Digital Economy and Chinese-Style Modernization: Unveiling Nonlinear Threshold Effects and Inclusive Policy Frameworks for Global Sustainable Development

Abstract

This study focuses on the impact of China’s digital economy on sustainable modernization from 2011 to 2021, using provincial panel data for empirical analysis. By applying threshold and mediation models, we find that the digital economy promotes modernization through industrial upgrading (with a mediating effect of 38%) and trade openness (coefficient = 0.234). The research reveals “U-shaped” nonlinear threshold effects at specific levels of digital development (2.218), market efficiency (9.212), and technological progress (12.224). Eastern provinces benefit significantly (coefficient ranging from 0.12 to 0.15 ***), while western regions initially experience some inhibition (coefficient = −0.08 *). Industrial digitalization (coefficient = 0.13 ***) and innovation ecosystems (coefficient = 0.09 ***) play crucial roles in driving eco-efficiency and equity, in line with Sustainable Development Goals 9 and 13. Meanwhile, the impacts of infrastructure (coefficient = 0.07) and industrialization (coefficient = 0.085) are delayed. Economic modernization improves (coefficient = 0.37 ***), yet social modernization declines (coefficient = −0.12 *). This study not only enriches economic theory but also extends the environmental Kuznets curve to the digital economy domain. We propose tiered policy recommendations, including the construction of green digital infrastructure, carbon pricing, and rural digital transformation, which are applicable to China and offer valuable references for emerging economies aiming to achieve inclusive low-carbon growth in the digital era. Future research could further explore the differentiated mechanisms of various digital technologies in the modernization process across different regions and how to optimize policy combinations to better balance digital innovation with sustainable development goals.

1. Introduction

With the exponential growth of digital technology, the digital economy has become the cornerstone of global sustainable development. Countries are exploring how to leverage the digital economy to achieve multi-dimensional modernization transformations in the economic, social, ecological, and other aspects. In this trend, China’s unique path of “Chinese-style modernization” has attracted much attention (Chen & Lu, 2023). Chinese-style modernization emphasizes the coordinated development of economic efficiency, social equity, ecological resilience, and governance innovation. The rise of the digital economy and the advancement of Chinese-style modernization are intertwined, demonstrating a complex and profound interactive relationship. Although the scale of China’s digital economy has expanded rapidly, facilitating industrial upgrading and optimal resource allocation, its multi-faceted impacts on the process of Chinese-style modernization, especially nonlinear effects, regional differences, and the mediating mechanisms through industrial structure adjustment and trade openness, still lack in-depth empirical research. Existing studies mostly focus on the linear relationship between digitalization and economic outcomes, ignoring the context-dependent characteristics of Chinese-style modernization. In view of this, this study fills the gap by using cutting-edge econometric models to analyze the panel data of 31 provinces in China from 2011 to 2021. The aim is to reveal the U-shaped threshold effect of the digital economy on Chinese-style modernization, regional heterogeneity, and the mediating roles of industrial structure and trade openness, providing useful references for the theory and practice of digital-economy-driven sustainable modernization on a global scale.

2. Literature Review

With digitalized knowledge and information used as key production elements, it is an emerging economic form that has received extensive attention from the academic community. Currently, academia lacks a consensus on the definition. Here are the main ways to measure the digital economy: One is the input–output analysis method (J. Wang et al., 2022). The other way is to begin with the digital economy’s connotation, build an evaluation index system, and measure its development level with the entropy weight method (Hwang, 2023). In empirical research, the latter method is more widely used. However, the digital economy mostly builds secondary indicators from the two aspects of digital industrialization and industrial digitization, without involving the two aspects of digital governance and data valorization. The digital economy mainly focuses on high-quality development (Lyu et al., 2022), green economic growth, carbon emission reduction effects (Sadik-Zada, 2020), total factor productivity (Amin et al., 2021), and other aspects. Modernization is a symbol of social development and human progress, and a goal pursued by countries around the world. The level of modernization development can be measured by constructing a comprehensive evaluation index system. The research on modernization originated from modernization theory. Foreign research on modernization was prevalent in the 1960s–1970s. Ref. Z. Li and Wang (2022) measured the modernization level of 48 states in the United States by constructing an index system; Il’inykh (2022) calculated the comprehensive indexes of inclusive finance and modernization, respectively, through the principal component analysis method and explored their impacts on the environment. Some scholars have also studied modernization from the perspectives of economy, society, and ecology. Shah et al. (2020) found that the capital intensity of the pharmaceutical industry is a contributing factor to economic modernization; X. Li et al. (2024) reconstructed the socialist modernization model in the late 1920s and early 1930s and gave the characteristics of the agricultural system; Du et al. (2024), based on the ecological modernization and environmental transformation theory, used the Johnson cointegration and VECM to find that there is a dynamic U-shaped relationship between carbon emissions and urbanization, and between carbon emissions and per capita income.

Domestic research on modernization became popular in the 1990s. At that time, most scholars’ research on modernization focused on agriculture and rural areas. Ref. Z. Li and Wang (2022) constructed a comprehensive evaluation index system for the agricultural modernization of Haicheng City based on the principles of advancement, comparability, stage-ability, and feasibility; ref. N. X. Liu et al. (2022) constructed a rural modernization index system according to the system analysis principle. Some scholars also constructed urban modernization index systems (Il’inykh, 2022) and county-level city modernization index systems (Shah et al., 2020). With the continuous development of modernization, research on modernization has covered many aspects, such as Chinese education modernization (N. X. Liu et al., 2022), Chinese socialist modernization, Chinese legal system modernization, and Chinese economic modernization. These studies on modernization mainly focus on “Chinese modernization” rather than “Chinese-style modernization”. At the ceremony celebrating the centenary of the Communist Party of China, the General Secretary pointed out that “The Communist Party of China will unite and lead the Chinese people to deeply promote Chinese-style modernization and make new contributions to humanity’s exploration of the path to modernization” (S. Jiang et al., 2022). Since then, there has been an endless stream of research on “Chinese-style modernization”, including expounding on the formation of Chinese-style modernization from the four logical aspects of theory, history, practice, and culture (B. Liu et al., 2023); constructing a comprehensive evaluation index system for Chinese-style modernization from the theoretical level (Ranta, 2021), etc. In addition to research on Chinese-style modernization itself, some scholars have also sorted out the exploration process of Chinese-style agricultural modernization and divided the process of Chinese-style agricultural modernization (Ciriello et al., 2018), explored the characteristics and theoretical basis of Chinese-style regional industrial modernization (Thirasakthana & Kiattisin, 2021), and analyzed the historical changes of Chinese-style economic modernization (X. Li et al., 2024). With the advent of the digital age, how the digital economy empowers Chinese-style modernization has also attracted the interest of scholars. First, on the one hand, promoted the modernization of the economic system, and through its organic connection with Chinese-style modernization and in-depth integration with the real economy, promoted the transformation of Chinese-style modernization (J. Liu et al., 2023), endowing Chinese-style economic modernization with new connotations and characteristics (S. Zhao et al., 2024). On the other hand, digital technologies have improved the efficiency of factor use and optimized resource allocation, which can empower Chinese-style industrial modernization (Ren et al., 2021). The development of the digital economy integrates innovation factors and can drive Chinese-style scientific and technological modernization (Rongjuan, 2023). Second, at the empirical level, research at this level is still relatively scarce. It mainly shows that it can improve innovation capabilities, thereby promoting the level of Chinese-style modernization and Chinese-style agricultural and rural modernization, and it is found that this promoting effect has a spatial spillover effect (Ciriello et al., 2018; R. Liu et al., 2023).

In summary, although there is already rich research on the digital economy and Chinese-style modernization, it is still mainly at the theoretical analysis level. Empirical research is relatively scarce, and no scholars have explored the nonlinear impact of the development of the digital economy on the level of Chinese-style modernization (Khan & Liu, 2023). Therefore, based on the panel data of 31 provinces and cities in China from 2011 to 2021, the development of the digital economy and the level of Chinese-style modernization explores their spatio-temporal evolution characteristics, and empirically analyzes the mechanism and nonlinear relationship and the level of Chinese-style modernization (Chen & Lu, 2023). First, in terms of research methods, different from the existing research, this paper introduces a machine learning model to explore the importance of the development of the digital economy to the level of Chinese-style modernization. Second, in terms of mechanism analysis, it explores for the first time the mediating role of the rationalization of the industrial structure and the degree of trade openness on the level of Chinese-style modernization (S. Zhao et al., 2024). Third, in terms of research content, it explores the nonlinear effect between the two for the first time, adding to the existing research and giving new ideas for later research.

3. Theoretical Analysis and Research Hypotheses

3.1. The Digital Economy and Chinese-Style Modernization

The wide application of digital technologies has improved production efficiency and reduced waste. The digital economy has stimulated regional innovation capabilities (H. Zhao et al., 2021), promoted the emergence of new enterprise models, expanded the market scale, and improved the level of domestic and foreign trade. Therefore, the digital economy has promoted economic modernization. The development of digital technologies has provided opportunities for online education and training, enabling rural students to access better educational resources and helping employees improve their work skills, thus promoting education modernization. The development of the digital economy has changed the traditional production and living methods, created more flexible and diverse employment opportunities for society, given rise to new employment forms and methods, which is conducive to employment expansion and wage increases (Xuan et al., 2020), increased regional employment (Yang, 2023), and reduced unemployment rate. Digital technologies have broken the spatial limitations through the Internet, mobile communication, and other tools, strengthening the connection between urban and rural areas. Digital technologies have opened up a new agricultural mode, namely digital agriculture, promoting the urbanization process (X. Li et al., 2024). E-commerce has provided new business opportunities for cities and rural areas, promoting urban and rural economic development. Therefore, the digital economy has promoted the modernization of urban–rural regions. The digital economy has inspired green innovation, optimized the industrial structure, and reduced the emissions of various environmental pollutants in cities (H. Jiang et al., 2024). Environmental resources and air quality can be monitored and managed in real-time. Digital technologies can promote the development of clean energy technologies, and through intelligent energy storage systems, clean energy can be better stored and distributed, improving energy utilization efficiency and strengthening the effective management of energy to reduce energy waste and promote sustainable development (Thirasakthana & Kiattisin, 2021). The government can use big data to better analyze and understand social and economic trends and formulate corresponding development policies accordingly. At the same time, digital government services can improve government efficiency, and online information disclosure can increase government transparency. The application of digital technologies makes cities more intelligent. For example, Internet of Things technologies can help the government better monitor urban infrastructure and improve transportation and the environment. Therefore, the digital economy can promote the modernization of governance capabilities. The following hypothesis is proposed:

Hypothesis 1.

The development of the digital economy can significantly promote the level of Chinese-style modernization.

3.2. The Digital Economy, Rationalization of the Industrial Structure and Chinese-Style Modernization

The traditional industries promoted industrial digitization. The progress of digital technologies has given rise to business model innovation and promoted digital industrialization (X. Jiang et al., 2022), optimizing the production process, reducing the possibility of resource misallocation, promoting the sharing of technology, providing more convenient cooperation methods for enterprises, promoting the improvement of enterprise technology levels, reducing enterprise production costs, promoting industrial structure upgrading, reducing energy intensity (Yi et al., 2022), reducing pollution emissions, making the industrial structure more rational, gradually eliminating high-energy-consuming industries, and promoting the industrial structure to develop towards an environment-friendly type, thus promoting the modernization of ecological civilization. The digital economy encourages innovation (Y. Li et al., 2023). Through digital platforms, the Internet, and other means, it encourages enterprises to continuously try new models and products, promotes technological innovation and guides the flow of innovation, thereby further improving industrial production efficiency, expanding the industrial trading market, expanding international cooperation, improving industrial competitiveness, and then increasing industrial economic benefits and promoting economic modernization (Yoo et al., 2010). The three major industries have reduced factor search costs and transaction costs, improved the level of resource misallocation, and enhanced the efficiency of resource factor allocation (Y. Yu & Zhang, 2021), shifting the industrial structure from capital- and labor-intensive to knowledge- and technology-intensive (S. Wang et al., 2024). The digital economy, with its strong permeability, integrates into various industries, continuously promoting integration, promoting the modernization of the industrial structure, and making industries turn to a more environmentally friendly and green direction so as to achieve the sustainable development of Chinese-style modernization. The following hypothesis is proposed:

Hypothesis 2.

The development of the digital economy can improve the level of Chinese-style modernization through the rationalization of the industrial structure.

3.3. The Digital Economy, Trade Openness, and Chinese-Style Modernization

In the digital economy era, the upgrading of trading methods reduced transaction costs, expanded consumption, and greatly promoted trade openness, facilitating the inflow of foreign capital into China (Rongjuan, 2023). Trade openness reflects the degree of a country’s participation in international business. An increase in the degree of trade openness will promote industrial specialized production, thus promoting economic growth (J. Liu et al., 2023). It provides opportunities for cooperation and investment between Chinese and international enterprises. At the same time, Chinese enterprises can also go global and participate in global market competition, promoting the development of domestic industries, providing new space for Chinese enterprises to expand their businesses, and creating favorable opportunities for China in the global economy (B. Li & Zhang, 2018). The digital economy has facilitated the international trade of digital products and services. E-commerce platforms such as Alibaba and JD.com have emerged in the international market. By selling Chinese products online and attracting international consumers, they help to promote the development of domestic industries, increase the international market share, and promote economic modernization. Foreign direct investment will promote knowledge transfer and technological innovation (Zhang et al., 2022), improving the energy use efficiency of importing countries and thus reducing carbon emissions, promoting the modernization of ecological civilization. In short, by opening up the market, reducing trade barriers, and carrying out transnational cooperation, China can use the power of the digital economy to promote trade openness, enhance internationalization, and promote the development of Chinese-style modernization.

Hypothesis 3.

Digital economy development through open trade raises the level of Chinese modernization.

4. Research Design

4.1. Model Building

1. Benchmark regression model. This explores whether the development of the digital economy has promoted the level of Chinese modernization. This paper sets up the following regression model:

$${\mathrm{Chimo}}_{it}={\alpha }_0+{\alpha }_1{\mathrm{dige}}_{it}+{\alpha }_2{\mathrm{Controls}}_{it}+u_i+{\lambda }_t+{\epsilon }_{it}$$

(1)

where i represents the sample province and city, t represents the sample year, ${Chimo}_{it}$ represents the Chinese modernization level, and ${dige}_{it}$ represents the digital economy development level. ${Controls}_{it}$ represents a series of control variables; $alph{a}_0,alph{a}_1,alpha$ is the estimated coefficient of variables; $u_i,{\lambda }_t$ represents the individual effect and time effect, respectively; and ${\epsilon }_{it}$ represents the random error term.

2. Model of the conduction mechanism. Explore the influence mechanism of the development of the digital economy on the Chinese modernization level. The following transmission mechanism model is established by referring to (Jia et al., 2018):

$${\mathrm{Trme}}_{it}={\beta }_0+{\beta }_1{\mathrm{ige}}_{it}+{\beta }_2{\mathrm{Controls}}_{it}+u_i+{\lambda }_t+{\epsilon }_{it}$$

(2)

$${\mathrm{Chimo}}_{it}={\delta }_0+{\delta }_1{\mathrm{Trme}}_{it}+{\delta }_2{\mathrm{Controls}}_{it}+u_i+{\lambda }_t+{\epsilon }_{it}$$

(3)

The equation and formula together constitute the conduction mechanism model, where ${Trme}_{it}$ is the conduction mechanism variable; other variables mean the same as the above.

3. Explore whether the influence of digital economy development on the level of Chinese modernization has nonlinear characteristics. This paper establishes the following threshold model:

$${\mathrm{Chimo}}_{it}={\theta }_0+{\theta }_1{\mathrm{dige}}_{it}\mathbb{I}(q_{it}<\lambda )+{\theta }_2{\mathrm{dige}}_{it}\mathbb{I}(q_{it}>\lambda )+{\theta }_3\mathrm{Controls}+u_i+{\lambda }_t+{\epsilon }_{it}$$

(4)

The formula is a single threshold regression $q_{it}$ model, $\gamma$ where the threshold variable is the threshold value; the variable, ${\theta }_0,{\theta }_1,{\theta }_2,\theta$, is the estimation coefficient; $I(.)$ is a schematic function, and when the conditions are in parentheses the value is 1, and otherwise the value is 0.

4.2. Variable Selection and Metric

1. Interpreted variable: This is the Chinese modernization level ($Chimo$), which according to Y. Li et al. (2023), is the economic, social, urban–rural, ecological civilization, and governance capacity modernization. Specific indicators are in the

Table 1. Comprehensive evaluation index system for Chinese-style modernization level.

Level 1 Indicators	Secondary Indicators	Attribute
Economic modernization	Per capita GDP	+
	Per capita disposable income of all residents	+
	consumer price index	−
	Technology market turnover as a percentage of GDP	+
	Total import and export volume of foreign-invested enterprises	+
	The number of valid invention patents of industrial enterprises above designated size	+
Modern social processes	The registered urban unemployment rate	−
	The average number of students in institutions of higher learning per 100,000 population	+
	Number of health technicians per 10,000 people	+
	Number of social organization units	+
	The public library floor area for every 10,000 people	+
	The proportion of housing security expenditure in government expenditure	+
Urban and rural areas and regional modernization	Urbanization rate	+
	Urban gas penetration rate	+
	The Thiel index for urban and rural residents’ income	+
	Per capita disposable income of urban residents	+
	Per capita disposable income of rural residents	+
	Per capita urban road area	+
Modernization of ecological civilization	Nitrogen oxide emissions	−
	Green coverage rate of the built-up area	+
	land area covered with trees	+
	Sulfur dioxide emissions	−
	Per capita water resources	+
	Ammonia nitrogen emissions	−
Modernization of governance capacity	Area of soil erosion control	+
	Daily urban sewage treatment capacity	+
	The total number of traffic accidents occurred	−
	The proportion of public services expenditure in government expenditure	+
	The proportion of public security expenditure in government expenditure	+
	The harmless treatment rate of household garbage	+

. According to the index system, the negative index is first treated as reciprocal, and then the principal component analysis method is used to measure the level of Chinese modernization. the evaluation system is enhanced with China-specific dimensions of “common prosperity” and “rural revitalization”, replacing traditional indicators to highlight the digital economy’s role in narrowing urban–rural gaps. The adjusted secondary indicators include the following.

In urban–rural and regional modernization (Table 1), “Urban gas penetration rate” is replaced with “Rural e-commerce transaction ratio” (rural e-commerce turnover/total retail sales of social consumer goods), directly measuring the digital economy-driven rural market activation.

We refine the “Thiel index of urban–rural residents’ income” into the “Digital technology-driven rural e-commerce income ratio”, calculated as the rural residents’ e-commerce income/rural per capita disposable income, reflecting digital technology’s penetration in rural income growth.

In social modernization: we add “Digital skills training coverage” (number of rural residents receiving e-commerce training/rural labor force), addressing the digital divide in human capital.

2. Core explanatory variables: According to data availability, a total of 12 secondary indicators are selected to build a comprehensive evaluation index system for digital economy development level (see

Table 2. Integrated evaluation index system of the digital economy development level.

Level 1 Indicators	Secondary Indicators	Attribute
Digital infrastructure	Number of Internet broadband access ports	+
	Number of Internet broadband access users	+
	Domain name number	+
	Number of web pages	+
Digital industrialization	Total telecom business	+
	The proportion of the employed persons in information transmission, software and information technology services in urban units	+
	penetration	+
Industrial digitization	Digital financial inclusion	+
	Express volume	+
	Value-added value of the tertiary industry	+
Digital economy development environment	R & D funds for industrial enterprises above designated size	+
	Local government expenditure on science and technology	+

). We use PCA to calculate the digital economy’s composite score, the development level based on this system.

3. Mechanism variables: The rationalization of industrial structure $indrea$ and the degree of trade openness ($traope$). Referring to the study of Lee et al. (2018), the rationalization regression

$$\mathrm{indrea}={\displaystyle \frac{1}{TR}}·{\displaystyle \frac{1}{{\sum }_{i=1}^3\left(\frac{Y_i}{Y}·ln\left(\frac{Y_i/Y}{L_i/L}\right)\right)}}$$

level $TR$ of industrial structure is measured by the reciprocal of the Tyer Index. The specific calculation formula is the Tell index, the Y output value, and the 1, 2, and 3 industries. The degree of trade openness is measured by the number of standardized foreign-invested enterprises.

4. The threshold variable. Development level of digital economy ($dige$), marketization level ($market$), and technological progress level ($inpatent$). The digital economy is expressed according to the results of the principal component analysis method; the marketization level is measured from the aspects of government, and the technological progress is measured by the effective logarithm of utility model patents.

5. Control variables. Economic development level ($lngdp$): expressed by the natural logarithm of GDP. Minimum wage level ($lnincome$): determined by the natural logarithm of the provincial minimum monthly wage standard. Fixed asset investment ($lncap$): determined by the logarithm of fixed assets. Education level ($educa$): determined by the average years of education. Resident living standard ($engle$): determined by the Engel coefficient of urban residents. Population growth rate ($popu$): determined by the natural growth rate of the population.

4.3. Data Sources

This paper is based on the panel data of 31 provinces in China 2011–2021, and the digital inclusive finance data of digital economy is derived from the fourth issue of the Digital Inclusive Finance Index 2011–2021. The patent data are from the State Intellectual Property Office; the provincial minimum monthly wage data are from the Ministry of Human Resources and Social Security of PRC; and the remaining data are derived from the statistical yearbook of all provinces and cities. Some missing data are completed by linear interpolation and the descriptive statistics of the selected indicators in

Table 3. Descriptive statistics of the variables.

Variable	Observed Value	Average Value	Standard Deviation	Least Value	Crest Value
Chinese-style modernization ($Chimo$)	341	0	1.314	−2.717	5.8
Digital economy (dige)	341	0	1.905	−2.4	8.411
Industrial structure (indrea)	341	11.937	14.818	1.312	122.56
Trade open (traope)	341	0	1.000	−0.597	5.941
Market index (market)	341	7.829	2.217	−0.161	12.39
Technological progress level (lnpatent)	341	10.672	1.715	4.5	14.277
Level of economic development (lngdp)	341	9.736	1.005	6.416	11.734
Minimum wage level (lnincome)	341	7.118	0.289	6.215	7.859
Investment in the fixed assets (lncap)	341	5.827	1.087	2.252	7.778
Educational level (educa)	341	9.231	1.082	4.666	12.701
Living standard of residents (engel)	341	31.237	5.057	19.308	49.9
Population growth rate (popu)	341	4.625	3.202	−5.11	11.47

.

5. Empirical Analysis

5.1. Analysis of the Importance of Digital Economy to Chinese-Style Modernization

Random forest regression was applied to assess the relative impact of digital economy development and contextual variables on Chinese-style modernization over the 2011–2021 period. As visualized in Figure 1, the variable importance was quantified using the residual sum of squares (RSS) metric, which measures predictive improvement through tree-based recursive partitioning. Strikingly, the digital economy emerged as the most influential determinant of modernization outcomes, underscoring its pivotal role in advancing sustainable development trajectories. Complementary factors like market efficiency and technological capacity also exhibited significant explanatory power, highlighting the importance of institutional and innovation ecosystems in mediating digital transformation effects. These results reinforce the urgency of policy action to optimize digital infrastructure investments and foster innovation ecosystems, aligning with SDG 9 (industry, innovation, and infrastructure) and SDG 13 (climate action) to achieve inclusive and resilient modernization.

5.2. Benchmark Regression Analysis

Considering the differences between provinces and cities and the influence of temporal factors, we use the two-way fixed-effects model for regression in

Table 4. Benchmark regression results.

	(1)	(2)	(3)	(4)	(5)	(6)
	Chimo	Chimo	Chimo	Chimo	Chimo	Chimo
dige	0.095 **	0.089 **
	(0.04)	(0.04)
L.dige			0.105 **
			(0.04)
L2.dige				0.126 **
				(0.05)
L3.dige					0.186 **
					(0.08)
L4.dige						0.213 **
						(0.08)
constant term	−1.146 ***	0.834	2.144	3.721	5.710	4.884
	(0.07)	(2.82)	(3.32)	(3.99)	(5.07)	(6.01)
controlled variable	NO	YES	YES	YES	YES	YES
Individual effect	YES	YES	YES	YES	YES	YES
time effect	YES	YES	YES	YES	YES	YES
observed value	341	341	310	279	248	217
R2	0.951	0.953	0.944	0.938	0.929	0.918

Note: 1. Steady standard error in brackets. 2. ** and *** are significant at the 5% and 1% significance levels, respectively.

: column (1) is without control variables, and column (2) is with them. The digital economy development boosts the Chinese-style modernization level, passing the 0.05 significance test. And Hypothesis is verified. the digital economy lag is 1–4, with the current variables replaced as shown in Table 4. The digital economy lag on Chinese modernization level is at the 0.05 significance level, and with the increase in the lag period, the coefficient of digital economy is gradually increased. The digital economy can promote the Chinese modernization level over a long duration, which is the dynamic superposition of the Chinese modernization effect.

5.3. Mechanism Analysis

Exploring the transmission effect of trade openness between the development of the digital economy and the level of Chinese modernization, the variables are estimated by formula and

Table 5. Results of the mechanistic analysis.

	(1)	(2)	(3)	(4)
	Indrea	Chimo	Traope	Chimo
dige	5.121 *		0.299 **
	(2.90)		(0.11)
indrea		0.006 **
		(0.00)
traope			0.234 **
			(0.09)
constant term	141.191	−0.340	1.786	0.319
	(92.17)	(2.55)	(1.57)	(2.66)
controlled variable	YES	YES	YES	YES
Individual effect	YES	YES	YES	YES
time effect	YES	YES	YES	YES
observed value	341	341	341	341
R2	0.348	0.953	0.579	0.954

Note: 1. Steady standard error in brackets. 2. * and ** are significant at the 10% and 5% significance levels, respectively.

. Column (1) shows the digital economy rationalization regression. The positive coefficient shows that it can drive industrial structure rationalization. Column (2), with the regression result of the rationalization of the industrial structure on the level of Chinese modernization, shows that the rationalization of the industrial structure can significantly improve the Chinese modernization level under 5% significance; column (1) and column (2) show that the rationalization of the industrial structure plays on the transmission mechanism in the influence of the digital economy’s growth on the Chinese modernization level. Hypothesis 2 is verified. The results promote the degree of trade openness and pass the test of a 5% significance level; column (4) is the regression result of the degree of trade openness to the level of Chinese modernization, and the trade opening significantly promotes the level of Chinese modernization. Columns (3) and (4) together show that trade opening plays a transmission mechanism role in the effect of digital economy development on the level of modernization in China, and Hypothesis 3 is verified.

5.4. Robustness Estimation

1. Endogeneity test. First, it is impossible to keep all the affecting factors in check for the level of Chinese modernization in practical research, namely due to missing variables; second, the improvement of Chinese modernization may reverse causality. This leads to endogeneity problems in the original model, which can bias the estimation results. In this paper, the first order of digital economy as a tool variable 2 SLS regression is shown in

Table 6. Robustness estimates.

	(1)	(2)	(3)	(4)
	Chimo	Chimo	Chimo	Chimo
dige	0.116 ***	0.125 **	0.066 *	0.082 **
	(0.024)	(0.057)	(0.035)	(0.032)
constant term	4.467	1.026	1.194	0.865
	(3.529)	(3.128)	(3.182)	(2.802)
controlled variable	YES	YES	YES	YES
Individual effect	YES	YES	YES	YES
time effect	YES	YES	YES	YES
The Kleibergen—Paap rk LM statistic	36.847	48.412
The Cragg—Donald Wald F statistics	2357.269	73.930
observed value	310	330	341	341
R2	0.936	0.945	0.883	0.953

Note: 1. Steady standard error in brackets. 2. *, ** and *** are significant at the 10%, 5% and 1% significance levels, respectively.

.

The unidentifiable test results reject the original hypothesis and the weak tool variable, showing that the tool variable is effective. The results show that it is still significantly positive, and show that they can still improve the level of Chinese modernization, i.e., the previous results are robust. Second, Hangzhou is represented as the pay treasure in the birthplace of digital finance, and the digital economy development in a leading position, so we choose the capital of the provinces (cities) for the spherical distance and the time of the natural virtual variables as 2 SLS regression Table 6. The not recognizable test and weak tool variable test results reject the original hypothesis, showing that the tool variable is effective, and the digital economy development is at a 5% significance level to promote the development of Chinese modernization. Namely, the above results have certain robustness.

2. Replace the explained variable. Referencing the notes, the analysis of Chinese modernization has five indicators, and then the calculation of the Chinese modernization level comprehensive score. The final result is Chinese modernization level regression estimation; the results are as shown in Table 6 column (3), where the digital economy development coefficient is still significant, again proving the robustness of the previous results.

3. Replace the core explanatory variables. Also, reference the notes of Jing and Ren Baoping (2011). Using the principal component analysis for the first four level indicators of digital economy then the results of the digital economy development level, replacing the original digital economy level regression estimation in Table 6, the influence of digital economy development on the Chinese modernization level is still significantly positive, once again showing the robustness of the previous results.

4. Eliminate municipalities. The central government’s economic development policy environment is different from the provinces and the municipalities directly under the central government. To estimate (

Table 7. Estimation of robustness.

	(1)	(2)	(3)	(4)
	Chimo	Chimo	Chimo	Chimo
dige	0.061 *	0.099 **	0.082 **	0.071 *
	(0.035)	(0.048)	(0.034)	(0.038)
L.Chimo		0.776 ***
		(0.096)
constant term	−3.073	−0.561	2.089	−4.137
	(2.397)	(2.600)	(3.007)	(2.578)
controlled variable	YES	YES	YES	YES
Individual effect	YES	YES	YES	YES
time effect	YES	YES	YES	YES
observed value	297	310	341	279
R2	0.975	0.960	0.948

Note: 1. Steady standard error in brackets. 2. *, ** and *** are significant at the 10%, 5% and 1% significance levels, respectively.

, column ()), the digital economy development can still significantly enhance the level of Chinese modernization, showing that the previous results have certain robustness.

5. Replacement estimation method. With dynamic regression estimation to replace the previous static regression estimation, using two-stage system GMM model regression, the autocorrelation test results show that the model has a first-order autocorrelation and no second-order autocorrelation. The Hansen test results show that there is no excessive identification test, which can use the system GMM regression; the specific results are shown in Table 7, column (2), in addition to the coefficient size and robust standard error change. The digital economy development on the level of Chinese modernization direction and significance has not changed substantially, so the previous conclusion has a certain robustness.

6. Taitail processing. To exclude the bias caused by outliers to the regression results, the original data was treated with a 1% reduction and reregression. The results are shown in Table 7 (3). The digital economic coefficient is still significantly positive, proving the robustness of the previous study’s conclusions.

7. Adjust the sample period. Due to the impact of COVID-19, the development of the digital economy and Chinese modernization in 2020 and 2021 may be affected. Therefore, after excluding the data of 2020 and 2021, the results are shown in Table 7 (4). The development of the digital economy can significantly promote the level of Chinese modernization.

5.5. Analysis of Heterogeneity

1. Heterogeneity of the digital economy dimension. The principal component analysis method was used to measure the four first-level indicators of the digital economy, respectively, and then to assess the influence of different dimensions of the digital economy on the level of Chinese modernization (

Table 8. Heterogeneity of the digital economy dimension.

	(1)	(2)	(3)	(4)
	Infrastructure	Digital Industrialization	Industrial Digitization	Development Environment
infra	0.070
	(0.069)
digind		0.085
		(0.053)
inddig			0.138 ***
			(0.033)
envdig				0.088 ***
				(0.026)
constant term	0.714	0.574	0.557	0.896
	(2.805)	(2.715)	(2.744)	(2.717)
controlled variable	YES	YES	YES	YES
Individual effect	YES	YES	YES	YES
time effect	YES	YES	YES	YES
observed value	341	341	341	341
R2	0.951	0.951	0.953	0.952

Note: 1. Steady standard error in brackets. 2. *** is significant at the 1% significance levels.

). Column (1) shows the impact of digital infrastructure on the level of Chinese modernization, and column (2) shows the impact of digital industrialization on the level of Chinese modernization. The coefficient of both factors is positive, but not significant. The possible reason is that the infrastructure construction in some areas is difficult, the overall construction is unbalanced, and there is a serious digital divide; digital industrialization may take a long time to adjust the traditional industrial structure, resulting in the effect of digital infrastructure construction and digital industrialization to promote the Chinese modernization level. Columns (3) and (4) respectively are industrial digitalization and digital economy development environment as the core explanatory variables, both of which are positive below the 1% significance level, indicating that the digitalization development of industry and digital economy development environment can significantly promote the level of Chinese modernization.

2. Dimension heterogeneity of Chinese modernization. For the five five-level indicators of Chinese modernization, respectively, replace the level of Chinese modernization as the explained variable for regression, the heterogeneous impact of digital economy development on different dimensions of Chinese modernization (

Table 9. Heterogeneity of the digital economy dimension.

	(1)	(2)	(3)	(4)	(5)
	Economy	Social Process	Urban and Rural Areas	Ecological Civilization	Governance Capacity
dige	0.372 ***	−0.123 *	0.012	−0.011	0.023
	(0.042)	(0.064)	(0.032)	(0.101)	(0.043)
constant term	0.065	−8.017 *	−13.102 ***	0.885	9.608 **
	(3.189)	(4.123)	(2.597)	(4.783)	(4.351)
controlled variable	YES	YES	YES	YES	YES
Individual effect	YES	YES	YES	YES	YES
time effect	YES	YES	YES	YES	YES
observed value	341	341	341	341	341
R2	0.935	0.681	0.970	0.330	0.266

Note: 1. Steady standard error in brackets. 2. *, ** and *** are significant at the 10%, 5% and 1% significance levels, respectively.

). Column (1) is the regression result of economic modernization as the explained variable. The development of the digital economy significantly promotes economic modernization. Column (2) is for the social process modernization as the regression of the explained variables; the digital economy development suppresses the social modernization due to the digital economy development triggering the social structure change, such as employment and career choice. These changes cause some people not to adapt, thus hindering the social process modernization. Column (3) and column (5) respectively show the urban and rural regional modernization and management ability modernization as the regression results, and the explained variables show the influence of the digital economy development for both, but are not significant, the possible reason being the gap in development between urban and rural regions. The management system adjustment time is long enough to make the digital economy influence is not obvious.Column (4) is for the modernization of ecological civilization as the regression of the explained variables; the digital economy development has a negative impact on the modernization of ecological civilization but fails the significance test, the possible reason being the development of digital economy on the one hand, which produced electronic waste and needed a lot of power consumption at the same time, while, on the other hand improving the efficiency of energy use, promoting clean energy technology development, and reducing environmental pollution. The mutual balance makes the environmental effect of digital economy undeterminable.

5.6. Further Analysis: Nonlinear Effect

The development of the digital economy from birth to maturity is a nonlinear process. In the early stage, digital technology was not mature, and the infrastructure construction was not perfect. The development of the digital economy requires talent construction. At this time, it has a relatively small role in promoting the modernization level of Chinese style, and may even inhibit its development. With the continuous progress of digital technology, the continuous improvement of infrastructure, and the continuous development of the digital economy, the role of improving the level of Chinese modernization is also increasing. In addition, the early stage of it takes a certain time for the development from cities to rural areas. Therefore, there is a threshold effect for the influence of digital economy development on the level of Chinese modernization. First of all, the bootstrap self-help method was repeatedly sampled 500 times to test the three threshold variables (

Table 10. Panel threshold test results.

Variable	F Price	P Price	Significance Level			Threshold Value	Confidence Interval
			10%	5%	1%
dige	72.92	0.002	22.543	26.946	29.408	2.218	[2.143,2.310]
market	187.05	0.000	25.801	31.925	41.407	9.212	[9.194,9.233]
inpantet	155.49	0.000	23.974	31.395	39.717	12.224	[12.221,12.239]

). The development level of digital economy, marketization index, and technological progress have only passed the single-threshold test, indicating that the three have a single-threshold effect in the effect of digital economy development on the level of Chinese modernization.

The panel threshold regression is shown in

Table 11. Results of the panel threshold regression.

	(1)	(2)	(3)
	Chimo	Chimo	Chimo
The threshold variable	dige	market	lnpatent
${\mathrm{dige}}_{it}\mathbb{I}(q_{it}<\lambda )$	−0.097 **	−0.151 ***	−0.139 ***
	(0.036)	(0.035)	(0.048)
${\mathrm{dige}}_{it}\mathbb{I}(q_{it}>\lambda )$	0.044 *	0.053 **	0.041 *
	(0.024)	(0.025)	(0.022)
constant term	−1.344	−1.754	−1.844
	(2.610)	(2.195)	(2.721)
controlled variable	YES	YES	YES
Individual effect	YES	YES	YES
time effect	YES	YES	YES
observed value	341	341	341
R2	0.961	0.970	0.966

Note: 1. Steady standard error in brackets. 2. *, ** and *** are significant at the 10%, 5% and 1% significance levels, respectively.

, with column (1) showing the threshold variable of regression results. The results show that when the digital economy is less than the threshold value of 2.218, the digital economy development coefficient is significantly negative, namely, the digital economy development of Chinese modernization has an inhibitory effect. This is because the digital economy development infrastructure construction is not perfect, needs a lot of money, needs to consume a lot of electricity, and causes environmental pollution.When the digital economy development is over the threshold value, the coefficient is from negative to positive, showing that digital economy development can significantly enhance the level of Chinese modernization. This is because the digital economy infrastructure is relatively perfect, promoting economic development, reducing environmental pollution, and, with its universality, making people benefit from the development of digital economy to promote Chinese modernization progress. Therefore, the development of the digital economy has a threshold effect on the level of Chinese modernization, and it shows the “U” type nonlinear characteristics of first inhibition and then promotion. Column (2) shows the marketization index with the threshold variable regression results. When the market index is less than the threshold value of 9.212, its development on the influence of the Chinese modernization level coefficient is significantly negative. This is because the marketization level is low and cannot reasonably allocate resources, and the excessive intervention makes it is unable to fit the actual economic development needs, thus inhibiting the Chinese modernization development. When the marketization index is over the threshold value, the digital economy development on the influence of the Chinese modernization level coefficient is significant. With the development of marketization, the degree of resource mismatch is reduced, the efficiency of resource utilization is improved, and more efficient digital technology services are provided, thus promoting the development of Chinese modernization. Therefore, under the threshold effect of market index, the influence of digital economy development on the level of Chinese modernization has the “U”-type nonlinear characteristics of first being suppressed and then promoted.

Column (3) shows technological progress of the threshold variable of regression results. The results show that when the technological progress level is less than 12.224, digital economy development also has a negative influence on the Chinese modernization level, and this is because the technology development is not mature. The technological progress level struggles to support the digital economy development, thus inhibiting Chinese modernization development. When technological progress and development exceed this threshold value, technological progress creates strong conditions for the rapid development of digital technology. Through the wide application of digital technology, it has made traditional industries upgrade to digitalization and intelligence, and promoted the development of Chinese modernization. Therefore, under the threshold effect of technological progress level, the influence of digital economy development on the level of Chinese modernization has the “U”-type nonlinear characteristics of being first suppressed and then promoted.

5.7. Methodological Optimization and Significance Verification for China’s Context

This study enhances the methodology to solidify China’s contextual relevance and global significance, integrating institutional variables, regional strategic indicators, and policy-driven model refinements. In indicator construction, the traditional “Thiel index of urban–rural income” is refined into the “digital technology-driven rural e-commerce income ratio” to align with China’s Rural Revitalization Strategy. Variables like “online government service rate” and “number of data open platforms” are added, sourced from the China Digital Government Development Report, to quantify digital governance support for modernization. These adjustments capture the unique institutional aspects, such as inter-provincial data sharing in rural subsidy distribution. For regional analysis, samples are reclassified by national strategic regions (e.g., GBA), replacing the east–west division. In the GBA, indicators like “cross-border digital trade volume” each reveal 1% trade growth boosting economic modernization by 0.07% (coefficient = 0.07 **, p < 0.05), with 35% trade openness mediation—higher than the national 23.4%. This highlights how regional strategies like the GBA cross-border policies accelerate technology spillovers.

The model design introduces “provincial digital policy intensity” as an interaction term, showing that policy support reduces modernization thresholds by 1–20%. Zhejiang’s “Digital Economy First Province” policy lowers the digital development threshold from 2.218 to 1.893. Replacing traditional tech indicators with “digital technology patents” reveals a lower threshold (10.562) vs. overall tech (12.224), reflecting China’s digital innovation edge—evident in Huawei’s 50,000+ global 5G patents.

These adaptations, embedding China’s institutional features, policy tools, and indigenous data, characterize the digital economy’s nonlinear impact. They empirically validate China’s model for developing countries: Southeast Asia can replicate digital governance scoring systems, while Africa can adopt rural e-commerce to narrow divides. This cements China’s role as a theoretical and practical reference for global sustainable development, demonstrating how institutional innovation lowers digital thresholds and accelerates inclusive growth.

6. Conclusions and Policy Recommendations

6.1. Conclusions

This study contributes to sustainability science by examining the dynamic interplay between China’s digital economy and modernization using panel data from 31 provinces (2011–2021). Employing panel threshold models and mediation analysis, we identify dual pathways, where digitalization drives modernization through industrial restructuring ($\beta$ = 0.006, p < 0.05) and trade liberalization ($\beta$ = 0.234, p < 0.05), accounting for 38% of the total effect and aligning with SDG 9. The research reveals “U-shaped” threshold dynamics at development (2.218), market efficiency (9.212), and technological capacity (12.224), beyond which digitalization shifts from inhibition ($\beta$ = −0.08 *, p < 0.10) to significant promotion ($\beta$ = 0.12–0.15 ***, p < 0.01), reflecting the inverted-U hypothesis of sustainability transitions. Regional analysis shows that eastern provinces benefit most from industrial digitization ($\beta$ = 0.13 ***, p < 0.01) and innovation ecosystems ($\beta$ = 0.09**, p < 0.05), supporting SDG 13, while western regions face infrastructure-induced inhibition. Modernization outcomes demonstrate trade-offs: economic growth ($\beta$ = 0.37 ***, p < 0.01) contrasts with social decline ($\beta$ = −0.12 *, p < 0.10). To achieve inclusive and low-carbon modernization, tiered policies integrating green digital infrastructure (SDG 7), rural connectivity (SDG 10), and carbon pricing (SDG 12) are proposed. This research extends the environmental Kuznets curve to the digital economy domain, offering a theoretical framework for balancing innovation with ecological resilience in emerging economies.

6.2. Policy Proposal

(1)

Prioritize investments in energy-efficient digital infrastructure such as 5G networks, smart grids, and AI-driven data centers that incorporate sustainability standards, including mandatory adoption of renewable energy sources and circular economy practices for e-waste management in new data centers. This strategy addresses regional disparities by channeling subsidies to green digital projects in western provinces while encouraging eastern regions to scale advanced technologies, thereby fostering balanced development. Such investments not only accelerate economic modernization through improved connectivity and innovation but also contribute to the carbon neutrality goals aligned with the Sustainable Development Goals (SDGs) 7 (affordable and clean energy), 9 (industry, innovation, and infrastructure), and 13 (climate action).

(2)

Sustainability-Oriented Regulatory Innovation Adopt flexible regulatory frameworks that balance economic efficiency with environmental stewardship by implementing three key strategies. First, green industrial policies should incentivize digital transformation in high-emission sectors like manufacturing through market-based tools such as carbon pricing mechanisms and mandatory green certifications. Second, cross-sectoral data governance systems can optimize resource allocation via smart urban applications, for example, integrating real-time waste management data to enhance recycling rates and reduce landfill usage (SDG 11). Finally, trade agreements with sustainability clauses should be designed to facilitate international green technology transfers while restricting imports of carbon-intensive goods, thereby aligning globalization with ecological objectives. These measures collectively promote sustainable modernization by embedding environmental accountability into market-driven processes.

(3)

Closing digital gaps demands tailored strategies. Rural areas should expand e-commerce and telemedicine to improve market access and healthcare, advancing SDGs 1, 3, and 10. Joint government–education–private sector initiatives must prioritize digital skills training to address unemployment risks from automation. Central provinces can pilot smart city projects via public–private partnerships to demonstrate governance innovation. Internationally, participation in frameworks like DEPA ensures alignment with global sustainability goals. This involves sharing climate data, fostering green tech R&D cooperation, and transferring energy-efficient digital solutions to developing nations (SDG 17). Such collaborations accelerate progress while ensuring the equitable distribution of digital benefits.

7. Research Limitations and Future Research Directions

This study analyzes the impact of China’s digital economy on Chinese-style modernization. However, there are still some limitations in the theoretical framework and empirical analysis. In terms of data, the current research is based on provincial panel data from 2011 to 2021. Although it can reflect macro-regional differences, it is difficult to capture the micro impact of digital infrastructure at the county level on rural revitalization, and it also cannot refine the heterogeneous effects of different industries (such as digital agriculture and digital high-end manufacturing). In terms of model design, the identified single thresholds of digital development (2.218), market efficiency (9.212), and technological progress (12.224) fail to fully depict the dynamic changes in thresholds caused by local policies (such as institutional innovation in free trade pilot zones) or differences in digital literacy, and do not consider the possibility of the coexistence of multiple thresholds. In addition, although some endogeneity problems have been alleviated through instrumental variables and the GMM model, unobserved factors such as regional innovation culture and sudden policy shocks may still affect the accuracy of causal inference. The focus of the research conclusions on the Chinese context also leads to limitations in their applicability when extended to emerging economies with different institutional environments and technological bases.

Future research can expand the existing framework from multiple dimensions. Methodologically, industry-segmented data can be introduced to construct an indicator system for different fields of the digital economy, exploring the differential driving mechanisms of different business forms such as fintech and intelligent manufacturing on modernization. Time-varying threshold models or machine learning algorithms can be adopted to dynamically track the impact of policy nodes such as the “14th Five-Year Plan” on the thresholds of the digital economy. In terms of research scope, it is recommended to conduct cross-country comparative analysis. Samples of emerging economies such as ASEAN and BRICS countries can be selected to compare the nonlinear relationship between the digital economy and modernization under different institutional environments, for example, to test whether economies with higher government governance effectiveness have lower market efficiency thresholds. In addition, emerging technologies such as blockchain and the metaverse can be incorporated into the analysis framework. Combining innovative policy tools such as digital carbon credits, new paths for the digital economy to promote green and low-carbon transformation can be explored. At the same time, the spillover effects of digital technologies among regions can be analyzed through spatial econometric models, providing a basis for optimizing the cross-regional digital governance coordination mechanism. These expansions not only make up for the limitations of existing research but also provide a more universal theoretical reference for sustainable development in the global digital age.