and green development, creating recursive improvement loops in both operational efficiency and environmental performance. In light of the above analysis, the following research hypothesis is formulated:

H2. 

Fintech enhances enterprises’ digital–green synergy by facilitating resource allocation efficiency.

Driven by carbon peak targets and neutrality targets and increasingly fierce market competition, green technology innovation has developed into a core path for manufacturing enterprises to achieve sustainable development. Through technological breakthroughs in energy saving and consumption reduction, enterprises can not only effectively reduce operating costs but also significantly enhance the endogenous motivation of enterprises to carry out green innovation [36]. Fintech demonstrates multidimensional catalytic effects through digital financial platforms, effectively alleviating both financing bottlenecks and risk apprehension in green innovation ecosystems. From an informational economics perspective, fintech remedies structural market failures by deploying intelligent matching mechanisms that reconcile information asymmetries between capital allocators and technology developers. Sophisticated predictive modeling techniques incorporating advanced data analytics and machine learning algorithms substantially elevate credit assessment accuracy, thereby directing financial resources toward environmentally impactful projects with optimal market viability [37]. From another angle, blockchain’s immutable architecture establishes reliable verification mechanisms for environmental intellectual property management. Distributed ledger technology ensures audit-proof documentation throughout green patent lifecycle processes—from certification protocols to carbon emission tracking. Such cryptographic assurance mechanisms significantly diminish transactional uncertainties in technology commercialization while preventing ethical violations in innovation transfers. Through the cost-efficient modernization of ecological practices and amplified technological convergence effects, fintech ultimately facilitates the harmonious integration of digital transformation and environmental sustainability objectives, creating self-reinforcing mechanisms for circular economic development. In light of the above analysis, the following research hypothesis is formulated:

H3. 

Fintech enhances enterprises’ digital–green synergy by promoting enterprises’ green technology innovation.

Enhanced environmental information disclosure acts as a pivotal mechanism to address enterprises’ environmental information gaps [38], with its theoretical and practical logic for enabling digital-environment integration rooted in the dual drivers of external pressure and signaling theories [39], fulfilling the essential function of systematically conveying organizational ecological initiatives, sustainability metrics, and associated operational risks to external entities through standardized, verifiable communication channels. The implementation of distributed ledger systems in ecological reporting frameworks substantially elevates data integrity, effectively neutralizing distortionary effects caused by informational imbalances within sustainable investment ecosystems [40]. Such operational transparency generates a dual operational advantage within green finance markets: financial intermediaries acquire advanced capabilities for the precise identification of environmentally responsible enterprises through algorithmically processed environmental data streams, while enterprises face strengthened accountability pressures to optimize ecological transition outcomes. Empirical investigations reveal statistically significant correlations between rigorous disclosure regimes and demonstrable reductions in industrial emission intensity metrics [16], establishing self-perpetuating market dynamics that incentivize ecological stewardship. Through the environmental information disclosure mechanism, fintech solutions effectively dismantle traditional information barriers between capital allocators and enterprises. This technological mediation enables the precision-targeted deployment of environmental financing instruments while simultaneously driving process innovations in enterprises’ sustainability management, resulting in synergistic improvements across both operational efficiency parameters and environmental impact mitigation trajectories throughout digital–green transformation journeys. In light of the above analysis, the following research hypothesis is formulated:

H4. 

Fintech enhances enterprises’ digital–green synergy by reducing information asymmetry through environmental information disclosure.

4. Research Design

4.1. Research Sample

This paper selects the data of Chinese A-share companies listed on the Shanghai Stock Exchange (SHSE) and the Shenzhen Stock Exchange (SZSE) from 2011 to 2022 as the initial research sample, and the raw data were obtained from the China Stock Market & Accounting Research Database (CSMAR), the Chinese Research Data Services Platform (CNRDS), and the Wind Economic Database, while the data of the annual reports were obtained from the official websites of the SHSE and the SZSE. Following rigorous data curation procedures, the sample selection process excluded the following: (1) financially distressed entities (ST/*ST-labeled) and PT delisted corporations; (2) financial sector enterprises; (3) enterprises undergoing initial public offerings during the observation window. The final analytical sample retains only enterprises demonstrating uninterrupted five-year data completeness through longitudinal inclusion criteria and winsorizes the continuous variables by 1% to mitigate outlier influences. The paper ends up with a total of 34,561 observations, containing 3491 listed enterprises located in 384 prefecture-level cities.

4.2. Measurement Model Design

The benchline regression model is shown in Equations (1)–(3).

$${Digital}_{i,j,t}={\alpha }_0+{\alpha }_1{Fintech}_{i,j,t}+{\alpha }_2{Controls}_{i,j,t}+{\mu }_i+{\phi }_i+{\eta }_i+{\epsilon }_{i,j,t}$$

(1)

$${Green}_{i,j,t}={\beta }_0+{\beta }_1{Fintech}_{i,j,t}+{\beta }_2{Controls}_{i,j,t}+{\mu }_i+{\phi }_i+{\eta }_i+{\epsilon }_{i,j,t}$$

(2)

$${DG}_{i,j,t}={\gamma }_0+{\gamma }_1{Fintech}_{i,j,t}+{\gamma }_2{Controls}_{i,j,t}+{\mu }_i+{\phi }_i+{\eta }_i+{\epsilon }_{i,j,t}$$

(3)

where subscripts i, j, and t are firms, industries, and years, respectively. ${Digital}_{i,j,t}$ is the digitization level of firm i of industry j in year t; ${Green}_{i,j,t}$ is the greening level of firm i of industry j in year t; and ${DG}_{i,j,t }$ is the digital–green synergy of firm i of industry j in year t. ${Fintech}_{i,j,t}$ is the level of fintech development of firm i of industry j in year t. ${Controls}_{i,j,t}$ is the set of control variables. The sample data passes the Hausman test, so this paper draws on the methodology of Awais, Afzal [41], using a fixed-effects model to control for the unobservable invariant characteristics of firms and factors that can affect all the individuals but change over time. ${ \mu }_i,{ \phi }_i$, and ${\eta }_i$ are the firm, industry, and year fixed effects, respectively; ${ \epsilon }_{i,j,t}$ is the residual term.

4.3. Definition of Variables

4.3.1. Explained Variable

Enterprises’ digitization: Referring to the methodology of Jiang, Du [42], we conducted automated web scraping via Python 3.10 to collect textual data from all the A-share firms listed. Full-text extraction was performed using Java PDFbox 3.0, followed by a quantitative text mining framework to categorize word frequencies related to core digital technologies, as well as their convergence with digital business application scenarios. Statistical aggregation was applied to generate two distinct metrics: the foundational technology lexicon frequency and the applied scenario lexicon frequency. To mitigate the right-skewed distribution in the dataset, both the total and the categorized word frequencies were logarithmically transformed (the natural logarithm of the frequency count plus one) to construct a composite digitization index.

Enterprises’ greening: Referring to Loughran and McDonald [43], we choose annual reports of listed enterprises as the observation text. First, environmental sustainability constitutes strategically critical information for publicly traded entities, which is systematically documented in annual reports—the most broadly disseminated and institutionally curated disclosure medium. This approach aligns with the dual function of annual reports as both summative retrospectives and forward-looking strategic guides [44]. Second, the standardized disclosure requirements governing the listed companies’ annual reports, encompassing stringent structural templates and terminological conventions, substantially optimize the accuracy of keyword identification and semantic pattern recognition. Consequently, the operationalization of corporate greening metrics through a frequency analysis of sustainability-related lexemes in annual reports demonstrates robust methodological validity. Methodologically, we used a comprehensive taxonomy of 113 environmental sustainability indicators across five strategic dimensions: (1) public engagement initiatives, (2) governance philosophies, (3) eco-innovation advancements, (4) emission mitigation protocols, and (5) environmental monitoring systems. Utilizing computational text mining techniques, we quantified the occurrence frequency of these predefined lexemes within the annual report corpus. To construct the final environmental performance metric, we applied logarithmic transformation (applying the natural logarithm to the frequency counts plus one), thereby normalizing distributional skewness and enhancing metric interpretability.

Enterprises’ digital–green synergy: Since enterprises’ digital–green synergy is a strategic, synergistic relationship of mutual support and mutual promotion, the coupling coordination degree model can reflect the overall efficacy and synergistic effect of multiple subsystems in a comprehensive way [45]. First, digital and green were standardized by using the Min-Max normalization method for data standardization. The digitalization level and the greening level of the enterprise after the deviation standardization are named Digital_std and Green_std, respectively. Secondly, the formula for defining the two-dimensional coupling degree is as follows:

$$C=2\times {\left[{\displaystyle \frac{\mathrm{Digital\_std}\times \mathrm{Green\_std}}{\mathrm{Digital\_std}+\mathrm{Green\_std}}}\right]}^{1/2}$$

(4)

Third, in order to more accurately measure the degree of synergy between the digitization and the greening of enterprises, the coupled coordination degree model is introduced for the next calculation:

$$D=\sqrt{C\times T}, T=\alpha \times \mathrm{Digital\_std}+\beta \times \mathrm{Green\_std}$$

(5)

D represents the coupling synergy degree of the composite system, T represents the coordination of enterprises’ digitization and greening, and α and β are the weights of the system. Since enterprises’ digitization and enterprises’ greening are of equal importance in this study, α = β = 0.5 is set. Finally, the degree of enterprises’ digital–green synergy is calculated according to the above formula.

4.3.2. Core Explanatory Variable

Fintech: Existing studies have three main types of methods for measuring the level of fintech development: (1) The utilization of the China Digital Inclusive Finance Index, curated by Peking University’s Digital Finance Research Center [46]. Derived from Ant Group’s transactional data infrastructure, this index quantifies fintech sophistication through tripartite dimensions: spatial penetration, transactional intensity, and technological integration. (2) The measurement of fintech development via the spatial aggregation of registered Fintech enterprises within jurisdictional boundaries [47]. (3) The construction of Fintech indices through the semantic pattern recognition of domain-specific lexemes in corporate disclosures. The first two measures are mostly used for macro studies at the national and provincial levels, while the fintech index constructed based on the text-mining method is more suitable for the micro analysis of enterprises. Aligning with microfoundational research objectives, this study implements the computational linguistics paradigm articulated by Ge, Yang [48]. Through the machine learning-enhanced textual interrogation of annual reports, we extracted frequency distributions of 124 fintech-related lexemes spanning six innovation clusters: blockchain architectures, internet finance ecosystems, artificial intelligence applications, sustainable finance mechanisms, distributed cloud infrastructure, and digital transaction platforms. Following algorithmic validation, logarithmic normalization (the base-of-term frequency plus one) was applied to derive annualized firm-specific fintech level indices.

4.3.3. Control Variables

In order to eliminate interference, this article selects a series of control variables, and the specific data are shown in

Table 1. Control variables.

Variables	Method of Measurement
Shareholder	The shareholding ratio of the first largest shareholder
Enterprises’ age (lnAge)	ln (year of index measurement − year of listing + 1)
Asset–liability ratio (ALR)	The ratio of total liabilities to total assets of a firm
Audit opinion (audit)	The audit is to be 0 if the accounting firm issues a standard unqualified opinion; otherwise, the audit is 1
Separation degree of two rights (SOR)	The proportion of the control of the actual controller minus the proportion of ownership
Enterprises’ size (size)	The logarithm of the total assets of the enterprise
Nature of equity (NOE)	One for state-owned enterprises, and zero for otherwise

:

4.3.4. Mechanism Variable

Efficiency: Resource allocation efficiency refers to how efficiently a firm converts resources into outputs. We refer to the idea of Teng, Du [49], and we use the total asset turnover ratio of the enterprises to measure their resource allocation efficiency.

Green innovation level (Green_ino): This study employs environmentally oriented patent filings as an empirical proxy for corporate eco-innovation outputs, specifically encompassing two distinct intellectual property classes: green invention patents and green utility model patents. The methodological rationale for prioritizing patent application volumes over granted patent counts derives from their superior temporal alignment with innovation activities. Whereas authorized patents inherently incorporate temporal distortions through extended bureaucratic vetting processes [50], application metrics provide a contemporaneous reflection of organizational commitments to sustainability-driven R&D efforts. This operational choice ensures chronological congruence between firms’ green innovation investments and their measurable outputs within the analytical framework.

EID: This paper uses environmental information disclosure metrics by adapting the content analysis framework pioneered by Cho and Patten [51]. Leveraging a systematically evaluated scoring rubric, we quantify the quality of corporate environmental disclosures through the textual interrogation of listed firms’ sustainability reports. Specifically, domain-specific indicators are calibrated against disclosed environmental governance practices, operational impacts, and compliance initiatives, with weighted scores algorithmically aggregated into a composite EID quality index.

4.3.5. Moderating Variable

In this paper, referring to Li, Han [52], the selected moderating variables are the age and occupational heterogeneity of the executive team. Heterogeneity is constructed and measured using the Herfindal–Hirschman index method, and the formula is $\mathrm{H}=1-\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{p}}_{\mathrm{i}}^2$, H ∈ [0, 1], where ${\mathrm{p}}_{\mathrm{i}}$ denotes the ratio of the members classified under category i to the total executive cohort; n indicates the number of members in each category, where higher values reflect greater diversity. The measurement classification is operationalized through two dimensions: (1) age heterogeneity, measured on an ordinal scale with five cohorts (≤30 years = 1; 31–40 = 2; 41–50 = 3; 51–60 = 4; ≥61 = 5); (2) professional background heterogeneity, classified into nine mutually exclusive domains (Production = 1; R&D = 2; Design = 3; Human Resources = 4; Management = 5; Marketing = 6; Financial Management = 7; Corporate Finance = 8; Legal = 9).

4.4. Descriptive Statistics

As shown in

Table 2. Descriptive statistics.

Variables	N	Mean	Median	SD	Min	Max
Digital_std	34,561	0.224	0.172	0.222	0	0.988
Green_std	34,561	0.415	0.408	0.193	0	0.988
DG	34,561	0.374	0.501	0.298	0	0.936
Fintech	34,561	3.178	3.219	1.437	0	6.263
lnAge	34,561	2.92	2.944	0.343	0.693	4.025
Shareholder	34,561	0.339	0.315	0.15	0.003	0.9
SOP	34,561	0.045	0.000	0.074	0	0.603
Size	34,561	22.237	22.056	1.351	17.641	28.636
ALR	34,561	0.43	0.421	0.211	0.007	3.513
Audit	34,561	0.054	0.000	0.226	0	1
NOE	34,561	0.351	0.000	0.477	0	1
Green_ino	34,561	3.686	0.000	24.953	0	1369
Efficiency	34,561	0.617	0.513	0.525	0.001	13.914
EID	34,561	7.649	5.000	6.966	0	37

, the median of enterprises’ digitalization being substantially lower than the mean suggests that a significant proportion of the enterprises falls below the average in digital maturity, with some yet to initiate digital transformation initiatives. Regarding green practices, the enterprises exhibit a mean score of 0.415 (SD = 0.193) and a median of 0.408, highlighting pronounced disparities in environmental sustainability efforts across firms. The digital–green synergy degree spans from 0 to 0.936, underscoring substantial heterogeneity in the integration of dual transformation strategies among the enterprises. Notably, the fintech development metric displays a standard deviation of 1.437, with values ranging from 0 to 6.263, which starkly illustrates systemic gaps in fintech innovation capabilities across the industry participants. The minimal datasets covered in this paper are available in the Supplementary Material.

5. The Empirical Results and Analysis

5.1. Basemark Regression

Table 3. Baseline regression.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	Digital_std	Digital_std	Green_std	Green_std	DG	DG
Fintech	0.093 *** (0.004)	0.091 *** (0.004)	0.032 *** (0.002)	0.029 *** (0.002)	0.124 *** (0.003)	0.123 *** (0.003)
lnAge		0.036 (0.024)		−0.012 (0.021)		0.009 (0.022)
Shareholder		−0.097 *** (0.015)		0.040 ** (0.019)		−0.031 (0.026)
SOP		−0.006 (0.012)		0.031 (0.022)		0.032 (0.020)
Size		0.019 *** (0.002)		0.019 *** (0.003)		0.014 *** (0.004)
DLR		0.010 (0.008)		−0.004 (0.009)		−0.007 (0.014)
Audit		0.001 (0.002)		−0.005 (0.005)		0.002 (0.006)
Govcon		0.007 (0.005)		−0.002 (0.007)		0.005 (0.008)
_cons	−0.072 *** (0.014)	−0.572 *** (0.085)	0.314 *** (0.006)	−0.079 (0.113)	−0.021 ** (0.009)	−0.346 *** (0.120)
Industry_Fixed	YES	YES	YES	YES	YES	YES
Year_Fixed	YES	YES	YES	YES	YES	YES
Firm_Fixed	YES	YES	YES	YES	YES	YES
N	34,561	34,561	34,561	34,561	34,561	34,561
${\mathrm{R}}^2$	0.852	0.854	0.695	0.697	0.673	0.673

Note: the figures in the brackets are the robust standard errors clustered to the industry level; the same below. ** p < 0.05, *** p < 0.01.

presents the baseline regression results, with columns (1), (3), and (5) reporting the estimates excluding the control variables and columns (2), (4), and (6) reporting the estimates incorporating the control variables. The findings indicate that fintech’s coefficients remain positive and statistically significant in all of them, confirming its robust positive impact on corporate digitization, greening, and digital–green synergy. These results substantiate Hypothesis H1.

5.2. Endogeneity Test

While the baseline OLS estimates in Table 3 provide the initial evidence of fintech’s role in digital–green synergy, potential endogeneity concerns require rigorous causal identification. Specifically, (1) Reverse Causality: Digital–green synergy outcomes may incentivize enterprises/regions to adopt fintech, creating bidirectional causality. (2) Omitted Variables: Unobserved factors, such as regional innovation culture, could jointly drive Fintech and digital–green synergy. (3) Measurement Error: Fintech proxies may contain non-classical noise. Endogeneity is addressed below, using the instrumental variables approach.

First, employing the temporal lag of core explanatory variables (Fintech_L) as instrumental variables, this study draws methodological precedent from Li, Du [53]. The selection rationale operates on two dimensions: (1) Fintech_L maintains a substantial correlation with contemporaneous fintech measures; (2) its temporal precedence ensures theoretical exogeneity relative to current-period dependent variables, thereby mitigating reverse causality concerns. The 2SLS estimation outcomes, detailed in columns (1)–(2) of

Table 4. Endogeneity test.

Variables	(1)	(2)	(3)	(4)
	Fintech	DG	Fintech	DG
Fintech		0.120 ***		0.156 ***
		(0.006)		(0.045)
Fintech_L	0.424 ***
	(0.014)
Post*Index			0.012 ***
			(0.002)
Controls	YES	YES	YES	YES
Industry_Fixed	YES	YES	YES	YES
Year_Fixed	YES	YES	YES	YES
Firm_Fixed	YES	YES	YES	YES
N	26,370	26,370	29,692	29,692
${\mathrm{R}}^2$	0.215	0.157	0.149	0.837
Under identification test	1111.150 ***		38.090 ***
Weak identification test	971.320		66.221

Note: *** p < 0.01.

, confirm instrument relevance through statistically significant first-stage associations. Crucially, second-stage results demonstrate that fintech’s positive impact remains significant (p < 0.01), corroborating fintech’s facilitative role in enterprises’ digital–green synergy.

Second, the interaction term between the historical fixed-line telephone density (1984 per capita) and the prior-year digital economy index serves as an instrumental variable. Fintech development inherently depends on digital infrastructure evolution, where historical telecommunication penetration (proxied by landline prevalence) shaped early internet accessibility and subsequent digital advancement. Regions with greater historical telephone adoption typically exhibit more mature digital economic foundations. This study operationalizes this relationship through the interaction instrument Post*Index (1984 telephones per capita × lagged digital economy index). The 2SLS estimates in Table 4, columns (3)–(4), confirm the instrument relevance through the statistically significant first-stage associations. The second-stage results maintain fintech’s positive coefficient significantly, further confirming its facilitative role in digital–green synergy development.

6. The Further Analysis

6.1. Mechanism Test

6.1.1. Test of Efficiency Enhancement Mechanism

Based on the proxy variables selected above (efficiency), the regression results presented in column (1) of

Table 5. Mechanism test.

Variables	(1)	(2)	(3)
	Efficiency	Green_ino	EID
Fintech	0.018 ***	0.577 **	0.289 ***
	(0.005)	(0.277)	(0.058)
Controls	YES	YES	YES
_cons	1.287 ***	−99.759 **	−13.787 ***
	(0.334)	(37.794)	(3.234)
Industry_Fixed	YES	YES	YES
Year_Fixed	YES	YES	YES
Firm_Fixed	YES	YES	YES
N	34,561	34,561	34,561
${\mathrm{R}}^2$	0.776	0.637	0.74

Note: ** p < 0.05, *** p < 0.01.

show a positive and statistically significant coefficient at the 1% level. These outcomes empirically validate the operational efficacy of the efficiency improvement mechanism, thereby confirming Hypothesis H2.

6.1.2. Test of Green Innovation Mechanism

Based on the analysis above and the selected variables (Green_ino), the estimation outcomes presented in column (2) of Table 5 exhibit a statistically significant and positive coefficient (p < 0.01). This empirical pattern validates the operational significance of the green innovation mechanism, consequently providing robust confirmation for Hypothesis H3.

6.1.3. The Test of the EID Mechanism

Building upon the investigations into the disclosure mechanisms above, this study incorporates the comprehensive corporate EID quality index as an operational proxy. As evidenced by the empirical results documented in column (3) of Table 5, the regression analysis yields a statistically significant positive coefficient (p < 0.01), demonstrating the operational validity of environmental disclosure mechanisms. This systematic pattern provides conclusive empirical verification for Hypothesis H4.

6.2. Heterogeneity Test

6.2.1. Enterprises’ Size Heterogeneity

Building upon the observed organizational disparities in technological adoption capacities, the established enterprises demonstrate enhanced capabilities to rapidly assimilate fintech solutions through their substantial financial reserves, dedicated technical personnel, and accumulated data assets. Conversely, small and medium-sized enterprises (SMEs) exhibit heightened sensitivity to fintech implementation costs, constrained by precarious liquidity positions and limited technological assimilation capabilities. Consequently, it is reasonable to hypothesize that the impact of fintech on firms’ digital–green synergy will vary depending on the heterogeneity of the firms’ size.

This paper employs stratified regression analysis based on enterprises’ size, categorizing firms into large enterprises (total assets exceeding the industry-specific median thresholds) and SMEs (sub-median asset holdings). As demonstrated in columns (1)–(2) of

Table 6. Heterogeneity test.

Variables	(1)	(2)	(3)	(4)
	Large Enterprises	SMEs	Heavily Polluting	Non-Heavily Polluting
Fintech	0.130 ***	0.117 ***	0.125 ***	0.121 ***
	−0.003	−0.003	−0.005	−0.003
Controls	YES	YES	YES	YES
_cons	−0.16	−0.062	−0.526 **	−0.400 ***
	−0.13	−0.118	−0.239	−0.125
Industry_Fixed	YES	YES	YES	YES
Year_Fixed	YES	YES	YES	YES
Firm_Fixed	YES	YES	YES	YES
N	17,147	17,131	7681	21,962
${\mathrm{R}}^2$	0.683	0.706	0.615	0.681
Coefficient difference p-value	0.092 *		0.000 ***

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. The coefficient difference p-values were calculated from the estimates of the Chow test for the interaction term model.

, both cohorts exhibit statistically significant positive coefficients, aligning with the baseline regression outcomes. Notably, the marginal effect of fintech adoption on digital–green integration proves substantially stronger for large enterprises, and the Chow test p-value is less than 0.1 at the 10% level, thereby indicating significant differences in the coefficients between the groups. This differential impact likely stems from large enterprises’ superior absorptive capacity—manifested through mature digital ecosystems and accumulated green technological capital—enabling the efficient exploitation of fintech synergies while amortizing technological transition costs through scale efficiencies; meanwhile, SMEs face constraints from capital scarcity, limited technological assimilation capabilities, and inadequate data asset accumulation, preventing the attainment of the minimum viable scale threshold for fintech implementation, thereby diminishing their marginal synergy gains.

6.2.2. Industry Heterogeneity

On the one hand, heavily polluting industries are characterized by high energy consumption, high emissions, a high degree of industrialization, and a total pollutant output that is much larger than that of non-heavily polluting industries [54]; on the other hand, heavily polluting enterprises have a strong, rigid demand for green transformation but face a dual bottleneck, namely of their weak digital foundation and the locking of the specialization of their assets; as a result, they rely more on fintechs to break down the sunk-cost barriers. Therefore, it is logical to speculate that the impact of fintech on firms’ digital–green synergy will be different due to the heterogeneity of the degree of pollution emission of enterprises.

Referring to Wang, Chen [55], this paper performs a group regression by categorizing firms into heavily polluting industries and non-heavily polluting industries to test the effect of the industry’s nature in the development of fintech on enterprises’ digital–green synergy. The industry-specific heterogeneity analysis, presented in columns (3)–(4) of Table 6, reveals statistically significant positive coefficients for both the heavily polluting and the non-heavily polluting industry cohorts. Notably, fintech’s catalytic influence on digital–green integration demonstrates amplified effects in environmentally intensive sectors, evidenced by a Chow test statistic of less than 0.01 at the 1% level, indicating significant differences in the coefficients between the groups. This differential efficacy suggests that regulatory-intensive industries experience heightened responsiveness to fintech-enabled sustainability solutions, potentially attributable to institutional pressures. Under the stringent environmental mandates that are characteristic of pollution-intensive sectors, firms face intensified motivations to leverage digital–green synergy as compliance mechanisms while mitigating regulatory risks through technological innovation.

6.3. Test of the Moderating Effect

According to the theory of high-ranking teams [52], executive teams function as pivotal determinants of organizational strategic trajectories, wherein individual demographic characteristics and cognitive schemas fundamentally shape corporate decision-making paradigms. The age heterogeneity within leadership cohorts systematically moderates fintech’s catalytic efficacy in advancing digital–green synergy. Junior executives typically demonstrate greater receptivity to experimental technologies and disruptive innovation frameworks, thereby accelerating organizational digitization. Conversely, senior executives exhibit stronger inclinations toward longitudinal sustainability prioritization, ensuring methodological stability in ecological transition strategies. This intergenerational cognitive synergy effectively plays a moderating role between radical digital innovation trajectories and evolutionary environmental adaptation processes, reconciling technological discontinuity with incremental green institutionalization through complementary governance mechanisms.

The heterogeneity of professional backgrounds enables executive teams to integrate knowledge and resources from multiple fields to drive synergies between digitization and greening. Executives with technological backgrounds can drive digital innovation, for example, by introducing blockchain technology to optimize supply chain transparency, while executives with environmental backgrounds can ensure that greening goals are achieved, for example, by developing a scientific carbon emissions monitoring system. Diverse professional backgrounds help executive teams to identify the interface between digitalization and greening and drive cross-sectoral innovation.

The moderation analysis results presented in columns (1)–(2) of

Table 7. Test of mechanism and moderating effect.

Variables	(1)	(2)
	Age	Occupation
age	−0.031
	(0.048)
Fintech × age	0.291 ***
	(0.035)
occ		0.056 **
		(0.025)
Fintech × occ		0.138 ***
		(0.007)
Controls	YES	YES
_cons	−0.463 ***	−0.493 ***
	(0.167)	(0.157)
Industry_Fixed	YES	YES
Year_Fixed	YES	YES
Firm_Fixed	YES	YES
N	27,926	27,946
${\mathrm{R}}^2$	0.643	0.671

Note: ** p < 0.05, *** p < 0.01.

exhibit statistically significant positive coefficients. The empirical evidence confirms that executives’ age diversity and occupational diversity significantly intensify fintech’s ability to strengthen enterprises’ digital–green synergy.

6.4. Robustness Check

6.4.1. Substitution of Explanatory Variables

This paper uses a new fintech measure (Fintech_M) through the logarithmic transformation of the Digital Financial Inclusion Index (from the Peking University Digital Finance Research Center) then employs this adjusted variable in alternative model specifications. The results in

Table 8. Robustness check.

Variables	(1)	(2)
	DG	DG
Fintech/Fintech_M	0.009 *	0.124 ***
	(0.005)	(0.003)
Controls	YES	YES
_cons	−0.584 ***	−0.299 *
	(0.156)	(0.152)
Industry_Fixed	YES	YES
Year_Fixed	YES	YES
Firm_Fixed	YES	YES
N	34,561	23,403
${\mathrm{R}}^2$	0.614	0.659

Note: * p < 0.1, *** p < 0.01.

column (1) confirm fintech’s persistent statistically significant positive effect on enterprises’ digital–green synergy, validating the baseline regression’s robustness to measurement variations.

6.4.2. Changing the Sample Size of the Observations

Considering that a complete dataset can better reflect the operational and business changes of enterprises and, thus, provide more accurate results in the regression analysis, this paper excludes enterprises with less than 12 years of data in the study period. As shown in column (2) of Table 8, the coefficients are still positive and significant, validating the baseline regression’s robustness to changes in the dataset.

7. Conclusions

This study investigates the mechanisms through which fintech influences the synergistic development of enterprises’ digital–green synergy. The findings reveal three key results: First, fintech exerts a significant positive impact on enterprises’ digitalization, enterprises’ greening, and enterprises’ digital–green synergistic development. Second, the heterogeneity analyses indicated stronger fintech-driven effects on digital–green synergy for large firms compared to SMEs and greater impacts in highly polluting industries relative to less-polluting sectors. Third, the mechanism tests identified efficiency enhancement, green innovation, and environmental disclosure as three pathways through which fintech facilitates synergy development. Additionally, increased age and occupational diversity within executive teams amplify fintech’s positive influence on enterprises’ digital–green synergy.

Based on the above research conclusions, the challenges for the current enterprises’ digital–green synergistic development include the existence of a high technology application threshold, insufficient data governance, weak ecological synergy, and further obstacles and difficulties; we put forward the following fintech-enabled proposals for enterprises’ digital–green synergistic development.

First, the government should strengthen the overall enabling role of fintech in digital–green synergy. The government can introduce a “small and beautiful” technology inclusion program and join hands with financial institutions to launch a “technology + green” special loan to break the scale difference; it can also focus on heavily polluting industries and utilize technological bundling and regulation to force enterprises to use real-time data uploading tools when applying for technological reform loans or subsidies. For example, for high-carbon industries, enterprises applying for technological reform loans or subsidies must use fintech tools to upload data in real time.

Second, the government needs to smooth and deepen the path of efficiency, innovation, and transparency. The government can promote the “industrial internet + green finance” model, automatically matching green credit lines to enterprises accessing the national industrial internet platform; set up a “fintech green patent gas pedal”, which can provide priority review channels for green technology patents based on blockchain and AI; and compel enterprises to apply for technological reform loans or subsidies by using fintech tools to upload data in real time. The establishment of a “FinTech Green Patent Accelerator” can provide a priority review channel for blockchain- and AI-based green technology patents; listed companies can be mandated to disclose environmental data through standardized API interfaces, and natural language processing technology can be used to automatically verify “greenwashing” behavior and incorporate it into credit ratings.

Third, enterprises should focus on the heterogeneous capacity building of their executive team. The government can launch the professional certification of “enterprise green manager”, require at least one person in the executive team of heavily polluting industries and key emission enterprises to hold the certification, and incorporate it into the governance assessment index of listed enterprises. At the same time, enterprises should actively promote cross-industry executive exchanges and organize the posting of manufacturing executives in financial and technological enterprises so as to promote the transfer of cross-border experience and the enhancement of their composite decision-making capacity.

Although this paper explores the impact of fintech on enterprises’ digital–green synergy, there are some limitations. Specifically, our study relies on data from Chinese A-share companies listed on the SHSE and the SZSE from 2011 to 2022, for which there are limited corresponding findings in the Chinese region. Also, since our research is primarily based on data from large companies, it cannot be directly generalized to small enterprises. Moreover, future research could examine how different types of fintech innovations (e.g., digital payments, network security technology) specifically contribute to the observed synergy effects.