Digital Economy Promotes the Improvement of Urban Living Environment: Evidence from Provincial Panel Data of China

Abstract

The digital economy has become an important aspect of global competition and a key force in restructuring global factor resources, reshaping economic structures, and changing the landscape of global competition. This paper defines the concept of urban living environment, analyzes the mechanisms through which the digital economy affects it, and empirically examines the effects and transmission mechanisms of the digital economy in enhancing urban living environments based on panel data from 30 Chinese provinces from 2012 to 2022. The study finds that the accelerated development of the digital economy significantly contributes to improvements in urban living environments. Specifically, the digital economy exerts its positive influence through the intermediary paths of technological innovation, government governance, and changes in resident behavior. Moreover, its effect is more pronounced in provinces with lower investment in industrial pollution control and relatively lax environmental regulations. From a regional perspective, the digital economy has a greater impact on improving urban living environments in the eastern region than in the central and western regions.

1. Introduction

With the continuous advancement of next-generation information technologies, such as artificial intelligence, big data, cloud computing, the Internet of Things, blockchain, and 5G, the digital economy has become a transformative force in reorganizing global production factors and reshaping the economic landscape. As outlined in the 14th Five-Year Plan for Digital Economy Development of China, the digital economy represents a new economic model after the agricultural and industrial economies. It is driven by data as a core production factor, supported by modern information networks, and powered by the integration of information and communication technologies across all sectors, promoting a model that seeks a balance between equity and efficiency [1,2].

The development of the digital economy exerts substantial influence on the economic, ecological, and social environments.

In terms of the economic environment, estimates from the Global Digital Economy White Paper (2024) released by the China Academy of Information and Communications Technology indicate that in 2023, the digital economy accounted for 60% of global GDP. Among the world’s leading economies, namely the United States, China, Germany, Japan, and South Korea, digital economy development was particularly remarkable, with a combined output exceeding USD 33 trillion, representing a year-on-year growth rate of over 8%. The digital economy has transformed the provision and consumption of products and services. Technological innovations in the digital sector have accelerated digital transformation in the industrial sphere, addressing key requirements of Industry 4.0 [3]. Research on 21 international cities further suggests that the rapid growth of the digital economy has fostered the emergence of new industries, new business models, and new forms of economic activity, thereby creating new employment opportunities and making a significant contribution to job creation [4].

Ecologically, digital and green technologies have contributed to the transformation and upgrading of traditional industries, promoting a more intelligent, high-end, and environmentally friendly manufacturing sector [5,6]. Shared platforms for environmental monitoring and transportation data have strengthened regulatory capacity and enhanced public environmental awareness.

Socially, digital governance, smart cities, and e-government initiatives have improved service efficiency for urban and rural residents, helped to resolve grassroots-level social tensions, and contributed to greater public satisfaction and social harmony.

Collectively, these developments illustrate that the digital economy profoundly reshapes economic, ecological, and social systems. In this study, we conceptualize the “urban living environment” as a composite measure that integrates these three dimensions.

The quality of the urban living environment is directly linked to the development, health, and well-being of urban residents. President Xi Jinping has emphasized that “the people’s aspiration for a better life is our goal”. The Third Plenary Session of the 20th Central Committee of the Communist Party of China called for institutional reforms to promote high-quality economic development, encourage the adoption of digital and green technologies by enterprises, and advance the intelligent, high-end, and green transformation of the manufacturing sector. These initiatives aim to align high-quality development with the people’s needs—delivering better education, stable employment, higher incomes, reliable social security, advanced healthcare, improved housing, and a more beautiful environment.

Therefore, exploring the interaction between the digital economy and the urban living environment—and understanding the mechanisms through which the digital economy facilitates improvements—have both theoretical significance and practical value. This study combines theoretical analysis with empirical investigation using panel data from 30 Chinese provinces between 2012 and 2022 to reveal the positive influence of the digital economy on urban living environments and its underlying transmission pathways.

2. Literature Review

Existing research has focused on the integration of the digital and real economies, the advancement of high-quality manufacturing, the promotion of urban-rural integration, and human settlement.

First, the integration of the digital economy with the real economy has emerged as a new engine and driver of China’s high-quality economic development. Deep integration of digital technologies with traditional industries unifies digitalization with new industrialization, facilitating industrial upgrading toward intelligence, automation, and digitalization, thereby shaping a new type of real economy [7]. This integration aligns with the inevitable trajectory of historical development, providing a strong technological foundation for Chinese-style modernization and serving as a cornerstone for constructing a modern industrial system. At its core, the integration process is one in which data becomes a key production factor [8]. Owing to its intangible and virtual nature, data blends seamlessly with traditional production factors, such as labor, capital, technology, and management, penetrating all stages of production and reshaping the types and proportions of inputs, thus reducing resource misallocation and market distortions [9].

Second, the digital economy plays a vital role in promoting high-quality development of the manufacturing sector. In terms of market information, digital technologies alleviate the information asymmetry between producers and consumers, enabling manufacturers to uncover genuine customer needs and deliver better products and services [10]. In terms of efficiency, new-generation information technologies help optimize the structure of input factors and enhance resource allocation efficiency, thereby improving overall productivity [11,12]. From the perspective of innovation, Hu Yanan et al. [13] argue that the digital economy breaks down the boundaries among innovation agents and shortens the time needed to collect and integrate innovation resources, allowing new knowledge and technologies to be rapidly incorporated into R&D processes and significantly improving innovation efficiency in manufacturing. Regarding green transformation, existing research suggests that digital transformation significantly advances green manufacturing [14]. Mechanistically, this transformation is achieved by improving technological levels and adjusting industrial structures [15], as well as by fostering green innovation, attracting highly skilled labor, and promoting financial agglomeration [16]. Dai Xiang et al. further point out that the digital economy promotes green transformation through scale and technological progress effects. The digital empowerment of manufacturing not only facilitates green transitions within enterprises but also generates positive spillover effects along the industrial chain, encouraging upstream and downstream firms to follow suit [17]. Cao Yu et al. [18] provide a qualitative analysis of the mechanisms through which the digital economy promotes green transformation, including: digital infrastructure driving green structuring, digital bundling enabling green capability-building, and digital leverage facilitating green scaling. The digital economy also improves energy efficiency [19,20], fosters inclusive green growth in cities [21], and contributes to carbon reduction by enhancing resource allocation, environmental regulation, and the quality of green innovation [22]. Li Jin et al. [23] identify an inverted U-shaped relationship between digital economy development and per capita carbon emissions, noting that carbon reductions are achieved through mechanisms such as trade openness, financial development, and improvements in government efficiency.

Third, the digital economy contributes to common prosperity [24,25] and fosters economic, spatial, social, and ecological integration between urban and rural areas [26,27,28]. It optimizes the spatial layout of urban and rural functional zones and supports the formation of integrated living circles for urban and rural residents. By enhancing entrepreneurial activity, it also empowers cities to pursue high-quality development [29]. In terms of improving the urban living environment, studies have found a coupling and synergistic relationship between the digital economy and the quality of human settlements [30].

Fourth, the urban human settlement environment. Cities serve as vital political entities and act as regional social, economic, technological, educational, and cultural centre. The sustainable development of cities is therefore essential for the broader sustainable development of regions and even nations. Drawing upon China’s social realities, Chinese scholar Wu Liangyong established the discipline of human settlement science, which emphasizes that the human settlement environment comprises five interrelated systems: the natural system, the human system, the social system, the residential system, and the supporting system [31]. The United Nations Human Settlements Programme (UN-Habitat) stresses that sustainable urban development must be grounded in economic, social, and ecological sustainability. The Sustainable Development Goals (SDGs) encompass a broad array of targets, including poverty eradication, the elimination of hunger and the achievement of food security, the promotion of health, equitable and quality education, decent work, and sustained economic growth [32]. According to the European Community, sustainable cities should not only provide the necessary environmental support for residents’ production and daily activities, but also ensure that urban systems remain resilient to external disturbances while meeting the cities’ own development needs [33].

In summary, existing literature predominantly focused on the integration of the digital and real economies, the green transformation of manufacturing, carbon emission reduction, environmental improvement, and the digital economy’s role in achieving common prosperity and urban-rural integration. However, research on how the digital economy specifically influences urban development—particularly its impact on the urban living environment and the underlying mechanisms—remains limited. More precisely, There is a lack of comprehensive, quantitative studies examining the role of the digital economy in enhancing urban living environments.

Few studies utilized nationwide provincial panel data to analyze both the effect and mediating mechanisms of the digital economy on urban living environments.

This paper seeks to contribute in the following ways:

• Developing a comprehensive evaluation index system for the urban living environment;
• Conducting a quantitative analysis using provincial panel data to examine the impact of the digital economy on urban living conditions;
• Exploring the mediating effects through which the digital economy influences the urban living environment;
• Employing empirical models such as the two-way fixed-effect model, along with quantile regression for empirical analysis, and assess the heterogeneity of these effects under varying levels of industrial pollution control investment, environmental regulation intensity, and across eastern, central, and western regions.

3. Theoretical Analysis and Research Hypotheses

3.1. Theoretical Analysis

As a new economic paradigm, the digital economy exhibits distinct characteristics compared to traditional economic models, namely, low marginal costs and economies of scale, strong integration and platform-based structures, and significant scalability. When traditional industries incorporate digital technologies, two key development trajectories emerge: (1) complementary integration, which fosters the emergence of new industrial forms, and (2) structural optimization, where digital transformation disrupts conventional production methods, enhances technological innovation, and streamlines production processes to reduce costs and improve efficiency.

The digital economy broadly encompasses two major dimensions: digital industrialization and industrial digitalization. Digital industrialization refers to the development of digital industries that support industrial digitalization through advancements in technologies, products, services, infrastructure, and integrated solutions. Industrial digitalization, on the other hand, involves the application of digital technologies and data resources to empower traditional industries, improve production efficiency, and boost output, signifying a deep integration between the digital and traditional economies.

The integration of the digital economy and traditional industries is reflected in several core aspects:

• Digitalized Management. An increasing number of manufacturing enterprises are incorporating industrial internet systems into their operations, enabling the comprehensive collection and analysis of data across the entire production process, product lifecycle, and supply chain. This facilitates more effective decision-making, improves management efficiency, optimizes resource allocation, and reduces energy consumption.
• Intelligent Manufacturing. The digital economy promotes the comprehensive analysis and integration of production elements, such as equipment, products, personnel, and tools. Real-time insights into production status and environmental changes support process optimization and the adoption of AI-driven technologies, thereby improving productivity and automation levels.
• Technological Innovation. Digital transformation accelerates technological upgrading and the transition of traditional industries toward greener, lower-pollution, and lower-emission production models.
• Networked Collaboration. By breaking down traditional information silos, the digital economy establishes interconnected data systems among production factors, supply chain actors, and between enterprises and society. This enables holistic coordination across production and manufacturing, thereby improving collaborative efficiency.

The digital economy is inherently a green economy, as its growth is not constrained by spatial limitations or by the availability of resources such as water and energy. Moreover, its marginal expansion does not necessarily lead to increased energy consumption or higher emissions of pollutants. The application of digital technologies, such as 5G, the Internet of Things (IoT), and the industrial internet, has fostered the emergence of new business models, driving industrial upgrading, high-quality regional development, and advancements in employment, innovation, and entrepreneurship. Consequently, these developments contribute to higher income levels and improved urban living environments.

Based on the above theoretical analysis, the following hypothesis is proposed:

Hypothesis 1 (H1).

The development of the digital economy significantly contributes to the improvement of urban living environments.

ULenv_i,t = α₀ + α₁Digit_i,t + α₂Contrls_i,t + η_t + μ_i + ε_i,t

(1)

In the model, the variable ULenv_i,t represents the level of urban living environment in province i during year t. The variable Digit_i,t denotes the level of digital economy development in province i at year t. The term η_t captures year fixed effects, while μ_i represents province-specific fixed effects. Contrls_i,t includes a set of control variables that may also influence urban livability in province i, and ε_i,t is the random error term. The coefficient α₁ is the primary parameter of interest, which measures the net effect of digital economy development on the urban livability of each province.

The digital economy empowers traditional industries through digital technologies and innovation, facilitating the transformation of production methods and technological upgrading. This process promotes the transition of traditional industries toward greener, low-pollution, and low-emission modes of operation. Such technological innovations generate not only better economic returns but also improvements in the ecological environment, thereby enhancing the quality of urban human settlements.

Furthermore, the digital economy strengthens government governance capacity, improves urban quality, and enhances citizens’ quality of life. Through initiatives such as smart cities and smart governance, it delivers more convenient online public services and fosters transformations in urban governance models. The circulation and application of data elements in the digital economy improve cross-sectoral communication, break down data silos, and enhance the efficiency of both enterprises and governments, as well as policy transparency. Data-sharing technologies contribute to raising public environmental awareness and strengthening government oversight of environmental protection, ultimately improving urban human settlements.

The digital economy also changes residents’ lifestyles. Digital trade and online marketplaces diversify shopping channels, reduce business operating costs, and expand the scope of economic and trade activities. Online healthcare enables patients to upload their health data and medical information to digital platforms, allowing doctors to access these records remotely and, with the aid of 5G and high-speed internet, even perform surgical operations, overcoming temporal and spatial constraints and partially alleviating issues caused by uneven distribution of medical resources. Internet-based pharmacies allow patients to purchase non-prescription drugs directly, reducing time spent acquiring medications. Cloud-based office applications supported by 5G remove spatial and temporal constraints on corporate operations and workforce distribution. These emerging scenarios and applications of the digital economy transform both commercial and residential behavior, fostering green, low-carbon, and convenient lifestyles. They also reshape traditional industries and trade, drive business model innovation, optimize industrial structures, and ultimately enhance urban living environments.

Based on the above analysis, we propose the following three hypotheses regarding the mechanisms through which the digital economy contributes to the improvement of urban living environments:

Hypothesis 2 (H2).

The digital economy improves urban living environments by promoting technological advancement.

Hypothesis 3 (H3).

The digital economy improves urban living environments by enhancing public governance.

Hypothesis 4 (H4).

The digital economy improves urban living environments by behavioral transformation of residents.

To explore the mechanism by which digital economy development contributes to improvements in urban livability, this study constructs a mediation model to empirically test the intermediary role of technological progress. Specifically, we examine the causal pathway: Digital Economy Development → Technological Progress, Public Governance, Behavioral Transformation of Residents → Urban Living Environment Enhancement. The mediation effect model is specified as follows:

M_i,t = β₀ + β₁Digit_i,t + β₂Contrls_i,t + μ_i + ε_i,t

(2)

ULenv_i,t = γ₀ + γ₁Digit_i,t + γ₂M_i,t + γ₃Contrls_i,t + μ_i + ε_i,t

(3)

Equation (2) captures the effect of digital economy development (Digit) on the mediating variable (M), while Equation (3) estimates the effect of both digital economy development and the mediating variable (e.g., technological innovation, data factors) on the dependent variable (urban livability). Regression analyses are conducted for both equations. If the coefficient β₁ in Equation (2) for digital economy development and the coefficient γ₂ for the mediating variable in Equation (3) are both significantly positive, this indicates that digital economy development indirectly affects urban living environment through the mediating effect of technological progress, public governance, behavioral transformation of residents.

Figure 1 illustrates the theoretical framework developed in this study, showing how the digital economy influences urban living environment through various mechanisms and transmission pathways.

3.2. Variable Description

• Dependent Variable

ULenv (Urban Living Environment Index) Referring to the research by Wei Heqing [30], this study constructs a comprehensive urban livability evaluation index system, grounded in indicators reflecting the public’s pursuit of a better life, including access to better education, more stable employment, more satisfactory income, more reliable social security, higher-quality healthcare, more comfortable housing conditions, and a cleaner environment. The index system consists of three primary dimensions, Economic Environment (include economic development, housing conditions, resource allocation, infrastructure), Ecological Environment, and Social Environment (include public services, social governance level). As shown in

Table 1. Urban Livability Evaluation Index System.

Primary Indicator	Secondary Indicator	Tertiary Indicator	Attribute
Economic Environment	Economic Development	GDP per capita	(+)
		Urban per capita disposable income	(+)
		Rural per capita disposable income	(+)
		Total retail sales of consumer goods	(+)
		Urban unemployment rate	(–)
	Housing Conditions	Per capita residential land area	(+)
		Population density	(–)
	Resource Allocation	Per capita domestic water consumption	(+)
		Per capita domestic electricity consumption	(+)
	Infrastructure	Per capita urban road area	(+)
		Per capita number of library books	(+)
Ecological Environment	Environmental Quality	Per capita park green space	(+)
		Green coverage rate of built-up areas	(+)
		Industrial SO2 emissions	(–)
		Industrial wastewater discharge	(–)
		PM2.5 concentration	(–)
Social Environment	Public Services	Number of primary and secondary school teachers per capita	(+)
		Number of hospital beds per capita	(+)
		Enrollment rate in compulsory education	(+)
		Public education expenditure as a share of GDP	(+)
	Social Governance	Government effectiveness	(+)
		Urbanization rate	(+)
		Proportion of fiscal expenditure on public welfare	(+)

, a total of 23 tertiary-level indicators are included. The Entropy Weight Method is applied to construct a composite index of urban living environment, ensuring an objective weighting of indicators based on data variability.

• Explanatory Variable

Core explanatory variable is Digital economy development index. Drawing on the work of Zhao Tao, et al. [34], this study adopts a set of fundamental indicators to measure the digital economy, including internet penetration rate, number of employees in internet-related sectors, output of internet-related industries, number of mobile internet users, development of digital inclusive finance, industrial scale, and R&D investment. The entropy weighting method is employed to determine the weights of these indicators. The specific indicators are shown in

Table 2. Indicators of Digital Economy Development.

Indicator Type	Indicator	Calculation Method	Attribute
Digital Industrialization	Internet Penetration Rate	Number of internet users per 100 people	(+)
	Internet-related Employment	Proportion of employees in computer services and software sector	(+)
	Internet Output	Per capita volume of telecommunications services	(+)
	Mobile Internet Users	Number of mobile phone users per 100 people	(+)
Industrial Digitalization	Digital Inclusive Finance Index	Digital inclusive finance development index	(+)
	industrial scale	E-commerce sales revenue	(+)
	R&D investment	Large-scale industrial enterprises R&D investment	(+)

.

• Mediating Variables

Technological progress (RDI): measured by R&D intensity, defined as the ratio of R&D investment to regional GDP.

Public governance (GOV): represented by the natural logarithm of general government budget expenditures.

Behavioral transformation of residents (RB): proxied by the volume of express delivery services in the region.

• Control Variables

To more comprehensively analyze the spillover effects of the digital economy on the improvement of urban living environments, several control variables that may influence urban livability are included:

Degree of Openness (odo): Measured by the ratio of total imports and exports to regional GDP.

Government Intervention (dgi): Measured by the ratio of government fiscal expenditure to regional GDP.

Human Capital Level (hc): Measured by the proportion of college students enrolled to the total population.

Scientific Research Input (sci): Measured by the logarithm of the number of scientific researchers in urban areas of each region.

3.3. Data Description

Considering data availability and timeliness, this study selects panel data from 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) covering the period from 2012 to 2022.

The core variable, the Digital Inclusive Finance Index, is sourced from the Digital Finance Research Center of Peking University in collaboration with Ant Financial Services Group. Other data are obtained from authoritative sources, including the China Environmental Statistics Yearbook, China Statistical Yearbook, China Population Census Yearbook, China Rural Statistical Yearbook, China Financial Statistical Yearbook, China Science and Technology Yearbook, provincial statistical yearbooks and bulletins, as well as the Wind financial database.

To address the issue of missing data, interpolation based on the average annual growth rate is employed, ensuring the completeness and consistency of the dataset.

4. Empirical Results

4.1. Regression Analysis

The baseline regression results of the fixed-effects model are presented in columns (1)–(5) of

Table 3. The Impact of the Digital Economy on Urban Living Environment.

Variables	Regression					Quantile Regression
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	FE	FE	FE	FE	FE	25th Percentile	50th Percentile	75th Percentile
Digit	0.2606 ***	0.3713 ***	0.3633 ***	0.3743 ***	0.2469 ***	0.4657 ***	0.5080 ***	0.5257 ***
	(0.057)	(0.058)	(0.058)	(0.056)	(0.064)	(0.061)	(0.017)	(0.029)
Odo		0.0793 ***	0.0801 ***	0.0613 ***	0.0658 ***	0.0246	0.0145 ***	0.0203 *
		(0.015)	(0.015)	(0.015)	(0.015)	(0.019)	(0.003)	(0.010)
Dgi			0.1206 ***	0.1184 ***	0.1080 **	−0.1351 ***	−0.1284 ***	−0.0354 ***
			(0.045)	(0.044)	(0.043)	(0.006)	(0.015)	(0.013)
Hc				0.2581 ***	0.2554 ***	−0.1338 ***	−0.0877 ***	0.0464 *
				(0.056)	(0.054)	(0.006)	(0.132)	(0.019)
Lnsci					0.0324 ***	0.0194 ***	0.0159 ***	0.0169 ***
					(0.009)	(0.001)	(0.005)	(0.004)
Constant	0.2852 ***	0.2455 ***	0.2146 ***	0.1577 ***	0.1024 ***
	(0.007)	(0.010)	(0.015)	(0.019)	(0.024)
Observations	330	330	330	330	330	330	330	330

Note: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. All other values in parentheses represent robust standard errors, unless otherwise indicated.

. Model (1) includes only the digital economy variable (Digit) as the explanatory variable, while the remaining models sequentially incorporate the corresponding control variables. Across all five models, the estimated coefficients of the digital economy variable are consistently positive and statistically significant, aligning with the theoretical expectations. The core findings remain robust before and after controlling for other factors.

Specifically, Model (5) reports an estimated coefficient of 0.2469 for the digital economy, significant at the 1% level. This result indicates a positive association between the digital economy and the quality of urban living environment during the sample period: a one-percentage-point increase in the digital economy is associated with a 0.2469-percentage-point improvement in urban living environment.

Given that mean regression captures only the average marginal effect of the digital economy on urban human settlements, its impact may differ under various conditional distributions. To comprehensively characterize this relationship, quantile regressions are conducted at the 25th, 50th, and 75th percentiles. Columns (6)–(8) of Table 3 report the corresponding results. At all three quantiles, the coefficients of the digital economy variable remain significantly positive. (For the detailed principles and methodology of the two-way fixed-effects and quantile regression models, see [35]).

Overall, these econometric results provide strong empirical support for the theoretical analysis presented earlier, demonstrating that the digital economy and the quality of urban human settlements move in the same direction. Growth in the digital economy significantly promotes improvements in urban living environment, thereby verifying Hypothesis 1.

Regarding the effects of the control variables on urban living environment (Ulenv), the level of openness to the outside world (Odo) exhibits a significant positive impact, indicating that greater openness can promote improvements in urban living environment. Openness facilitates the inflow of foreign capital, technology, and managerial expertise, which in turn fosters urban economic growth and industrial upgrading, thereby improving infrastructure, public services, and the urban environment.

The degree of government intervention (Dgi) shows a significant negative effect on urban human settlements in the quantile regressions (at the 25th, 50th, and 75th percentiles), suggesting that excessive government intervention may lead to resource misallocation, inefficiency, and other issues that negatively affect urban living environment.

Human capital (Hc) has a significantly positive effect on urban living environment under the individual-time fixed-effects model, implying that human capital enhances innovation capacity, supports industrial agglomeration, stimulates consumption, and ultimately improves urban human settlements. However, in the quantile regressions (at the 25th and 50th percentiles), human capital exhibits a significantly negative effect. This may be because increases in human capital are often accompanied by higher resource consumption, which can exert greater short-term pressure on regions with relatively low levels of urban living environment.

Scientific research investment (Sci) demonstrates a significant positive effect on urban human settlements in both the individual-time fixed-effects model and the quantile regressions (at the 25th, 50th, and 75th percentiles). This can be attributed to the fact that scientific research investment facilitates the development of new technologies and products, thereby improving production efficiency and the quality of life for residents, which ultimately enhances urban living environment.

4.2. Robustness Checks

To ensure the reliability and robustness of the empirical results, two robustness tests are conducted:

• Exclusion of municipalities directly under the central government (i.e., Beijing, Shanghai, Tianjin, and Chongqing). These cities differ substantially from other provinces in terms of economic, social, and cultural development. Removing them from the sample allows us to assess whether the main findings are overly dependent on these outlier cases. The corresponding results are presented in Column (1) of

  Table 4. Results of robustness check.

  Variables	(1)	(2)
  Digit	0.4090 ***	0.2972 ***
  	(0.069)	(0.073)
  Odo	0.0084	0.0541 ***
  	(0.020)	(0.016)
  Dgi	0.0607	0.1018 **
  	(0.043)	(0.048)
  Hc	0.1497 ***	0.2712 ***
  	(0.057)	(0.064)
  Lnsci	0.0308 ***	0.0231 **
  	(0.009)	(0.010)
  Constant	0.1504 ***	0.1194 ***
  	(0.024)	(0.028)
  Individual FE	YES	YES
  Year FE	YES	YES
  Observations	286	270
  R-Squared	0.955	0.940
  F	344.8	272.8

  Note: *** and ** denote statistical significance at the 1% and 5% levels, respectively.

  .
• Exclusion of abnormal time periods (i.e., the years 2020 and 2021). These years were severely affected by the COVID-19 pandemic, during which the normal trajectory of digital economic development may have been disrupted. Excluding these years helps eliminate the impact of extraordinary shocks and assess the robustness of the findings. The results of this robustness check are reported in Column (2) of Table 4.

As shown in Column (1) of Table 4, after excluding the four centrally administered municipalities, the regression coefficient of the digital economy (Digit) on urban livability (Ulenv) is 0.4090, which is statistically significant at the 1% level. Similarly, Column (2) of Table 4 indicates that after excluding the abnormal years 2020 and 2021, the coefficient of the digital economy remains significantly positive at 0.2972, also significant at the 1% level. These results confirm that the positive impact of the digital economy on urban livability remains robust after removing special samples, consistent with the baseline regression results presented earlier.

4.3. Endogeneity Analysis

To enhance the robustness of the research conclusions and mitigate potential endogeneity issues, this study employs an instrumental variable approach to re-estimate the effect of digital economy development on urban living environment. Following Huang Qunhui et al. [36], the development of internet technology in China originated with the popularization of fixed-line telephones. The number of fixed-line telephones is therefore a suitable instrumental variable for regional internet development, as it satisfies the relevance condition. Moreover, the historical number of fixed-line telephones is unlikely to directly affect the current quality of urban living environment, thus meeting the exogeneity requirement. Accordingly, the interaction term between the number of fixed-line telephones in each region in 1998 and the number of internet users (ivph) is adopted as the instrumental variable in this study.

After incorporating the instrumental variable, the results of the endogeneity tests are reported in

Table 5. Results of endogeneity analysis.

Variables	(1)	(2)
	Digit	Ulenv
Ivph	0.1509 ***
	(0.000)
Digit		0.6801 ***
		(0.000)
Odo	−0.0101	−0.0109
	(0.779)	(0.451)
Dgi	0.3463 ***	−0.1795 ***
	(0.000)	(0.001)
Hc	0.8789 ***	−0.1940 ***
	(0.000)	(0.006)
Lnsci	−0.0189	0.0080
	(0.315)	(0.788)
Constant	−0.1337 *	0.3173 ***
	(0.077)	(0.000)
Observations	330	330
F	45.05	139.21
Kleibergen-Paap rk LM statistic	14.04
(p-val)	(0.0002)
KP-F	45.05
Hansen J	0.000

Note: *** and * denote statistical significance at the 1% and 10% levels, respectively.

. The KP-LM statistic is 14.04 with a corresponding p-value of 0.0002, indicating no problem of under-identification. The KP-F statistic is 45.05, which exceeds the Stock–Yogo critical value of 16.38 at the 10% significance level, suggesting that weak instrument concerns are not present. The Hansen J statistic is 0.000, implying that the model is exactly identified and the instrumental variable is exogenous. Furthermore, the first-stage regression results show a positive and statistically significant correlation (at the 1% significance level) between the instrumental variable and the endogenous variable.

These results confirm that the instrumental variable satisfies both the relevance and exogeneity conditions, with no evidence of weak instruments or identification failure, thus making it a valid instrument. As shown in column (2) of Table 5, even after addressing potential endogeneity, the digital economy still exerts a significant positive effect on urban living environment, supporting the validity of the theoretical analysis presented in this study.

4.4. Heterogeneity Analysis

4.4.1. Heterogeneity by Industrial Governance Investment

Given the significant differences in digital economy integration, environmental governance effectiveness, resource allocation efficiency, and sustainable development capacity across cities with varying levels of industrial governance investment, the impact of the digital economy on urban living environment may also differ accordingly. To explore this potential heterogeneity, this study divides the samples into two groups based on industrial pollution control investment levels: a high industrial pollution control investment group and a low industrial pollution control investment group.

The level of industrial pollution control investment is measured by the cumulative investment in instruments and equipment for industrial pollution control. Regions are categorized into two groups:

High industrial pollution control investment group: investment greater than or equal to the median value.

Low industrial pollution control investment group: investment less than the median value.

The detailed analysis results are presented in

Table 6. Heterogeneity by industrial pollution control investment.

Variables	(1)	(2)
	High Industrial Pollution Control Investment Group	Low Industrial Pollution Control Investment Group
Digit	0.0812	0.2262 **
	(0.059)	(0.095)
Odo	−0.0295 *	0.0628 ***
	(0.017)	(0.019)
Dgi	0.1828 ***	−0.0156
	(0.069)	(0.047)
Hc	0.2900 ***	0.2721 ***
	(0.064)	(0.066)
Lnsci	0.0392 ***	0.0080
	(0.010)	(0.010)
Constant	0.1179 ***	0.1820 ***
	(0.029)	(0.029)
Individual FE	YES	YES
Year FE	YES	YES
Observations	165	165
R-Squared	0.982	0.943
F	484.5	144.2

Note: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

.

As shown in column (2) of Table 6, in the low industrial pollution control investment group, the coefficient of the digital economy (Digit) on urban living environment (Ulenv) is 0.2262 and is statistically significant at the 5% level. In contrast, in the high industrial pollution control investment group, the effect of the digital economy on urban living environment is not significant.

This suggests that the digital economy exerts a stronger positive impact on urban living environment in regions with lower levels of industrial pollution control investment. One possible explanation is that in such regions, industrial pollution control systems and environmental protection mechanisms are less developed. The introduction of the digital economy and related technologies, such as intelligent monitoring and big data analytics, can therefore substantially improve environmental governance efficiency, facilitate optimal resource allocation, and significantly enhance the quality of urban living environment. In contrast, cities with higher levels of industrial pollution control investment already possess a stronger foundation for environmental governance, making the incremental effect of digital economy development on environmental improvement less pronounced.

4.4.2. Heterogeneity by Environmental Regulation Level

Given that cities with different levels of environmental regulation may vary significantly in their strategies, implementation effectiveness, policy enforcement, and environmental responsiveness when leveraging the digital economy to improve human settlements, such differences may lead to heterogeneous effects of the digital economy on urban human settlements. Therefore, to gain a deeper understanding of this relationship, cities are classified into two categories based on their level of environmental regulation: a high environmental regulation group and a low environmental regulation group.

Environmental regulation is measured by the ratio of total manufacturing pollution control expenditure to total manufacturing output value. Cities with environmental regulation levels greater than or equal to the median are classified as the high environmental regulation group, while those below the median are classified as the low environmental regulation group. The detailed results of the analysis are presented in

Table 7. Heterogeneity by environmental regulation level.

Variables	(1)	(2)
	High Environmental Regulation Group	Low Environmental Regulation Group
Digit	0.0854	0.2737 **
	(0.079)	(0.137)
Odo	0.0781 ***	0.0445 *
	(0.023)	(0.023)
Dgi	0.0357	0.1947 *
	(0.048)	(0.106)
Hc	0.2567 ***	0.3013 ***
	(0.094)	(0.085)
Lnsci	0.0462 ***	0.0284 **
	(0.012)	(0.014)
Constant	0.1073 ***	0.0876 **
	(0.037)	(0.042)
Individual FE	YES	YES
Year FE	YES	YES
Observations	165	165
R-Squared	0.943	0.941
F	156.1	108.9

Note: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

.

As shown in column (1) of Table 7, in the high environmental regulation group, the coefficient of the digital economy (Digit) on urban living environment (Ulenv) is 0.0854 and is not statistically significant. In contrast, in the low environmental regulation group, the coefficient is 0.2737 and is statistically significant at the 5% level.

This indicates that the digital economy has a stronger positive effect on urban living environment in cities with lower levels of environmental regulation. A possible explanation is that in cities with high environmental regulation, a series of stringent environmental protection measures and regulations have already been implemented, leaving relatively limited room for further environmental improvement. Consequently, the integration of the digital economy has a less pronounced impact on enhancing their urban living environment. Conversely, in cities with low environmental regulation, environmental governance is relatively less stringent. The introduction of the digital economy can therefore provide more efficient environmental monitoring, more precise resource allocation, and more innovative applications of environmental protection technologies, thereby significantly improving the quality of urban living environment.

4.4.3. Regional Heterogeneity

China’s regions differ significantly in economic development levels, industrial structure, resource endowment, policy environment, and other aspects. These differences may cause the impact of the digital economy on urban living environment to vary across regions. To gain a more comprehensive and in-depth understanding of how the digital economy affects urban living environment in different regions, this study divides cities into two categories based on their geographic location: Eastern Region and Central & Western Region. The Eastern region includes Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; the Central region includes Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan; the Western region includes Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Ningxia, Qinghai, Xinjiang, Guangxi, and Inner Mongolia. The regression results are presented in

Table 8. Heterogeneity by industrial governance investment.

Variables	(1)	(2)
	Eastern Region	Central & Western Region
Digit	0.4773 ***	0.1426 *
	(0.109)	(0.081)
Odo	0.0615 ***	0.1773 ***
	(0.023)	(0.039)
Dgi	0.4907 ***	0.0298
	(0.113)	(0.042)
Hc	0.6641 ***	0.1265 **
	(0.117)	(0.057)
Lnsci	−0.0128	0.0399 ***
	(0.016)	(0.009)
Constant	−0.0193	0.1352 ***
	(0.048)	(0.024)
p value	0.008
Individual FE	Yes	Yes
Year FE	Yes	Yes
Observations	121	209
R-Squared	0.941	0.961
F	101.0	289.5

Note: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

.

The p-value for the coefficient difference test between groups is calculated using Fisher’s combined probability test with 1000 bootstrap samples.

As shown in Table 8, the inter-group coefficient difference test yields a p-value below 0.05, indicating that the coefficients are comparable across groups. In the eastern region, the coefficient of the digital economy (Digit) on urban human settlements (Ulenv) is 0.4773 and is statistically significant at the 1% level. In the central and western regions, the corresponding coefficient is 0.1426, significant at the 10% level.

These results suggest that the digital economy exerts a stronger positive effect on urban living environment in the eastern region than in the central and western regions. One possible explanation is that the eastern region has a stronger foundation in traditional industries and greater industrial accumulation. The development of the digital economy in this region integrates more effectively with traditional industries, fostering new products and business models, driving industrial transformation and upgrading, cultivating new sources of economic growth, and promoting both economic development and environmental protection. Together, these factors contribute more substantially to the improvement of urban living environment in the eastern region.

4.5. Mechanism Analysis

The preceding analysis has examined only the relationship between the digital economy and urban living environment, while the underlying mechanisms of this relationship remain unclear and warrant further investigation. Building on the earlier analysis, this section tests the mediating role of technological innovation, public governance and behavioral transformation of residents.

Table 9. The results of mediation effect test.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	Rdi	Ulenv	Gov	Ulenv	Rb	Ulenv
Digit	0.0283 ***	0.2131 ***	0.4512 *	0.2170 ***	3.2189 ***	0.1125 *
	(0.005)	(0.067)	(0.233)	(0.063)	(0.768)	(0.057)
Rdi		1.1946 *
		(0.716)
Gov				0.0663 ***
				(0.016)
Rb						0.0417 ***
						(0.004)
Constant	0.0081 ***	0.0927 ***	0.3508 ***	0.0792 ***	−1.2819 ***	0.1559 ***
	(0.002)	(0.024)	(0.086)	(0.024)	(0.285)	(0.021)
Controls	YES	YES	YES	YES	YES	YES
Individual FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
Observations	330	330	330	330	330	330
R-Squared	0.720	0.944	0.943	0.947	0.442	0.958
F	48.77	301.5	317.0	318.0	15.02	403.9

Note: *** and * denote statistical significance at the 1% and 10% levels, respectively.

reports the mediation effects of technological innovation (Rdi), public governance (Gov), and behavioral transformation of residents (Rb) in the relationship between the digital economy (Digit) and urban living environment (Ulenv). As shown in Models (1), (3), and (5), the regress results of the coefficients of digital economy to technological innovation, government governance, and behavioral transformation of residents are all significantly positive, and in Models (2), (4), (6), the coefficients of technological innovation, public governance, and behavioral transformation of residents on urban living environment are also significantly positive, indicating a significant transmission effect. Hypotheses 2–4 are thus supported.

Technological innovation, such as intelligent environmental monitoring technologies, smart cities, and energy conservation and emission reduction initiatives, directly influences the quality of urban human settlements. The development of the digital economy not only enhances the efficiency of information dissemination and integration but also fosters numerous new business models and application scenarios through industrial digitalization, transforming both commercial activities and residents’ lifestyles.

Through improved government governance, including targeted policy measures and refined urban management, the quality of the urban environment and residents’ living standards can be significantly improved. Furthermore, the digital economy facilitates the deep integration of technological innovation with urban planning, construction, and management, thereby promoting the intelligent and green development of urban human settlements. This integration not only enhances the efficiency and sophistication of urban governance but also strengthens cities’ adaptability and resilience to environmental changes, ultimately driving substantial improvements in the quality of urban human settlements.

5. Policy Recommendations and Conclusions

5.1. Policy Recommendations

Given the significant role of the digital economy in enhancing urban livability and promoting a better quality of life, this paper proposes the following policy recommendations:

• Strengthen, Optimize, and Expand China’s Internet Platforms

Internet platforms are the primary organizational form of the digital economy. The development of a robust digital economy depends on a cohort of competitive internet platforms. How to effectively develop, utilize, and govern these platforms has become a global concern. The issue in China is not the excess of strong platforms, but rather the shortage. It is essential to pursue both development and regulation simultaneously, ensuring that digital platforms serve the people, support national strategies, and assist small and medium-sized enterprises (SMEs), thereby promoting a stronger, better, and larger platform economy.

2.

Accelerate the Construction of New Digital Infrastructure

Efforts should be made to take a forward-looking approach in building a comprehensive digital information infrastructure that is high-speed, ubiquitous, integrated across land, sea, and space, cloud-network converged, intelligent and agile, green and low-carbon, and secure and controllable. Equal emphasis must also be placed on ensuring inclusivity in digital infrastructure to bridge the digital divide between different regions and groups. As internet platforms become central organizers of online market transactions and key components of the digital economy ecosystem, it is recommended that large platforms be incorporated into the scope of critical information infrastructure. In parallel, China should foster globally influential software enterprises, focus on breakthroughs in key software technologies, and enhance innovation and supply capacity in the software sector.

3.

Accelerate the Development of a Data Factor Allocation System

Building an open and shared data transaction system and unlocking the value of data remains a complex and ongoing task. Key current priorities include:

• Deepening cross-level, cross-regional, and cross-departmental sharing of government data to enable safe and orderly public data openness;
• Cultivating intermediary institutions for data circulation, encouraging market forces to tap into the value of commercial data, and exploring the creation of a multi-tiered data factor market;
• Establishing sound foundational systems for data factors, ensuring a high-quality data supply, and mobilizing participation from industry associations, research institutes, and enterprises to develop innovative mechanisms for data utilization;
• Actively exploring the secure cross-border flow of data and proposing a “Chinese solution” for international data governance rules.

4.

Promote Deep Integration of Digital Technology and the Real Economy

Many enterprises still face challenges such as lack of knowledge, capacity, or confidence in digital transformation. Digitalization must be grounded in reality and tailored to individual business conditions—transformation for its own sake should be avoided. Efforts should focus on expanding inclusive services such as cloud adoption, data utilization, and intelligent upgrades, as well as building digital transformation support centers. In addition to nurturing “specialized, refined, distinctive, and innovative” enterprises and industry champions, China should aim to develop internationally competitive digital industry clusters. Attention must also be paid to overcoming the digital development barriers faced by SMEs. New business models such as instant retail, community group-buying, crowdsourcing, mass entrepreneurship, personalized customization, flexible manufacturing, digital agriculture, and smart tourism should be actively developed to foster a labor-friendly digital economy.

5.

Improve the Digital Economy Governance System

It is crucial to recognize the strategic importance of strengthening internet platforms. Governance should be guided by market-oriented, law-based, and international principles. Clarifying platform responsibilities, implementing inclusive and prudent regulatory frameworks, and establishing new governance mechanisms suited to platform economy dynamics are key. Efforts must be made to foster collaborative governance involving government, platforms, enterprises, industry organizations, media, and the public. A digitalized regulatory model—integrating digital technology and data factors—should be established to replace campaign-style regulation with functional, transparent, and coordinated oversight.

6.

Expand International Cooperation in the Digital Economy and Support Platform Globalization

China should encourage internet platforms to engage in cooperation with international partners and lead pilot initiatives. Efforts should be made to build a mutually beneficial framework for international digital trade rules. Through fiscal and financial policy support, platform companies should be encouraged to expand abroad, promote digital products and services, and advance cross-border e-commerce, with a focus on constructing a “Digital Silk Road” with ASEAN and other regions.

5.2. Future Research

While promoting the development of the digital economy, attention should also be paid to avoiding its potential adverse impacts, including environmental issues and data security concerns such as data leakage. According to the United Nations Conference on Trade and Development (UNCTAD), for example, in terms of the physical carriers of the digital economy, producing a two-kilogram personal computer requires approximately 800 kg of raw materials, while manufacturing a smartphone requires around 70 kg of raw materials. End-user devices such as smartphones, along with the internet and data centers, generate more carbon dioxide emissions during their usage phase than during the production phase. In terms of energy consumption, Bitcoin mining provides a striking example: between 2015 and 2023, global energy consumption for mining increased by about 34 times, reaching an estimated 121 terawatt-hours.

The limitations of this study and future research are as follows. First, the indicator system for evaluating urban living environment could be further optimized by incorporating micro-level survey data, such as residents’ perceptions of life satisfaction. Second, the sample period could be extended to include longer time series data, enabling a deeper understanding of the evolving relationship between the digital economy and urban living environment. Third, as urban living environment is an issue of global concern, future research could select representative international cities—covering both developed and developing countries—as case studies. Comparative analysis of different urban governance approaches could then be undertaken to explore how the digital economy and urban living environment interact and reinforce each other across regions and stages of development.

5.3. Conclusions

Accelerating the development of the digital economy can significantly enhance urban living environment. This impact is largely transmitted through technological innovation, government governance, and changes in resident behavior mechanisms. Moreover, its effect is more pronounced in provinces with lower investment in industrial pollution control and relatively lax environmental regulations. From a regional perspective, the digital economy has a greater impact on improving urban living environments in the eastern region than in the central and western regions.

In the new era and at a new stage of development, it is essential to grasp firmly the trends and patterns of digital economy development, strengthen the capacity to develop the digital economy, and maintain full autonomy over its advancement. By leveraging the digital economy to restructure global factor resources, reshape the global economic structure, and transform the landscape of international competition, high-quality development can be effectively aligned with the people’s aspirations for a better life, thereby enhancing the quality of urban living environment.