How Fintech Impacts Urban Economic Resilience: A Perspective on the Empowerment of Digital Inclusive Finance

Abstract

Fintech is recognized as a pivotal driver of future developments in the financial sector. Investigating the impact of fintech on urban economic resilience is of significant theoretical and practical importance. This study explores how fintech influences urban economic resilience and its internal mechanisms, utilizing panel data from 280 prefecture-level cities in China over the period 2011–2022. The key findings are as follows: (1) The development of fintech significantly boosts urban economic resilience. (2) Fintech strengthens urban economic resilience by advancing the level of digital inclusive finance. (3) A favorable business environment and well-developed digital infrastructure positively moderate the effect of fintech on urban economic resilience. (4) The heterogeneity analysis shows that fintech’s impact on urban economic resilience is more pronounced in inland cities and those with a strong service-oriented industry presence. This study enriches the understanding of factors influencing urban economic resilience and offers valuable insights into the role of fintech in enhancing it.

1. Introduction

The world is currently undergoing a historic transformation, the likes of which have not been seen in a century. The global economy is confronted with a range of unforeseen risks, including the global financial crisis, trade tensions between China and the United States, the COVID-19 pandemic, and the ongoing Russian-Ukrainian conflict. These challenges are testing the capacity of nations to manage uncertainty. With half of the global population now residing in urban areas, cities have become the focal point of human activity [1]. Since 2015, the United Nations Human Settlements Programme (UN-Habitat) has focused on promoting sustainable urban development from a resilience perspective and launched a series of policy practices. It can be observed that resilience is a key factor in achieving sustainable urban development. General Secretary Xi Jinping has emphasized the need to “comprehensively promote the construction of resilient cities”. The Outline of the Fourteenth Five-Year Plan for the National Economic and Social Development of the People’s Republic of China and the Vision 2035 both highlight economic resilience as a key guiding principle. Similarly, in its World Economic Situation and Prospects 2021 report, the United Nations Department of Economic and Social Affairs asserts that governments worldwide must reassess their fiscal and debt sustainability frameworks to strengthen economic resilience and foster an inclusive recovery. The existing literature offers extensive research on enhancing economic resilience, with scholars identifying key factors such as urban governance [2], infrastructure development [3], industrial clustering [4], and the level of technological innovation [5] as critical influences on regional economic resilience. Based on the theory of endogenous growth [6], the connection between fintech and urban economic resilience essentially reflects the shaping of the dynamic adaptability of economic systems by “endogeneity of technological innovation”. The theory of endogenous growth emphasizes that the core driver of economic growth is not exogenous capital or labor input, but rather knowledge creation, technological progress, and human capital accumulation within the economic system itself. As a typical “technology-finance” integrated innovation, fintech has become a core driving force for enhancing urban economic resilience through endogenous mechanisms such as knowledge spillover, human capital accumulation, innovation incentives, and dynamic learning. Fintech promotes the transformation of urban economies from a “linear growth” model that relies on traditional factor inputs to an “endogenous growth” model that depends on technological innovation and efficiency improvement. Consequently, urban economies can demonstrate stronger abilities of adaptation, recovery, and transformation when facing internal and external shocks. The People’s Bank of China, in its Financial Technology Development Plan (2022–2025), emphasizes the importance of embedding fintech within the real economy. Fintech is steadily emerging as a driving force in shaping the future of the financial industry. Therefore, exploring the impact of fintech on urban economic resilience, as well as understanding its underlying mechanisms, holds both theoretical and practical significance. China is undergoing rapid urbanization, with the National Bureau of Statistics (NBS) reporting that the urbanization rate will reach 66.16% by the end of 2023, reflecting an average annual increase of 0.75 percentage points. While this rapid urbanization presents significant opportunities, it also brings a host of challenges. Using Chinese cities as case studies is essential for understanding urban economic resilience. Additionally, as the world’s second-largest economy, China boasts a vast domestic market and a diverse industrial structure. The economic development levels and industrial compositions of cities vary significantly, ranging from the highly developed coastal regions to the less developed inland areas. This diversity provides a valuable sample for research, offering insights into how fintech influences urban economic resilience under different conditions.

Compared to existing literature, the main contributions of this study are as follows: (1) Integrating fintech development and urban economic resilience into a unified analytical framework, this study empirically examines the impact of fintech on urban economic resilience using panel data from China’s prefecture-level cities. Through detailed empirical research, it is found that fintech has a significant promoting effect on the improvement of urban economic resilience. This study addresses gaps in current empirical research and expands the set of factors influencing economic resilience; (2) Introducing digital inclusive finance as an intermediary variable, it is found that fintech can promote the improvement of urban economic resilience by enhancing the level of digital inclusive finance in cities. By considering the diverse characteristics of multiple cities, this study highlights the heterogeneous effects of fintech development on urban economic resilience, shedding light on the underlying mechanisms and pathways through which fintech influences economic resilience; (3) Extending the existing literature, this paper examines the moderating roles of the business environment and digital infrastructure in shaping the impact of fintech on urban economic resilience. Empirical research shows that both the business environment and digital infrastructure have positive moderating effects, significantly enhancing the promoting role of fintech development level in urban economic resilience; (4) Recognizing the early benefits of reform and opening up, as well as the geographic advantages of China’s coastal cities, this study highlights the economic, infrastructural, and developmental differences between coastal and inland cities. Rather than relying on traditional East, Central, and West regional classifications, the analysis divides cities into coastal and inland groups to accurately capture the differentiated impacts based on geographical location. Meanwhile, against the backdrop of China’s industrial upgrading, the study classifies the samples into those dominated by the tertiary industry (service industry) and those that are not. The research shows that the role of fintech in enhancing urban economic resilience is stronger in inland areas than in coastal cities, and its impact on the economic resilience of cities with a high-level industrial structure dominated by the tertiary industry is relatively significant.

2. Literature Review

2.1. The Impact of Fintech

In recent years, with the continuous evolution and widespread adoption of digital technology, the global industrial system is undergoing a wave of intelligent and digital transformation. Fintech, which represents the integration and innovative application of digital technology in the financial sector, has increasingly become a focal point for both academic researchers and industry practitioners worldwide. By leveraging technologies such as big data, cloud computing, blockchain, and artificial intelligence, fintech drives the creation of new business models, technological applications, and service innovations. These developments, in turn, have a significant impact on the growth of financial markets and institutions [7]. A review of the existing literature on fintech reveals that most current studies concentrate on its impact at the micro level. Specifically, research has explored the effects of fintech development on corporate innovation [8], corporate debt default [9,10], and bank credit [11,12]. At the macro level, fintech plays a significant role in promoting urban entrepreneurial activities by enhancing human capital and driving industrial upgrading [13], making a substantial contribution to economic growth. Additionally, fintech is crucial in advancing sustainability goals. In their study, Cheng et al. [5] demonstrate that fintech can notably reduce carbon emissions, with this finding remaining robust even after accounting for the effects of low-carbon pilot city policies.

2.2. Definition and Measurement Methods of Urban Economic Resilience

The term “resilience” originally emerged in the field of physics to describe the ability of a material to return to its original shape after being subjected to an external force. In 1973, ecologist Holling introduced the concept of resilience into ecology, defining it as the capacity of an ecosystem to maintain its function, structure, and feedback processes in the face of disturbances [14]. Since then, researchers have expanded the concept of resilience to various fields, including society, ecosystems, economy, and energy systems. Economic resilience is defined as the ability to effectively withstand external disturbances, manage various risk shocks, adapt to natural disasters, and recover from them to achieve sustainable economic development [15]. As complex social systems, cities are crucial vehicles for resilience and must be reflexive, adaptive, robust, redundant, flexible, and inclusive [16]. The assessment of urban economic resilience is influenced by various factors. Kitsos and Bishop [17] noted in their study that urban economic resilience can be shaped by elements such as the degree of urbanization, demographic composition, and geographic location. There are two primary methods for measuring economic resilience: the core variable method and the multidimensional indicator method. Researchers using the core variable method treat resilience as an inherent attribute of the urban economy and assess the stability of urban economic development by examining changes in core variables in response to external shocks [18]. Commonly used core indicators for measuring urban economic resilience include the unemployment rate and GDP growth rate, as these variables reflect the trajectory of economic development under shocks [19]. Although the core variable approach simplifies the measurement of economic resilience, some scholars argue that economic resilience should be viewed as a complex, systemic outcome resulting from the collective actions of key actors both within and outside the region [20]. In response, they have developed indicator systems to measure regional economic resilience using a multidimensional, multi-indicator approach. For instance, Sensier et al. [21] introduced a methodology to assess the economic resilience of the entire European region across three main dimensions. Their study analyzed the varying responses of different regions to multiple economic shocks since the early 1990s. To clearly demonstrate the distinction between economic resilience and its related concepts, we have listed the connotations and key focuses of the relevant terms in

Table 1. Terms Related to Economic Resilience.

Related Terms	Connotation	Differences from Economic Resilience	Author and Year
Sustainability	Sustainability science seeks to address the major challenges facing society while ensuring that human well-being is undiminished and the basic Earth systems continue to operate.	It focuses on long-term balance and pays attention to the “survival bottom line” of the system, while economic resilience focuses on the dynamic adaptation and upgrading of economic systems under shocks.	Charles L. Redman (2014) [22]
stability	Measure the ability of a system to maintain relatively stable functions or services over a certain period under external disturbances or pressures.	It emphasizes adherence to the original model, while resilience allows the system to adjust its state after a shock.	Grimm et al. (1992) [23]
shock resistance	The capacity of individuals, communities, and institutions to withstand, adapt to, and recover from shocks.	It focuses on the “resistance effect” when a shock occurs, while resilience covers resistance, recovery, and transformation.	Razzano and Bernardi (2024) [24]

.

2.3. Fintech and Urban Economic Resilience

We conducted a comprehensive review of the relevant literature and found that while there is a substantial body of research on financial technology and economic resilience, no studies have specifically examined financial technology as a factor influencing urban economic resilience. A closely related area of research is the impact of digital finance on economic resilience. Since digital finance and financial technology share significant conceptual overlap, the findings from this body of work provide a useful theoretical foundation for the present study. A review of the literature on the impact of digital finance on regional economic resilience reveals that, despite the use of various research methodologies, the overall conclusion is consistent: digital finance has a positive effect on enhancing urban economic resilience. For instance, Tang et al. [25] employed a coupled scheduling model to examine the relationship between digital finance and economic resilience across provinces and cities in the Yangtze River Economic Belt. Their findings indicate that the level of coordination between digital finance and economic resilience is generally increasing. Similarly, Hou et al. [26] demonstrated through empirical analysis of panel data from 283 cities in China that digital finance enhances regional economic resilience by improving capital allocation efficiency, strengthening regional innovation capacity, and boosting residents’ consumption. Notably, this effect is more pronounced in central and western cities. In contrast, Yang et al. [27] also acknowledge the positive impact of digital finance on urban economic resilience but find that this effect is more pronounced in eastern cities. The digital economy is a comprehensive system driven by the integration of digital technologies across various sectors, with fintech serving as its specialized branch focused on financial services. As a crucial interface between the digital economy and the financial system, fintech plays a pivotal role in directly illustrating the dynamic evolution of a city’s economic resilience in response to technological advancements.

In conclusion, existing literature has yet to explicitly address the intrinsic relationship between fintech and urban economic resilience, including whether fintech has a positive or negative impact, and the specific mechanisms through which it influences urban economic resilience. These remain unexplored and unanswered questions. As one of the few countries to recover rapidly from the pandemic’s economic shock, studying China’s urban economic resilience offers valuable insights for both developing countries and the global community. Building on this context, this paper leverages data from 280 prefecture-level cities in China, spanning from 2011 to 2022, to investigate the impact of fintech on urban economic resilience and the underlying mechanisms at play.

3. Theoretical Analysis and Assumptions

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

3.1. Direct Effects: The Impact of Fintech on the Urban Economic Resilience

The theoretical framework for studying the impact of fintech on urban economic resilience can be constructed across three dimensions: resistance and resilience, adaptation and regulation, and transformation and development of urban economic systems. First, the banking sector plays a critical role in driving the economic growth and development of the region in which it operates [28]. Fintech has the potential to reshape liquidity creation by altering the structure of bank credit, thereby enhancing the risk-taking capacity of banks [29]. Fintech enhances the ability of financial institutions to manage and monitor financial risks through the use of big data analysis, risk modeling, and real-time monitoring systems. This facilitates more accurate risk assessments and crisis prevention, ultimately fostering financial stability and promoting sustainable economic development [30]. Some studies have shown that negative income shocks can significantly undermine household resilience; however, the development of financial technology has bolstered household resilience in the aftermath of such shocks, effectively mitigating their negative impact on households [31]. Second, the development of fintech can significantly reduce labor mismatches and improve the efficiency of labor allocation within enterprises by mitigating overinvestment [32]. By making resource allocation more efficient and equitable, fintech contributes to the promotion of entrepreneurial employment, the optimization of industrial structures, and the enhancement of overall economic stability and risk resilience within society. The greater the impact of fintech, the faster the adjustment of enterprise leverage [33]. This swift adjustment to optimal leverage not only enhances financial flexibility and market competitiveness but also boosts shareholder confidence, ultimately maximizing enterprise value. Furthermore, fintech has the potential to significantly enhance urban innovation capacity by increasing the willingness to invest in research and development, boosting entrepreneurial vitality, and reducing financial pressures [34]. Su et al. [35] highlighted the positive impact of fintech adoption on the development of rural e-commerce, using Taobao villages in China as a case study. Their findings suggest that fintech plays a crucial role in regional industrial transformation. Building on this theoretical foundation, we propose the following hypothesis:

Hypothesis 1.

The development of fintech can effectively enhance urban economic resilience.

3.2. Indirect Effects: The Mediating Role of Digital Inclusive Finance

The development of digital inclusive finance is closely linked to the technical support provided by fintech. Leveraging advanced digital technologies such as big data, blockchain, and cloud computing, fintech enhances the accessibility and reach of financial services. This enables small and micro-enterprises at the bottom of the economic pyramid to access credit resources, thereby improving financial inclusion [36]. Furthermore, existing research demonstrates that fintech development significantly enhances financial access for marginalized firms, a phenomenon known as financial inclusion [37]. Fintech is not only a financial innovation but also an inclusive one, as it adapts cutting-edge financial services to the lower end of the market. This helps achieve a more equitable distribution of the benefits of innovation and economic growth, thereby promoting social justice and equity [38]. Digital inclusive finance plays a crucial role in enhancing the financial accessibility of disadvantaged groups and is a key mechanism for improving urban economic resilience. Chinese scholars Du et al. [39] employed panel data from 285 prefectural-level cities and above in China, spanning from 2011 to 2020, and provided empirical evidence demonstrating the significant contribution of digital inclusive finance to strengthening economic resilience. Similarly, Shang and Liu (2024) [40] showed in their study that digital inclusive finance can bolster urban economic resilience by improving innovation capacity and resource allocation efficiency. Verma and Chatterjeel [41], Indian scholars, concluded that digital inclusive finance enhances financial resilience, based on data from 13 emerging market countries. Building on this theoretical framework, this paper proposes the following hypothesis:

Hypothesis 2.

Fintech can enhance urban economic resilience by increasing the level of digital inclusive finance.

3.3. Moderating Effects

3.3.1. Moderating Effects of the Business Environment

The business environment encompasses the rules, regulations, and conditions that govern market activities, including laws, policies, taxes, and other institutional factors [42]. In the context of fintech development, the effectiveness of financial institutions in providing funding to mitigate external risks is influenced by the local business environment [39]. In regions with favorable business environments, robust legal and policy frameworks can offer the necessary legal safeguards for fintech, thereby enhancing market trust and fostering the adoption and dissemination of technological innovations and applications. By optimizing the business environment, local governments create more favorable conditions for innovative and entrepreneurial activities, which in turn enable enterprises to unleash greater market potential, foster independent innovation, and strengthen the urban economic resilience [43]. Furthermore, a market-oriented, rule-of-law-based business environment helps reduce rent-seeking behaviors among enterprises, lowers transaction costs, protects intellectual property rights, enhances regional investment attractiveness, promotes fair competition, encourages entrepreneurial activities, and boosts industrial agglomeration effects. Together, these factors establish a solid foundation for the sustained and healthy development of the local economy [44]. Building on the theoretical framework outlined above, this paper proposes the following hypothesis:

Hypothesis 3.

The business environment moderates the impact of fintech on the urban economic resilience.

3.3.2. The Moderating Role of Digital Infrastructure

The term “digital infrastructure” refers to the fundamental systems and elements that enable the operation and connectivity of the digital economy and society [45]. Fintech innovations, particularly the application of mobile payments, blockchain technology, and smart contracts, rely heavily on advanced digital infrastructures for support. The development of digital infrastructures, such as communication networks, data centers, cloud computing, and artificial intelligence [46], has significantly enhanced the speed and coverage of financial transactions. These advancements also enable financial services to deliver the necessary products and services to businesses and individuals more rapidly, particularly during economic fluctuations or crises, thereby strengthening the market’s capacity to adapt to uncertainty. Information network-based digital infrastructure, as a critical pillar of modern societies and economies, plays a crucial role in reshaping urban development patterns and boosting urban economic resilience [47]. Cities can leverage their digital infrastructures to maintain the continuity of financial markets in the face of economic crises or catastrophic events, ensuring that essential economic functions remain intact. In particular, during financial crises, digital infrastructure facilitates the rapid deployment of financial technologies, such as micro-credit and real-time payment systems, which are essential for supporting economic recovery and social stability. Based on the theoretical analysis presented above, this paper proposes the following hypothesis:

Hypothesis 4.

Digital infrastructure moderates the impact of fintech on urban economic resilience.

4. Research Design

4.1. Sample Selection and Data Sources

In this study, data from selected prefecture-level cities in China spanning from 2012 to 2022 are used as the sample. After excluding and matching missing data, the panel dataset includes 280 cities, resulting in a final sample size of 3360 observations. The primary variables utilized in this paper are sourced from the China Urban Statistical Yearbook and local statistical yearbooks. Missing values for a small number of years and cities are addressed through linear interpolation.

4.2. Variable Selection and Definition

4.2.1. Explained Variable

Urban economic resilience (Resi): Existing research on the measurement of economic resilience has yet to establish a unified method of assessment. Drawing on the work of Guo et al. [20] and Fang et al. [48], and considering the availability of city-level data, this study constructs a multidimensional index system based on fourteen indicators across three key dimensions: resistance and recovery capacity, adaptation and adjustment capacity, and transformation and development capacity, as shown in

Table 2. Comprehensive evaluation index system for urban economic resilience.

Level 1 Indicator	Level 2 Indicators	Level 3 Indicators	Attribute of Index
Urban Economic Resilience	Resistance and recovery	Regional GDP per capita (yuan)	+
		Disposable income per capita (yuan)	+
		Total residential savings (yuan)	+
		Number of registered unemployed (people)	-
		Total exports and imports as a share of GDP (%)	-
	Adaptive and regulatory capacity	Local government revenue to expenditure ratio (%)	+
		Total retail sales of consumer goods (million dollars)	+
		Share of tertiary sector in GDP (%)	+
		Deposit and loan ratio of financial institutions (%)	+
		Total long-term investment in fixed assets (million yuan)	+
	Transformation and development capacity	Number of patents granted (item)	+
		Number of students enrolled in general higher education institutions (people)	+
		Financial investment in scientific research (million yuan)	+
		Financial education expenditure (million yuan)	+

The “+” symbol indicates that the indicator attribute is positive, while the “-“ symbol indicates that the indicator attribute is negative.

. To ensure the scientific rigor of the evaluation system, this study employs the entropy method to calculate the composite economic resilience index for each city (Entropy method: First, the original data are standardized (for positive indicators, the max-min normalization method is adopted; for negative indicators, normalization is performed after taking their reciprocals first). Second, the information entropy and entropy weight of each indicator are calculated. Finally, the comprehensive index of urban economic resilience is obtained through a weighted summation. This method avoids biases caused by subjective weighting and is suitable for multi-dimensional, multi-indicator comprehensive evaluation scenarios.).

To clearly illustrate the temporal and spatial changes in urban economic resilience, ArcGIS is used to generate spatial distribution maps for urban economic resilience in 2011 and 2022 (see Figure 2).

4.2.2. Core Explanatory Variable

Fintech (Fin): Building on the methodology of Liu et al. [49], this study uses the number of newly registered fintech companies in the sample cities within a given year as an indicator of the local fintech level. To collect the relevant business registration data, we searched for keywords such as “fintech”, “artificial intelligence”, “online payment”, “cloud computing”, “Internet of Things”, and “blockchain” on the “Tianyancha” website. Companies that had been in business for less than one year or were not operating normally were excluded. The number of fintech companies in each prefecture-level city is then used as a proxy variable for the local level of fintech development, with a higher number of fintech companies indicating a more developed fintech sector in that city.

4.2.3. Mediating Variable

Digital inclusive finance (Dif): This study employs the “Digital Inclusive Finance Index” developed by the Digital Finance Research Center of Peking University and the Ant Group Research Institute as a measure of digital inclusion development across different regions.

4.2.4. Moderating Variables

The business environment (Env) and digital infrastructure (Dinf) are selected as moderating variables. Data on the business environment are sourced from the “China Provincial Business Environment Research Report”, with missing values filled using linear interpolation. Digital infrastructure data is derived using the entropy method, encompassing three primary indicators: information infrastructure, convergence infrastructure, and innovation infrastructure.

4.2.5. Control Variables

Referring to the studies of Du et al. [50] and Ye et al. [51], this paper selects the following indicators as control variables: market openness (open), government intervention (gov), cultural soft power (book), urban health care level (med), infrastructure (bas), and regional informatization level (inf). Specific definitions of these variables are provided in

Table 3. List of variable definitions.

Variable Type	Variable	Symbol	Variable Definition
Explained variables	Urban economic resilience	Resi	The entropy method calculates
Explanatory variables	Fintech	Fin	(Number of fintech companies in prefecture-level cities + 1) taken in pairs
Mediating variables	Digital inclusive finance	Dif	Digital inclusive finance index divided by 100
Control variables	Market openness	open	Logarithm of the amount of foreign capital actually utilized
	Government intervention	gov	Government fiscal expenditure/local GDP
	Cultural accessibility	book	The total number of books in public libraries is taken to be the right
	Urban health care level	med	Number of hospitals, health centers
	Infrastructure	bas	Road freight volume taken in pairs
	Regional informatization level	inf	Cell phone subscribers at the end of the year in pairs

.

4.3. Model Construction

4.3.1. Baseline Regression Model

In order to verify the impact of fintech on the urban economic resilience, this paper constructs the following benchmark regression model:

Resi_it = α₀ + α₁Fin_it + α₂∑Controls_it + μ_i + δ_t + ε_it

(1)

In Equation (1), i denotes prefecture-level city and t denotes year; the explanatory variable Resi_it denotes the urban economic resilience of prefecture-level city i in year t calculated by the entropy method; the explanatory variable Fin_it denotes the fintech index of i in year t; α₀ is a constant term; Controlsit is the control variables, including: market openness (open), government intervention (gov), cultural soft power (book), urban medical level (med), infrastructure (bas), and regional informatization level (inf); μ_i is an individual fixed effect; δ_t is a time fixed effect; and ε_it denotes a random interference term. This paper focuses on the coefficient of the explanatory variable Fin_it. If α₁ is significantly positive, it indicates that the development of regional financial technology level can significantly promote the level of urban economic resilience, and Hypothesis 1 holds.

4.3.2. Mediating Effect Model

Drawing on previous studies, to verify the internal mechanism through which fintech affects urban economic resilience, this paper constructs the following mediating effect model:

Dif_it = β₀ + β₁Fin_it + β₂ + ∑Controls_it + μ_i + δ_t + ε_it

(2)

Resi_it = γ₀ + γ₁Fin_it + γ₂Dif_it + γ₃∑Controls_it + μ_i + δ_t + ε_it

(3)

Dif_it in Equation (2) is the mediating variable, which indicates the digital inclusive finance index of prefecture-level city i in year t. The meanings of the rest of the variables are the same as in Equation (1). Using model (1) to test the impact of fintech on the urban economic resilience, the regression coefficient α1 is obtained, and if α₁ is significant, the next step of mediating effect identification can be carried out. Using model (2) to test the effect of fintech on digital inclusive finance, the regression coefficient β₁ is obtained, and if β₁ is significant, the next step of the test is carried out. Add the mediating variable into Equation (3) to test whether the mediation effect exists, and get the regression coefficient γ₂. When γ₂ is significant and γ₃ > α₁, if γ₂ is significant, it means that there is a partial mediation effect, and if γ₂ is not significant, it means that there is a complete mediation effect.

4.3.3. Moderating Effects Model

To test whether the business environment and digital infrastructure moderate the impact of fintech on the urban economic resilience, this paper constructs the following moderating effect model:

Resi_it = θ₀ + θ₁Fin_it + θ₂Env_it + θ₃Fin_it*Env_it + θ₄∑Controls_it + μ_i + δ_t + ε_it

(4)

Resi_it = θ₀ + θ₁Fin_it + θ₂Dinf_it + θ₃Fin_it*Dinf_it + θ₄∑Controls_it + μ_i + δ_t + ε_it

(5)

Env_it in Equation (4) denotes the business environment, and Fin_it*Env_it is the interaction term between fintech and the business environment. Dinf_it in Equation (5) denotes digital infrastructure, and Fin_it*Dinf_it is the interaction term between Fintech and digital infrastructure. The estimated coefficient θ₃ of the interaction term indicates the moderating effect; if θ₃ is significantly positive, it indicates positive moderation; on the contrary, if θ₃ is significantly negative, it indicates the existence of negative moderation.

5. Empirical Results and Analysis

5.1. Descriptive Statistics

To avoid bias in the regression results, the variables are tested for multicollinearity. The test results of the variance inflation factor (VIF) of each variable in the sample show that the average VIF value is 2.43, with the maximum VIF value is 4.61, and the inflation factor of each variable is much less than 10, indicating that there is no multicollinearity among the main variables. The descriptive statistical results of each variable are shown in

Table 4. Descriptive statistics.

	(1)	(2)	(3)	(4)	(5)
Variables	N	Mean	Sd	Min	Max
Resi	3360	0.258	0.116	0.0587	0.815
Fin	3360	2.211	1.520	0	8.052
open	3360	10.34	1.666	1.099	14.15
gro	3360	0.204	0.103	0.0439	0.916
book	3360	7.411	0.945	4.159	11.01
med	3360	4.804	0.769	2.079	8.024
bas	3360	9.033	0.858	3.664	13.23
inf	3360	5.835	0.730	3.584	8.296
Dif	3360	1.928	0.758	0.170	3.611
Number of cities	280	280	280	280	280

, the model uses clustered standard errors at the city level, and it can be found by observing Table 3: the maximum value of the explanatory variable urban economic resilience (Resi) is 0.815, the minimum value is 0.0587, and the standard deviation is 0.116, which indicates that the level of urban economic resilience varies among regions, but it is still within a reasonable range; the maximum value of the explanatory variable fintech (Fin) is 8.052, the minimum value is 0, with a standard deviation of 1.52, indicating that there is a large gap in the level of development of financial technology in different cities; the mediating variable digital inclusive finance (Dif) has a maximum value of 3.611, a minimum value of only 0.758, and a mean value of 1.928, indicating that there is an imbalance in regional development.

5.2. Baseline Regression Results

This study uses the Hausman test to determine whether to select fixed effects or random effects. The results of the Hausman test indicate that the null hypothesis is rejected at the 1% significance level, leading to the choice of a fixed effects model for the benchmark regression. The results of the benchmark regression are presented in

Table 5. Panel two-way fixed effects model regression results.

	(1)	(2)
Variables	Resi	Resi
Fin	0.0104 ***	0.0082 ***
	(4.06)	(3.50)
open		−0.0004
		(−0.58)
gro		−0.0922 ***
		(−3.87)
book		0.0023
		(1.05)
med		0.0103 ***
		(3.40)
bas		0.0002
		(0.10)
inf		0.0044
		(0.63)
Constant	0.2307 ***	0.1579 ***
	(71.64)	(3.30)
year	Yes	Yes
city	Yes	Yes
Observations	3360	3360
R-squared	0.576	0.588
Number of cities	280	280

Robust t-statistics in parentheses, *** p < 0.01.

. Column (1) displays the regression results for the impact of financial technology development on urban economic resilience before adding control variables. It shows that the regression coefficient for financial technology (Fin) to urban economic resilience (Resi) is significant at the 1% level. This suggests that financial technology has a significant positive effect on urban economic resilience, confirming that the development of financial technology contributes to enhancing urban economic resilience. Thus, Hypothesis 1 is supported. Column (2) presents the regression results after controlling for both city and year fixed effects and adding control variables. The regression coefficient is 0.0082, which remains significant at the 1% level. This further strengthens the conclusion that the development of regional financial technology plays a more robust role in improving urban economic resilience.

The possible reasons are as follows: first, fintech promotes the innovation of financial products and services, which improves the quality and efficiency of financial services while reducing transaction costs, providing new momentum for the development of the city’s economy; second, with the help of advanced data analytics tools and artificial intelligence algorithms, financial institutions can monitor real-time market dynamics and customer behaviors, identify potential risks promptly and take measures to address them, which not only helps to protect their sound operations but also enhances the risk-resistant ability of the entire urban economic system. Furthermore, with the application of fintech, it is easier for enterprises to obtain financial support and expand their markets, and the government can also supervise and serve the enterprises more effectively, and realize the precision of policy implementation, to promote the effective allocation of resources and the optimization and upgrading of the economic structure.

5.3. Mediating Effect Test

Table 6. Mediating effect test results.

	(1)	(2)	(3)
Variables	Resi	Dif	Resi
Dif			0.0524 ***
			(3.21)
Fin	0.0082 ***	0.0198 ***	0.0071 ***
	(3.50)	(3.88)	(3.14)
open	−0.0004	−0.0067 ***	−0.0001
	(−0.58)	(−5.29)	(−0.09)
gro	−0.0922 ***	−0.3799 ***	−0.0723 ***
	(−3.87)	(−4.65)	(−3.05)
book	0.0023	0.0158 ***	0.0015
	(1.05)	(3.00)	(0.69)
med	0.0103 ***	0.0242 ***	0.0090 ***
	(3.40)	(4.65)	(3.06)
bas	0.0002	0.0085 **	−0.0003
	(0.10)	(2.02)	(−0.14)
inf	0.0044	0.0036	0.0042
	(0.63)	(0.25)	(0.61)
Sobel		Z = 4.574 ***
Constant	0.1579 ***	0.2934 ***	0.1425 ***
	(3.30)	(2.79)	(2.99)
year	Yes	Yes	Yes
city	Yes	Yes	Yes
Observations	3360	3360	3360
R-squared	0.588	0.995	0.593
Number of cities	280	280	280

Robust t-statistics in parentheses, *** p < 0.01, ** p < 0.05.

presents the regression results examining the impact of fintech on urban economic resilience through the mediating effect of digital inclusive finance. As shown in column (2), the regression coefficient for fintech is positive and significant at the 1% level, indicating that the development of fintech promotes the level of digital inclusive finance. The results in column (3) demonstrate that, after including digital inclusive finance in the model, the estimated coefficient for fintech remains significantly positive at the 1% level, although its value has decreased compared to the original regression. Additionally, the regression coefficient for digital inclusive finance is also significant at the 1% level, suggesting that digital inclusive finance partially mediates the relationship between fintech and urban economic resilience. In other words, the impact of fintech on urban economic resilience is partially mediated by digital inclusive finance, alongside its direct effect. This supports Hypothesis 2.

5.4. Moderating Effect Test

Table 7. Moderating effect test results.

	(1)	(2)	(3)	(4)
Variables	Resi	Resi	Resi	Resi
Fin	0.0059 ***	0.0039 *	0.0068 ***	0.0065 ***
	(2.64)	(1.76)	(3.03)	(2.95)
Env	0.1035 ***	0.0883 ***
	(6.80)	(5.96)
Dinf			0.2627 ***	0.1759 ***
			(5.79)	(3.01)
Fin*Env		0.0152 ***
		(4.31)
Fin*Dinf				0.0249 **
				(2.04)
open	0.0001	0.0009	0.0002	0.0009
	(0.17)	(1.30)	(0.33)	(1.16)
gro	−0.0533 **	−0.0556 **	−0.0673 ***	−0.0756 ***
	(−2.25)	(−2.32)	(−2.92)	(−3.13)
book	−0.0003	0.0003	−0.0003	0.0001
	(−0.11)	(0.14)	(−0.14)	(0.04)
med	0.0090 ***	0.0048 *	0.0084 ***	0.0067 **
	(3.10)	(1.87)	(2.91)	(2.53)
bas	−0.0018	−0.0016	−0.0005	−0.0007
	(−0.95)	(−0.87)	(−0.27)	(−0.42)
inf	−0.0002	0.0065	0.0066	0.0112 *
	(−0.03)	(1.05)	(1.00)	(1.80)
Constant	0.2712 ***	0.2260 ***	0.2091 ***	0.1759 ***
	(6.06)	(5.11)	(4.70)	(3.98)
year	Yes	Yes	Yes	Yes
city	Yes	Yes	Yes	Yes
Observations	3360	3360	3360	3360
Number of cities	280	280	280	280
R-squared	0.603	0.615	0.603	0.606

Robust t-statistics in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

presents the results of the moderating effect test. The regression results show that the coefficients for the business environment (Env) and digital infrastructure (Dinf) are 0.0152 and 0.0249, respectively, both of which are significant at the 1% level. These findings indicate that both variables have a positive moderating effect, significantly strengthening the impact of fintech development on urban economic resilience. Thus, Hypotheses 3 and 4 are supported.

The interaction diagrams illustrating the moderating effects are presented in Figure 3 and Figure 4. As shown in Figure 3, the optimization of the regional business environment significantly enhances the impact of fintech development on urban economic resilience. The effect of fintech on economic resilience is notably greater in regions with a high level of business environment compared to regions with a low level. Similarly, Figure 4 demonstrates that as the level of digital infrastructure improves, the role of fintech in enhancing urban economic resilience becomes significantly more pronounced. The impact of fintech on urban economic resilience is particularly strong in regions with high levels of digital infrastructure.

5.5. Robustness and Endogeneity Tests

5.5.1. Replacement of the Explanatory Variables Measurement Approach

Referring to the method of Martin et al. [52], the urban economic resilience is re-measured using the sensitivity coefficient method based on the real GDP of each city.

R_i^t = (ΔY_i^t − ΔE)/|ΔE|

(6)

ΔY_i^t = Y_i^t − Y_i^t−k

(7)

ΔE = ((Y_A^t − Y_A^t−k)/Y_A^t−k)Y_i^t−k

(8)

In the formula, R_i^t represents the relative economic resilience of city i in year t; ΔY_i^t represents the change in the actual economic output of city i in year t, as shown in Equation (6). Y_A^t and Y_A^t−k represent the national economic output in years t and t − k, respectively. ΔE denotes the change in national economic output, predicted based on the overall performance of the country’s economy, as outlined in Equation (8).

For convenience, Equations (6)–(8) are combined and simplified into Equation (9).

R_i^t = ((Y_i^t − Y_i^t−k)/Y_i^t−k − (Y_f^t − Y_f^t−k)/Y_f^t−k)/|(Y_f^t − Y_f^t−k)/Y_f^t−k|

(9)

To facilitate comparative analysis, Equation (9) is centered, resulting in Equation (10):

Resi_2 = R_i^t − ∑_iⁿ₌₁R_i^t/n

(10)

In Equation (10), n represents the total number of cities in the sample, and Resi_2 denotes the standardized urban economic resilience, which replaces the original explanatory variables in the robustness regression. The results, presented in column (1) of Table 7, show that the regression coefficients are significant at the 1% level, consistent with the findings of the benchmark regression.

5.5.2. Excluding Special Time Samples

Given that the outbreak of COVID-19 in 2020 may have influenced regional fintech development and urban economic resilience, this study excludes the samples from 2020 and 2021. A new time window is created, and the regression is re-run. The results, shown in column (2) of Table 7, indicate that the impact of fintech on urban economic resilience remains significantly positive at the 1% confidence level, thereby confirming the robustness of the conclusions from the baseline regression.

5.5.3. Excluding the Sample of Provincial Capital Cities

Provincial capital cities typically have higher administrative status and often serve as the economic centers of their respective provinces, thereby securing more resources and favorable policies. This leads to a significant disparity in fintech development and regional economic resilience between provincial capitals and ordinary prefecture-level cities. To eliminate potential bias in the estimation results, the sample of provincial capital cities is excluded, and the regression is re-run. The results, presented in column (3) of Table 7, show that the regression coefficients remain significantly positive at the 1% level, confirming the robustness of the findings.

5.5.4. Core Explanatory Variable Lagged

Given that the impact of fintech on urban economic resilience may involve a time lag and that there could be reverse causality between the two, this study lags the core explanatory variables by one period in the regression to address the endogeneity issue. This approach allows for testing the impact of fintech in the previous period on urban economic resilience in the current period. The regression results, shown in column (4) of

Table 8. Robustness test results.

	(1)	(2)	(3)	(4)
Variables	Resi_2	Resi	Resi	Resi
lag_Fin				0.0067 ***
				(2.96)
Fin	1.4340 ***	0.0076 ***	0.0079 ***
	(2.95)	(3.28)	(3.47)
open	−1.0506 ***	0.0007	−0.0012	−0.0006
	(−4.66)	(0.92)	(−1.64)	(−0.90)
gro	−20.2468 ***	−0.0855 ***	−0.0845 ***	−0.0866 ***
	(−3.97)	(−3.49)	(−3.52)	(−3.72)
book	0.0441	0.0027	0.0029	0.0021
	(0.09)	(1.32)	(1.33)	(0.88)
med	3.2563 ***	0.0097 ***	0.0093 ***	0.0096 ***
	(4.10)	(3.27)	(3.06)	(3.12)
bas	1.4328 **	−0.0002	−0.0005	0.0001
	(2.41)	(−0.13)	(−0.24)	(0.06)
inf	−7.0255 ***	0.0037	0.0038	0.0015
	(−3.76)	(0.58)	(0.54)	(0.19)
Constant	−20.3377 **	0.1541 ***	0.1613 ***	0.1854 ***
	(−2.24)	(3.45)	(3.50)	(3.45)
year	Yes	Yes	Yes	Yes
city	Yes	Yes	Yes	Yes
Observations	3360	2800	3060	3080
Number of cities	280	280	255	280
R-squared	0.145	0.494	0.609	0.604

Robust t-statistics in parentheses, *** p < 0.01, ** p < 0.05.

, indicate that the regression coefficient for fintech remains significantly positive at the 1% level. Although the coefficient value is slightly lower than that in the benchmark regression, the results continue to be robust.

5.5.5. Instrumental Variable Method

As a leading region in fintech development, Hangzhou exerts a stronger technological spillover effect on areas that are geographically closer to it. This satisfies the relevance requirement for instrumental variables. Meanwhile, geographical distance is an objective existence that does not affect urban economic resilience, thus meeting the exogeneity requirement for instrumental variables. Additionally, the development of fintech is influenced by the growth of the Internet industry and the level of conventional communication infrastructure. Building on the work of Yang and Zhang [53] and Li et al. [54], this study employs two instrumental variables to address potential endogeneity issues: (1) the cross-multiplier term of the logarithm of the spherical distance from each city to Hangzhou, adjusted for the time trend (IV1), and (2) the interaction between the number of landline telephones per 100 people in 1984 and the number of Internet users per 100 people in the previous period (IV2).

The regression results are presented in

Table 9. Results of the instrumental variable method.

	(1)	(2)
	First	Second
Variables	Fin	Resi
IV1	−0.0285 ***
	(−7.33)
IV2	−0.0000 ***
	(−3.01)
Fin		0.0920 ***
		(7.00)
controls	Yes	Yes
Constant	398.3542 ***	0.1666 **
	(7.34)	(2.16)
year	Yes	Yes
city	Yes	Yes
Kleibergen-Paap rk LM statistic	61.96 ***
Cragg-Donald Wald F statistic	35.4 ***
	(19.93)
Kleibergen-Paap Wald rk F statistic	33.23 ***
	(19.93)
Observations	3360	3360
R-squared		0.898

Robust t-statistics in parentheses, *** p < 0.01, ** p < 0.05.

. In the first stage, the coefficients of the instrumental variables are significant at the 1% level. In the second stage, the regression coefficient for the explanatory variable, Fin, remains significant at the 1% level. These results suggest that the positive correlation between fintech and urban economic resilience is robust and consistent with the findings from the baseline regression. Furthermore, from an econometric perspective, the Cragg-Donald Wald F statistic is 35.4, indicating the absence of a weak instrumental variable problem. The Kleibergen-Paap rk LM statistic is 61.96, with a p-value of 0.000, which rejects the null hypothesis of “insufficient identification of instrumental variables”.

5.6. Heterogeneity Test

5.6.1. Geographic Location Heterogeneity

Geographic location contributes to significant differences in economic development, urban and rural construction, industrial structure, and other factors between coastal and inland areas. In this study, the sample is divided into coastal and inland cities according to whether they are directly adjacent to the sea. The regression results are presented in Table 10, with column (1) showing the results for coastal cities and column (2) displaying those for inland cities. The regression results reveal that, compared to coastal cities, the positive impact of fintech on urban economic resilience is more pronounced in inland cities.

The possible reasons for this are as follows: The “late-mover advantage” theory in development economics [55] points out that economically underdeveloped regions can achieve “leapfrog development” at a lower cost through technological imitation and institutional innovation. Due to low population density and small enterprise scales in inland cities, traditional financial institutions tend to focus their branch layouts and credit allocation on eastern coastal or central cities, leaving local enterprises and residents facing “financial exclusion”. In addition, the industrial structure of inland cities is mostly dominated by agriculture and resource-based industries, which are in urgent need of transformation toward the service sector and high-tech industries. However, they lack effective financial support, such as supply chain financing for green agriculture and microcredit for the cultural and tourism industry. In recent years, China has significantly narrowed the digital gap between inland cities and coastal regions through projects such as the “Eastern Data and Western Computing” initiative and rural digital infrastructure development, laying a hardware foundation for the implementation of fintech. With the characteristics of “digitalization” and “geographical dependence elimination”, fintech can precisely break through the “spatial constraints” of traditional finance. This “leapfrog empowerment” enables inland cities to bypass the “heavy asset investment” stage of traditional finance and directly achieve the “popularization” and “inclusiveness” of financial services through fintech, thereby promoting economic structure transformation more efficiently and enhancing economic resilience.

5.6.2. Heterogeneity of Industrial Structure

The theory of structural economic transformation [56] suggests that economic development follows a phased evolution, transitioning from an agriculture-dominated economy to an industry-dominated one, and eventually to a service-dominated economy. Within this process, the growth of the service sector—particularly producer services—serves as a key indicator of economic advancement. This integration has generated a demand for specialized services while simultaneously improving production efficiency. The rising prominence of the production service industry has provided more efficient support and services to manufacturing. Additionally, the emergence of new industries has expanded the business models of traditional industries, with manufacturing enterprises branching into the service sector. In turn, the service industry has offered value-added solutions to manufacturing, resulting in a mutually reinforcing relationship that drives the optimization and upgrading of the industrial structure.

To measure the industrial structure of cities, this paper uses the ratio of the tertiary industry to the secondary industry and divides the sample into cities with high and low industrial structures based on the median. Group regressions are then conducted. The regression results, presented in Table 10, show that columns (3) and (4) display the results for cities dominated by service-oriented industries and those dominated by non-service-oriented industries, respectively. The results reveal that fintech significantly enhances the urban economic resilience led by service-oriented industries, while its effect is not significant in cities dominated by non-service-oriented industries.

The possible reasons for this can be explained as follows: First, in service-oriented industry-led cities, the tertiary sector plays a dominant role. These industries are particularly dependent on information flow, data processing, and efficient service delivery models. The development of fintech directly addresses these needs, enhancing both operational efficiency and service quality, thereby improving the overall economic vitality and risk resilience of the city. Second, service-oriented cities typically exhibit a higher level of informatization and greater acceptance of technology, which facilitates the market’s adoption of fintech applications, enabling them to be rapidly transformed into tangible productivity gains. Additionally, service-dominant cities typically possess more advanced financial infrastructure and a more favorable regulatory environment compared to non-service-dominant cities. This supports the growth and development of fintech firms, further stimulating innovation and optimizing services. In contrast, while manufacturing and other industrial sectors in non-service-dominant cities can also benefit from fintech, their core strengths lie primarily in production capacity and cost control. As a result, the direct impact of fintech on these sectors is relatively limited. Its more indirect effects, such as improving the financing environment and capital utilization efficiency, are not substantial enough to significantly alter the overall economic resilience of these cities.

Table 10. Results of the heterogeneity test.

	(1)	(2)	(3)	(4)
	Geographic Location Heterogeneity		Heterogeneity of Industrial Structure
Variables	Resi	Resi	Resi	Resi
Fin	0.0080	0.0071 ***	0.0131 ***	0.0052
	(1.23)	(2.88)	(3.46)	(1.44)
open	0.0033	−0.0006	0.0028 **	−0.0021 ***
	(1.39)	(−0.80)	(2.20)	(−2.91)
gro	−0.2164 *	−0.0777 ***	−0.0998 ***	−0.0496
	(−1.78)	(−3.22)	(−2.80)	(−1.36)
book	0.0008	0.0016	0.0060 **	−0.0012
	(0.13)	(0.67)	(2.09)	(−0.33)
med	0.0253 ***	0.0045	0.0075 *	0.0077 **
	(3.52)	(1.48)	(1.73)	(2.07)
bas	0.0003	0.0000	0.0012	−0.0014
	(0.05)	(0.02)	(0.56)	(−0.75)
inf	−0.0392 **	0.0146 **	−0.0016	0.0164 *
	(−2.25)	(2.10)	(−0.18)	(1.70)
Constant	0.4017 **	0.1226 ***	0.1428 **	0.1596 ***
	(2.59)	(2.60)	(2.08)	(2.66)
year	Yes	Yes	Yes	Yes
city	Yes	Yes	Yes	Yes
Observations	612	2748	1680	1680
R-squared	0.656	0.589	0.463	0.684

Robust t-statistics in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

Table 10. Results of the heterogeneity test.

	(1)	(2)	(3)	(4)
	Geographic Location Heterogeneity		Heterogeneity of Industrial Structure
Variables	Resi	Resi	Resi	Resi
Fin	0.0080	0.0071 ***	0.0131 ***	0.0052
	(1.23)	(2.88)	(3.46)	(1.44)
open	0.0033	−0.0006	0.0028 **	−0.0021 ***
	(1.39)	(−0.80)	(2.20)	(−2.91)
gro	−0.2164 *	−0.0777 ***	−0.0998 ***	−0.0496
	(−1.78)	(−3.22)	(−2.80)	(−1.36)
book	0.0008	0.0016	0.0060 **	−0.0012
	(0.13)	(0.67)	(2.09)	(−0.33)
med	0.0253 ***	0.0045	0.0075 *	0.0077 **
	(3.52)	(1.48)	(1.73)	(2.07)
bas	0.0003	0.0000	0.0012	−0.0014
	(0.05)	(0.02)	(0.56)	(−0.75)
inf	−0.0392 **	0.0146 **	−0.0016	0.0164 *
	(−2.25)	(2.10)	(−0.18)	(1.70)
Constant	0.4017 **	0.1226 ***	0.1428 **	0.1596 ***
	(2.59)	(2.60)	(2.08)	(2.66)
year	Yes	Yes	Yes	Yes
city	Yes	Yes	Yes	Yes
Observations	612	2748	1680	1680
R-squared	0.656	0.589	0.463	0.684

Robust t-statistics in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

6. Conclusions and Policy Recommendations

6.1. Conclusions

This paper systematically analyzes the impact of fintech on urban economic resilience, using panel data from 280 prefecture-level cities in China spanning from 2011 to 2022. The analysis employs the fixed effect model, mediation effect model, and panel threshold regression model, complemented by robustness and heterogeneity tests. The study yields the following key conclusions:

(1)

Fintech has a significant positive effect on urban economic resilience. This conclusion remains robust even after conducting various robustness checks, such as altering the measurement of explanatory variables, changing the sample range and time intervals, and introducing a one-period lag for the explanatory variables. These findings suggest that the ongoing development of regional financial technology contributes to enhancing urban economic resilience.

(2)

Fintech can indirectly enhance urban economic resilience by improving the level of digital inclusive finance. This suggests that, with the development of fintech, technological advancements have overcome the physical limitations and information barriers of traditional finance, enabling services to reach micro and small enterprises, low-income groups, and individuals in remote areas. These services, characterized by lower costs and broader coverage, foster the development of digital inclusive finance, thereby improving financial inclusiveness and strengthening the region’s capacity to withstand economic risks.

(3)

An investigation into the mechanisms through which fintech influences urban economic resilience reveals that, as the business environment and digital infrastructure continue to improve, the positive impact of fintech on urban economic resilience steadily increases.

(4)

There is clear regional and industrial structure heterogeneity in the impact of fintech on urban economic resilience. Fintech plays a more prominent role in enhancing the urban economic resilience in inland areas compared to coastal cities. Furthermore, its impact is more significant in cities dominated by service-oriented industries.

6.2. Policy Recommendations

Based on the above conclusions, this paper proposes the following policy recommendations:

6.2.1. Domestic Level

(1)

Building a Collaborative Empowerment Mechanism of “Business Environment—Digital Infrastructure—Fintech”: Local governments shall coordinate the establishment of a “Fintech Service Middle Platform” to integrate the achievements of business environment optimization and digital infrastructure capabilities, providing financial institutions with standardized and low-cost digital service interfaces. Meanwhile, leveraging the computing power advantages of digital infrastructure, a “policy-finance” linkage model will be established to form a closed-loop ecosystem characterized by “business environment optimization reducing institutional costs—digital infrastructure supporting data circulation—fintech enabling precise targeted support—dynamic enhancement of economic resilience”. In addition, a “policy effect reverse calibration” mechanism will be put in place. By monitoring indicators such as the service coverage rate of fintech products and changes in enterprise financing costs, the direction of business environment reforms and the focus of digital infrastructure investment will be dynamically optimized, ensuring the three elements work in synergy to continuously amplify the positive effect of fintech on urban economic resilience.

(2)

Strengthen the top-level design and establish a “national” framework for fintech development. Currently, there are significant disparities in the level of fintech application across regions. Coastal areas have developed technology-intensive ecosystems due to their first-mover advantages, while inland cities are still constrained by underdeveloped digital infrastructure and limited industrial diversity. Without proper coordination and guidance, this imbalance could worsen, reinforcing the “Matthew effect” and further widening regional development gaps. Furthermore, fintech innovation exhibits significant externalities. The flow of cross-regional data, as well as the interconnection and interoperability of payment and clearing systems, requires standardization. Without this, local individualism may lead to issues such as regulatory arbitrage and data silos. For example, disparities in the scale of implementing the pilot digital RMB policy across regions could undermine the efficiency of monetary policy transmission. Additionally, the systemic risks associated with FinTech are characterized by their network-wide nature, meaning that regional financial risks can rapidly spread through digital channels. To effectively contain the cross-domain spread of risks, it is essential to establish a comprehensive regulatory framework that covers the entire country. Building a national framework is crucial for unlocking the potential of data as a key element. Currently, government and industry data are dispersed across various levels, and the only way to harness the multiplier effect of data on financial innovation is by promoting cross-domain data-sharing mechanisms through top-level design.

(3)

Focus on cities led by the tertiary industry and further deepen the integration and innovation of “technology + services”. Tertiary-industry-led cities typically rely on service sectors such as tourism, commerce, logistics, and culture as their core pillars. Their economic structure is heavily dependent on market consumption, technological application, and industry chain synergies, making them more vulnerable to external shocks. However, these cities also have the potential to achieve significant growth through technological innovation, enabling them to “leapfrog” traditional development paths. The tertiary industry plays a pivotal role in expanding domestic demand and promoting consumption upgrades. The integration of technology and services can foster new business models, create personalized consumption scenarios, and unlock the potential of domestic demand. Cities led by the tertiary sector are typically located inland or in regional centers, and their development is closely tied to the balance in constructing a unified national market. By leveraging Fintech to connect digital platforms for cultural, tourism, and local specialty consumption, these cities can not only stimulate regional economies but also support the expansion of coastal industrial chains into inland markets. Deepening the innovation of “technology + services” is not only crucial for enhancing the economic resilience of these cities but also a strategic approach to advancing industrial structure upgrades, narrowing regional disparities, and achieving high-quality development.

6.2.2. International Level

(1)

Strengthen international cooperation and knowledge-sharing to foster the inclusive development of financial technology. In the era of deepening global economic integration and the rapid advancement of digital technologies, enhancing international collaboration and exchanging experiences is essential for promoting the inclusive development of fintech. This approach also serves as a vital mechanism for amplifying fintech’s positive impact on the economic resilience of cities. By leveraging global or regional platforms such as the United Nations, the G20, and the BRICS Cooperation Mechanism, a tripartite cooperation network encompassing government, market, and society can be established to advance fintech inclusion. By focusing on the two core elements of “technology adaptability” and “scene localization”, we have facilitated the transition of fintech from “technology output” to “capability output”. In response to common challenges such as weak digital infrastructure and low financial literacy in cities of developing countries, both developed and developing economies can collaborate on “Fintech Adaptability Research”. This approach aims to ensure that the inclusive development of fintech is no longer a “technological privilege” limited to a few countries, but rather a “development dividend” that can be shared by cities worldwide.

(2)

Promote regional differentiation strategies to develop fintech applications tailored to local conditions. Policymakers in different countries should establish a precise development framework that considers the industrial structure, digital infrastructure levels, and economic resilience gaps of various regions. This approach will help avoid a “one-size-fits-all” model for fintech promotion and ensure that fintech solutions are deeply adapted to the specific economic needs of cities, ultimately enhancing the economic resilience of diverse urban types. For instance, inland cities can leverage satellite communications, edge computing, and other technologies to overcome geographical constraints, developing mobile payment terminals and offline financial services that have low dependence on network connectivity. Coastal cities should prioritize the application of cross-border financial technologies, exploring blockchain-driven multi-currency clearing systems and AI-based cross-border credit assessment tools. At the same time, they should establish capital flow risk early-warning models to mitigate the impact of international economic and trade fluctuations. Service-oriented cities, on the other hand, can focus on sectors such as culture and tourism, healthcare, and more, integrating consumption data to create a “digital wallet + cross-border services” platform. Leading cities in the service industry should deeply cultivate scenarios related to culture, tourism, and healthcare, combining consumption data to build a comprehensive platform that enables features such as “credit residence” and “instant tax refunds” for tourists. At the policy level, differentiation is crucial: special subsidies should be provided for inland digital infrastructure, cross-border financial sandbox pilots should be authorized along the coast, and public data interfaces should be opened for service-oriented cities. By fostering technological adaptation, cultivating specific scenarios, and ensuring policy coordination, a gradient development model of “strengthening inland areas, risk prevention in coastal cities, and service improvements” will emerge. This approach will enable fintech to become a tailored resilience tool that helps cities of all types withstand economic fluctuations.

6.3. Research Limitations and Future Research Directions

This study selects data from 280 cities in China as samples. Although the amount of data is relatively large, it does not cover data from all types of cities; for example, data from cities in some ethnic minority autonomous regions and special administrative regions are not included. In addition, the study mainly focuses on the positive impacts of fintech while neglecting potential drawbacks, such as important issues like the digital divide and cybersecurity threats, which introduce certain limitations to the study. Future research should utilize more comprehensive data to include more types of regions and groups, thereby making the research more accurate. Additionally, addressing the potential drawbacks of fintech will also benefit the study.