China’s new-type urbanization is narrowing the urban-rural human well-being gap through heterogeneous pathways while confronting the new challenge of the digital economy

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To examine the impact of new-type urbanization on the urban-rural human well-being gap, this paper constructed a theoretical analytical framework, in which their levels were evaluated and spatiotemporal evolution patterns were explored. Furthermore, a two-way fixed effects model was employed to quantitatively test the mechanism through which new-type urbanization affected urban-rural human well-being gap. Besides, the moderating effects of transportation infrastructure and the digital economy were also discussed. The results suggested that new-type urbanization in China had significantly narrowed the human well-being gap between urban and rural areas, and the effect presented both urban-rural and spatiotemporal heterogeneity. Transportation infrastructure development enhanced the effect of new-type urbanization in reducing the well-being gap, whereas the development of the digital economy may become a challenge to this narrowing process. Additionally, unexpected findings offered further insights: (1) industrial structure upgrading and higher levels of environmental governance both expanded the well-being gap between urban and rural residents; (2) new-type urbanization improved well-being more strongly for rural residents than for urban ones; and (3) the process of reducing the urban-rural human well-being gap may susceptible to major public events such as the COVID‑19 pandemic, in which rural areas recovering more slowly than urban areas. Based on the research results, the paper proposed policy recommendations to advance future urbanization strategies under the objective of promoting well-being equalization between urban and rural residents. Social science/Development studies Earth and environmental sciences/Environmental social sciences New-type urbanization Human well-being Urban-rural gap influence mechanism China Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Urbanization is an inevitable path for the development of all countries worldwide and will remain the most prominent social transformation globally in the 21st century (Liu, 2023 ; Huang et al., 2024 ). According to data from the United Nations, the proportion of the global urban population has surged from 30% in 1950 to 57% in 2023, and is projected to exceed 68% by 2050 (UN-Habitat, 2023). As a dynamic process with far-reaching impacts on the economy, society, ecology, and other dimensions, urbanization has long been a focus of global concern (Ahmad, N. et al., 2023 ; Wang, 2024 ; Moreno-García et al., 2025 ). However, traditional urbanization which characterized by an overemphasis on scale expansion and growth speed has given rise to numerous problems in the economic, social, and ecological spheres (Mishra et al., 2023 ; Zhang & Kim, 2024 ; Salvi & Kumar, 2024 ). It has brought about negative impacts such as excessive resource consumption and increased carbon dioxide emissions (Zhou et al., 2021 ; Liu et al., 2022 ). Moreover, it has exerted uncertain effects on the multidimensional human well-being of urban and rural areas, including income levels, accessibility to public services, and rights to a sound ecological environment (Zhao et al., 2022 ; Hu et al., 2022 ). In fact, the relationship between urbanization and human well-being, particularly issues such as urban-rural human well-being inequality, has long been a global research hotspot (Udayanga, 2025 ; Skeggs & Orben, 2025 ). Many developed countries have gradually achieved equalization of human well-being between urban and rural areas in the process of their urbanization (Collins et al., 2024 ; Saqib et al., 2024 ). As the world's largest and fastest-paced practitioner of urbanization, China has witnessed its urbanization rate soar from 21.62% in 1983 to 66.16% in 2023 over the past four decades, creating the largest-scale population spatial migration in human history (Zhang et al., 2022 ). However, behind this rapid growth in China's urbanization rate lied complex and severe practical challenges: On the one hand, the accelerated advancement of urbanization has significantly improved urban residents' well-being across dimensions such as income levels, accessibility to public services, and life convenience (Lin & Huang, 2018 ). On the other hand, significant gaps persist in rural areas, especially in key well-being dimensions including infrastructure, educational resources, medical services, social security, cultural and recreational facilities, and environmental quality (Liang et al., 2019 ). These gaps manifest not only as "hard divides" in material terms but also as "soft gaps" in developmental opportunities, social integration, cultural identity, and other aspects. They have become deep-seated hidden barriers restricting the coordinated and sustainable development of urban and rural societies. The Chinese government has long attached great importance to the issue of unbalanced urban-rural development (Zhang et al., 2024 ). In 2014, it formally proposed "People-Centered New-type urbanization", aiming to promote the equalization of human well-being between urban and rural areas (Liu et al., 2024 ). Distinct from traditional urbanization, new-type urbanization emphasizes a people-centered approach: it not only focuses on economic growth and urban scale expansion but also pursues the synchronized improvement of basic public service, infrastructure, resource utilization efficiency and environmental quality between urban and rural areas. Its core goal is to enable urban and rural residents to share the dividends of development and achieve a higher level of well-being. Relevant studies have shown that for urban areas, new-type urbanization has alleviated the urban heat island effect by increasing urban green spaces, thereby improving the health and quality of life of urban residents (Beele et al., 2024 ). However, for rural areas, new-type urbanization has led to the loss of farmers' land and pollution of their living environments (Li et al., 2018 ). In terms of the connotation of human well-being itself, urban-rural human' well-being is a comprehensive reflection of the happy life state of urban and rural residents, encompassing basic material conditions required for maintaining high-quality life and health, the ability to acquire knowledge, and sound social relationships (VanderWeele & Johnson, 2025). The urban-rural human well-being gap based on this connotation refers to the comprehensive disparity across all the above dimensions of well-being. Nevertheless, most existing studies have only focused on the relationship between new-type urbanization and the urban-rural human well-being gap in a single dimension (e.g., income), and their conclusions remain inconsistent (Peng et al., 2024 ; Zhao, 2024 ; Chen & Hu, 2025 ). For instance, some studies have indicated that new-type urbanization has narrowed the urban-rural income gap through channels such as industrial structure upgrading, technological innovation, and digital inclusive finance (Hong et al., 2024 ; Mo et al., 2024 ; Wang et al., 2024 ). However, other studies have pointed out that due to factors such as urban-biased land development policies and infrastructure gaps, new-type urbanization may also widen the urban-rural income gap (Feng et al., 2019 ). Additionally, some studies have found an inverted "U"-shaped relationship between new-type urbanization and the urban-rural income gap: only after crossing a certain threshold will new-type urbanization shift from widening to narrowing the gap (He & Zhang, 2022 ; Zhang et al., 2023 ). Against this backdrop, questions such as whether China's people-centered New-type urbanization has truly achieved its policy goal of narrowing the urban-rural human well-being gap after more than a decade of development and what its operational mechanism is still require further theoretical construction and empirical research. Based on the above context, this study intends to address the following three key questions: (1) How to construct a scientific indicator system to evaluate the level of new-type urbanization (abbreviated as "NU" in this study) and the urban-rural human well-being gap (abbreviated as "URHWG" in this study) in China? What spatiotemporal evolution patterns have they respectively presented? (2) How did NU effect URHWG? Did this effect exhibit urban-rural heterogeneity and spatiotemporal heterogeneity? What is the underlying mechanism? (3) In terms of the effect of NU on URHWG, what roles have the transportation infrastructure development (vigorously promoted in China during the past period) and the digital economy (representing the future development trend) played—positive or negative? By exploring and answering the above three questions, this study aims to propose optimization suggestions for promoting the long-term and effective narrowing of URHWG in China through NU in the future. Additionally, it intends to provide policy references for other developing countries to achieve rapid and coordinated urban-rural development and the well-being equalization of urban-rural residents. 2. Theoretical analysis and research hypothesis As elaborated in the previous section, China’s early-stage urbanization was characterized by being land finance-driven, economic growth-oriented, and city-centric (Feng et al., 2023 ). This progress of urbanization accelerated the flow of production factors from rural to urban areas, spurred the agglomeration of population and capital in cities, and significantly boosted the economic and social development of China’s urban areas. However, it also led to widespread decline in rural areas and exerted negative impacts on the ecological environment of areas surrounding cities. Consequently, URHWG was not narrowed; instead, it widened progressively. Distinct from traditional urbanization, NU exerts effects on URHWG primarily through the following pathways. Firstly, NU promotes the establishment of unified urban-rural markets for human resources and construction land. This facilitates equal employment opportunities for urban and rural workers, ensures that farmers fairly share the land value-added benefits, and thereby narrows the well-being gap between urban and rural residents in the dimension of income. Secondly, NU advances the rational allocation of educational resources, with a priority on tilting such resources toward rural areas. This enhances the quality of compulsory education in rural areas and the level of balanced development in education, narrowing the well-being gaps between urban and rural residents in the dimension of education. Lastly, NU drives the allocation of basic medical and health services to rural areas and optimizes the three-tier rural medical and health service network (covering county, township, and village levels). This enables rural residents to access safe, affordable, and accessible basic medical and health services, thereby narrowing the well-being gap between urban and rural residents in the dimension of health. Based on the above analysis, this study proposes Hypothesis 1 and Hypothesis 2. H1:NU can significantly advance the well-being level of urban - rural residents. H2:NU can narrow URHWG. Furthermore, transportation infrastructure, as a development foundation independent of China’s NU evaluation system, plays a pivotal role (Wang et al., 2023 ). Only a well-developed transportation network can provide guidance and support for NU, and help extend the improvements in residents’ well-being brought about by NU to the vast rural areas, thereby narrowing URHWG. With the advancement of NU, the comprehensive transportation network and inter-regional trunk transportation network have been continuously improved, and transportation connections among urban agglomerations have been gradually strengthened. This has enhanced the outward transportation conditions of small and medium-sized cities as well as small towns. In particularly, local governments have increased fiscal expenditures on transportation: ordinary national highways now basically cover all counties, national expressways generally serve cities with a population of over 200,000, and road access to every village has been realized in most regions. These developments have significantly promoted the economic and social progress of rural areas and strengthened their connections with surrounding cities and towns, enabling rural residents to share the fruits of NU. This will contribute to the achievement of the goal to equal the well-being of urban-rural residents. Based on this, the study proposes Hypothesis 3. H3: The effects of NU on URHWG can be positively modulated by transportation infrastructure development. Meanwhile, as the world enters the era of the digital economy, China has witnessed the rapid development of digital technologies represented by the Internet, artificial intelligence, blockchain, cloud computing, and big data in recent years. The digital economy has not only broken the temporal and spatial constraints between traditional industries and regions but also provided a strong driving force for the coordinated development of urban and rural areas. On the one hand, the digital economy can exert a positive effect through two main pathways amid the advancement of NU: It first realizes urban-rural connectivity by upgrading traditional industrial formats and production methods, thereby enabling more rational and balanced allocation of resource factors during the NU process (Wang et al., 2025 ); It applies knowledge, technologies, and other spillover effects from urban areas to agriculture in the context of NU, optimizes the agricultural industrial structure, and promotes rural economic and social development. From this perspective, it helps narrow the urban-rural income gap, and in turn, reduces the URHWG (Lin & Peng, 2025 ). On the other hand, due to factors such as the weak digital infrastructure in rural areas and the generally low educational level of rural residents, the urban-rural "digital divide" has become increasingly prominent. The existence of this divide may prevent urban and rural residents from fairly sharing the achievements brought by NU on an equal basis, which is not conducive to narrowing the URHWG (Xia et al., 2024 ). Consequently, this study proposes Hypothesis 4. H4: The effects of NU on URHWG can be modulated by digital economy with uncertainty. Based on the above analysis, the theoretical mechanism of the impact of NU on URHWG can be represented by Fig. 1 . 3. Study design 3.1 Selection and measurement of variables 3.1.1 Independent variable The independent variable in this study is NU. With reference to the NU development indicators proposed in the National New-type urbanization Plan (2014–2020) and relevant research findings by scholars, this study constructed an indicator system for NU level from four dimensions: population urbanization, basic public services, supporting facilities, and resource and environment. The system comprised 4 primary indicators and 12 secondary indicators (Table 1 ). Table 1 indicator system of NU Primary indicator Secondary indicator Quantitative indicator (Unit) properties weight Population urbanization Urbanization rate of permanent population Urban permanent population / resident population in the end of the year (%) + 0.087 Urban population density Urban permanent population/built-up area (people/km 2 ) + 0.133 Basic public services Coverage rate of basic endowment insurance for urban permanent population Urban permanent population participating in basic endowment insurance / Urban permanent population (%) + 0.062 Coverage rate of basic medical insurance for urban permanent population Urban permanent population participating in basic medical insurance / Urban permanent population (%) + 0.104 Coverage rate of unemployment insurance for urban permanent population Urban permanent population participating in basic unemployment insurance / Urban permanent population (%) + 0.188 Supporting facilities Popularization rate of public water supply Popularization degree of public water supply (%) + 0.047 Sewage treatment rate Centralized treatment rate of sewage treatment plants (%) + 0.049 Harmless treatment rate of domestic waste Harmlessly treated domestic waste / domestic waste generated (%) + 0.036 Popularization rate of gas supply Popularization degree of gas supply (%) + 0.047 Resource and environment Per capita construction land area Construction land area / urban permanent Residents (%) + 0.123 Urban afforestation Green space area / built-up area (%) + 0.056 Air quality Air quality index (AQI) - 0.069 NU serves as an objective measure for evaluating the modernization of comprehensive urban development. To avoid subjectivity and one-sidedness caused by human factors when measuring the NU level, this study adopted a combined objective weight method integrating the CRITIC method and the entropy weight method. The CRITIC (Criteria Importance Through Intercriteria Correlation) method is an objective weight method that determines the objective weights of each indicator by considering the contrast intensity and conflict among indicators. However, the single CRITIC method fails to measure the degree of dispersion among indicators. In contrast, the entropy weight method—another commonly used objective weight method—can effectively capture the dispersion degree among indicators, thereby making up for this limitation of the CRITIC method. In this study, the average value of the weights derived from the two methods was used as the final weight for the NU indicators. 3.1.2 Dependent variable URHWG serves as the dependent variable in this study. To measure the URHWG index, this research drew on the Human Development Index (HDI) proposed by the United Nations Development Programme (UNDP). A URHWG indicator system was constructed using the urban-rural ratio of each indicator (see Table 2 ). Table 2 Indicator system of URHWG Factors Indicators Properties AHP weight Entropy weight Comprehensive weight Life quality Per capita disposable income + 0.373 0.200 0.286 Ratio of Engel's coefficient - 0.246 0.246 0.246 Life expectancy Health technical personnel per 1,000 residents + 0.104 0.152 0.128 Medical and health institution beds per 1,000 residents + 0.056 0.111 0.084 Education Students per full-time teacher in primary and secondary schools + 0.038 0.178 0.108 Average years of schooling + 0.183 0.113 0.148 Indicators in the table refer to the ratio between urban and rural aeras. This study adopted a combination of Analytic Hierarchy Process (AHP) method and entropy weight method to calculate China’s URHWG index. The average value of the two was taken as the final comprehensive weight for indicators. Figure 2 presents the standardization results and scatter plot fitting of the independent variable (NU) and the dependent variable (URHWG). 3.1.3 Moderating variables ① Transportation development (Tran). Based on the theoretical analysis above, this study employed per capita highway mileage to measure the development of transportation infrastructure. ② Digital economy (Dig). As the digital economy is a general and comprehensive concept that is difficult to measure directly. However, the digital inclusive finance index offers unique advantages benefiting various social classes especially including the vulnerable groups such as small enterprises, farmers, and urban low-income groups (Jin & Zhong, 2024 ). Therefore, with reference to relevant studies, the digital inclusive finance index (2014–2023) which released by Peking University was adopted to assess the development of the digital economy across Chinese provinces. 3.1.4 Control variables ① Economic development (LnGDP). Considering that rural areas adjacent to cities may be driven by urban economic growth, resulting in a smaller urban-rural income gap, and thus narrowing the URHWG. The natural logarithm of real per capita GDP of each province was employed to measure its economic development. ② Social security (Soc). Social security directly affects residents’ living standards and serves as the most fundamental service for society. A sound social security system raises the benchmark of residents’ quality of life, thereby narrowing the gap in human well-being between urban and rural areas. This study measured the social security of each province by the ratio of social security and employment expenditure to local fiscal expenditure. ③ Fixed-asset investment (LnFin). Fixed-asset investment reflects the scale of infrastructure construction and the growth of economic investment. A high level of fixed-asset investment improves residents’ quality of life, which may in turn reduce the URHWG. Thereby, this study used the natural logarithm of total social fixed-asset investment of each province to measure its fixed-asset investment. ④ Industrial structure upgrading (Ind). Existing studies have shown that industrial structure upgrading narrowed the urban-rural income gap through promoting integrated urban-rural development, and consequently reduces the human well-being gap between urban and rural areas. Therefore, this study measured industrial structure upgrading of each province by the ratio of the added value of the tertiary industry to that of the secondary industry. ⑤ Environmental governance (Env). In the process of rapid urbanization, issues such as industrial pollution and fragile ecological environments affect residents’ daily lives. A sound environment is conducive to residents’ physical and mental health, thereby improving their well-being and narrowing the URHWG. The environmental governance in this study was measured by the ratio of the sum of industrial pollution treatment expenditure and forestry investment to local fiscal expenditure. 3.1.5 Data sources and descriptive statistics Considering the consistency with the process of new urbanization, this paper set the research period from 2014 to 2023. A total of 30 Chinese provinces, municipalities directly under the central government, and autonomous regions were selected as research units. Xizang Autonomous Region, Hong Kong, Macao, and Taiwan are excluded due to severe data absence. The data are mainly sourced from the following statistical yearbooks for 2015–2024: China Statistical Yearbook, China Urban Statistical Yearbook, China Urban Construction Statistical Yearbook, China Population and Employment Statistical Yearbook, China Labor Statistical Yearbook, China Environmental Statistical Yearbook, and the statistical yearbooks of individual provinces. For a small amount of missing data, linear prediction and moving average methods are used for interpolation. Furthermore, to eliminate the impact of inflation, price-related indicators (e.g., fixed-asset investment and per capita GDP) are deflated using 2014 as the base year. All variables were standardized to enhance the interpretability and stability of the model. The descriptive statistical results of each variable are presented in Fig. 3 . Furthermore, this study employs the Variance Inflation Factor (VIF) test and Levin-Lin-Chu (LLC) test to conduct multicollinearity test and stationarity test on all variables. The results were presented in Table 3 . The test results showed that: the maximum value of VIF is 4.97 and the average value is 2.23, indicating that there was no severe multicollinearity in the model; the LLC test results rejected the null hypothesis that all cross-sectional units in the panel data have unit roots, which proved that the series of panel data was stationary. Table 3 Multicollinearity and panel unit root test results. Variable VIF LLC test NU 4.970 -15.208*** Dig 3.260 -10.528*** LnGDP 2.670 -2.799*** Ind 1.560 -6.870*** LnFin 1.460 -43.093*** Tran 1.400 -6.225*** Soc 1.300 -2.143** Env 1.240 -2.761*** URHWG -7.461*** Mean VIF 2.230 3.2 Model construction To test the direct effect of NU on the URHWG, this study conducted regression using the Pooled Ordinary Least Squares (Pooled OLS) and Fixed Effects (FE) models based on the research hypotheses proposed above. The results showed an F-statistic of 69.73 with a p-value of 0.0000, indicating that the FE model outperforms the Pooled OLS model. Second, the Random Effects (RE) model was employed, followed by the Lagrange Multiplier (LM) test; the results revealed a p-value significantly smaller than 0.01, demonstrating that the RE model also performs better than the Pooled OLS model. Next, time-fixed effects tests were conducted for both the FE and RE models, and the p-values of both tests were significantly smaller than 0.01, confirming the necessity of incorporating time-fixed effects. Additionally, inter-sectional heteroskedasticity test, intra-sectional autocorrelation test, and cross-sectional correlation test were performed on the panel data; all p-values were significantly smaller than 0.01, indicating the presence of heteroskedasticity, intra-sectional autocorrelation, and cross-sectional correlation in the panel data. Finally, considering the above issues (e.g., heteroskedasticity), this study conducted regression by adding clustered robust standard error to the RE model and performs an over-identification test. The results showed a p-value significantly smaller than 0.01, suggesting that the FE model is superior to the RE model. Therefore, this study ultimately adopted the Two-Way fixed effects (TWFE) model for regression analysis, with clustered robust standard error used by default. The benchmark regression model is as follows: where, i and t represent province and year respectively; URHWG it denotes the dependent variable, NU it is the independent variable, Control it stands for a set of control variables, α is the constant, β is the coefficient to be estimated for NU it , δ represents the regression coefficients of the control variables, µ i denotes the individual fixed effects, λ t denotes the time fixed effects, ε it is the random error term. In line with the hypotheses proposed above, Tran and Dig as moderating variables were selected for regression analysis. The moderating effect model is specified as follows: where, M it represents the moderating variable, β 2 is the main effect coefficient of NU, β 3 is the main effect coefficient of the moderating variable, β 4 is the moderating effect coefficient, other symbols are consistent with those in the TWFE model. 4. Results and analysis 4.1 Spatiotemporal evolution patterns of NU and URHWG in China 4.1.1 Spatiotemporal evolution pattern of NU The spatial distribution pattern of NU was illustrated by Fig. 4 a-d. It revealed that the spatial distribution of China’s NU level exhibited significant heterogeneity. The level in eastern regions was higher than that in central and western regions. High-level NU has spread from eastern to western regions over time. Specifically, the NU level of most provinces (primarily concentrated in central and western China) was below 0.4 in 2014, while only a few eastern provinces had an NU level exceeding 0.4. By 2017, the NU level of central and western provinces was mainly between 0.4 and 0.5, whereas that of eastern regions had risen to above 0.5. In 2020, the NU level of central provinces had approached that of eastern regions (all exceeding 0.5), while some western provinces remaining in the 0.4–0.5 range. By 2023, except for Yunnan and Guizhou provinces, all other provinces had achieved an NU level of over 0.5, and some provinces (e.g., Beijing, Shanghai, and Guangdong) even reached an NU level exceeding 0.6. From a local perspective, the average NU levels of eastern provinces in 2014, 2017, 2020, and 2023 were 0.452, 0.516, 0.580, and 0.596 respectively. The high level and rapid growth of NU in eastern regions can be primarily attributed to the following factors: a solid economic foundation, a high degree of openness to the outside world, and a high level of population and industrial agglomeration. Additionally, eastern regions initiated urbanization earlier and possess unique geographical advantages over central and western regions in terms of population quality, public services, and infrastructure construction. These advantages has promoted balanced development across all dimensions of NU, enabling eastern regions to maintain a significant leading edge in NU process for a long time. Meanwhile, the NU level of central and western provinces also showed a steady upward trend during the study period, with the average value increasing from 0.339 in 2014 to 0.543 in 2023. This indicated that the development gap between central/western regions and eastern regions has been continuously narrowing with the in-depth advancement of China’s "Western Development Strategy" and "Central China Rise Strategy". Infrastructure construction, population growth, industrial transfer, and technological progress has all promoted the development of NU in central and western provinces to a certain extent. Figure 4 e-f presents the temporal variation trends of the annual average NU levels across China as a whole and across eastern, and mid-western regions. The national average NU level increased from 0.380 in 2014 to 0.563 in 2023, indicating an overall upward trend in NU during the study period. Furthermore, the pie charts in figures showed that the number of provinces with an NU level exceeding the national average has increased year by year, reaching 93.3% in 2023. This demonstrated that the implementation of China’s new-type urbanization policy has achieved remarkable results. 4.1.2 Spatiotemporal Evolution Patterns of URHWG The spatial distribution pattern of the URHWG was shown as Fig. 5 a-d. As observed from the figures, the number of provinces with a URHWG exceeding 0.3 has gradually decreased, while the number of provinces with a URHWG below 0.2 has continuously increased. URHWG in China exhibits distinct heterogeneity and agglomeration characteristics in spatial distribution. Provinces with a URHWG below 0.2 have gradually expanded to provinces in central and western regions while it initially concentrated mainly in eastern regions. However, the study also identifies an interesting phenomenon: Beijing and Shanghai, as China’s two most developed regions, have consistently maintained a URHWG above 0.3, forming a sharp contrast with their surrounding areas. This revealed that while these regions have achieved rapid economic development, they have also witnessed a widening of the URHWG to a certain extent. In contrast, Xinjiang, which URHWG reached 0.066 in 2023 has become the smallest URHWG among western province despite its relatively underdeveloped economy. This indicated that Xinjiang has achieved a relatively balanced urban-rural human well-being level with a relatively low level of economic development. The URHWG of western provinces have gradually narrowed with the average value decreasing from 0.363 in 2014 to 0.267 in 2023 over time. Figure 5 e-f illustrated the temporal variation trends of the annual average URHWG across China as a whole and across eastern, mid-western regions. The national average URHWG declined from 0.280 in 2014 to 0.223 in 2023, indicating an overall narrowing trend during the entire study period. Notably, however, the URHWG experienced a slight widening between 2019 and 2021. This phenomenon may be attributed to the global COVID-19 pandemic that emerged in late 2019. During this period, the global socioeconomic development stagnated, leading to a precipitous drop in the income of urban and rural residents and severe threats to their health, which in turn reduced the well-being of both urban and rural residents. Nevertheless, urban residents may have experienced a slighter decline in well-being compared to rural residents thanks to the availability of urban infrastructure, accumulated savings, and better living environments. This discrepancy resulted in the widening of the URHWG during that period. With the effective control of global COVID-19 pandemic, socioeconomic activities gradually recovered, and the URHWG continued to narrow. Similarly, the pie charts in figures showed that the number of provinces with a URHWG exceeding the national average has decreased year by year, dropping to 33.3% by 2023. This indicated that China’s goal of narrowing the URHWG has achieved initial success. 4.2 Model exploration of NU impact on URHWG 4.2.1 Benchmark regression analysis Table 4 presented the results of the benchmark regression. Column (1) corresponded to the estimation results without incorporating any control variables. The results showed that the coefficient of NU is significantly negative, indicating that NU significantly promotes the narrowing of the URHWG (see Table 4 ). Columns (2) to (6) displayed the estimation results after the stepwise incorporation of control variables. It was observed that even after adding the control variables, the direction of the NU coefficient remains significantly negative, demonstrating that NU still exerted a significant facilitating effect on narrowing the URHWG. Thus, hypothesis 2 (H2) has been verified. In addition, the coefficient of LnGDP was significant at the 10% confidence level and exerted a facilitating effect on narrowing the URHWG, suggesting that the improvement of the overall regional economic level can promote the narrowing of the URHWG. This may be because as the regional economic level advances, the income gap between urban and rural residents may become less pronounced. Interestingly, the coefficients of Ind (industrial structure upgrading) and Env (environmental governance level) were significant at the 5% and 1% confidence levels respectively, but both are positive. This indicated that Ind and Env significantly contribute to the expansion of the URHWG. A plausible explanation for this may that the primary focus of China’s industrial structure upgrading and environmental governance enhancement remained on urban areas, with the benefits primarily accruing to urban residents. The impact of these initiatives on rural residents was still insufficient, which consequently led to the expansion of the URHWG. In contrast, the coefficients of the other control variables were not statistically significant, meaning they did not have a significant impact on the URHWG. Table 4 Benchmark regression results. Explanatory variable (1) (2) (3) (4) (5) (6) NU -0.223 *** -0.240 *** -0.245 *** -0.246 *** -0.213 *** -0.203 *** (0.065) (0.078) (0.085) (0.084) (0.072) (0.072) LnGDP -0.233 * -0.186 * -0.190 * -0.185 * -0.184 * (0.124) (0.095) (0.098) (0.092) (0.093) Soc 0.078 0.079 0.082 0.084 (0.076) (0.075) (0.074) (0.074) LnFin 0.010 0.001 0.003 (0.028) (0.030) (0.030) Ind 0.212 ** 0.207 ** (0.085) (0.086) Env 0.052 *** (0.019) _cons 0.365 *** 0.467 *** 0.434 *** 0.430 *** 0.398 *** 0.389 *** (0.029) (0.060) (0.052) (0.053) (0.045) (0.047) Province Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes N 300 300 300 300 300 300 R 2 0.287 0.368 0.377 0.378 0.401 0.409 R 2 _a 0.263 0.344 0.351 0.350 0.372 0.378 F 12.323 14.084 17.899 16.737 20.424 15.448 Data in()are Cluster robust standard error; *, **, and *** indicate significance at 10%, 5%, and 1% confidence levels, respectively. 4.2.2 Endogeneity analysis In order to alleviate endogeneity caused by omitted variables, the time-fixed and individual-fixed effects has been incorporated into the benchmark regression model and controlled for a set of variables. However, there may still exist a bidirectional causal relationship between the URHWG and NU, which could lead to endogeneity in the model and make it difficult to accurately identify the effect of NU on the URHWG. Therefore, instrumental variables (IVs) for NU were employed to address potential endogeneity. NU with a one-period lag was highly correlated with the current NU, while it had no direct correlation with the current URHWG. This satisfied both the correlation and exogeneity conditions of instrumental variables. Thus, NU with a one-period lag was chosen as the first instrumental variable (IV1) for NU. Regions with low relief amplitude are more conducive to urban construction and expansion, as they reduce construction costs and thus tend to have a higher level of NU. However, relief amplitude itself has no direct impact on the URHWG, satisfying the correlation and exogeneity conditions of instrumental variables. Notably, relief amplitude is a time-invariant variable and thus cannot be directly included in the benchmark regression model. To address this, an interaction term between relief amplitude and a time dummy variable was constructed as the second instrumental variable (IV2) for NU. Table 5 Endogenous regression results. Explanatory variable (1) (2) IV1 IV2 NU -0.256** -0.721*** (0.104) (0.156) Control variables YES YES F value of the first stage 86.34*** 15.15*** LM statistic 34.323*** 20.392** Wald F statistic 86.341{16.38} 15.148{11.46} N 270 300 R 2 0.380 0.261 R 2 _a 0.259 0.130 F 10.803 10.989 Data in()are Cluster robust standard error; data in {}are Stock-Yogo weak ID test critical values at 10%; *, **, and *** indicate significance at 10%, 5%, and 1% confidence levels, respectively. Table 5 presented the regression results of the endogeneity analysis. First, the F-statistics of the first stage for both instrumental variables (IVs) exceed 10, which rejected the null hypothesis that the IVs are uncorrelated with NU. This confirmed the correlation between the IVs and NU. Second, both IVs passed the unidentifiable test and weak instrumental variable test, rejecting the null hypothesis that the IVs are weak instruments. This verified the validity of the selected IVs. Finally, after addressing the endogeneity, the regression results showed that the coefficient of NU remains significantly negative (-0.256 and − 0.721, respectively), indicating that NU still exerted a significant promoting effect on narrowing the URHWG. Compared with the coefficient in the benchmark regression (-0.203), the endogeneity would lead to an underestimation of the promoting effect of NU on narrowing the URHWG. 4.2.3 Robustness tests To ensure the robustness of the benchmark regression results, this study conducted robustness tests through the following methods, with the results reported in Table 6 . ①Replacement of independent variable The core goal of people-centered NU is to increase the proportion of the urban population, which can directly and effectively reflect the regional NU level. Therefore, this study re-conducted the benchmark regression using the proportion of permanent urban residents (Urb) as the independent variable instead of NU. The results remain robust, as shown in Column (1) of Table 6 . ②Exclusion of municipal sample Considering the potential particularities of the four municipalities directly under the central government (Beijing, Tianjin, Shanghai, and Chongqing) in terms of national policy support and funding allocation, this study excluded these municipalities from the sample and re-conducts the benchmark regression. The results remain robust, as presented in Column (2) of Table 6 . ③Winsorization To avoid sample selection bias, this study performed 5% winsorization on both the upper and lower tails of all variables included in the benchmark regression. After excluding sample selection bias, the results remain robust as presented in Column (3) of Table 6 . Table 6 Results of robustness tests Explanatory variable (1) (2) (3) Replace key variables Excluding municipalities Winsorization NU -0.184** -0.182** (0.070) (0.074) Urb -0.011*** (0.003) Control variables YES YES YES _cons 1.881*** 1.300*** 1.375*** (0.530) (0.401) (0.431) Province Yes Yes Yes Year Yes Yes Yes N 300.000 260.000 300.000 R 2 0.500 0.538 0.420 R 2 _a 0.474 0.510 0.390 F 15.427 26.146 12.609 Data in()are Cluster robust standard error; *, **, and *** indicate significance at 10%, 5%, and 1% confidence levels, respectively. 4.2.4 Heterogeneity analysis Considering that the effect of NU on the URHWG may exhibit heterogeneity, this study further examined and revealed the heterogeneous effects of NU on the URHWG from three perspectives: urban-rural dual structure, geospatial location, and time. The heterogeneity characteristics are shown in Fig. 6 . ①Urban-rural heterogeneity The long-standing urban-rural dual structure is the fundamental cause of the urban-rural gap, leading to significant disparities between urban and rural areas in terms of resources, industries, infrastructure and so on. Therefore, it is necessary to explore the differentiated effects of NU on human well-being under the urban-rural dual structure. Using the URHWG evaluation method mentioned above, this study measured the urban human well-being (UHW) and rural human well-being (RHW) and conducted regressions of UHW and RHW with NU respectively. It was shown that the temporal variation trends of UHW and RHW were basically consistent in Fig. 6 a. They both grew rapidly between 2014 and 2019 and fluctuated around 2020 but then resumed rapid growth. This fluctuation may be attributed to the global COVID-19 pandemic that emerged in late 2019. Notably, UHW recovered significantly faster than RHW after 2021. This phenomenon would be further discussed in the subsequent temporal heterogeneity analysis. Furthermore, Fig. 6 b revealed that the regression coefficients of NU on human well-being were positive in both urban and rural areas, and their confidence intervals do not include 0. This indicated that NU can significantly improve the human well-being of both urban and rural areas. Thereby, hypothesis 1(H1) that proposed in the theoretical analysis has been verified. However, an unexpected finding emerges: the regression coefficient of NU on RHW was larger than that on UHW, meaning NU had a stronger promoting effect on human well-being in rural areas than urban areas. Two plausible explanations for this result were as follows: On the one hand, the implementation of the NU policy had been accompanied by the simultaneous advancement of the Rural Revitalization Strategy. These two policies had generated a synergistic effect, significantly increasing rural residents’ income, improving the medical and health services, enhancing infrastructure, and optimizing educational resources in rural areas—thus greatly boosting RHW. On the other hand, the baseline level of UHW in China had long been much higher than that of RHW. During the advancement of NU, although UHW has further improved, its growth rate may be lower than that of RHW. This difference in growth rates ultimately led to the observed results. ②Spatial Heterogeneity The significant imbalance of regional development in China may lead to heterogeneous effects of NU on the URHWG. To further explore the differentiated effects of NU on the URHWG across various regions in China, this study divided the sample of 30 provinces into two groups—eastern regions and mid-western regions—and conducts regression analysis separately. The results were presented in Fig. 6 c. The regression coefficient of NU was positive but statistically insignificant in eastern regions, whereas it was significantly negative in mid-western regions. This indicated that NU significantly promoted the narrowing of the URHWG in mid-western regions, but its effect on eastern regions was not remarkable. A plausible explanation for this difference was as follows: For eastern regions, the overall urbanization level was relatively high, and the urban-rural gap was already small. As a result, the promoting effect of NU on narrowing the URHWG was weak. Even there may even be a tendency toward widening the URHWG especially in megacities such as Beijing and Shanghai that mentioned in the previous analysis. For mid-western regions, urbanization started at a relatively low level. Additionally, issues such as unequal resource allocation between urban and rural areas, large gaps in economic foundations, and disparities in population quality have led to a relatively large URHWG. With the gradual advancement of NU, the overall population quality has improved, and various resources have been allocated more rationally between urban and rural areas, gradually narrowing the urban-rural gap. Consequently, all dimensions of the URHWG have been narrowed. Thus, NU exerted a more significant effect on narrowing the URHWG in mid-western regions compared with eastern regions. ③Temporal Heterogeneity In light of the fluctuations observed after 2019 in the urban-rural heterogeneity results and the potential impact of the global COVID-19 pandemic identified above, the sample period was divided into two phases: pre-2019 and post-2019. The results of regression analysis for each phase were shown in Fig. 6 d. The regression coefficient of NU is significantly negative in the pre-2019 phase but significantly positive in the post-2019 phase. These findings indicated that NU significantly narrowed the URHWG in the pre-2019 period. However, its effect reversed to significantly widen the URHWG after 2019. The outbreak of the global COVID-19 pandemic in late 2019 led to a stagnation in global economic development and severely disrupted the lives of both urban and rural residents. Nevertheless, urban regions outperformed rural regions in terms of economic foundations and infrastructure, giving them stronger resilience to external shocks. Urban residents are less affected than rural residents and demonstrate greater ability to recover from hardships when facing major shocks such as the COVID-19 pandemic. This explains why NU contributed to the widening of the URHWG during the post-2019 period. 4.2.5 Moderating Effect Analysis Results from the benchmark regression and robustness tests have confirmed that NU significantly promotes the narrowing of the URHWG. To further explore the mechanism through which NU influences the URHWG, this study empirically analyzes the moderating effects of transportation development (Tran) and digital economy (Dig) on the mechanism through which NU narrows URHWG. The results were presented in Table 7 . Table 7 Mechanism regression results. Explanatory variable (1) (2) NU -0.102 0.128 (0.093) (0.089) LnTran -0.059 (0.039) NU×LnTran -0.270 ** (0.109) Dig 0.001 *** (0.000) NU×Dig 0.002 *** (0.001) Control variables YES YES _cons 1.752 *** 1.920 *** (0.485) (0.451) Province YES YES Year YES YES N 300.000 300.000 R 2 0.501 0.558 R 2 _a 0.470 0.531 F 21.139 34.403 The coefficient of the interaction term between NU and Tran (NU×Tran) is -0.270, which was significant at the 5% confidence level (as seen in Column (1)). This indicated that a high level of transportation infrastructure can effectively enhance the role of NU in narrowing the URHWG. In other words, the construction of transportation infrastructure significantly strengthened the promoting effect of NU on reducing the URHWG. Accordingly, hypothesis 3 (H3) has been verified. Nevertheless, the coefficient of the interaction term between NU and Dig was 0.002, and it is significant at the 1% confidence level (as seen in Column (2)). This result revealed that with the development of the digital economy, the effect of NU on narrowing the URHWG is significantly weakened and may even reverse to widen the URHWG. That is to say, the development of the digital economy significantly inhibited the promoting effect of NU on reducing the URHWG. From this perspective, hypothesis 4 (H4) has been revised as: the development of the digital economy may pose challenges to China's goal of narrowing the URHWG through NU. 5. Discussion 5.1 Spatiotemporal heterogeneity of the effects of China's NU on the URHWG The results of the above model analysis indicated that NU significantly narrowed China's URHWG on the whole. However, there existed heterogeneity in the effects of NU on the human well-being of urban and rural areas respectively, which manifested in three dimensions: urban-rural, spatial, and temporal. Firstly, it’s the urban-rural heterogeneity. For urban areas, NU enhance urban residents' quality of life, health status, and educational attainment by improving basic public services, infrastructure, living environment significantly. UHW has also been ultimately promoted as a result. This mechanism is intuitive and easy to understand. However, this study has found that NU exerted a stronger promoting effect on RHW than on UHW. From the perspective of NU's connotation, its core lied in being "people-centered", where "people" include not only urban residents but also rural residents. Thus, China's NU strategy takes into account both urban and rural areas, aiming to achieve the coordinated improvement of human well-being in both sectors. Moreover, NU involves both the renewal of existing urban areas and the construction of new urban areas. For rural areas, the renewal of adjacent old urban areas creates abundant employment opportunities for rural residents and expands sales channels for agricultural products, which enhances the economic vitality of rural areas and narrows the human well-being gap between urban and rural areas in the dimension of life quality. Additionally, NU has improved medical and health resources as well as educational resources in rural areas. Unlike the urban-biased land policies under traditional urbanization, NU adheres to the principle of integrated urban-rural development, promoting the coordination between urbanization and agricultural modernization. At the same time, the coverage of public finance has expanded in rural areas and raised the level of infrastructure and public service security in the process of NU. In terms of medical and health care, NU has promoted the construction of county-level hospitals and improved the "county-township-village three-tier rural medical and health service network" (with county-level hospitals as the core, and township health centers and village clinics as the foundation). This network provides rural residents with accessible and affordable basic medical and health services. From the perspective of education, NU advocates the rational allocation of educational resources with a focus on tilting resources toward rural areas. This has promoted the popularization of compulsory education in rural areas and significantly improved the quality and balanced development of rural compulsory education. Of course, the improvement of RHW is not solely attributed to NU. It may also be associated with other national strategies such as the Rural Revitalization Strategy. These policies complement each other and jointly drive the enhancement of RHW. Secondly, the impact of NU on the URHWG has spatial heterogeneity. This result can be explained by the following factors: Eastern regions have attracted a large amount of capital, talent, and technological resources, giving them significant advantages in urban construction and social production compared with central and western regions. However, the URHWG in eastern regions may have already been relatively small due to their advanced development. As a result, the marginal utility of NU on narrowing the URHWG is not as significant as expected. In contrast, central and western regions have received strong policy support in terms of capital and technology, benefiting from national strategies such as the Western Development Strategy and Central China Rise Strategy. A large number of industries have transferred from eastern regions to central and western regions, which has rapidly raised the NU level in these areas and generated a high marginal utility of NU. Consequently, NU in central and western regions can significantly promote the narrowing of the URHWG. Lastly, it’s the temporal heterogeneity. 2019 was identified as a significant threshold for temporal heterogeneity in the effect of NU on the URHWG. Two main reasons may account for this phenomenon: First, the global COVID-19 pandemic broke out in late 2019, leading to a stagnation in economic development and the suspension of production and construction activities. This hindered the rapid advancement of NU and severely disrupted the lives of both urban and rural residents. However, urban areas, with their more solid economic foundations and better infrastructure, demonstrated greater stability and resilience in the face of shocks compared with rural areas. This disparity resulted in a tendency toward the widening of the URHWG after the pandemic. Second, with the rapid advancement of NU in most regions, the marginal utility of NU has gradually diminished. Its effect on narrowing the URHWG has become negligible and even shown a tendency to reverse. Therefore, China's NU should avoid repeating the old path of traditional urbanization that prioritized speed over sustainability, and focus more on improving quality rather than pursuing scale expansion in the future. 5.2 Moderating effects of transportation development and digital economy on the effect of NU on the URHWG The results indicated that the rapid development of transportation infrastructure in China had facilitated the role of NU in narrowing the URHWG. In fact, transportation development has been proven by relevant research to be a key component of urban expansion and exerts a positive effect on urbanization (Wu et al., 2023 ; Akhtar et al., 2024 ). Additionally, transportation infrastructure acts as a vital link for promoting urban-rural integration and rural sustainable development, with a significant influence on the URHWG. Specifically, a higher level of transportation infrastructure enhances connectivity and circulation between urban and rural areas. This facilitates rural residents' access to urban areas for employment, medical care, and education, while also enabling the smooth consumption of agricultural products in cities. Ultimately, it promotes the rational allocation and equitable sharing of public services, infrastructure, and resources between urban and rural regions—thereby contributing to the narrowing of the URHWG. However, it is interesting to note that the digital economy inhibits the role of NU in narrowing the URHWG. From existing research, it was generally believed that there is a mutually reinforcing relationship between the digital economy and NU. On the one hand, digital technology promotes resource allocation, environmental management, and social equity, thereby advancing more coordinated and sustainable urbanization (Chen, 2025 ); On the other hand, NU provides essential infrastructure and market expansion opportunities for digital economy which further stimulate the digital economy in turn (Zhao et al., 2023 ). However, academic opinions on the relationship between the digital economy and the URHWG remain divided. Some studies argued that the digital economy has a positive effect on narrowing urban-rural gaps in income and human capital, indicating that expanded digital infrastructure and services can promote urban-rural integration. Other studies contend that the digital economy may exacerbate inequalities between urban and rural residents in accessing health care, education, and economic opportunities (Zhao et al., 2024 ). Consistent with the latter view, this study finds that the digital economy exerts an inhibitory impact on NU's role in narrowing the URHWG. Thus, the digital economy can be regarded as a double-edged sword in the process of NU narrowing the URHWG. With proper guidance to align its development with urban-rural integration, the digital economy can become a powerful tool for narrowing urban-rural gaps and advancing rural revitalization. Conversely, it may aggravate the URHWG by widening the "digital literacy gap" between urban and rural residents. From the perspective of narrowing the URHWG, China should continue to prioritize transportation infrastructure construction in the process of advancing NU. For the digital economy, efforts should be made to strengthen its rational popularization in rural areas—for example, by integrating it with the Rural Revitalization Strategy to promote its systematic development in rural regions. 5.3 Policy implications This study systematically explored the effects of NU on the URHWG by examining the influential mechanisms, heterogeneous characteristics, and moderating effects. The research findings can provide scientific basis and effective guidance for countries worldwide to promote high-quality development of urbanization and achieve balanced improvement of urban and rural human well-being. The following policy recommendations were proposed based on the empirical results. 5.3.1 Focus on high-quality and coordinated development of urbanization For most countries, the level of urbanization in the early stage tends to be generally low with unbalanced regional development. For instance, this study found that the NU level of eastern provinces in China is generally higher than that of central and western provinces. In this stage, priority should be given to regions with low NU levels in central and western China to promote coordinated development of new-type urbanization across regions. Specifically, differentiated urbanization policies should be formulated based on local conditions, combined with geographical conditions, resource endowments, and comparative advantages. In underdeveloped regions with backward economies and complex terrain, policies should be actively promoted to construct transportation networks, popularize compulsory education, improve infrastructure, and advance ecological revitalization. These measures can enhance population quality and citizenization, thereby accelerating urbanization and narrowing the URHWG. In developed regions with strong economic strength and superior geographical conditions, to ultimately narrowing the URHWG, efforts should be made to improve urbanization quality through policies such as intensive land use, upgrading public service, advancing industrial structure upgrading, strengthening environmental protection and governance, and optimizing infrastructure accessibility. In the later stage, when the overall urbanization level becomes relatively high, reference should be made to the strategies adopted by the above developed regions, with a focus on the high-quality development of new-type urbanization. Promoting urban-rural integration through the equal exchange of factors and the balanced allocation of public resources, and make it as a key development model in future urbanization process. Especially for rural areas with low resilience, it is necessary to effectively improve the comprehensive agricultural production capacity, risk resistance, and sustainable development capacity by promoting agricultural modernization. 5.3.2 Optimize transportation infrastructure construction It was revealed that transportation infrastructure construction significantly facilitates the role of NU in narrowing the URHWG. Therefore, efforts should be made to further improve comprehensive transport channels and inter-regional transportation networks. On the one hand, it is necessary to strengthen the transportation connections between urban agglomerations and large cities, and accelerate the planning and construction of integrated urban regional transportation. At the same time, improving the external transportation of small and medium-sized cities and towns and enhancing their modernization level of transportation are also of great significance. For rural areas, the focus should be on improving the level and coverage of transportation infrastructure, and optimizing the rational planning of transportation networks. For China in particular, the construction of rural road infrastructure should be closely integrated with ongoing policies such as village planning, the integrated development of the primary, secondary, and tertiary industries in rural areas, and rural land institutional reform to achieve scientific and systematic layout and optimization. 5.3.3 Promote the rational development of the digital economy in rural areas The digital economy has inevitably become the main form of social and economic development in the future. However, this study has found that the digital economy may exert an inhibitory impact on NU's role in narrowing the URHWG. The primary reason is that the current development of the digital economy in rural areas lags far behind that in urban areas, and the gap in digital economy between urban and rural areas has widened the URHWG. Therefore, future efforts should focus on promoting the development of the digital economy in rural areas. Specifically, the following measures should be implemented: Firstly, strengthen the construction of digital economy infrastructure in rural areas and enhance rural residents' digital literacy by expanding the coverage of digital technologies such as artificial intelligence (AI) and 5G networks. Secondly, attach importance to the cultivation of digital talents in rural areas and improve rural residents' ability to apply digital technologies to realize rural digitalization. Thirdly, promote the integration and coordinated development of digital economy and other important development opportunities in rural areas such as the rural revitalization strategy to narrow the digital divide between urban and rural areas. 5.4 Future research directions Although this study investigated the relationship between NU and the URHWG using econometric models, the analysis is limited to the provincial level in China. As a result, the research only reflected the relatively macro-level effect of NU on the URHWG, and failed to fully reveal the mechanisms underlying this effect at more granular scales. Theoretically, a higher level of NU tends to exert a stronger attraction on residents (both urban and rural) in surrounding areas, implying the potential existence of spatial spillover effects of NU. Therefore, future studies can focus on refining the research scale to the prefecture-level cities or even smaller units (e.g., county-level). Such research should prioritize exploring the micro-level mechanisms through which NU influences the URHWG and the spatial spillover effects of NU on URHWG. 6. Conclusions This study constructed a theoretical mechanism and analytical framework for the effects of NU on the URHWG, and proposed corresponding research hypotheses. Taking Chinese provinces as the research units, it evaluated the levels of NU and URHWG, analyzed their spatiotemporal evolution patterns. Subsequently, a two-way fixed effects model was employed to reveal the mechanism through which NU influences the URHWG with a particular focus on discussing the spatiotemporal and urban-rural heterogeneity of these effects. Furthermore, in the context of China's long-term efforts to advance transportation infrastructure construction and the global trend of digital economy development, this study explored the moderating effects of these two variables on the mechanism through which NU narrows the URHWG. The main conclusions are as follows. Firstly, NU significantly promoted the narrowing of China's URHWG. This conclusion was supported by benchmark regression analysis, endogeneity analysis, and a series of robustness tests. Secondly, the effects of NU on the URHWG exhibited distinct spatiotemporal and urban-rural heterogeneity. Before the outbreak of the global COVID-19 pandemic (pre-2019), NU significantly narrowed the URHWG; however, after 2019, this effect reversed. NU exerted a significant URHWG-narrowing effect in mid-western China, but its effects were insignificant in eastern provinces. At the same time, NU's promoting effect on rural human well-being (RHW) is significantly stronger than that on urban human well-being (UHW). Lastly, from the perspective of moderating effects, transportation infrastructure amplified NU's positive effects in narrowing the URHWG while digital economy inhibits this narrowing effect. Additionally, this study has made some interesting findings: In spite of their high level of economic development and rapid urbanization, megacities like Beijing and Shanghai maintained a relatively large URHWG which expose the imbalance between economic growth and urban-rural human well-being. In contrast, Xinjiang (a less economically developed western region) has witnessed a steady narrowing of its URHWG, achieving relatively balanced urban-rural human well-being at a lower economic development level. Besides, both industrial structure upgrading and improved environmental governance are found to widen the URHWG. This indicated that current policies related to industrial development and environmental improvement remain to be improved. Furthermore, the effects of NU enhancing the human well-being has urban-rural heterogeneity, in which NU has stronger promoting effect on RHW compared to UHW. It seemingly aligned with the policy goal of balanced urban-rural human well-being. However, this study's analysis reveals a harsh reality: traditional urbanization severely undermined human well-being in rural areas. Last but not least, the influence of NU on the URHWG fluctuated after 2019, indicating that NU's URHWG-narrowing effect is vulnerable to major public emergencies (e.g., the COVID-19 pandemic). The widening of the URHWG after the pandemic highlights that urban areas have stronger resilience and recovery capacity than rural areas. This suggest that governments should prioritize supporting rural development in the aftermath of such crises. Declarations Ethical Approval This article does not contain any studies with human participants performed by any of the authors’. Informed Consent This article does not contain any studies with human participants performed by any of the authors’. Competing interests The authors declare no competing interests. Author Contribution Jun Yang and Enyi Zhao: wrote the main manuscript textXiao Lyu: Designed framework and ideas of the paperXiao Liu: prepared data and figuresAll authors reviewed the manuscript. Acknowledgement The authors thank Key Projects of Humanities and Social Science Foundation of the Ministry of Education in China (21YJC630154) and National Natural Science Foundation of China (42171249). Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Ahmad N, Raid M, Alzyadat J et al (2023) Impact of urbanization and income inequality on life expectancy of male and female in South Asian countries: a moderating role of health expenditures. 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Cite Share Download PDF Status: Published Journal Publication published 02 May, 2026 Read the published version in Humanities and Social Sciences Communications → Version 1 posted Editorial decision: Revision requested 20 Feb, 2026 Reviews received at journal 26 Dec, 2025 Reviews received at journal 24 Dec, 2025 Reviews received at journal 22 Dec, 2025 Reviewers agreed at journal 06 Dec, 2025 Reviewers agreed at journal 02 Dec, 2025 Reviewers agreed at journal 23 Nov, 2025 Reviewers invited by journal 04 Nov, 2025 Editor assigned by journal 04 Nov, 2025 Editor invited by journal 29 Oct, 2025 Submission checks completed at journal 15 Oct, 2025 First submitted to journal 15 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7790181","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":542183529,"identity":"291ce1f6-b57a-4cb0-b3c2-fbb9058130f0","order_by":0,"name":"Jun Yang","email":"","orcid":"","institution":"School of Public Policy \u0026 Management, China University of Mining and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Yang","suffix":""},{"id":542183530,"identity":"e3805473-dc2c-45fa-925a-24ccec2bef51","order_by":1,"name":"Enyi Zhao","email":"","orcid":"","institution":"School of Public Policy 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18:48:17","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":171347,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7790181/v1/4b95e0d04ee0dc64c11fd345.html"},{"id":95864320,"identity":"a86d9a1a-b6e5-4359-9421-ed2a53dd6e58","added_by":"auto","created_at":"2025-11-13 18:48:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56920,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical mechanism of the impact of NU on URHWG\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7790181/v1/3b460a579164ee8c24f7ae0a.jpg"},{"id":95864321,"identity":"b4298605-4e74-4f33-91c8-ab1abb6437da","added_by":"auto","created_at":"2025-11-13 18:48:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":108285,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of NU and URHWG\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7790181/v1/572699b7e5bacad95c6c6f9f.jpg"},{"id":96240924,"identity":"cffec106-64b8-45fc-9c24-0923aadb9e9e","added_by":"auto","created_at":"2025-11-19 07:09:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72674,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive Statistics chart of variables\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7790181/v1/68466413e5e490fe50ad0a43.jpg"},{"id":95864323,"identity":"03e36e97-c0e6-421a-895b-ea457b70bed6","added_by":"auto","created_at":"2025-11-13 18:48:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":611330,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal distribution of NU in China\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7790181/v1/59c7cbedca4079524f8715c5.png"},{"id":96242048,"identity":"762c368f-fcfa-41fb-a804-37ed8d5dc535","added_by":"auto","created_at":"2025-11-19 07:11:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":573752,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal distribution of URHWG in China\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7790181/v1/484fc0c9a1b1d832d0f8e124.png"},{"id":95864326,"identity":"e73b0557-add2-4358-ac9f-125ab7026083","added_by":"auto","created_at":"2025-11-13 18:48:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":105453,"visible":true,"origin":"","legend":"\u003cp\u003eResults of heterogeneity analysis\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-7790181/v1/139a251d450081a83dafe50c.png"},{"id":108809357,"identity":"d311bcd3-1b1a-42ec-913b-31e462e4841d","added_by":"auto","created_at":"2026-05-08 15:52:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2022995,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7790181/v1/38147f6c-9bda-4764-a29f-146ca952a62d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"China’s new-type urbanization is narrowing the urban-rural human well-being gap through heterogeneous pathways while confronting the new challenge of the digital economy","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUrbanization is an inevitable path for the development of all countries worldwide and will remain the most prominent social transformation globally in the 21st century (Liu, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). According to data from the United Nations, the proportion of the global urban population has surged from 30% in 1950 to 57% in 2023, and is projected to exceed 68% by 2050 (UN-Habitat, 2023). As a dynamic process with far-reaching impacts on the economy, society, ecology, and other dimensions, urbanization has long been a focus of global concern (Ahmad, N. et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Moreno-Garc\u0026iacute;a et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, traditional urbanization which characterized by an overemphasis on scale expansion and growth speed has given rise to numerous problems in the economic, social, and ecological spheres (Mishra et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang \u0026amp; Kim, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Salvi \u0026amp; Kumar, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It has brought about negative impacts such as excessive resource consumption and increased carbon dioxide emissions (Zhou et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, it has exerted uncertain effects on the multidimensional human well-being of urban and rural areas, including income levels, accessibility to public services, and rights to a sound ecological environment (Zhao et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn fact, the relationship between urbanization and human well-being, particularly issues such as urban-rural human well-being inequality, has long been a global research hotspot (Udayanga, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Skeggs \u0026amp; Orben, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Many developed countries have gradually achieved equalization of human well-being between urban and rural areas in the process of their urbanization (Collins et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Saqib et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As the world's largest and fastest-paced practitioner of urbanization, China has witnessed its urbanization rate soar from 21.62% in 1983 to 66.16% in 2023 over the past four decades, creating the largest-scale population spatial migration in human history (Zhang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, behind this rapid growth in China's urbanization rate lied complex and severe practical challenges: On the one hand, the accelerated advancement of urbanization has significantly improved urban residents' well-being across dimensions such as income levels, accessibility to public services, and life convenience (Lin \u0026amp; Huang, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). On the other hand, significant gaps persist in rural areas, especially in key well-being dimensions including infrastructure, educational resources, medical services, social security, cultural and recreational facilities, and environmental quality (Liang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These gaps manifest not only as \"hard divides\" in material terms but also as \"soft gaps\" in developmental opportunities, social integration, cultural identity, and other aspects. They have become deep-seated hidden barriers restricting the coordinated and sustainable development of urban and rural societies.\u003c/p\u003e\u003cp\u003eThe Chinese government has long attached great importance to the issue of unbalanced urban-rural development (Zhang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In 2014, it formally proposed \"People-Centered New-type urbanization\", aiming to promote the equalization of human well-being between urban and rural areas (Liu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Distinct from traditional urbanization, new-type urbanization emphasizes a people-centered approach: it not only focuses on economic growth and urban scale expansion but also pursues the synchronized improvement of basic public service, infrastructure, resource utilization efficiency and environmental quality between urban and rural areas. Its core goal is to enable urban and rural residents to share the dividends of development and achieve a higher level of well-being. Relevant studies have shown that for urban areas, new-type urbanization has alleviated the urban heat island effect by increasing urban green spaces, thereby improving the health and quality of life of urban residents (Beele et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, for rural areas, new-type urbanization has led to the loss of farmers' land and pollution of their living environments (Li et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In terms of the connotation of human well-being itself, urban-rural human' well-being is a comprehensive reflection of the happy life state of urban and rural residents, encompassing basic material conditions required for maintaining high-quality life and health, the ability to acquire knowledge, and sound social relationships (VanderWeele \u0026amp; Johnson, 2025). The urban-rural human well-being gap based on this connotation refers to the comprehensive disparity across all the above dimensions of well-being. Nevertheless, most existing studies have only focused on the relationship between new-type urbanization and the urban-rural human well-being gap in a single dimension (e.g., income), and their conclusions remain inconsistent (Peng et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhao, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chen \u0026amp; Hu, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For instance, some studies have indicated that new-type urbanization has narrowed the urban-rural income gap through channels such as industrial structure upgrading, technological innovation, and digital inclusive finance (Hong et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mo et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, other studies have pointed out that due to factors such as urban-biased land development policies and infrastructure gaps, new-type urbanization may also widen the urban-rural income gap (Feng et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, some studies have found an inverted \"U\"-shaped relationship between new-type urbanization and the urban-rural income gap: only after crossing a certain threshold will new-type urbanization shift from widening to narrowing the gap (He \u0026amp; Zhang, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Against this backdrop, questions such as whether China's people-centered New-type urbanization has truly achieved its policy goal of narrowing the urban-rural human well-being gap after more than a decade of development and what its operational mechanism is still require further theoretical construction and empirical research.\u003c/p\u003e\u003cp\u003eBased on the above context, this study intends to address the following three key questions: (1) How to construct a scientific indicator system to evaluate the level of new-type urbanization (abbreviated as \"NU\" in this study) and the urban-rural human well-being gap (abbreviated as \"URHWG\" in this study) in China? What spatiotemporal evolution patterns have they respectively presented? (2) How did NU effect URHWG? Did this effect exhibit urban-rural heterogeneity and spatiotemporal heterogeneity? What is the underlying mechanism? (3) In terms of the effect of NU on URHWG, what roles have the transportation infrastructure development (vigorously promoted in China during the past period) and the digital economy (representing the future development trend) played\u0026mdash;positive or negative? By exploring and answering the above three questions, this study aims to propose optimization suggestions for promoting the long-term and effective narrowing of URHWG in China through NU in the future. Additionally, it intends to provide policy references for other developing countries to achieve rapid and coordinated urban-rural development and the well-being equalization of urban-rural residents.\u003c/p\u003e"},{"header":"2. Theoretical analysis and research hypothesis","content":"\u003cp\u003eAs elaborated in the previous section, China\u0026rsquo;s early-stage urbanization was characterized by being land finance-driven, economic growth-oriented, and city-centric (Feng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This progress of urbanization accelerated the flow of production factors from rural to urban areas, spurred the agglomeration of population and capital in cities, and significantly boosted the economic and social development of China\u0026rsquo;s urban areas. However, it also led to widespread decline in rural areas and exerted negative impacts on the ecological environment of areas surrounding cities. Consequently, URHWG was not narrowed; instead, it widened progressively. Distinct from traditional urbanization, NU exerts effects on URHWG primarily through the following pathways. Firstly, NU promotes the establishment of unified urban-rural markets for human resources and construction land. This facilitates equal employment opportunities for urban and rural workers, ensures that farmers fairly share the land value-added benefits, and thereby narrows the well-being gap between urban and rural residents in the dimension of income. Secondly, NU advances the rational allocation of educational resources, with a priority on tilting such resources toward rural areas. This enhances the quality of compulsory education in rural areas and the level of balanced development in education, narrowing the well-being gaps between urban and rural residents in the dimension of education. Lastly, NU drives the allocation of basic medical and health services to rural areas and optimizes the three-tier rural medical and health service network (covering county, township, and village levels). This enables rural residents to access safe, affordable, and accessible basic medical and health services, thereby narrowing the well-being gap between urban and rural residents in the dimension of health. Based on the above analysis, this study proposes Hypothesis 1 and Hypothesis 2.\u003c/p\u003e\u003cp\u003eH1:NU can significantly advance the well-being level of urban - rural residents.\u003c/p\u003e\u003cp\u003eH2:NU can narrow URHWG.\u003c/p\u003e\u003cp\u003eFurthermore, transportation infrastructure, as a development foundation independent of China\u0026rsquo;s NU evaluation system, plays a pivotal role (Wang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Only a well-developed transportation network can provide guidance and support for NU, and help extend the improvements in residents\u0026rsquo; well-being brought about by NU to the vast rural areas, thereby narrowing URHWG. With the advancement of NU, the comprehensive transportation network and inter-regional trunk transportation network have been continuously improved, and transportation connections among urban agglomerations have been gradually strengthened. This has enhanced the outward transportation conditions of small and medium-sized cities as well as small towns. In particularly, local governments have increased fiscal expenditures on transportation: ordinary national highways now basically cover all counties, national expressways generally serve cities with a population of over 200,000, and road access to every village has been realized in most regions. These developments have significantly promoted the economic and social progress of rural areas and strengthened their connections with surrounding cities and towns, enabling rural residents to share the fruits of NU. This will contribute to the achievement of the goal to equal the well-being of urban-rural residents. Based on this, the study proposes Hypothesis 3.\u003c/p\u003e\u003cp\u003eH3: The effects of NU on URHWG can be positively modulated by transportation infrastructure development.\u003c/p\u003e\u003cp\u003eMeanwhile, as the world enters the era of the digital economy, China has witnessed the rapid development of digital technologies represented by the Internet, artificial intelligence, blockchain, cloud computing, and big data in recent years. The digital economy has not only broken the temporal and spatial constraints between traditional industries and regions but also provided a strong driving force for the coordinated development of urban and rural areas. On the one hand, the digital economy can exert a positive effect through two main pathways amid the advancement of NU: It first realizes urban-rural connectivity by upgrading traditional industrial formats and production methods, thereby enabling more rational and balanced allocation of resource factors during the NU process (Wang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); It applies knowledge, technologies, and other spillover effects from urban areas to agriculture in the context of NU, optimizes the agricultural industrial structure, and promotes rural economic and social development. From this perspective, it helps narrow the urban-rural income gap, and in turn, reduces the URHWG (Lin \u0026amp; Peng, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). On the other hand, due to factors such as the weak digital infrastructure in rural areas and the generally low educational level of rural residents, the urban-rural \"digital divide\" has become increasingly prominent. The existence of this divide may prevent urban and rural residents from fairly sharing the achievements brought by NU on an equal basis, which is not conducive to narrowing the URHWG (Xia et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, this study proposes Hypothesis 4.\u003c/p\u003e\u003cp\u003eH4: The effects of NU on URHWG can be modulated by digital economy with uncertainty.\u003c/p\u003e\u003cp\u003eBased on the above analysis, the theoretical mechanism of the impact of NU on URHWG can be represented by Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"3. Study design","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Selection and measurement of variables\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Independent variable\u003c/h2\u003e\u003cp\u003eThe independent variable in this study is NU. With reference to the NU development indicators proposed in the National New-type urbanization Plan (2014\u0026ndash;2020) and relevant research findings by scholars, this study constructed an indicator system for NU level from four dimensions: population urbanization, basic public services, supporting facilities, and resource and environment. The system comprised 4 primary indicators and 12 secondary indicators (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eindicator system of NU\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary indicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary indicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQuantitative indicator (Unit)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eproperties\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eweight\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePopulation urbanization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrbanization rate of permanent\u003c/p\u003e\u003cp\u003epopulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrban permanent population / resident population in the end of the year (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban population density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrban permanent population/built-up area (people/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eBasic public services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoverage rate of basic endowment insurance for urban permanent population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrban permanent population participating in basic endowment insurance / Urban permanent population (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoverage rate of basic medical insurance for urban permanent population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrban permanent population participating in basic medical insurance / Urban permanent population (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoverage rate of unemployment insurance for urban permanent population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrban permanent population participating in basic unemployment insurance / Urban permanent population (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eSupporting facilities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePopularization rate of public water supply\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePopularization degree of public water supply (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSewage treatment rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCentralized treatment rate of sewage treatment plants (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHarmless treatment rate of domestic waste\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHarmlessly treated domestic waste / domestic waste generated (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePopularization rate of gas supply\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePopularization degree of gas supply (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eResource and environment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer capita construction land area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConstruction land area / urban permanent Residents (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban afforestation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGreen space area / built-up area (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAir quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAir quality index (AQI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNU serves as an objective measure for evaluating the modernization of comprehensive urban development. To avoid subjectivity and one-sidedness caused by human factors when measuring the NU level, this study adopted a combined objective weight method integrating the CRITIC method and the entropy weight method. The CRITIC (Criteria Importance Through Intercriteria Correlation) method is an objective weight method that determines the objective weights of each indicator by considering the contrast intensity and conflict among indicators. However, the single CRITIC method fails to measure the degree of dispersion among indicators. In contrast, the entropy weight method\u0026mdash;another commonly used objective weight method\u0026mdash;can effectively capture the dispersion degree among indicators, thereby making up for this limitation of the CRITIC method. In this study, the average value of the weights derived from the two methods was used as the final weight for the NU indicators.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Dependent variable\u003c/h2\u003e\u003cp\u003eURHWG serves as the dependent variable in this study. To measure the URHWG index, this research drew on the Human Development Index (HDI) proposed by the United Nations Development Programme (UNDP). A URHWG indicator system was constructed using the urban-rural ratio of each indicator (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndicator system of URHWG\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProperties\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAHP weight\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEntropy weight\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eComprehensive weight\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLife quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer capita disposable income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRatio of Engel's coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLife expectancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHealth technical personnel per 1,000 residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical and health institution beds per 1,000 residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudents per full-time teacher in primary and secondary schools\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage years of schooling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIndicators in the table refer to the ratio between urban and rural aeras.\u003c/p\u003e\u003cp\u003eThis study adopted a combination of Analytic Hierarchy Process (AHP) method and entropy weight method to calculate China\u0026rsquo;s URHWG index. The average value of the two was taken as the final comprehensive weight for indicators. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the standardization results and scatter plot fitting of the independent variable (NU) and the dependent variable (URHWG).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3 Moderating variables\u003c/h2\u003e\u003cp\u003e① Transportation development (Tran). Based on the theoretical analysis above, this study employed per capita highway mileage to measure the development of transportation infrastructure.\u003c/p\u003e\u003cp\u003e② Digital economy (Dig). As the digital economy is a general and comprehensive concept that is difficult to measure directly. However, the digital inclusive finance index offers unique advantages benefiting various social classes especially including the vulnerable groups such as small enterprises, farmers, and urban low-income groups (Jin \u0026amp; Zhong, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, with reference to relevant studies, the digital inclusive finance index (2014\u0026ndash;2023) which released by Peking University was adopted to assess the development of the digital economy across Chinese provinces.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.1.4 Control variables\u003c/h2\u003e\u003cp\u003e① Economic development (LnGDP). Considering that rural areas adjacent to cities may be driven by urban economic growth, resulting in a smaller urban-rural income gap, and thus narrowing the URHWG. The natural logarithm of real per capita GDP of each province was employed to measure its economic development.\u003c/p\u003e\u003cp\u003e② Social security (Soc). Social security directly affects residents\u0026rsquo; living standards and serves as the most fundamental service for society. A sound social security system raises the benchmark of residents\u0026rsquo; quality of life, thereby narrowing the gap in human well-being between urban and rural areas. This study measured the social security of each province by the ratio of social security and employment expenditure to local fiscal expenditure.\u003c/p\u003e\u003cp\u003e③ Fixed-asset investment (LnFin). Fixed-asset investment reflects the scale of infrastructure construction and the growth of economic investment. A high level of fixed-asset investment improves residents\u0026rsquo; quality of life, which may in turn reduce the URHWG. Thereby, this study used the natural logarithm of total social fixed-asset investment of each province to measure its fixed-asset investment.\u003c/p\u003e\u003cp\u003e④ Industrial structure upgrading (Ind). Existing studies have shown that industrial structure upgrading narrowed the urban-rural income gap through promoting integrated urban-rural development, and consequently reduces the human well-being gap between urban and rural areas. Therefore, this study measured industrial structure upgrading of each province by the ratio of the added value of the tertiary industry to that of the secondary industry.\u003c/p\u003e\u003cp\u003e⑤ Environmental governance (Env). In the process of rapid urbanization, issues such as industrial pollution and fragile ecological environments affect residents\u0026rsquo; daily lives. A sound environment is conducive to residents\u0026rsquo; physical and mental health, thereby improving their well-being and narrowing the URHWG. The environmental governance in this study was measured by the ratio of the sum of industrial pollution treatment expenditure and forestry investment to local fiscal expenditure.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.1.5 Data sources and descriptive statistics\u003c/h2\u003e\u003cp\u003eConsidering the consistency with the process of new urbanization, this paper set the research period from 2014 to 2023. A total of 30 Chinese provinces, municipalities directly under the central government, and autonomous regions were selected as research units. Xizang Autonomous Region, Hong Kong, Macao, and Taiwan are excluded due to severe data absence. The data are mainly sourced from the following statistical yearbooks for 2015\u0026ndash;2024: China Statistical Yearbook, China Urban Statistical Yearbook, China Urban Construction Statistical Yearbook, China Population and Employment Statistical Yearbook, China Labor Statistical Yearbook, China Environmental Statistical Yearbook, and the statistical yearbooks of individual provinces. For a small amount of missing data, linear prediction and moving average methods are used for interpolation. Furthermore, to eliminate the impact of inflation, price-related indicators (e.g., fixed-asset investment and per capita GDP) are deflated using 2014 as the base year. All variables were standardized to enhance the interpretability and stability of the model. The descriptive statistical results of each variable are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurthermore, this study employs the Variance Inflation Factor (VIF) test and Levin-Lin-Chu (LLC) test to conduct multicollinearity test and stationarity test on all variables. The results were presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The test results showed that: the maximum value of VIF is 4.97 and the average value is 2.23, indicating that there was no severe multicollinearity in the model; the LLC test results rejected the null hypothesis that all cross-sectional units in the panel data have unit roots, which proved that the series of panel data was stationary.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMulticollinearity and panel unit root test results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLLC test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-15.208***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDig\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-10.528***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLnGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.799***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-6.870***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLnFin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-43.093***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTran\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-6.225***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.143**\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.761***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eURHWG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-7.461***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean VIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Model construction\u003c/h2\u003e\u003cp\u003eTo test the direct effect of NU on the URHWG, this study conducted regression using the Pooled Ordinary Least Squares (Pooled OLS) and Fixed Effects (FE) models based on the research hypotheses proposed above. The results showed an F-statistic of 69.73 with a p-value of 0.0000, indicating that the FE model outperforms the Pooled OLS model. Second, the Random Effects (RE) model was employed, followed by the Lagrange Multiplier (LM) test; the results revealed a p-value significantly smaller than 0.01, demonstrating that the RE model also performs better than the Pooled OLS model. Next, time-fixed effects tests were conducted for both the FE and RE models, and the p-values of both tests were significantly smaller than 0.01, confirming the necessity of incorporating time-fixed effects. Additionally, inter-sectional heteroskedasticity test, intra-sectional autocorrelation test, and cross-sectional correlation test were performed on the panel data; all p-values were significantly smaller than 0.01, indicating the presence of heteroskedasticity, intra-sectional autocorrelation, and cross-sectional correlation in the panel data. Finally, considering the above issues (e.g., heteroskedasticity), this study conducted regression by adding clustered robust standard error to the RE model and performs an over-identification test. The results showed a p-value significantly smaller than 0.01, suggesting that the FE model is superior to the RE model. Therefore, this study ultimately adopted the Two-Way fixed effects (TWFE) model for regression analysis, with clustered robust standard error used by default.\u003c/p\u003e\u003cp\u003eThe benchmark regression model is as follows:\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1763059068.png\" style=\"width: 481px;\"\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003ewhere, \u003cem\u003ei\u003c/em\u003e and \u003cem\u003et\u003c/em\u003e represent province and year respectively; \u003cem\u003eURHWG\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e denotes the dependent variable, \u003cem\u003eNU\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the independent variable, \u003cem\u003eControl\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e stands for a set of control variables, \u003cem\u003eα\u003c/em\u003e is the constant, \u003cem\u003eβ\u003c/em\u003e is the coefficient to be estimated for \u003cem\u003eNU\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e, δ\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e represents the regression coefficients of the control variables, \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e denotes the individual fixed effects, \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e denotes the time fixed effects, \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the random error term.\u003c/p\u003e\u003cp\u003eIn line with the hypotheses proposed above, Tran and Dig as moderating variables were selected for regression analysis. The moderating effect model is specified as follows:\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1763059197.png\" style=\"width: 565px;\"\u003e\u003c/p\u003e\u003cp\u003ewhere, \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e represents the moderating variable, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e is the main effect coefficient of NU, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e is the main effect coefficient of the moderating variable, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e is the moderating effect coefficient, other symbols are consistent with those in the TWFE model.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results and analysis","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Spatiotemporal evolution patterns of NU and URHWG in China\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e4.1.1 Spatiotemporal evolution pattern of NU\u003c/h2\u003e\u003cp\u003eThe spatial distribution pattern of NU was illustrated by Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-d. It revealed that the spatial distribution of China\u0026rsquo;s NU level exhibited significant heterogeneity. The level in eastern regions was higher than that in central and western regions. High-level NU has spread from eastern to western regions over time. Specifically, the NU level of most provinces (primarily concentrated in central and western China) was below 0.4 in 2014, while only a few eastern provinces had an NU level exceeding 0.4. By 2017, the NU level of central and western provinces was mainly between 0.4 and 0.5, whereas that of eastern regions had risen to above 0.5. In 2020, the NU level of central provinces had approached that of eastern regions (all exceeding 0.5), while some western provinces remaining in the 0.4\u0026ndash;0.5 range. By 2023, except for Yunnan and Guizhou provinces, all other provinces had achieved an NU level of over 0.5, and some provinces (e.g., Beijing, Shanghai, and Guangdong) even reached an NU level exceeding 0.6. From a local perspective, the average NU levels of eastern provinces in 2014, 2017, 2020, and 2023 were 0.452, 0.516, 0.580, and 0.596 respectively. The high level and rapid growth of NU in eastern regions can be primarily attributed to the following factors: a solid economic foundation, a high degree of openness to the outside world, and a high level of population and industrial agglomeration. Additionally, eastern regions initiated urbanization earlier and possess unique geographical advantages over central and western regions in terms of population quality, public services, and infrastructure construction. These advantages has promoted balanced development across all dimensions of NU, enabling eastern regions to maintain a significant leading edge in NU process for a long time. Meanwhile, the NU level of central and western provinces also showed a steady upward trend during the study period, with the average value increasing from 0.339 in 2014 to 0.543 in 2023. This indicated that the development gap between central/western regions and eastern regions has been continuously narrowing with the in-depth advancement of China\u0026rsquo;s \"Western Development Strategy\" and \"Central China Rise Strategy\". Infrastructure construction, population growth, industrial transfer, and technological progress has all promoted the development of NU in central and western provinces to a certain extent. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee-f presents the temporal variation trends of the annual average NU levels across China as a whole and across eastern, and mid-western regions. The national average NU level increased from 0.380 in 2014 to 0.563 in 2023, indicating an overall upward trend in NU during the study period. Furthermore, the pie charts in figures showed that the number of provinces with an NU level exceeding the national average has increased year by year, reaching 93.3% in 2023. This demonstrated that the implementation of China\u0026rsquo;s new-type urbanization policy has achieved remarkable results.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e4.1.2 Spatiotemporal Evolution Patterns of URHWG\u003c/h2\u003e\u003cp\u003eThe spatial distribution pattern of the URHWG was shown as Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-d. As observed from the figures, the number of provinces with a URHWG exceeding 0.3 has gradually decreased, while the number of provinces with a URHWG below 0.2 has continuously increased. URHWG in China exhibits distinct heterogeneity and agglomeration characteristics in spatial distribution. Provinces with a URHWG below 0.2 have gradually expanded to provinces in central and western regions while it initially concentrated mainly in eastern regions. However, the study also identifies an interesting phenomenon: Beijing and Shanghai, as China\u0026rsquo;s two most developed regions, have consistently maintained a URHWG above 0.3, forming a sharp contrast with their surrounding areas. This revealed that while these regions have achieved rapid economic development, they have also witnessed a widening of the URHWG to a certain extent. In contrast, Xinjiang, which URHWG reached 0.066 in 2023 has become the smallest URHWG among western province despite its relatively underdeveloped economy. This indicated that Xinjiang has achieved a relatively balanced urban-rural human well-being level with a relatively low level of economic development. The URHWG of western provinces have gradually narrowed with the average value decreasing from 0.363 in 2014 to 0.267 in 2023 over time. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee-f illustrated the temporal variation trends of the annual average URHWG across China as a whole and across eastern, mid-western regions. The national average URHWG declined from 0.280 in 2014 to 0.223 in 2023, indicating an overall narrowing trend during the entire study period. Notably, however, the URHWG experienced a slight widening between 2019 and 2021. This phenomenon may be attributed to the global COVID-19 pandemic that emerged in late 2019. During this period, the global socioeconomic development stagnated, leading to a precipitous drop in the income of urban and rural residents and severe threats to their health, which in turn reduced the well-being of both urban and rural residents. Nevertheless, urban residents may have experienced a slighter decline in well-being compared to rural residents thanks to the availability of urban infrastructure, accumulated savings, and better living environments. This discrepancy resulted in the widening of the URHWG during that period. With the effective control of global COVID-19 pandemic, socioeconomic activities gradually recovered, and the URHWG continued to narrow. Similarly, the pie charts in figures showed that the number of provinces with a URHWG exceeding the national average has decreased year by year, dropping to 33.3% by 2023. This indicated that China\u0026rsquo;s goal of narrowing the URHWG has achieved initial success.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Model exploration of NU impact on URHWG\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Benchmark regression analysis\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presented the results of the benchmark regression. Column (1) corresponded to the estimation results without incorporating any control variables. The results showed that the coefficient of NU is significantly negative, indicating that NU significantly promotes the narrowing of the URHWG (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Columns (2) to (6) displayed the estimation results after the stepwise incorporation of control variables. It was observed that even after adding the control variables, the direction of the NU coefficient remains significantly negative, demonstrating that NU still exerted a significant facilitating effect on narrowing the URHWG. Thus, hypothesis 2 (H2) has been verified. In addition, the coefficient of LnGDP was significant at the 10% confidence level and exerted a facilitating effect on narrowing the URHWG, suggesting that the improvement of the overall regional economic level can promote the narrowing of the URHWG. This may be because as the regional economic level advances, the income gap between urban and rural residents may become less pronounced. Interestingly, the coefficients of Ind (industrial structure upgrading) and Env (environmental governance level) were significant at the 5% and 1% confidence levels respectively, but both are positive. This indicated that Ind and Env significantly contribute to the expansion of the URHWG. A plausible explanation for this may that the primary focus of China\u0026rsquo;s industrial structure upgrading and environmental governance enhancement remained on urban areas, with the benefits primarily accruing to urban residents. The impact of these initiatives on rural residents was still insufficient, which consequently led to the expansion of the URHWG. In contrast, the coefficients of the other control variables were not statistically significant, meaning they did not have a significant impact on the URHWG.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBenchmark regression results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplanatory variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.223\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.240\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.245\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.246\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.213\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.203\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.065)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.078)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.084)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.072)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.072)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLnGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.233\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.186\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.190\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.185\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.184\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.124)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.095)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.098)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.092)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.093)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.076)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.075)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.074)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.074)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLnFin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.030)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.212\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.207\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.086)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.052\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.365\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.467\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.434\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.430\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.398\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.389\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.029)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.052)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.053)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.047)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e_a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.448\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eData in()are Cluster robust standard error; *, **, and *** indicate significance at 10%, 5%, and 1% confidence levels, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Endogeneity analysis\u003c/h2\u003e\u003cp\u003eIn order to alleviate endogeneity caused by omitted variables, the time-fixed and individual-fixed effects has been incorporated into the benchmark regression model and controlled for a set of variables. However, there may still exist a bidirectional causal relationship between the URHWG and NU, which could lead to endogeneity in the model and make it difficult to accurately identify the effect of NU on the URHWG. Therefore, instrumental variables (IVs) for NU were employed to address potential endogeneity. NU with a one-period lag was highly correlated with the current NU, while it had no direct correlation with the current URHWG. This satisfied both the correlation and exogeneity conditions of instrumental variables. Thus, NU with a one-period lag was chosen as the first instrumental variable (IV1) for NU. Regions with low relief amplitude are more conducive to urban construction and expansion, as they reduce construction costs and thus tend to have a higher level of NU. However, relief amplitude itself has no direct impact on the URHWG, satisfying the correlation and exogeneity conditions of instrumental variables. Notably, relief amplitude is a time-invariant variable and thus cannot be directly included in the benchmark regression model. To address this, an interaction term between relief amplitude and a time dummy variable was constructed as the second instrumental variable (IV2) for NU.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEndogenous regression results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExplanatory variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIV1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIV2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.256**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.721***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.104)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.156)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF value of the first stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86.34***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.15***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLM statistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.323***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.392**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWald F statistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86.341{16.38}\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.148{11.46}\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e_a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.989\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eData in()are Cluster robust standard error; data in {}are Stock-Yogo weak ID test critical values at 10%; *, **, and *** indicate significance at 10%, 5%, and 1% confidence levels, respectively.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presented the regression results of the endogeneity analysis. First, the F-statistics of the first stage for both instrumental variables (IVs) exceed 10, which rejected the null hypothesis that the IVs are uncorrelated with NU. This confirmed the correlation between the IVs and NU. Second, both IVs passed the unidentifiable test and weak instrumental variable test, rejecting the null hypothesis that the IVs are weak instruments. This verified the validity of the selected IVs. Finally, after addressing the endogeneity, the regression results showed that the coefficient of NU remains significantly negative (-0.256 and \u0026minus;\u0026thinsp;0.721, respectively), indicating that NU still exerted a significant promoting effect on narrowing the URHWG. Compared with the coefficient in the benchmark regression (-0.203), the endogeneity would lead to an underestimation of the promoting effect of NU on narrowing the URHWG.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.2.3 Robustness tests\u003c/h2\u003e\u003cp\u003eTo ensure the robustness of the benchmark regression results, this study conducted robustness tests through the following methods, with the results reported in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e①Replacement of independent variable\u003c/p\u003e\u003cp\u003eThe core goal of people-centered NU is to increase the proportion of the urban population, which can directly and effectively reflect the regional NU level. Therefore, this study re-conducted the benchmark regression using the proportion of permanent urban residents (Urb) as the independent variable instead of NU. The results remain robust, as shown in Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e②Exclusion of municipal sample\u003c/p\u003e\u003cp\u003eConsidering the potential particularities of the four municipalities directly under the central government (Beijing, Tianjin, Shanghai, and Chongqing) in terms of national policy support and funding allocation, this study excluded these municipalities from the sample and re-conducts the benchmark regression. The results remain robust, as presented in Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e③Winsorization\u003c/p\u003e\u003cp\u003eTo avoid sample selection bias, this study performed 5% winsorization on both the upper and lower tails of all variables included in the benchmark regression. After excluding sample selection bias, the results remain robust as presented in Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of robustness tests\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExplanatory variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReplace key variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExcluding municipalities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWinsorization\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.184**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.182**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.074)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.011***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.881***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.300***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.375***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.530)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.401)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.431)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e260.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e300.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e_a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.390\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.609\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eData in()are Cluster robust standard error; *, **, and *** indicate significance at 10%, 5%, and 1% confidence levels, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.2.4 Heterogeneity analysis\u003c/h2\u003e\u003cp\u003eConsidering that the effect of NU on the URHWG may exhibit heterogeneity, this study further examined and revealed the heterogeneous effects of NU on the URHWG from three perspectives: urban-rural dual structure, geospatial location, and time. The heterogeneity characteristics are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e①Urban-rural heterogeneity\u003c/p\u003e\u003cp\u003eThe long-standing urban-rural dual structure is the fundamental cause of the urban-rural gap, leading to significant disparities between urban and rural areas in terms of resources, industries, infrastructure and so on. Therefore, it is necessary to explore the differentiated effects of NU on human well-being under the urban-rural dual structure. Using the URHWG evaluation method mentioned above, this study measured the urban human well-being (UHW) and rural human well-being (RHW) and conducted regressions of UHW and RHW with NU respectively. It was shown that the temporal variation trends of UHW and RHW were basically consistent in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea. They both grew rapidly between 2014 and 2019 and fluctuated around 2020 but then resumed rapid growth. This fluctuation may be attributed to the global COVID-19 pandemic that emerged in late 2019. Notably, UHW recovered significantly faster than RHW after 2021. This phenomenon would be further discussed in the subsequent temporal heterogeneity analysis. Furthermore, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb revealed that the regression coefficients of NU on human well-being were positive in both urban and rural areas, and their confidence intervals do not include 0. This indicated that NU can significantly improve the human well-being of both urban and rural areas. Thereby, hypothesis 1(H1) that proposed in the theoretical analysis has been verified. However, an unexpected finding emerges: the regression coefficient of NU on RHW was larger than that on UHW, meaning NU had a stronger promoting effect on human well-being in rural areas than urban areas. Two plausible explanations for this result were as follows: On the one hand, the implementation of the NU policy had been accompanied by the simultaneous advancement of the Rural Revitalization Strategy. These two policies had generated a synergistic effect, significantly increasing rural residents\u0026rsquo; income, improving the medical and health services, enhancing infrastructure, and optimizing educational resources in rural areas\u0026mdash;thus greatly boosting RHW. On the other hand, the baseline level of UHW in China had long been much higher than that of RHW. During the advancement of NU, although UHW has further improved, its growth rate may be lower than that of RHW. This difference in growth rates ultimately led to the observed results.\u003c/p\u003e\u003cp\u003e②Spatial Heterogeneity\u003c/p\u003e\u003cp\u003eThe significant imbalance of regional development in China may lead to heterogeneous effects of NU on the URHWG. To further explore the differentiated effects of NU on the URHWG across various regions in China, this study divided the sample of 30 provinces into two groups\u0026mdash;eastern regions and mid-western regions\u0026mdash;and conducts regression analysis separately. The results were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec. The regression coefficient of NU was positive but statistically insignificant in eastern regions, whereas it was significantly negative in mid-western regions. This indicated that NU significantly promoted the narrowing of the URHWG in mid-western regions, but its effect on eastern regions was not remarkable. A plausible explanation for this difference was as follows: For eastern regions, the overall urbanization level was relatively high, and the urban-rural gap was already small. As a result, the promoting effect of NU on narrowing the URHWG was weak. Even there may even be a tendency toward widening the URHWG especially in megacities such as Beijing and Shanghai that mentioned in the previous analysis. For mid-western regions, urbanization started at a relatively low level. Additionally, issues such as unequal resource allocation between urban and rural areas, large gaps in economic foundations, and disparities in population quality have led to a relatively large URHWG. With the gradual advancement of NU, the overall population quality has improved, and various resources have been allocated more rationally between urban and rural areas, gradually narrowing the urban-rural gap. Consequently, all dimensions of the URHWG have been narrowed. Thus, NU exerted a more significant effect on narrowing the URHWG in mid-western regions compared with eastern regions.\u003c/p\u003e\u003cp\u003e③Temporal Heterogeneity\u003c/p\u003e\u003cp\u003eIn light of the fluctuations observed after 2019 in the urban-rural heterogeneity results and the potential impact of the global COVID-19 pandemic identified above, the sample period was divided into two phases: pre-2019 and post-2019. The results of regression analysis for each phase were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed. The regression coefficient of NU is significantly negative in the pre-2019 phase but significantly positive in the post-2019 phase. These findings indicated that NU significantly narrowed the URHWG in the pre-2019 period. However, its effect reversed to significantly widen the URHWG after 2019. The outbreak of the global COVID-19 pandemic in late 2019 led to a stagnation in global economic development and severely disrupted the lives of both urban and rural residents. Nevertheless, urban regions outperformed rural regions in terms of economic foundations and infrastructure, giving them stronger resilience to external shocks. Urban residents are less affected than rural residents and demonstrate greater ability to recover from hardships when facing major shocks such as the COVID-19 pandemic. This explains why NU contributed to the widening of the URHWG during the post-2019 period.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e4.2.5 Moderating Effect Analysis\u003c/h2\u003e\u003cp\u003eResults from the benchmark regression and robustness tests have confirmed that NU significantly promotes the narrowing of the URHWG. To further explore the mechanism through which NU influences the URHWG, this study empirically analyzes the moderating effects of transportation development (Tran) and digital economy (Dig) on the mechanism through which NU narrows URHWG. The results were presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMechanism regression results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplanatory variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.093)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.089)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLnTran\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNU\u0026times;LnTran\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.270\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.109)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDig\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNU\u0026times;Dig\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.752\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.920\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.485)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.451)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.558\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e_a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.531\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.403\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe coefficient of the interaction term between NU and Tran (NU\u0026times;Tran) is -0.270, which was significant at the 5% confidence level (as seen in Column (1)). This indicated that a high level of transportation infrastructure can effectively enhance the role of NU in narrowing the URHWG. In other words, the construction of transportation infrastructure significantly strengthened the promoting effect of NU on reducing the URHWG. Accordingly, hypothesis 3 (H3) has been verified. Nevertheless, the coefficient of the interaction term between NU and Dig was 0.002, and it is significant at the 1% confidence level (as seen in Column (2)). This result revealed that with the development of the digital economy, the effect of NU on narrowing the URHWG is significantly weakened and may even reverse to widen the URHWG. That is to say, the development of the digital economy significantly inhibited the promoting effect of NU on reducing the URHWG. From this perspective, hypothesis 4 (H4) has been revised as: the development of the digital economy may pose challenges to China's goal of narrowing the URHWG through NU.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Spatiotemporal heterogeneity of the effects of China's NU on the URHWG\u003c/h2\u003e\u003cp\u003eThe results of the above model analysis indicated that NU significantly narrowed China's URHWG on the whole. However, there existed heterogeneity in the effects of NU on the human well-being of urban and rural areas respectively, which manifested in three dimensions: urban-rural, spatial, and temporal. Firstly, it\u0026rsquo;s the urban-rural heterogeneity. For urban areas, NU enhance urban residents' quality of life, health status, and educational attainment by improving basic public services, infrastructure, living environment significantly. UHW has also been ultimately promoted as a result. This mechanism is intuitive and easy to understand. However, this study has found that NU exerted a stronger promoting effect on RHW than on UHW. From the perspective of NU's connotation, its core lied in being \"people-centered\", where \"people\" include not only urban residents but also rural residents. Thus, China's NU strategy takes into account both urban and rural areas, aiming to achieve the coordinated improvement of human well-being in both sectors. Moreover, NU involves both the renewal of existing urban areas and the construction of new urban areas. For rural areas, the renewal of adjacent old urban areas creates abundant employment opportunities for rural residents and expands sales channels for agricultural products, which enhances the economic vitality of rural areas and narrows the human well-being gap between urban and rural areas in the dimension of life quality. Additionally, NU has improved medical and health resources as well as educational resources in rural areas. Unlike the urban-biased land policies under traditional urbanization, NU adheres to the principle of integrated urban-rural development, promoting the coordination between urbanization and agricultural modernization. At the same time, the coverage of public finance has expanded in rural areas and raised the level of infrastructure and public service security in the process of NU. In terms of medical and health care, NU has promoted the construction of county-level hospitals and improved the \"county-township-village three-tier rural medical and health service network\" (with county-level hospitals as the core, and township health centers and village clinics as the foundation). This network provides rural residents with accessible and affordable basic medical and health services. From the perspective of education, NU advocates the rational allocation of educational resources with a focus on tilting resources toward rural areas. This has promoted the popularization of compulsory education in rural areas and significantly improved the quality and balanced development of rural compulsory education. Of course, the improvement of RHW is not solely attributed to NU. It may also be associated with other national strategies such as the Rural Revitalization Strategy. These policies complement each other and jointly drive the enhancement of RHW.\u003c/p\u003e\u003cp\u003eSecondly, the impact of NU on the URHWG has spatial heterogeneity. This result can be explained by the following factors: Eastern regions have attracted a large amount of capital, talent, and technological resources, giving them significant advantages in urban construction and social production compared with central and western regions. However, the URHWG in eastern regions may have already been relatively small due to their advanced development. As a result, the marginal utility of NU on narrowing the URHWG is not as significant as expected. In contrast, central and western regions have received strong policy support in terms of capital and technology, benefiting from national strategies such as the Western Development Strategy and Central China Rise Strategy. A large number of industries have transferred from eastern regions to central and western regions, which has rapidly raised the NU level in these areas and generated a high marginal utility of NU. Consequently, NU in central and western regions can significantly promote the narrowing of the URHWG.\u003c/p\u003e\u003cp\u003eLastly, it\u0026rsquo;s the temporal heterogeneity. 2019 was identified as a significant threshold for temporal heterogeneity in the effect of NU on the URHWG. Two main reasons may account for this phenomenon: First, the global COVID-19 pandemic broke out in late 2019, leading to a stagnation in economic development and the suspension of production and construction activities. This hindered the rapid advancement of NU and severely disrupted the lives of both urban and rural residents. However, urban areas, with their more solid economic foundations and better infrastructure, demonstrated greater stability and resilience in the face of shocks compared with rural areas. This disparity resulted in a tendency toward the widening of the URHWG after the pandemic. Second, with the rapid advancement of NU in most regions, the marginal utility of NU has gradually diminished. Its effect on narrowing the URHWG has become negligible and even shown a tendency to reverse. Therefore, China's NU should avoid repeating the old path of traditional urbanization that prioritized speed over sustainability, and focus more on improving quality rather than pursuing scale expansion in the future.\u003c/p\u003e\u003cp\u003e5.2 Moderating effects of transportation development and digital economy on the effect of NU on the URHWG\u003c/p\u003e\u003cp\u003eThe results indicated that the rapid development of transportation infrastructure in China had facilitated the role of NU in narrowing the URHWG. In fact, transportation development has been proven by relevant research to be a key component of urban expansion and exerts a positive effect on urbanization (Wu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Akhtar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, transportation infrastructure acts as a vital link for promoting urban-rural integration and rural sustainable development, with a significant influence on the URHWG. Specifically, a higher level of transportation infrastructure enhances connectivity and circulation between urban and rural areas. This facilitates rural residents' access to urban areas for employment, medical care, and education, while also enabling the smooth consumption of agricultural products in cities. Ultimately, it promotes the rational allocation and equitable sharing of public services, infrastructure, and resources between urban and rural regions\u0026mdash;thereby contributing to the narrowing of the URHWG.\u003c/p\u003e\u003cp\u003eHowever, it is interesting to note that the digital economy inhibits the role of NU in narrowing the URHWG. From existing research, it was generally believed that there is a mutually reinforcing relationship between the digital economy and NU. On the one hand, digital technology promotes resource allocation, environmental management, and social equity, thereby advancing more coordinated and sustainable urbanization (Chen, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); On the other hand, NU provides essential infrastructure and market expansion opportunities for digital economy which further stimulate the digital economy in turn (Zhao et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, academic opinions on the relationship between the digital economy and the URHWG remain divided. Some studies argued that the digital economy has a positive effect on narrowing urban-rural gaps in income and human capital, indicating that expanded digital infrastructure and services can promote urban-rural integration. Other studies contend that the digital economy may exacerbate inequalities between urban and rural residents in accessing health care, education, and economic opportunities (Zhao et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consistent with the latter view, this study finds that the digital economy exerts an inhibitory impact on NU's role in narrowing the URHWG. Thus, the digital economy can be regarded as a double-edged sword in the process of NU narrowing the URHWG. With proper guidance to align its development with urban-rural integration, the digital economy can become a powerful tool for narrowing urban-rural gaps and advancing rural revitalization. Conversely, it may aggravate the URHWG by widening the \"digital literacy gap\" between urban and rural residents. From the perspective of narrowing the URHWG, China should continue to prioritize transportation infrastructure construction in the process of advancing NU. For the digital economy, efforts should be made to strengthen its rational popularization in rural areas\u0026mdash;for example, by integrating it with the Rural Revitalization Strategy to promote its systematic development in rural regions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Policy implications\u003c/h2\u003e\u003cp\u003eThis study systematically explored the effects of NU on the URHWG by examining the influential mechanisms, heterogeneous characteristics, and moderating effects. The research findings can provide scientific basis and effective guidance for countries worldwide to promote high-quality development of urbanization and achieve balanced improvement of urban and rural human well-being. The following policy recommendations were proposed based on the empirical results.\u003c/p\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003e5.3.1 Focus on high-quality and coordinated development of urbanization\u003c/h2\u003e\u003cp\u003eFor most countries, the level of urbanization in the early stage tends to be generally low with unbalanced regional development. For instance, this study found that the NU level of eastern provinces in China is generally higher than that of central and western provinces. In this stage, priority should be given to regions with low NU levels in central and western China to promote coordinated development of new-type urbanization across regions. Specifically, differentiated urbanization policies should be formulated based on local conditions, combined with geographical conditions, resource endowments, and comparative advantages. In underdeveloped regions with backward economies and complex terrain, policies should be actively promoted to construct transportation networks, popularize compulsory education, improve infrastructure, and advance ecological revitalization. These measures can enhance population quality and citizenization, thereby accelerating urbanization and narrowing the URHWG. In developed regions with strong economic strength and superior geographical conditions, to ultimately narrowing the URHWG, efforts should be made to improve urbanization quality through policies such as intensive land use, upgrading public service, advancing industrial structure upgrading, strengthening environmental protection and governance, and optimizing infrastructure accessibility. In the later stage, when the overall urbanization level becomes relatively high, reference should be made to the strategies adopted by the above developed regions, with a focus on the high-quality development of new-type urbanization. Promoting urban-rural integration through the equal exchange of factors and the balanced allocation of public resources, and make it as a key development model in future urbanization process. Especially for rural areas with low resilience, it is necessary to effectively improve the comprehensive agricultural production capacity, risk resistance, and sustainable development capacity by promoting agricultural modernization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e5.3.2 Optimize transportation infrastructure construction\u003c/h2\u003e\u003cp\u003eIt was revealed that transportation infrastructure construction significantly facilitates the role of NU in narrowing the URHWG. Therefore, efforts should be made to further improve comprehensive transport channels and inter-regional transportation networks. On the one hand, it is necessary to strengthen the transportation connections between urban agglomerations and large cities, and accelerate the planning and construction of integrated urban regional transportation. At the same time, improving the external transportation of small and medium-sized cities and towns and enhancing their modernization level of transportation are also of great significance. For rural areas, the focus should be on improving the level and coverage of transportation infrastructure, and optimizing the rational planning of transportation networks. For China in particular, the construction of rural road infrastructure should be closely integrated with ongoing policies such as village planning, the integrated development of the primary, secondary, and tertiary industries in rural areas, and rural land institutional reform to achieve scientific and systematic layout and optimization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003e5.3.3 Promote the rational development of the digital economy in rural areas\u003c/h2\u003e\u003cp\u003eThe digital economy has inevitably become the main form of social and economic development in the future. However, this study has found that the digital economy may exert an inhibitory impact on NU's role in narrowing the URHWG. The primary reason is that the current development of the digital economy in rural areas lags far behind that in urban areas, and the gap in digital economy between urban and rural areas has widened the URHWG. Therefore, future efforts should focus on promoting the development of the digital economy in rural areas. Specifically, the following measures should be implemented: Firstly, strengthen the construction of digital economy infrastructure in rural areas and enhance rural residents' digital literacy by expanding the coverage of digital technologies such as artificial intelligence (AI) and 5G networks. Secondly, attach importance to the cultivation of digital talents in rural areas and improve rural residents' ability to apply digital technologies to realize rural digitalization. Thirdly, promote the integration and coordinated development of digital economy and other important development opportunities in rural areas such as the rural revitalization strategy to narrow the digital divide between urban and rural areas.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e5.4 Future research directions\u003c/h2\u003e\u003cp\u003eAlthough this study investigated the relationship between NU and the URHWG using econometric models, the analysis is limited to the provincial level in China. As a result, the research only reflected the relatively macro-level effect of NU on the URHWG, and failed to fully reveal the mechanisms underlying this effect at more granular scales. Theoretically, a higher level of NU tends to exert a stronger attraction on residents (both urban and rural) in surrounding areas, implying the potential existence of spatial spillover effects of NU. Therefore, future studies can focus on refining the research scale to the prefecture-level cities or even smaller units (e.g., county-level). Such research should prioritize exploring the micro-level mechanisms through which NU influences the URHWG and the spatial spillover effects of NU on URHWG.\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThis study constructed a theoretical mechanism and analytical framework for the effects of NU on the URHWG, and proposed corresponding research hypotheses. Taking Chinese provinces as the research units, it evaluated the levels of NU and URHWG, analyzed their spatiotemporal evolution patterns. Subsequently, a two-way fixed effects model was employed to reveal the mechanism through which NU influences the URHWG with a particular focus on discussing the spatiotemporal and urban-rural heterogeneity of these effects. Furthermore, in the context of China's long-term efforts to advance transportation infrastructure construction and the global trend of digital economy development, this study explored the moderating effects of these two variables on the mechanism through which NU narrows the URHWG. The main conclusions are as follows.\u003c/p\u003e\u003cp\u003eFirstly, NU significantly promoted the narrowing of China's URHWG. This conclusion was supported by benchmark regression analysis, endogeneity analysis, and a series of robustness tests. Secondly, the effects of NU on the URHWG exhibited distinct spatiotemporal and urban-rural heterogeneity. Before the outbreak of the global COVID-19 pandemic (pre-2019), NU significantly narrowed the URHWG; however, after 2019, this effect reversed. NU exerted a significant URHWG-narrowing effect in mid-western China, but its effects were insignificant in eastern provinces. At the same time, NU's promoting effect on rural human well-being (RHW) is significantly stronger than that on urban human well-being (UHW). Lastly, from the perspective of moderating effects, transportation infrastructure amplified NU's positive effects in narrowing the URHWG while digital economy inhibits this narrowing effect.\u003c/p\u003e\u003cp\u003eAdditionally, this study has made some interesting findings: In spite of their high level of economic development and rapid urbanization, megacities like Beijing and Shanghai maintained a relatively large URHWG which expose the imbalance between economic growth and urban-rural human well-being. In contrast, Xinjiang (a less economically developed western region) has witnessed a steady narrowing of its URHWG, achieving relatively balanced urban-rural human well-being at a lower economic development level. Besides, both industrial structure upgrading and improved environmental governance are found to widen the URHWG. This indicated that current policies related to industrial development and environmental improvement remain to be improved. Furthermore, the effects of NU enhancing the human well-being has urban-rural heterogeneity, in which NU has stronger promoting effect on RHW compared to UHW. It seemingly aligned with the policy goal of balanced urban-rural human well-being. However, this study's analysis reveals a harsh reality: traditional urbanization severely undermined human well-being in rural areas. Last but not least, the influence of NU on the URHWG fluctuated after 2019, indicating that NU's URHWG-narrowing effect is vulnerable to major public emergencies (e.g., the COVID-19 pandemic). The widening of the URHWG after the pandemic highlights that urban areas have stronger resilience and recovery capacity than rural areas. This suggest that governments should prioritize supporting rural development in the aftermath of such crises.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval\u003c/h2\u003e\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors\u0026rsquo;.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors\u0026rsquo;.\u003c/p\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJun Yang and Enyi Zhao: wrote the main manuscript textXiao Lyu: Designed framework and ideas of the paperXiao Liu: prepared data and figuresAll authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank Key Projects of Humanities and Social Science Foundation of the Ministry of Education in China (21YJC630154) and National Natural Science Foundation of China (42171249).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad N, Raid M, Alzyadat J et al (2023) Impact of urbanization and income inequality on life expectancy of male and female in South Asian countries: a moderating role of health expenditures. \u003cem\u003eHUMANITIES \u0026amp; SOCIAL SCIENCES COMMUNICATIONS\u003c/em\u003e, 10, 552\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAkhtar M, Zaman K, Khan M (2024) The impact of governance indicators, renewable energy demand, industrialization, and travel \u0026amp; transportation on urbanization: A panel study of selected Asian economies. 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SUSTAINABLE CITIES Soc, 66\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"New-type urbanization, Human well-being, Urban-rural gap, influence mechanism, China","lastPublishedDoi":"10.21203/rs.3.rs-7790181/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7790181/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cdiv language=\"En\" class=\"ArticleSubTitle\"\u003eWhether the decade-long development of China\u0026rsquo;s new-type urbanization has genuinely achieved its intended goal of narrowing the well-being gap between urban and rural residents remains a question without certain answer for both theoretical and empirical investigation. To examine the impact of new-type urbanization on the urban-rural human well-being gap, this paper constructed a theoretical analytical framework, in which their levels were evaluated and spatiotemporal evolution patterns were explored. Furthermore, a two-way fixed effects model was employed to quantitatively test the mechanism through which new-type urbanization affected urban-rural human well-being gap. Besides, the moderating effects of transportation infrastructure and the digital economy were also discussed. The results suggested that new-type urbanization in China had significantly narrowed the human well-being gap between urban and rural areas, and the effect presented both urban-rural and spatiotemporal heterogeneity. Transportation infrastructure development enhanced the effect of new-type urbanization in reducing the well-being gap, whereas the development of the digital economy may become a challenge to this narrowing process. Additionally, unexpected findings offered further insights: (1) industrial structure upgrading and higher levels of environmental governance both expanded the well-being gap between urban and rural residents; (2) new-type urbanization improved well-being more strongly for rural residents than for urban ones; and (3) the process of reducing the urban-rural human well-being gap may susceptible to major public events such as the COVID‑19 pandemic, in which rural areas recovering more slowly than urban areas. Based on the research results, the paper proposed policy recommendations to advance future urbanization strategies under the objective of promoting well-being equalization between urban and rural residents.\u003c/div\u003e","manuscriptTitle":"China’s new-type urbanization is narrowing the urban-rural human well-being gap through heterogeneous pathways while confronting the new challenge of the digital economy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 18:48:11","doi":"10.21203/rs.3.rs-7790181/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-20T08:10:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-26T11:53:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-24T14:20:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-23T02:20:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270369160825781897192184935621397531802","date":"2025-12-07T02:27:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265186762415682630445012759682644511645","date":"2025-12-03T03:29:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81470598519042451686600804381425270730","date":"2025-11-24T01:32:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T05:18:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-04T05:17:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-29T08:14:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-16T01:21:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-10-16T01:17:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"04be6fa4-d0db-4f89-aaac-99d966a6cc2f","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57677775,"name":"Social science/Development studies"},{"id":57677776,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-05-08T15:17:05+00:00","versionOfRecord":{"articleIdentity":"rs-7790181","link":"https://doi.org/10.1057/s41599-026-07402-w","journal":{"identity":"humanities-and-social-sciences-communications","isVorOnly":false,"title":"Humanities and Social Sciences Communications"},"publishedOn":"2026-05-02 15:57:34","publishedOnDateReadable":"May 2nd, 2026"},"versionCreatedAt":"2025-11-13 18:48:11","video":"","vorDoi":"10.1057/s41599-026-07402-w","vorDoiUrl":"https://doi.org/10.1057/s41599-026-07402-w","workflowStages":[]},"version":"v1","identity":"rs-7790181","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7790181","identity":"rs-7790181","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}