FinTech Adoption and Sustainable Development Performance: Moderating Role of Local Environmental Concern Across Industries

FinTech Adoption and Sustainable Development Performance: Moderating Role of Local Environmental Concern Across Industries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article FinTech Adoption and Sustainable Development Performance: Moderating Role of Local Environmental Concern Across Industries Juan Hou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8025487/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Under the background of China's “Dual Carbon” strategy, corporate sustainable development performance has become a core indicator of high-quality development. As a key driver for improving resource allocation efficiency, FinTech adoption at the enterprise level requires further investigation regarding its impact on sustainable development. Drawing on institutional theory and the resource-based view, this study develops a theoretical framework to empirically examine the influence of FinTech adoption on firms' sustainable development performance and its underlying mechanisms. The model incorporates local government environmental concern as a moderating variable to explore its conditional effects, and further investigates industrial heterogeneity. The empirical results reveal that FinTech adoption significantly enhances sustainable development performance. The effect is more pronounced among non-state-owned enterprises and heavily polluting firms. Additionally, local government environmental concern exhibits a substitution effect in the relationship between FinTech adoption and sustainability performance, reflecting cross-industry differences in resource endowments, technological capacity, and policy responsiveness. FinTech adoption Sustainable development performance Environmental regulation Government environmental concern Industry heterogeneity Institutional and resource-based theory 1 Introduction Under the dual pressure of intensified global climate change and environmental degradation, achieving sustainable development has become a common goal of the international community. The United Nations’ 2030 Agenda integrating economic growth, social progress, and environmental protection. Guided by this global consensus, the Chinese government has prioritized sustainable development. Subsequently, the National Development and Reform Commission issued the “Action Plan for Carbon Peak by 2030” in 2021, systematically deploying key tasks, policy paths, and guarantees to accelerate the transformation toward a green and low-carbon economy. As the basic unit of market economies, enterprises play a pivotal role in national ecological advancement and green development. Enterprise sustainable development emphasizes continuous technological upgrading, the adoption of advanced equipment, and talent cultivation to meet industry demands, while simultaneously considering economic, environmental, and social dimensions in delivering products or services, thereby enhancing their competitive edge. Under the constraints of the dual carbon policy, evolving environmental regulations, and increasing external uncertainties, enterprises are compelled to seek a new dynamic balance between environmental protection and operational performance, transforming fundamentally from traditional extensive growth models toward low-carbon, green, and digitalized pathways. More importantly, the rapid development of financial technologies like artificial intelligence, blockchain, cloud computing, and big data, has profoundly reshaped enterprise operations. FinTech not only transforms traditional financing models but also optimizes risk management and enhances resource allocation efficiency, offering technological support for sustainable enterprise development. In the Chinese context, the interaction between local government environmental regulation intensity and industry characteristics introduces institutional and industry heterogeneity into the FinTech–sustainability relationship. Current research on FinTech shows two main tendencies. First, most studies focus on the impact of FinTech on financial institutions. For example, Gozman (2018) and Chen (2024) emphasize the technological support FinTech offers to the financial industry, while He (2023) notes its disruption of traditional indirect financing mechanisms. Others such as Yao et al. (2023) and Wang (2023) examine how innovative financial products improve information acquisition. Second, many studies evaluate the macro-level impact of FinTech on corporate capital structure, ESG performance, digital transformation, technological innovation, total factor productivity, regional innovation efficiency, green innovation, and economic growth. However, there is still a lack of micro-level analyses from the perspective of FinTech application at the enterprise level. Moreover, most literature has primarily assessed the impact of FinTech on economic performance, while a balanced focus on social–environmental and financial outcomes is essential to evaluate enterprise sustainability more comprehensively. To address this gap, this study adopts an enterprise-level view of FinTech application, measuring sustainable development performance from both social–environmental and financial perspectives. It investigates how FinTech adoption influences enterprise sustainability, examines local government environmental concern, and explores industry-level heterogeneity. This paper offers three main contributions. First, it expands existing research by investigating the impact of FinTech adoption on enterprise sustainability performance within a unified framework, providing empirical evidence to support corporate sustainability and national goals for dual carbon and high-quality growth. Second, it introduces local government environmental concern as a moderating variable, capturing the influence of informal institutional factors beyond formal regulations and enriching the literature on environmental governance and corporate behavior. Third, the study explores the heterogeneous impacts of FinTech across industries, especially between high-pollution and low-pollution sectors, offering insights for differentiated industrial policy formulation. 2 Theoretical Analysis and Research Hypotheses 2.1 FinTech Adoption on Sustainable Development Performance Enterprise sustainable development emphasizes continuous technological upgrading, the adoption of advanced equipment, and talent cultivation to meet industry demands, while simultaneously considering economic, environmental, and social dimensions in delivering products or services, thereby enhancing their competitive edge. The rise of financial technology (FinTech) has brought profound changes to enterprises, providing new momentum for sustainable development by improving capital acquisition efficiency, optimizing resource allocation, and reducing operational risks (Wang & Zhang, 2025). Firstly, Traditional financing channels, such as bank loans and equity financing, are often restricted by high entry barriers and information asymmetry. In contrast, FinTech applications enable enterprises to secure financing more efficiently and flexibly (Zeng, 2024). For example, companies can collaborate with upstream and downstream firms through supply chain finance platforms, leveraging blockchain technology to ensure transaction transparency and security, thereby obtaining lower-cost financing. This directly boosts the economic performance of enterprises, as they can better allocate resources and respond to market changes, improving profitability and market competitiveness. Furthermore, traditional risk management primarily relied on experience and qualitative judgment. Modern FinTech, through AI and blockchain, allows for more precise data analysis and risk forecasting, enabling enterprises to make timely adjustments to decisions, ensuring proper capital allocation and use, thus reducing the cost and complexity of risk management (Cheng et al., 2024). The application of these technologies helps enterprises reduce the frequency of risk events and enhances the scientific nature of decision-making, further contributing to their sustainability. Secondly, the impact of FinTech on social and environmental performance is also gradually becoming evident. As global attention to environmental protection and sustainable development increases, enterprises' environmental responsibilities are rising. FinTech can promote environmental performance by supporting green investments and advancing the development of green financial products (Xue & Pan, 2024). Green financial products can provide financial support for enterprises’ environmental projects, driving investments in reducing carbon emissions, improving energy efficiency, and adopting sustainable technologies (Chen, 2020). By promoting green investment through FinTech, enterprises not only fulfill their social responsibilities but also achieve long-term economic returns. Moreover, enterprises use FinTech to enhance the transparency and fairness of resource allocation, facilitating public oversight and building trust among the public and investors. This technological push for social equity and responsibility enables enterprises to balance economic benefits with social responsibility. It is important to note that different industries exhibit significant differences in pollution levels, environmental requirements, and technological innovation capabilities, leading to heterogeneous effects of FinTech on corporate sustainable development performance across industries. Specifically, differences in pollution levels, technological advancement, and green innovation capabilities among industries determine the role and effectiveness of FinTech in promoting sustainable development. Based on this analysis, it can be proposed: H1: The application of FinTech can significantly enhance corporate sustainable development performance. 2.2 The Moderating Effect of Local Government Environmental Attention The introduction of China’s "carbon peak, carbon neutrality" strategic goals marks a systematic transformation of the environmental governance system (Zhang & Huang, 2023). In this context, the environmental attention of local governments not only influences corporate environmental behaviors but also affects enterprises’ decisions and actions through policy guidance, incentive measures, and market signals. Governments guide enterprises toward green development paths by strengthening environmental policy formulation, enhancing law enforcement and supervision, and providing green incentives (Du et al., 2021; Xie et al., 2023). For instance, local governments often adopt command-and-control environmental regulations, imposing punitive constraints on high-polluting and high-energy-consuming enterprises, thus promoting technological innovation and energy efficiency optimization (Wu & Li, 2024). Research by Tang et al. (2022) also indicates that such policies significantly promote the application and authorization of green patents. At the same time, the rise of FinTech provides enterprises with more precise and efficient tools for green financing and risk control, improving the way resources for environmental protection investments are obtained and stimulating innovation in green technologies. In this context, that may create a complementary effect: the former promotes green development goals through policies, while the latter provides digital technology support. However, innovaation, patcularly green innovation, as the core approach to improving social and environmental performance in sustainable development, often entails higher initial investments and longer technological transformation cycles. In the short term, enterprises may experience a decline in profitability due to increased green investments, which can impact the economic dimension of sustainable development (Li & Zhang, 2024). Additionally, improvements in environmental performance exhibit a time lag and may not be immediately visible. In this context, if the local government imposes excessive environmental pressure, it may force enterprises to over-invest in green projects, increasing their financial burden and operational stress, thereby weakening the role of FinTech in optimizing resource allocation and possibly creating a substitution effect with the company’s digital transformation efforts. For instance, Li et al. (2021) point out that excessive environmental regulations may hinder corporate green innovation, while Li et al. (2023) argue that strong regulations still have a positive effect in heavily polluting industries. Therefore, local government environmental attention may play a complementary or substitution role in the relationship between FinTech application and sustainable development performance, depending on the context. Specifically, when enterprises have a solid foundation in green financial technology, moderate government attention can strengthen the synergistic efficiency of technological investment and green transformation, resulting in a complementary effect. Conversely, in the context of excessive environmental regulations and uncertain returns from innovation, government intervention may weaken the enterprise’s ability to leverage FinTech to improve performance, resulting in a substitution effect. Based on this, it can be proposed: H2a The environmental attention of local governments and FinTech application have a complementary effect on improving corporate sustainable development performance. H2b The environmental attention of local governments and FinTech application have a substitution effect on improving corporate sustainable development performance. 3 Research Design and Variable Explanation 3.1 Data Sources and Sample Selection This study selects non-financial A-share listed companies from 2006 to 2023 as the research subjects to empirically test the impact of FinTech on corporate sustainable development performance. The original data is sourced from the CSMAR database, the "China Statistical Yearbook," and government work reports from official government websites. Data related to FinTech application is obtained through Python-based word frequency statistics on the annual reports of the companies. Data regarding local government environmental attention is derived from word frequency statistics on government work reports. 3.2 Variable Explanation 3.2.1 Dependent Variable Drawing on the studies of Feng et al. (2020), Long (2022), Xie and Zhu (2021), the corporate sustainable development performance (SDP) in this study is a dual performance measure consisting of economic performance and social-environmental performance. The final score is obtained by standardizing the average weight. Economic performance is represented by the ROE (Return on Equity) indicator. For social responsibility performance, this study uses the CSMAR database which includes 12 standards to measure the quality of corporate social and environmental responsibility information. These 12 standards ensuring the authenticity and standardization of information disclosure. After third-party validation, each standard is assigned a value of 0 or 1: a disclosure earns a value of 1, and non-disclosure earns a value of 0. The total score of the 12 standards for each company is then summed and standardized (i.e., the total score divided by 12). A higher value of this index indicates a higher level and quality of social and environmental responsibility fulfillment. 3.2.2 Independent Variables The core independent variable in this study is the level of FinTech application by enterprises (FinTech). Given the lack of a unified quantitative index system for FinTech, this study follows the methods of Li Chuntao et al. (2020), Song Xinlei et al. (2022), and Huang Juan et al. (2023) and adopts a text mining approach. Using Python, the study performs word frequency statistics on FinTech-related keywords in the annual reports of listed companies and standardizes the frequency of occurrence (Table 1 ). This results in the construction of an enterprise FinTech application index. A higher value of this index indicates deeper application of FinTech within the company. Moreover, to ensure the relevance of FinTech application to corporate operations, keywords unrelated to the company’s main business or mentioned only in passing are excluded during the keyword screening process, enhancing the accuracy and representativeness of the index. Table 1 FinTech adoption Keyword Dictionary Dimension FinTech Keywords Artificial Intelligence Artificial intelligence, robots, machine learning, deep learning, neural networks, facial recognition, biometric recognition, voiceprint recognition, pattern recognition, image recognition, virtual reality, augmented reality, knowledge graph, smart technology, smart deposits, smart marketing, intelligent, intelligent technology, intelligent risk control, automation, natural language processing Blockchain Blockchain, distributed ledger, supply chain, Internet of Things, near-field, quantum, quantum communication, quantum communications, data encryption, digital currency, electronic money Big Data Big data, big data analytics, big data services, big data technology, big data models, big data mining, data warehouse, data technology, data models, data mining, data governance, data center, digitalization, digital transformation, digital signature, digital ecosystem, digital marketing Cloud Computing Distributed, distributed storage, distributed computing, distributed architecture, distributed database, trusted computing, cloud adoption, private cloud, virtualization, privacy computing, cloud-based, cloud services, cloud service platform, cloud computing, cloud architecture, cloud platform, cloud system, cloud disaster recovery 3.2.3 Moderating Variable This study selects local government environmental concern (Local Government Environmental Concern, LGEC) as the moderating variable to explore whether the degree of local government attention to environmental issues moderates the relationship between FinTech application and corporate sustainable development performance. Drawing on the methods of Zhou Jintang et al. (2020) and Shao Shuai et al. (2024), this study employs a text mining approach. Using Python tools, the study performs word frequency statistics on the occurrence of keywords related to environmental protection, carbon peak, carbon neutrality, and green development in local government annual work reports. The frequency of these keywords is then matched to corresponding listed company samples by region and year. The intensity of local government environmental concern is measured by the proportion of the number of environmental protection-related keywords to the total word count of the entire government report. This index is statistically standardized to eliminate the impact of differences in report word counts across different years and regions. A higher value of this index indicates a greater tendency for local governments to promote environmental responsibility and green transformation among enterprises within their jurisdiction through policy formulation, supervision and enforcement, and financial incentives, thereby affecting the role of FinTech in corporate sustainable development. Table 2 Local Government Environmental Concern Keyword Dictionary Dimension Selected Keywords Environmental Goals Environmental protection, eco-friendly, green, clean, low-carbon, blue sky, green water, green mountains, sustainable, carbon neutrality, carbon peak, ecological civilization, green development Environmental Factors Ecology, air, climate Environmental Pollution Pollution, sulfur dioxide, chemical oxygen demand, smog, particulate matter, carbon dioxide, energy, carbon emissions, coal burning, pollutant discharge, illegal discharge, exhaust gas, wastewater, waste gas, solid waste, microplastics, heavy metal pollution Environmental Measures Energy saving, emission reduction, desulfurization, denitrification, carbon capture, carbon trading, carbon tax, environmental subsidies, pollution control, clean production, green manufacturing 3.2.4 Control Variables Following the methods of Yang Wei et al. (2021), Pan Yi and Zhang Jinchang (2023), this study select enterprise- and regional-level control variables: firm size (Size), firm age (Firmage), firm growth (Growth), managerial shareholding ratio (Mshare), debt-to-equity ratio (Lev), ownership concentration (Top1), dual roles (dual), board independence (Indep), regional economic development level (AGDP), and industrial structure (IS). In Table 3 , the mean of sustainable development performance (SDP) is 0.384(MIN = 0.02, MAX = 0.917), show that most enterprises score relatively low in sustainable development performance, while some enterprises perform exceptionally well. The standard deviation is 0.141, showing a moderate degree of variation between the samples. The mean of the FinTech application index (FIA) is 2.543(SD = 1.714, MAX = 7.537), indicating considerable variation in FinTech application among the companies. The mean of local government environmental concern (LGEC) is 0.944, with a minimum value of 0 and a maximum value of 11, reflecting significant differences in the level of environmental concern across regions. The numerical data of the variables are of good overall quality, with high differentiation and a wide range. Moreover, there are certain variations between the independent and control variables in the sample, which is conducive to subsequent regression analysis. This allows for a comprehensive reflection of the level of FinTech application and corporate sustainable development, ensuring that the research results possess practical significance. Table 3 Description and Descriptive Statistics of Key Variables Type Variable Name Code Mean P50 SD Min Max Dependent Sustainable Development Performance SDP 0.384 0.421 0.141 0.02 0.917 Independent FinTech Application Index FIA 2.543 2.639 1.714 0 7.537 Moderator Local Government Environmental Concern LGEC 0.944 0.905 0.299 0 11 Control Firm Size Size 22.14 21.93 1.306 19.41 26.44 Firm Age FirmAge 2.859 2.89 0.385 0 3.638 Firm Growth Growth 0.187 0.121 0.385 -0.671 3.808 Managerial Shareholding Mshare 14.01 0.558 20.2 0 70.6 Debt-to-Asset Ratio Lev 0.406 0.399 0.2 0.027 0.925 Shareholding Concentration Top1 0.35 0.33 0.149 0 0.758 Duality Dual 0.284 0 0.451 0 1 Board Independence Indep 37.39 33.33 5.434 0 60 Regional Economic Development Level AGDP 11.39 11.48 0.602 8.253 12.49 Industry Structure IS 37.73 41.13 15.76 0 90.97 3.3 Model Specification Based on the research questions and theoretical hypotheses, to test the impact of FinTech application on corporate sustainable development performance (H1), the following baseline model is constructed: SDPi,t represents the sustainable development performance of firm i at time t. FIAi,t is the FinTech application level for firm i at time t. Controlsi,t are the control variables as explained earlier, including factors like firm size, firm age, growth, managerial shareholding ratio, debt-to-equity ratio, ownership concentration, dual roles, board independence, regional economic development level, and industrial structure. µi represents the firm fixed effects, which control for time-invariant individual characteristics of the firms. λt represents the year fixed effects, controlling for time trends and macroeconomic shocks. εi,t is the random error term. 4 Empirical Analysis 4.1 Baseline Regression Table 4 presents the regression results from models ( 1 ) to ( 4 ), examining the effect of the firm's FinTech application level (FIA) on its sustainable development performance (SDP). The positive effect of FinTech application on corporate sustainable development performance is highly significant across all models. The regression coefficients consistently remain positive, demonstrating the robustness of the results. This conclusion supports the research hypothesis H1, which states that FinTech application can effectively improve corporate sustainable development performance. Table 4 Baseline Regression ( 1 ) SDP ( 2 ) SDP ( 3 ) SDP ( 4 ) SDP FIA 0.042*** (100.334) 0.007*** (6.812) 0.011*** (19.481) 0.005*** (4.925) Size 0.030*** (34.802) 0.020*** (9.195) FirmAge 0.085*** (31.770) 0.052*** (4.678) Growth -0.005*** (-4.231) -0.002 (-1.363) Mshare -0.000*** (-3.558) -0.001*** (-6.157) Lev -0.006 (-1.419) 0.045*** (5.421) Top1 -0.025*** (-4.108) -0.024* (-1.707) Dual 0.002 (1.291) -0.004 (-1.476) Indep 0.000 (1.605) -0.000 (-0.511) AGDP 0.054*** (28.036) -0.010 (-1.624) IS -0.000*** (-3.172) -0.000 (-0.754) cons 0.273*** (156.175) 0.164*** (46.980) -1.150*** (-48.908) -0.249*** (-3.209) N 42744 42744 38523 38523 Individual Fixed Effects NO YES NO YES Time Fixed Effects NO YES NO YES R-squared 0.230 0.410 0.384 0.425 Note: t statistics in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01 4.2 Robustness Test Firstly, to verify the robustness of the impact of Financial Technology Adoption (FIA) on corporate Sustainable Development Performance (SDP), this study conducts a robustness test by replacing the dependent variable. Specifically, the original dependent variable, SDP, is substituted with corporate ESG data released by the China Research Data Services (CNRDS) platform to examine the consistency and robustness of the results. As shown in Columns ( 1 ) and ( 2 ) of Table 5 , the coefficient of FIA remains significantly positive in relation to the new dependent variable, thereby confirming the robustness of the regression results. Secondly, based on the original sample data, the study selects the years 2017–2021 as the research period, excluding the early development years of FinTech application (when it was less mature) and the years severely impacted by the pandemic. This adjustment aims to reduce the impact of time heterogeneity on the regression results. From Table 5 , columns ( 3 ), we can see that the correlation between FIA and SDP remains significantly positive (0.004). Thirdly, considering that municipalities directly under the central government in China (e.g., Beijing, Chongqing, Shanghai) have advantages in administrative level and are more likely to receive policy preferences, the study removes samples from these municipalities, following conventional research practices. The model ( 1 ) is then re-applied for regression analysis. From Table 5 , columns ( 3 ), we can see that the correlation between FIA and SDP remains significantly positive (0.004). From Table 3 , columns ( 3 ), we can see that the correlation between FIA and SDP remains significantly positive (0.005). The results are consistent with the previous findings. Table 5 Robustness Test Replace the Dependent Variable Narrow the Sample Interval ( 1 )ESG ( 2 ) ESG ( 3 )SDP ( 4 )SDP FIA 0.740*** 0.679*** 0.004*** 0.005*** (10.687) (9.273) (3.040) (4.105) cons 0.354*** 1.164*** -0.433*** -0.400*** (40.287) (3.646) (-3.993) (-4.322) Control Variables NO YES YES YES Fixed Effects YES YES YES YES N 41721 37589 14296 11173 R2 0.266 0.268 0.102 0.106 Note: t statistics in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01 4.3 Endogeneity Test To avoid reverse causality and reduce the interference of endogeneity, this study adopts two methods to perform the endogeneity test. The first method is to use lagged values of financial technology application level (FIA) as instrumental variables (IV). Specifically, the lagged first and second periods of FIA are used as instruments for regression analysis. The results indicate that the regression coefficients are 0.003 and 0.004, respectively, both significantly positive at the 1% level. These results are consistent with the main regression results, suggesting that the lagged values of financial technology application levels effectively address endogeneity concerns. The second method is the difference-in-differences (DID) approach. Given that the People's Bank of China issued the "FinTech Development Plan" in 2019, which had a significant exogenous shock on the rapid development of financial technology services for the real economy, this policy provides a suitable instrument for exogenous variation. A time dummy variable, "time," is defined as 0 for years prior to 2019 and 1 for years after 2019. Based on the median level of financial technology in 2019, the sample is divided into the treatment and control groups. Firms with financial technology levels above the median are classified as the treatment group (value = 1). A difference-in-differences model is then constructed. The coefficient of the interaction term, 0.017, indicates a significant positive relationship at the 1% level. The results from both methods of endogeneity testing are consistent with the main regression findings, confirming the robustness of the positive impact of financial technology application on corporate sustainable development performance. Table 6 Endogeneity Test Lagged Explanatory Variable (One Period) Lagged Explanatory Variable (Two Periods) Difference-in-Differences (DID) SDP( 1 ) SDP( 2 ) SDP ( 3 ) FIA 0.003*** 0.004*** (5.718) (5.639) time×exper 0.017*** (9.926) _cons -0.151*** -0.168*** (-6.344) (-6.469) Control Variables YES YES YES Fixed Effects YES YES YES N 31189 27448 38524 R2 0.291 0.238 0.335 Note: t statistics in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01 4.4 Moderating Effect of Local Government Environmental Concern As analyzed earlier, the application of FinTech tools can effectively improve corporate sustainable development performance, and local government environmental concern has a certain moderating effect. To further examine whether the moderating effect of local government environmental concern is complementary or substitutive, this study constructs a moderating effect model ( 1 ) for testing. The interaction term (FIA × LGEC) shows a significant negative correlation with corporate sustainable development performance, indicating that local government environmental concern weakens the impact of FinTech on corporate ESG performance, demonstrating a substitutive effect (as Table 6 column ( 1 )). This supports hypothesis H2b. This finding is consistent with the research by Zhang et al. (2021), which suggests that there may be a threshold for the synergistic effect between environmental policies and FinTech. 4.5 Differences Based on Corporate Nature This study further divides the sample into state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs) based on ownership structure. The regression results in columns ( 2 ) and ( 3 ) of Table 7 show that the positive impact of FinTech application on the sustainable development performance of non-SOEs is more pronounced. This is because private enterprises are more susceptible to the inclusive influence of FinTech (Zhang Xun et al., 2019). On one hand, non-SOEs, especially private enterprises, typically face more severe financing and resource constraints. By applying FinTech, they can more effectively reduce information asymmetry, improve operational efficiency, and flexibly and effectively obtain funding support, thus significantly enhancing their sustainable development performance (SDP). In contrast, SOEs enjoy policy-based loans and implicit government guarantees, limiting the marginal improvement that FinTech can provide. On the other hand, non-SOEs have more flexible organizational structures, allowing them to quickly integrate FinTech tools to optimize resource allocation. In contrast, SOEs often have longer decision-making chains, and the application of technology may be constrained by administrative processes, leading to delayed effects of FinTech. Additionally, non-SOEs are more reliant on FinTech to mitigate environmental regulation risks (such as reducing compliance costs through carbon asset digital management), while SOEs, due to their close ties with the government, may mitigate environmental compliance pressure through non-market channels (such as administrative negotiations). Based on the China Securities Regulatory Commission's 2012 industry classification, this study further categorizes enterprises into heavy-pollution industries, such as steel, cement, coal, metallurgy, and chemicals, and non-heavy-pollution industries. The regression results in columns ( 4 ) and ( 5 ) of Table 7 show that the positive impact of FinTech application on the sustainable development performance of heavy-pollution enterprises is more pronounced. A possible explanation is that heavy-pollution industries (such as steel and chemicals) face strict environmental supervision and carbon emission constraints. FinTech (such as environmental big data monitoring and carbon accounting systems) can directly help these industries achieve precise emission reductions and avoid production shutdown penalties, significantly improving their SDP. In contrast, non-heavy-pollution enterprises experience less environmental pressure, so the urgency for technological application is lower. Table 7 Moderating Effect and Heterogeneity Test of Enterprises ( 1 ) ( 2 ) SDP ( 3 ) SDP SDP State-owned Enterprises Non-state-owned Enterprises Heavy-pollution Industries Non-heavy-pollution Industries FIA 0.005*** 0.004*** 0.005*** 0.006*** 0.005*** (4.283) (2.731) (3.523) (3.273) (3.758) FIA×LGEC -0.001* (-1.706) _cons -1.056*** -0.233** -0.285*** -0.087 -0.301*** (-18.284) (-2.065) (-2.684) (-0.656) (-3.077) Control Variables YES YES YES YES YES Fixed Effects YES YES YES YES YES N 38004 14958 23565 10792 27731 R2 0.387 0.459 0.410 0.439 0.406 Note: t statistics in parentheses; * p < 0.1, ** p < 0.05, *** p < 0.01 5 Research Conclusions and Policy Recommendations This study explores the impact of FinTech application on corporate sustainable development performance from both theoretical and empirical perspectives. The empirical analysis results indicate that FinTech application (FIA) significantly enhances corporate sustainable development performance (SDP), and this effect is more pronounced in non-state-owned enterprises and heavy-pollution industries. However, the local government's environmental concern (LGEC) has a substitutive effect on the positive impact of FinTech, meaning that higher environmental regulatory intensity may weaken the marginal contribution of FinTech to SDP. Endogeneity tests and robustness analyses further confirm the reliability of these conclusions. Based on these findings, the following recommendations are made: ( 1 ) Establish a Collaborative Mechanism for FinTech and Green Development Government departments should formulate differentiated policies to support FinTech, with a focus on enhancing technological support for non-state-owned enterprises and heavy-pollution industries. They should establish FinTech application demonstration projects and cultivate exemplary enterprises. Additionally, a green FinTech standard system should be developed to regulate industry development. ( 2 ) Promote FinTech Tools Differentiated by Industry For non-state-owned enterprises, the government can subsidize FinTech service platforms (e.g., green credit AI rating systems) to reduce the digitalization costs for private enterprises and alleviate their financing constraints. For heavy-pollution industries, efforts should be made to ensure the coordinated implementation of FinTech and environmental policies, such as the establishment of a "carbon data blockchain platform" to help companies precisely quantify emission reduction benefits and integrate with carbon market transactions. ( 3 ) Optimize the Collaborative Mechanism of Environmental Policies To avoid one-size-fits-all administrative interventions, local governments should leverage FinTech's dynamic monitoring capabilities to implement precise environmental regulations. ( 4 ) Strengthen Institutional Support and Capacity Building At the central level, FinTech applications should be incorporated into the "dual carbon" evaluation index, encouraging local governments to explore policy tool combinations through green FinTech innovation pilot zones. At the enterprise level, industry associations can collaborate with universities to conduct FinTech training, enhancing the digital governance capabilities of heavy-pollution enterprises and resolving technological adaptation barriers. Declarations Data Availability The data used in this study are available from the corresponding author upon reasonable request. Conflicts of Interest The author declares no conflicts of interest related to this research. Funding Statement This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution Juan Hou conducted the study design, data analysis, and manuscript preparation. Ethical Approval This study uses secondary publicly available data and did not require ethical approval. Consent to Participate Not applicable, as the study does not involve human participants. Consent to Publication The author consents to the publication of this manuscript. Competing Interests The author confirms that there are no competing interests. References Cheng C, Yang S, Tian X. The impact of supply chain finance empowered by fintech on enterprise value. J Manage Sci. 2024;27(02):95–119. Feng T, Tao J, Wang C. The impact of green entrepreneurial orientation on green innovation and corporate performance: The moderating effect of industry. Chin Circulation Econ. 2020;34(10):90–103. Guo N, Wang P, Lu Y. 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New Finance, (02), 56–63, 2023. Zhang Q, Huang L. Can the establishment of green financial reform and innovation pilot zones promote corporate technological innovation? Economic Syst Reform, (01), 182–90, 2023. Zhang X, Cao J. Fintech and corporate ESG performance. Acc Monthly. 2024;45(06):72–9. Zeng C. Fintech development, corporate ESG performance, and liquidity management. Wuhan Finance, (11), 3–14, 2024. Additional Declarations No competing interests reported. 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The United Nations\u0026rsquo; 2030 Agenda integrating economic growth, social progress, and environmental protection. Guided by this global consensus, the Chinese government has prioritized sustainable development. Subsequently, the National Development and Reform Commission issued the \u0026ldquo;Action Plan for Carbon Peak by 2030\u0026rdquo; in 2021, systematically deploying key tasks, policy paths, and guarantees to accelerate the transformation toward a green and low-carbon economy.\u003c/p\u003e\u003cp\u003eAs the basic unit of market economies, enterprises play a pivotal role in national ecological advancement and green development. Enterprise sustainable development emphasizes continuous technological upgrading, the adoption of advanced equipment, and talent cultivation to meet industry demands, while simultaneously considering economic, environmental, and social dimensions in delivering products or services, thereby enhancing their competitive edge. Under the constraints of the dual carbon policy, evolving environmental regulations, and increasing external uncertainties, enterprises are compelled to seek a new dynamic balance between environmental protection and operational performance, transforming fundamentally from traditional extensive growth models toward low-carbon, green, and digitalized pathways.\u003c/p\u003e\u003cp\u003eMore importantly, the rapid development of financial technologies like artificial intelligence, blockchain, cloud computing, and big data, has profoundly reshaped enterprise operations. FinTech not only transforms traditional financing models but also optimizes risk management and enhances resource allocation efficiency, offering technological support for sustainable enterprise development. In the Chinese context, the interaction between local government environmental regulation intensity and industry characteristics introduces institutional and industry heterogeneity into the FinTech\u0026ndash;sustainability relationship.\u003c/p\u003e\u003cp\u003eCurrent research on FinTech shows two main tendencies. First, most studies focus on the impact of FinTech on financial institutions. For example, Gozman (2018) and Chen (2024) emphasize the technological support FinTech offers to the financial industry, while He (2023) notes its disruption of traditional indirect financing mechanisms. Others such as Yao et al. (2023) and Wang (2023) examine how innovative financial products improve information acquisition. Second, many studies evaluate the macro-level impact of FinTech on corporate capital structure, ESG performance, digital transformation, technological innovation, total factor productivity, regional innovation efficiency, green innovation, and economic growth.\u003c/p\u003e\u003cp\u003eHowever, there is still a lack of micro-level analyses from the perspective of FinTech application at the enterprise level. Moreover, most literature has primarily assessed the impact of FinTech on economic performance, while a balanced focus on social\u0026ndash;environmental and financial outcomes is essential to evaluate enterprise sustainability more comprehensively. To address this gap, this study adopts an enterprise-level view of FinTech application, measuring sustainable development performance from both social\u0026ndash;environmental and financial perspectives. It investigates how FinTech adoption influences enterprise sustainability, examines local government environmental concern, and explores industry-level heterogeneity.\u003c/p\u003e\u003cp\u003eThis paper offers three main contributions. First, it expands existing research by investigating the impact of FinTech adoption on enterprise sustainability performance within a unified framework, providing empirical evidence to support corporate sustainability and national goals for dual carbon and high-quality growth. Second, it introduces local government environmental concern as a moderating variable, capturing the influence of informal institutional factors beyond formal regulations and enriching the literature on environmental governance and corporate behavior. Third, the study explores the heterogeneous impacts of FinTech across industries, especially between high-pollution and low-pollution sectors, offering insights for differentiated industrial policy formulation.\u003c/p\u003e"},{"header":"2 Theoretical Analysis and Research Hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 FinTech Adoption on Sustainable Development Performance\u003c/h2\u003e\u003cp\u003eEnterprise sustainable development emphasizes continuous technological upgrading, the adoption of advanced equipment, and talent cultivation to meet industry demands, while simultaneously considering economic, environmental, and social dimensions in delivering products or services, thereby enhancing their competitive edge. The rise of financial technology (FinTech) has brought profound changes to enterprises, providing new momentum for sustainable development by improving capital acquisition efficiency, optimizing resource allocation, and reducing operational risks (Wang \u0026amp; Zhang, 2025).\u003c/p\u003e\u003cp\u003eFirstly, Traditional financing channels, such as bank loans and equity financing, are often restricted by high entry barriers and information asymmetry. In contrast, FinTech applications enable enterprises to secure financing more efficiently and flexibly (Zeng, 2024). For example, companies can collaborate with upstream and downstream firms through supply chain finance platforms, leveraging blockchain technology to ensure transaction transparency and security, thereby obtaining lower-cost financing. This directly boosts the economic performance of enterprises, as they can better allocate resources and respond to market changes, improving profitability and market competitiveness. Furthermore, traditional risk management primarily relied on experience and qualitative judgment. Modern FinTech, through AI and blockchain, allows for more precise data analysis and risk forecasting, enabling enterprises to make timely adjustments to decisions, ensuring proper capital allocation and use, thus reducing the cost and complexity of risk management (Cheng et al., 2024). The application of these technologies helps enterprises reduce the frequency of risk events and enhances the scientific nature of decision-making, further contributing to their sustainability.\u003c/p\u003e\u003cp\u003eSecondly, the impact of FinTech on social and environmental performance is also gradually becoming evident. As global attention to environmental protection and sustainable development increases, enterprises' environmental responsibilities are rising. FinTech can promote environmental performance by supporting green investments and advancing the development of green financial products (Xue \u0026amp; Pan, 2024). Green financial products can provide financial support for enterprises\u0026rsquo; environmental projects, driving investments in reducing carbon emissions, improving energy efficiency, and adopting sustainable technologies (Chen, 2020). By promoting green investment through FinTech, enterprises not only fulfill their social responsibilities but also achieve long-term economic returns.\u003c/p\u003e\u003cp\u003eMoreover, enterprises use FinTech to enhance the transparency and fairness of resource allocation, facilitating public oversight and building trust among the public and investors. This technological push for social equity and responsibility enables enterprises to balance economic benefits with social responsibility. It is important to note that different industries exhibit significant differences in pollution levels, environmental requirements, and technological innovation capabilities, leading to heterogeneous effects of FinTech on corporate sustainable development performance across industries. Specifically, differences in pollution levels, technological advancement, and green innovation capabilities among industries determine the role and effectiveness of FinTech in promoting sustainable development. Based on this analysis, it can be proposed:\u003c/p\u003e\u003cp\u003eH1: The application of FinTech can significantly enhance corporate sustainable development performance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 The Moderating Effect of Local Government Environmental Attention\u003c/h2\u003e\u003cp\u003eThe introduction of China\u0026rsquo;s \"carbon peak, carbon neutrality\" strategic goals marks a systematic transformation of the environmental governance system (Zhang \u0026amp; Huang, 2023). In this context, the environmental attention of local governments not only influences corporate environmental behaviors but also affects enterprises\u0026rsquo; decisions and actions through policy guidance, incentive measures, and market signals. Governments guide enterprises toward green development paths by strengthening environmental policy formulation, enhancing law enforcement and supervision, and providing green incentives (Du et al., 2021; Xie et al., 2023). For instance, local governments often adopt command-and-control environmental regulations, imposing punitive constraints on high-polluting and high-energy-consuming enterprises, thus promoting technological innovation and energy efficiency optimization (Wu \u0026amp; Li, 2024). Research by Tang et al. (2022) also indicates that such policies significantly promote the application and authorization of green patents.\u003c/p\u003e\u003cp\u003eAt the same time, the rise of FinTech provides enterprises with more precise and efficient tools for green financing and risk control, improving the way resources for environmental protection investments are obtained and stimulating innovation in green technologies. In this context, that may create a complementary effect: the former promotes green development goals through policies, while the latter provides digital technology support.\u003c/p\u003e\u003cp\u003eHowever, innovaation, patcularly green innovation, as the core approach to improving social and environmental performance in sustainable development, often entails higher initial investments and longer technological transformation cycles. In the short term, enterprises may experience a decline in profitability due to increased green investments, which can impact the economic dimension of sustainable development (Li \u0026amp; Zhang, 2024). Additionally, improvements in environmental performance exhibit a time lag and may not be immediately visible. In this context, if the local government imposes excessive environmental pressure, it may force enterprises to over-invest in green projects, increasing their financial burden and operational stress, thereby weakening the role of FinTech in optimizing resource allocation and possibly creating a substitution effect with the company\u0026rsquo;s digital transformation efforts. For instance, Li et al. (2021) point out that excessive environmental regulations may hinder corporate green innovation, while Li et al. (2023) argue that strong regulations still have a positive effect in heavily polluting industries.\u003c/p\u003e\u003cp\u003eTherefore, local government environmental attention may play a complementary or substitution role in the relationship between FinTech application and sustainable development performance, depending on the context. Specifically, when enterprises have a solid foundation in green financial technology, moderate government attention can strengthen the synergistic efficiency of technological investment and green transformation, resulting in a complementary effect. Conversely, in the context of excessive environmental regulations and uncertain returns from innovation, government intervention may weaken the enterprise\u0026rsquo;s ability to leverage FinTech to improve performance, resulting in a substitution effect. Based on this, it can be proposed:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2a\u003c/strong\u003e\u003cp\u003eThe environmental attention of local governments and FinTech application have a complementary effect on improving corporate sustainable development performance.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2b\u003c/strong\u003e\u003cp\u003eThe environmental attention of local governments and FinTech application have a substitution effect on improving corporate sustainable development performance.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Research Design and Variable Explanation","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data Sources and Sample Selection\u003c/h2\u003e\u003cp\u003eThis study selects non-financial A-share listed companies from 2006 to 2023 as the research subjects to empirically test the impact of FinTech on corporate sustainable development performance. The original data is sourced from the CSMAR database, the \"China Statistical Yearbook,\" and government work reports from official government websites. Data related to FinTech application is obtained through Python-based word frequency statistics on the annual reports of the companies. Data regarding local government environmental attention is derived from word frequency statistics on government work reports.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Variable Explanation\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Dependent Variable\u003c/h2\u003e\u003cp\u003eDrawing on the studies of Feng et al. (2020), Long (2022), Xie and Zhu (2021), the corporate sustainable development performance (SDP) in this study is a dual performance measure consisting of economic performance and social-environmental performance. The final score is obtained by standardizing the average weight. Economic performance is represented by the ROE (Return on Equity) indicator. For social responsibility performance, this study uses the CSMAR database which includes 12 standards to measure the quality of corporate social and environmental responsibility information. These 12 standards ensuring the authenticity and standardization of information disclosure. After third-party validation, each standard is assigned a value of 0 or 1: a disclosure earns a value of 1, and non-disclosure earns a value of 0. The total score of the 12 standards for each company is then summed and standardized (i.e., the total score divided by 12). A higher value of this index indicates a higher level and quality of social and environmental responsibility fulfillment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Independent Variables\u003c/h2\u003e\u003cp\u003eThe core independent variable in this study is the level of FinTech application by enterprises (FinTech). Given the lack of a unified quantitative index system for FinTech, this study follows the methods of Li Chuntao et al. (2020), Song Xinlei et al. (2022), and Huang Juan et al. (2023) and adopts a text mining approach. Using Python, the study performs word frequency statistics on FinTech-related keywords in the annual reports of listed companies and standardizes the frequency of occurrence (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This results in the construction of an enterprise FinTech application index. A higher value of this index indicates deeper application of FinTech within the company. Moreover, to ensure the relevance of FinTech application to corporate operations, keywords unrelated to the company\u0026rsquo;s main business or mentioned only in passing are excluded during the keyword screening process, enhancing the accuracy and representativeness of the index.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFinTech adoption Keyword Dictionary\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDimension\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinTech Keywords\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArtificial Intelligence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArtificial intelligence, robots, machine learning, deep learning, neural networks, facial recognition, biometric recognition, voiceprint recognition, pattern recognition, image recognition, virtual reality, augmented reality, knowledge graph, smart technology, smart deposits, smart marketing, intelligent, intelligent technology, intelligent risk control, automation, natural language processing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlockchain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlockchain, distributed ledger, supply chain, Internet of Things, near-field, quantum, quantum communication, quantum communications, data encryption, digital currency, electronic money\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBig Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBig data, big data analytics, big data services, big data technology, big data models, big data mining, data warehouse, data technology, data models, data mining, data governance, data center, digitalization, digital transformation, digital signature, digital ecosystem, digital marketing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCloud Computing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistributed, distributed storage, distributed computing, distributed architecture, distributed database, trusted computing, cloud adoption, private cloud, virtualization, privacy computing, cloud-based, cloud services, cloud service platform, cloud computing, cloud architecture, cloud platform, cloud system, cloud disaster recovery\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Moderating Variable\u003c/h2\u003e\u003cp\u003eThis study selects local government environmental concern (Local Government Environmental Concern, LGEC) as the moderating variable to explore whether the degree of local government attention to environmental issues moderates the relationship between FinTech application and corporate sustainable development performance. Drawing on the methods of Zhou Jintang et al. (2020) and Shao Shuai et al. (2024), this study employs a text mining approach. Using Python tools, the study performs word frequency statistics on the occurrence of keywords related to environmental protection, carbon peak, carbon neutrality, and green development in local government annual work reports. The frequency of these keywords is then matched to corresponding listed company samples by region and year. The intensity of local government environmental concern is measured by the proportion of the number of environmental protection-related keywords to the total word count of the entire government report. This index is statistically standardized to eliminate the impact of differences in report word counts across different years and regions. A higher value of this index indicates a greater tendency for local governments to promote environmental responsibility and green transformation among enterprises within their jurisdiction through policy formulation, supervision and enforcement, and financial incentives, thereby affecting the role of FinTech in corporate sustainable development.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLocal Government Environmental Concern Keyword Dictionary\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDimension\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelected Keywords\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironmental Goals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnvironmental protection, eco-friendly, green, clean, low-carbon, blue sky, green water, green mountains, sustainable, carbon neutrality, carbon peak, ecological civilization, green development\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironmental Factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEcology, air, climate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironmental Pollution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePollution, sulfur dioxide, chemical oxygen demand, smog, particulate matter, carbon dioxide, energy, carbon emissions, coal burning, pollutant discharge, illegal discharge, exhaust gas, wastewater, waste gas, solid waste, microplastics, heavy metal pollution\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironmental Measures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnergy saving, emission reduction, desulfurization, denitrification, carbon capture, carbon trading, carbon tax, environmental subsidies, pollution control, clean production, green manufacturing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.2.4 Control Variables\u003c/h2\u003e\u003cp\u003eFollowing the methods of Yang Wei et al. (2021), Pan Yi and Zhang Jinchang (2023), this study select enterprise- and regional-level control variables: firm size (Size), firm age (Firmage), firm growth (Growth), managerial shareholding ratio (Mshare), debt-to-equity ratio (Lev), ownership concentration (Top1), dual roles (dual), board independence (Indep), regional economic development level (AGDP), and industrial structure (IS).\u003c/p\u003e\u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the mean of sustainable development performance (SDP) is 0.384(MIN\u0026thinsp;=\u0026thinsp;0.02, MAX\u0026thinsp;=\u0026thinsp;0.917), show that most enterprises score relatively low in sustainable development performance, while some enterprises perform exceptionally well. The standard deviation is 0.141, showing a moderate degree of variation between the samples. The mean of the FinTech application index (FIA) is 2.543(SD\u0026thinsp;=\u0026thinsp;1.714, MAX\u0026thinsp;=\u0026thinsp;7.537), indicating considerable variation in FinTech application among the companies. The mean of local government environmental concern (LGEC) is 0.944, with a minimum value of 0 and a maximum value of 11, reflecting significant differences in the level of environmental concern across regions. The numerical data of the variables are of good overall quality, with high differentiation and a wide range. Moreover, there are certain variations between the independent and control variables in the sample, which is conducive to subsequent regression analysis. This allows for a comprehensive reflection of the level of FinTech application and corporate sustainable development, ensuring that the research results possess practical significance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescription and Descriptive Statistics of Key Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP50\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSustainable Development Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndependent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinTech Application Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.537\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocal Government Environmental Concern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLGEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirm Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirm Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFirmAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.638\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirm Growth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGrowth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.808\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eManagerial Shareholding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMshare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e70.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDebt-to-Asset Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLev\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShareholding Concentration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTop1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.758\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDuality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBoard Independence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegional Economic Development Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndustry Structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e90.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Model Specification\u003c/h2\u003e\u003cp\u003eBased on the research questions and theoretical hypotheses, to test the impact of FinTech application on corporate sustainable development performance (H1), the following baseline model is constructed:\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"724\" height=\"73\"\u003e\u003c/p\u003e\u003cp\u003eSDPi,t represents the sustainable development performance of firm i at time t. FIAi,t is the FinTech application level for firm i at time t. Controlsi,t are the control variables as explained earlier, including factors like firm size, firm age, growth, managerial shareholding ratio, debt-to-equity ratio, ownership concentration, dual roles, board independence, regional economic development level, and industrial structure. \u0026micro;i represents the firm fixed effects, which control for time-invariant individual characteristics of the firms. λt represents the year fixed effects, controlling for time trends and macroeconomic shocks. εi,t is the random error term.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Empirical Analysis","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Baseline Regression\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the regression results from models (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), examining the effect of the firm's FinTech application level (FIA) on its sustainable development performance (SDP). The positive effect of FinTech application on corporate sustainable development performance is highly significant across all models. The regression coefficients consistently remain positive, demonstrating the robustness of the results. This conclusion supports the research hypothesis H1, which states that FinTech application can effectively improve corporate sustainable development performance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Regression\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eSDP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eSDP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eSDP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eSDP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.042***\u003c/p\u003e\u003cp\u003e(100.334)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.007***\u003c/p\u003e\u003cp\u003e(6.812)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.011***\u003c/p\u003e\u003cp\u003e(19.481)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005***\u003c/p\u003e\u003cp\u003e(4.925)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.030***\u003c/p\u003e\u003cp\u003e(34.802)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.020***\u003c/p\u003e\u003cp\u003e(9.195)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirmAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.085***\u003c/p\u003e\u003cp\u003e(31.770)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.052***\u003c/p\u003e\u003cp\u003e(4.678)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrowth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.005***\u003c/p\u003e\u003cp\u003e(-4.231)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003cp\u003e(-1.363)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMshare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.000***\u003c/p\u003e\u003cp\u003e(-3.558)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.001***\u003c/p\u003e\u003cp\u003e(-6.157)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLev\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003cp\u003e(-1.419)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.045***\u003c/p\u003e\u003cp\u003e(5.421)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTop1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.025***\u003c/p\u003e\u003cp\u003e(-4.108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.024*\u003c/p\u003e\u003cp\u003e(-1.707)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003cp\u003e(1.291)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003cp\u003e(-1.476)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e(1.605)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003cp\u003e(-0.511)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.054***\u003c/p\u003e\u003cp\u003e(28.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.010\u003c/p\u003e\u003cp\u003e(-1.624)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.000***\u003c/p\u003e\u003cp\u003e(-3.172)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003cp\u003e(-0.754)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003econs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.273***\u003c/p\u003e\u003cp\u003e(156.175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.164***\u003c/p\u003e\u003cp\u003e(46.980)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.150***\u003c/p\u003e\u003cp\u003e(-48.908)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.249***\u003c/p\u003e\u003cp\u003e(-3.209)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38523\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.425\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: t statistics in parentheses; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Robustness Test\u003c/h2\u003e\u003cp\u003eFirstly, to verify the robustness of the impact of Financial Technology Adoption (FIA) on corporate Sustainable Development Performance (SDP), this study conducts a robustness test by replacing the dependent variable. Specifically, the original dependent variable, SDP, is substituted with corporate ESG data released by the China Research Data Services (CNRDS) platform to examine the consistency and robustness of the results. As shown in Columns (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the coefficient of FIA remains significantly positive in relation to the new dependent variable, thereby confirming the robustness of the regression results.\u003c/p\u003e\u003cp\u003eSecondly, based on the original sample data, the study selects the years 2017\u0026ndash;2021 as the research period, excluding the early development years of FinTech application (when it was less mature) and the years severely impacted by the pandemic. This adjustment aims to reduce the impact of time heterogeneity on the regression results. From Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, columns (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), we can see that the correlation between FIA and SDP remains significantly positive (0.004).\u003c/p\u003e\u003cp\u003eThirdly, considering that municipalities directly under the central government in China (e.g., Beijing, Chongqing, Shanghai) have advantages in administrative level and are more likely to receive policy preferences, the study removes samples from these municipalities, following conventional research practices. The model (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) is then re-applied for regression analysis. From Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, columns (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), we can see that the correlation between FIA and SDP remains significantly positive (0.004). From Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, columns (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), we can see that the correlation between FIA and SDP remains significantly positive (0.005). The results are consistent with the previous findings.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness Test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eReplace the Dependent Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eNarrow the Sample Interval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)ESG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) ESG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)SDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)SDP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.740***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.679***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(10.687)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(9.273)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4.105)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003econs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.354***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.164***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.433***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.400***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(40.287)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.646)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-3.993)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-4.322)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: t statistics in parentheses; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Endogeneity Test\u003c/h2\u003e\u003cp\u003eTo avoid reverse causality and reduce the interference of endogeneity, this study adopts two methods to perform the endogeneity test.\u003c/p\u003e\u003cp\u003eThe first method is to use lagged values of financial technology application level (FIA) as instrumental variables (IV). Specifically, the lagged first and second periods of FIA are used as instruments for regression analysis. The results indicate that the regression coefficients are 0.003 and 0.004, respectively, both significantly positive at the 1% level. These results are consistent with the main regression results, suggesting that the lagged values of financial technology application levels effectively address endogeneity concerns.\u003c/p\u003e\u003cp\u003eThe second method is the difference-in-differences (DID) approach. Given that the People's Bank of China issued the \"FinTech Development Plan\" in 2019, which had a significant exogenous shock on the rapid development of financial technology services for the real economy, this policy provides a suitable instrument for exogenous variation. A time dummy variable, \"time,\" is defined as 0 for years prior to 2019 and 1 for years after 2019. Based on the median level of financial technology in 2019, the sample is divided into the treatment and control groups. Firms with financial technology levels above the median are classified as the treatment group (value\u0026thinsp;=\u0026thinsp;1). A difference-in-differences model is then constructed. The coefficient of the interaction term, 0.017, indicates a significant positive relationship at the 1% level.\u003c/p\u003e\u003cp\u003eThe results from both methods of endogeneity testing are consistent with the main regression findings, confirming the robustness of the positive impact of financial technology application on corporate sustainable development performance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEndogeneity Test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLagged Explanatory Variable (One Period)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLagged Explanatory Variable (Two Periods)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDifference-in-Differences (DID)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSDP(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSDP(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSDP (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(5.718)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(5.639)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etime\u0026times;exper\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.017***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(9.926)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.151***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.168***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-6.344)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-6.469)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38524\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.335\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: t statistics in parentheses; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Moderating Effect of Local Government Environmental Concern\u003c/h2\u003e\u003cp\u003eAs analyzed earlier, the application of FinTech tools can effectively improve corporate sustainable development performance, and local government environmental concern has a certain moderating effect. To further examine whether the moderating effect of local government environmental concern is complementary or substitutive, this study constructs a moderating effect model (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) for testing. The interaction term (FIA \u0026times; LGEC) shows a significant negative correlation with corporate sustainable development performance, indicating that local government environmental concern weakens the impact of FinTech on corporate ESG performance, demonstrating a substitutive effect (as Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)). This supports hypothesis H2b. This finding is consistent with the research by Zhang et al. (2021), which suggests that there may be a threshold for the synergistic effect between environmental policies and FinTech.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Differences Based on Corporate Nature\u003c/h2\u003e\u003cp\u003eThis study further divides the sample into state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs) based on ownership structure. The regression results in columns (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e show that the positive impact of FinTech application on the sustainable development performance of non-SOEs is more pronounced. This is because private enterprises are more susceptible to the inclusive influence of FinTech (Zhang Xun et al., 2019). On one hand, non-SOEs, especially private enterprises, typically face more severe financing and resource constraints. By applying FinTech, they can more effectively reduce information asymmetry, improve operational efficiency, and flexibly and effectively obtain funding support, thus significantly enhancing their sustainable development performance (SDP). In contrast, SOEs enjoy policy-based loans and implicit government guarantees, limiting the marginal improvement that FinTech can provide. On the other hand, non-SOEs have more flexible organizational structures, allowing them to quickly integrate FinTech tools to optimize resource allocation. In contrast, SOEs often have longer decision-making chains, and the application of technology may be constrained by administrative processes, leading to delayed effects of FinTech. Additionally, non-SOEs are more reliant on FinTech to mitigate environmental regulation risks (such as reducing compliance costs through carbon asset digital management), while SOEs, due to their close ties with the government, may mitigate environmental compliance pressure through non-market channels (such as administrative negotiations).\u003c/p\u003e\u003cp\u003eBased on the China Securities Regulatory Commission's 2012 industry classification, this study further categorizes enterprises into heavy-pollution industries, such as steel, cement, coal, metallurgy, and chemicals, and non-heavy-pollution industries. The regression results in columns (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e show that the positive impact of FinTech application on the sustainable development performance of heavy-pollution enterprises is more pronounced. A possible explanation is that heavy-pollution industries (such as steel and chemicals) face strict environmental supervision and carbon emission constraints. FinTech (such as environmental big data monitoring and carbon accounting systems) can directly help these industries achieve precise emission reductions and avoid production shutdown penalties, significantly improving their SDP. In contrast, non-heavy-pollution enterprises experience less environmental pressure, so the urgency for technological application is lower.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModerating Effect and Heterogeneity Test of Enterprises\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) SDP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) SDP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eState-owned Enterprises\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-state-owned Enterprises\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHeavy-pollution Industries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNon-heavy-pollution Industries\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.005***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.005***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4.283)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.731)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.523)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3.273)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(3.758)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIA\u0026times;LGEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.706)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.056***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.233**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.285***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.301***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-18.284)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.065)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-2.684)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-0.656)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-3.077)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27731\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: t statistics in parentheses; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Research Conclusions and Policy Recommendations","content":"\u003cp\u003eThis study explores the impact of FinTech application on corporate sustainable development performance from both theoretical and empirical perspectives. The empirical analysis results indicate that FinTech application (FIA) significantly enhances corporate sustainable development performance (SDP), and this effect is more pronounced in non-state-owned enterprises and heavy-pollution industries. However, the local government's environmental concern (LGEC) has a substitutive effect on the positive impact of FinTech, meaning that higher environmental regulatory intensity may weaken the marginal contribution of FinTech to SDP. Endogeneity tests and robustness analyses further confirm the reliability of these conclusions. Based on these findings, the following recommendations are made:\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003cb\u003eEstablish a Collaborative Mechanism for FinTech and Green Development\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGovernment departments should formulate differentiated policies to support FinTech, with a focus on enhancing technological support for non-state-owned enterprises and heavy-pollution industries. They should establish FinTech application demonstration projects and cultivate exemplary enterprises. Additionally, a green FinTech standard system should be developed to regulate industry development.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) \u003cb\u003ePromote FinTech Tools Differentiated by Industry\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor non-state-owned enterprises, the government can subsidize FinTech service platforms (e.g., green credit AI rating systems) to reduce the digitalization costs for private enterprises and alleviate their financing constraints. For heavy-pollution industries, efforts should be made to ensure the coordinated implementation of FinTech and environmental policies, such as the establishment of a \"carbon data blockchain platform\" to help companies precisely quantify emission reduction benefits and integrate with carbon market transactions.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) \u003cb\u003eOptimize the Collaborative Mechanism of Environmental Policies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo avoid one-size-fits-all administrative interventions, local governments should leverage FinTech's dynamic monitoring capabilities to implement precise environmental regulations.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) \u003cb\u003eStrengthen Institutional Support and Capacity Building\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAt the central level, FinTech applications should be incorporated into the \"dual carbon\" evaluation index, encouraging local governments to explore policy tool combinations through green FinTech innovation pilot zones. At the enterprise level, industry associations can collaborate with universities to conduct FinTech training, enhancing the digital governance capabilities of heavy-pollution enterprises and resolving technological adaptation barriers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The data used in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The author declares no conflicts of interest related to this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Juan Hou conducted the study design, data analysis, and manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study uses secondary publicly available data and did not require ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable, as the study does not involve human participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The author consents to the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The author confirms that there are no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCheng C, Yang S, Tian X. 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Wuhan Finance, (11), 3\u0026ndash;14, 2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"FinTech adoption, Sustainable development performance, Environmental regulation, Government environmental concern, Industry heterogeneity, Institutional and resource-based theory","lastPublishedDoi":"10.21203/rs.3.rs-8025487/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8025487/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnder the background of China's \u0026ldquo;Dual Carbon\u0026rdquo; strategy, corporate sustainable development performance has become a core indicator of high-quality development. As a key driver for improving resource allocation efficiency, FinTech adoption at the enterprise level requires further investigation regarding its impact on sustainable development. Drawing on institutional theory and the resource-based view, this study develops a theoretical framework to empirically examine the influence of FinTech adoption on firms' sustainable development performance and its underlying mechanisms. The model incorporates local government environmental concern as a moderating variable to explore its conditional effects, and further investigates industrial heterogeneity. The empirical results reveal that FinTech adoption significantly enhances sustainable development performance. The effect is more pronounced among non-state-owned enterprises and heavily polluting firms. Additionally, local government environmental concern exhibits a substitution effect in the relationship between FinTech adoption and sustainability performance, reflecting cross-industry differences in resource endowments, technological capacity, and policy responsiveness.\u003c/p\u003e","manuscriptTitle":"FinTech Adoption and Sustainable Development Performance: Moderating Role of Local Environmental Concern Across Industries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 08:02:36","doi":"10.21203/rs.3.rs-8025487/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-11T06:27:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T20:06:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-07T20:46:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-05T10:19:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146263267656381409612865257196654071587","date":"2025-12-01T14:39:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293166436293883733837817400680829795125","date":"2025-12-01T07:01:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-30T17:05:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330484650562748363423481539394238121460","date":"2025-11-30T13:41:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20682278290078155305899338292024590209","date":"2025-11-30T01:45:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T11:35:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189324458785480182709537867116069758544","date":"2025-11-07T11:07:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-07T09:17:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-07T07:05:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-05T08:40:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-05T08:39:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2025-11-04T06:58:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d7336b46-edd8-4303-b637-16ec13538210","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T06:23:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 08:02:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8025487","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8025487","identity":"rs-8025487","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}