
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12636-9
10.1016/j.heliyon.2024.e36605
e36605
Research Article
New urbanization construction and city economic resilience-based on multi-period DID tests for 278 cities
Li Fanglin lifanglin@ujs.edu.cn

Diao Ziyu 1047698517@qq.com
⁎
School of Finance and Economics, Jiangsu University, Zhenjiang, Jiangsu, 212013, China
⁎ Corresponding author. 1047698517@qq.com
25 8 2024
15 9 2024
25 8 2024
10 17 e366055 2 2024
29 7 2024
19 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
As a key measure to realize Chinese-style modernization, the construction of new urbanization injects new vitality into China ‘s urban economic growth by building a modern industrial system, and it is also of great significance to improve urban economic resilience. This study examines data from 278 Chinese cities spanning the period 2006–2022, utilizing a multi-period Difference-in-Differences model and a moderating effect model to investigate the impact of new urbanization on cities' economic resilience. The findings indicate that the adoption of modern urbanization significantly enhances cities' economic resilience. Notably, when examined from the perspectives of geographical location, urban scale, urban agglomeration, and urban economic development level, the impact of new urbanization is particularly pronounced in the eastern region, small cities, non-urban agglomeration cities, and cities with lower levels of economic development. The mechanism test demonstrates that new urbanization affects cities’ economic resilience by fostering technological innovation and upgrading the industrial structure. Essentially, technological innovation and industrial restructuring serve as intermediaries in fortifying economic resilience through the implementation of new urbanization. As a result, recommendations are formulated to bolster the resilience of urban economies.

Highlights

• The positive impact of new urbanization on the economic resilience of cities is explored.

• A quasi-natural experiment using "new urbanization pilot policies" was used to determine causal effects.

• Explored the heterogeneous effects of new urbanization on urban economic resilience.

• Economic agglomeration and population agglomeration development are used as channels.

• The spatial spillover effect of new urbanization on urban economic resilience is explored.

Keywords

New urbanization
Economic resilience
Technological innovation
Industrial structure upgrading
==== Body
pmc1 Introduction

In recent years, the increasing geopolitical risks have brought unprecedented severe challenges to the global economy. As an open economy, China ‘s economic development has inevitably been affected to a certain extent. In order to ensure the steady operation and long-term development of China ‘s economy, the Chinese government attaches great importance to and emphasizes the cultivation of strong economic resilience, that is, the ability of China ‘s economy to quickly recover and maintain continuous growth in the face of economic crisis. In view of the core position of cities in China ‘s economic system, the development level of urban economic resilience has undoubtedly become a key variable affecting the development of China ‘s overall economic resilience. Therefore, how to effectively enhance the resilience of urban economy and promote the sustainable development of urban economy is of great strategic significance for realizing the high-quality development of China ‘s economy.

The core concept of new urbanization, a pivotal initiative aimed at bridging the urban-rural gap in China, is to emphasize the comprehensive advancement of individuals and introduce a fresh approach to urbanization that seamlessly merges industries with urban areas [1]. This is achieved through strategic urban industrial planning and enhanced coordination and mutual progress among cities. This process not only enhances the optimization of cities' industrial structure and boosts production efficiency [2], thereby revitalizing China's urban economic growth, but also greatly enhances the resilience of the urban economy, ensuring its robust progress even in the face of external shocks. Therefore, undertaking a thorough investigation into the influence of China's recent urbanization endeavors on urban economic resilience and its fundamental mechanisms is of paramount theoretical and practical significance in promoting the sustainable, flourishing, and stable development of urban economies.

The contribution of this paper lies in two aspects: First, this paper takes the pilot policy of new urbanization as a quasi-natural experiment and applies a multi-period DID model to deeply investigate the effect of new urbanization on the economic resilience of cities. Second, to further explore the mechanisms through which new urbanization affects urban economic resilience, we intend to investigate the role of new urbanization in urban economic resilience from the perspectives of technological innovation and industrial structural upgrading, using the mediation effect model.

The remaining content of this article is arranged as follows: the second part is the literature review section, the third part is the research hypothesis section, the fourth part is the research design section, the fifth part is the empirical analysis section, and the final part is the conclusion and recommendations module.

2 Literature review

The present study focuses on urban economic resilience, with a primary focus on its conceptual understanding, measurement, and influencing factors. Initially, the term “resilience” originated in the field of ecology, with Holling defining ecological resilience as the ability of a system to bounce back to a stable state following a disturbance [3]. Scholars have extended and inferred the concept following this theory [4,5] Hundt and Holtermann contend that economic resilience pertains to a regional economy's capacity to rebound from a shock and return to its previous state of growth [6]. Additionally, urban economic resilience is frequently assessed in terms of development. Martin initially introduced the sensitivity coefficient as a measure of economic resilience's developmental level within a region [7]. Building on Martin's work, Faggian, Gemmiti developed a sensitivity index model to assess the economic resilience level in the Italian region, with the employment rate as the primary indicator [8]. Numerous scholars have evaluated regional economic resilience by establishing a comprehensive index system that considers three crucial aspects: resistance, resilience, and renewal [9,10]. Some researchers have explored the influence of emerging businesses on the regeneration and reconstruction of urban economies, highlighting the importance of forging new paths for growth in enhancing regional economic resilience [11]. Additionally, several academics have delved into the determinants that affect economic resilience. Rizzi, Graziano conducted a preliminary study on the factors influencing economic resilience in the European NUTS-2 region [12]. Their research revealed that the presence of economic disparities within the region has a detrimental effect on its economic resilience. Kitsos and Bishop argued that the progression of economic resilience in urban areas can be influenced by factors like the degree of urbanization, demographic makeup, and geographical location [13]. Martin and Sunley discovered that policies promoting regional integration play a vital role in strengthening regional ties and fostering a more resilient economic framework, ultimately enhancing economic resilience [14]. Wang and Ge illustrated that spatial correlation networks for economic resilience arise from factors such as geographic disparities and variations in human capital distribution [15]. They substantiate their claims by constructing correlation networks that are rooted in economic resilience.

New urbanization in China represents a unique concept that markedly diverges from urbanization patterns observed in other nations. This approach places particular emphasis on the advancement of small towns as the focal point of the urbanization process [16]. The development of new urbanization has faced various challenges since its inception. The idea of urbanization emerged in the early stages of reform, aiming to spur the rural population's migration to urban areas by fostering the expansion of non-agricultural sectors. Nevertheless, this rudimentary development strategy has led to inefficiencies stemming from the dispersed nature of small towns [17]. After the 18th Party Congress, the Chinese government unveiled a new urbanization policy emphasizing the attainment of balanced growth across towns. This policy prioritizes the advancement of small towns [18,19] with the goal of fostering high-quality urban development [20,21]. Within the framework of new urbanization initiatives, experts have initiated the assessment of its developmental stage. Initially, the evaluation relied solely on the urbanization rate, specifically the demographic transition to urban living [22,23]. However, this singular metric poses challenges in providing an accurate and impartial depiction of new urbanization's progress. Notably, it overlooks critical influences like employment and environmental factors [24]. Consequently, some scholars have proposed a comprehensive evaluation index system to more fairly assess new urbanization across four pivotal dimensions: economic, demographic, social, and environmental considerations [25]. In evaluating the economic dimension of new urbanization, essential metrics such as per capita GDP, fixed asset investment, added value of the tertiary sector, and total retail sales of social consumer products are commonly employed to gauge its economic ramifications [26]. In terms of the demographic aspect, indicators like the proportion of the urban population are pivotal [27,28]. Social aspects are evaluated through three core dimensions: access to basic necessities, dignity and capacities, and fundamental health considerations [29,30]. The environmental component is examined using indicators like green space coverage in urbanized areas, wastewater and industrial pollution emissions, and the efficiency of domestic waste collection [31].

There is a notable gap in the existing literature concerning the relationship between new urbanization and economic resilience. While the majority of extant studies concentrate on the linkage between new urbanization and urban resilience, as well as new urbanization and economic growth. Scholars predominantly employ the coupling coordination degree methodology to explore the correlation between new urbanization and urban resilience. Gao and Chen put forth a comprehensive conceptual framework, delineating new urbanization and urban resilience into three interconnected subsystems: economic, social, and ecological [32]. Examining 14 prefectural-level cities in Liaoning Province from 2009 to 2019, they assessed the fluctuating levels of dynamic coupling and coordination across these subsystems. In a similar vein, Xiong, Li underscored the crucial role of urbanization in enhancing the efficacy and quality of urban resilience as they unraveled the coupling and coordination coefficient between urbanization and urban resilience [33]. Scholars have studied different urban agglomerations. Peng, He conducted a study on the urban agglomeration of the Yangtze River Delta (YRD) and observed a growing level of coordination among different areas within the region, which marks the transition from different degrees of differentiation to regional integration [34]; Wang, Yuan focused on the Chengdu-Chongqing Economic Circle and identified a diagonal development pattern in both areas [35]. Moreover, the correlation between urbanization and economic advancement has been an area of scholarship exploration. Some studies postulate that urbanization holds the potential to stimulate perpetual economic expansion [36] and have identified spatial spillover effects of new urbanization on economic growth when examined from a spatial outlook [37]. Turok and McGranahan delved into the implications of urbanization on economic growth in African and Asian nations, underscoring the impact of institutional frameworks and the accessibility of public amenities on this interrelation [38]. Nevertheless, some scholars advocate a more prudent standpoint, asserting that the enduring influence of urbanization on economic growth is more pronounced in comparison to the transient effect [39], while dissenting views suggest the absence of such effects in the short term [21,40]. Only a minority studies suggest that urbanization may exacerbate income disparities between urban and rural populations, leading to a widening gap between the affluent and the impoverished, potentially impeding the rate of economic expansion [41].

Upon review, it is evident that the research on economic resilience and new urbanization is relatively abundant, with a focus on the conceptual connotations and measurements of economic resilience and new urbanization. However, there is insufficient discussion regarding the relationship between new urbanization and economic resilience: (1) Existing studies have mainly explored the impact of new urbanization on economic growth, indicating inconclusive effects, and some literature has examined the role of new urbanization in urban resilience from the perspective of coupling coordination. Few studies have directly investigated the impact of new urbanization on urban economic resilience; (2) Currently, the literature has not delved into the underlying mechanisms of how new urbanization affects urban economic resilience, failing to clarify the impact pathways of new urbanization on urban economic resilience.

3 Research hypotheses

As a comprehensive and systematic development strategy, the New Urbanization significantly enhances the resilience of urban economies [35]. Its impact is primarily manifested in two aspects:

Firstly, in terms of the production factor mobility mechanism, new urbanization, with human urbanization at its core, has propelled a high-quality agglomeration of population and industries in urban areas [42]. This agglomeration has not only optimized the allocation of production factors but also promoted market-oriented reforms of factor prices through the mechanism of market integration, rectifying price distortions and accelerating the maturation of factor markets [43]. In this process, new urbanization has exhibited its "selection effect," transforming and optimizing traditional industrial development models, guiding the flow of production factors to sectors with higher productivity and returns, thereby enhancing the overall efficiency and resilience of urban economies.

Secondly, in terms of the consumer spending enhancement mechanism, new urbanization has unleashed tremendous consumption potential by promoting the urbanization of agricultural migrant populations [44]. This demographic shift has not only increased the scale of consumer demand but also improved consumer psychology and expectations through enhancing the quality of urbanization, promoting the upgrading of consumption structures and the transformation of consumption concepts. Concurrently, new urbanization has propelled the transformation of employment structures and the optimal allocation of labor resources [45], providing urban residents with more high-quality employment opportunities and significantly increasing their wage income, further enhancing consumer capabilities and the certainty of expected income.

Furthermore, it is noteworthy that while the new urbanization enhances the resilience of urban economies, it also promotes the improvement of urban social functions and the optimization of spatial layout. This comprehensive development not only strengthens cities' ability to resist external shocks but also lays a solid foundation for their long-term, stable, and sustainable development. Based on this analysis, the hypothesis is proposed.H1 The construction of new urbanization can significantly improve the level of economic resilience of cities.

New urbanization construction significantly contributes to the promotion of urban innovation. Through investment in both traditional and new infrastructure, new urbanization not only enhances the physical environment of cities but also elevates the overall quality of urban life [46]. This kind of setting plays a crucial role in attracting talented individuals with fresh ideas and facilitates the aggregation of valuable resources for fostering creativity. Consequently, it results in a continuous improvement in the level of innovation within metropolitan areas. Urban innovation platforms have witnessed consistent growth and enhancement as a direct result of the implementation of new urbanization strategies. The emergence of modern urbanism has created an innovative ecosystem that fosters the free flow of factors and the efficient allocation of resources for innovation, utilizing national autonomous innovation demonstration zones, high-tech industrial development zones, and other advanced innovation platforms. Within such environments, collaborative interactions among stakeholders in innovation are more prevalent. Consequently, innovation activities become more vibrant and dynamic, leading to a higher level of innovation vitality in urban settings [47].

Enhancing innovation plays a crucial role in fortifying the economic resilience of cities. The theory of endogenous economic growth suggests that technological innovation can enhance production methods and organization, resulting in improved resource allocation and increased production efficiency [48]. Such progress not only advances the economic development of a city but also strengthens the capacity of the economic system to endure uncertainties. In times of economic upheavals, efficient resource allocation enables quick adaptations in production structures, thereby reducing losses and facilitating a rapid restoration of stability in the economic system. Consequently, the following research hypothesis is proposed.H2 New urbanization can enhance urban economic resilience by improving the level of technological innovation.

The construction of new urbanization serves as a pivotal driver for the advancement of cities' industrial framework towards modernization. Effective policy guidance and market mechanisms are essential for facilitating the transformation and upgrading of traditional industries towards high-end, intelligent, and sustainable pathways. This strategic initiative is aimed at fostering a diversified and contemporary industrial system, with a particular emphasis on emerging industries. The overarching objective is to provide a robust impetus for the high-quality development of the urban economy [49]. Moreover, the modern urbanization paradigm underscores the importance of strategically organizing industries. During the city's spatial planning process, a comprehensive analysis is conducted to assess the distinct characteristics and requirements of each industry, with the ultimate goal of achieving a balance between concentration and dispersion of industrial development [50]. Such a layout enables the harnessing of synergistic effects among industries, enhances resource utilization efficiency, lowers production costs, and ultimately heightens the competitiveness of industries. Furthermore, it promotes coordinated development among different regions within the city, facilitating a scenario of mutual advantages and shared growth.

The enhancement of the industrial structure plays a vital role in fortifying the resilience of the urban economy. Through the progression of the industrial structure, the urban economy cultivates a varied industrial system, augmenting the growth potential and bolstering its capacity to mitigate risks associated with dependence on a single industry [51]. In the event of a particular industry's disruption, alternative industries can swiftly adapt and provide complementary support, collectively mitigating risks and ensuring the stable operation of the city's economy. Moreover, the industrial structure upgrade entails significant adjustments to the city's economic framework, optimizing resource allocation and ultimately boosting economic efficiency [52]. These adaptations enable the urban economy to respond more flexibly to external disturbances, recover promptly, and sustain steady growth [53]. Therefore, the industrial structure upgrade not only acts as a critical method to facilitate the high-quality advancement of the urban economy but also serves as a pivotal strategy to enhance its resilience. Consequently, this study proposes the following hypothesis.H3 New urbanization can enhance urban economic resilience by promoting industrial structure upgrading.

4 Research design

4.1 Model specification

4.1.1 Benchmark regression model

The pilot policy for China's new urbanization was first implemented in 2014, followed by the release of the second and third batches of pilot city lists in 2015 and 2016, respectively. This formed a quasi-natural experimental environment consisting of treatment and control groups. The Difference-in-Differences (DID) model, as an important tool for evaluating the effects of economic policies, reveals the impact of these policies by comparing the differences between the treatment and control groups. Therefore, the multi-period DID model can be used to assess the net effect of the new urbanization pilot policy on urban economic resilience, thereby verifying hypothesis H1. Considering that the implementation of the new urbanization pilot policy did not start at the same time but spanned multiple time periods, this paper intends to construct a multi-period DID model to identify the impact of new urbanization on urban economic resilience. The specific model is as follows:(1) UERit=γ0+γ1treatit+βcontrolsit+εit

Where i and t represent city and time respectively; γ0 is a constant term; UER is an explained variable indicating the level of economic resilience of city i in year t; treat is the core explanatory variable in this paper, representing a dummy variable for new urbanization, with treatit = 1 indicating that city i has already implemented a new urbanization policy in year t and beyond, and treatit = 0 indicating that city i has not implemented a new urbanization policy in year t; The control variables are a series of control variables, and εit is the nuisance term that changes with i and t.

4.1.2 Mediating effect model

As a statistical method, the mediating effect model is used to explore whether the influence of independent variables on dependent variables is transmitted through one or more mediating variables. In order to test the previous hypotheses H2 and H3, that is, to explore the possible technological innovation and industrial upgrading effects in the process of new urbanization on urban economic resilience, this paper refers to the research method of H Yi et al. [54], and constructs the mediating effect model as follows:(2) Mit=δ0+δ1treatit+γcontrols+μi+λt+εit

Where Mit represents the mediating variables innov and is.

4.2 Data selection

4.2.1 Explained variable

The core explained variable is urban economic resilience (UER). According to Martin [7], the sensitivity index is used to measure the level of urban economic resilience in the long term, based on the evolutionary theory. It is calculated by comparing the actual change in the gross regional product with the expected change in the respective cities. The specific formula for calculating the sensitivity index is shown in Equation (1).(3) UER=Δci−(Δci)e(Δci)e,Δci=Δcit+k−Δcit,(Δci)e=cit⋅dt+k

Where Δci is the change in urban GDP, (Δci)e is the expected change based on the change in real GDP, and dt+k is the rate of change in real GDP.

4.2.2 Core explanatory variables

The core explanatory variable is the new urbanization pilot policy (treat), depicted as a binary variable. Initiated in late 2014, the new urbanization policy led to the designation of three rounds of new urbanization pilot cities in 2014, 2015, and 2016. Preceding the pilot policy year, a value of 0 is ascribed, while the designated new urbanization pilot cities are assigned a value of 1 during that year.

4.2.3 Mechanism variables

Technological innovation (innov) is assessed by the number of patents granted per 10,000 people. Patent itself is to protect the power of technology inventors, and is a form of expression directly related to the results of technological innovation. The number of patents granted rather than the number of patent applications is chosen because the patent granting process has a certain screening effect, avoiding the overestimation of the level of technological innovation by patent applications. Industrial structure upgrading(is), as described by Xue [55] and Lin [56], involves the use of the vector pinch angle method to quantify the extent of improvement in the industrial structure. Firstly, a set of three-dimensional vectors X0=(χ1,0,χ2,0,χ3,0) is set, in which each component corresponds to the proportion of the value-added of each industry in the GDP, and then the angles θ1,θ2,θ3 between the vector X0 and the vectors X1=(1,0,0),X2=(0,1,0),X3=(0,0,1), which is arranged according to the industrial hierarchy, is calculated to find out the index of upgrading of industrial structure W.(4) θj=arccos(∑i=13(χi.j⋅χi.0)(∑i=13(χi.j2)1/2.∑i=13(χi.02)1/2),j=1,2,3

(5) W=∑k=13∑j=1kθj

4.2.4 Control variables

In addition to the macro policy of new urbanization, urban economic resilience is influenced by various factors [5,57]. This study incorporates four control variables: Government intervention (gov) reflects the profound impact of public policy and resource allocation on economic resilience, characterized primarily by the ratio of general government fiscal expenditure to GDP [58]. Information infrastructure (info), the cornerstone of modern communication technologies, signifies the accessibility and utilization of these technologies [59], quantified specifically by the number of cell phone subscribers per capita. Green development (green) elucidates the influence of ecological sustainability on economic resilience [60], manifested conspicuously through the urban greening coverage rate. The degree of urban openness (open) reflects the level of connectivity between a city and its external economic environment [61], measured by the proportion of total import and export volume to GDP.

4.3 Data sources

This study incorporates data from 278 cities in China spanning the period from 2006 to2022.1 The compilation of pilot cities for new urbanization is carried out manually, following the guidelines of the National Development and Reform Commission's comprehensive pilot program. Data on the number of patented inventions granted were obtained from the Chinese Research Data Services (CNRDS). Additional variables are sourced from publications like the China Urban Statistical Yearbook and China Energy Statistical Yearbook. Missing values are imputed using linear interpolation methods (see Table 1).

5 Empirical analysis

5.1 Parallel trend hypothesis test

Before conducting multi-period Difference-in-Differences model analysis, it is essential to confirm a consistent trend in both the ex-ante treatment group and the control group to fulfill the criteria of the parallel trend test. The examination of Fig. 1 reveals that none of the variables exhibit statistical significance pre-policy adoption. During the first four periods post-implementation, these variables continue to show insignificance. However, in the subsequent period, the variables achieve statistical significance. This outcome may be linked to a temporal delay in the policy's impact, ultimately meeting the parallel trend test criteria.Fig. 1 Parallel trend test.

Fig. 1

5.2 Benchmark regression analysis

This study utilizes the multi-period Difference-in-Differences model with double fixed effects to investigate the impact of the new urbanization policy on a city's economic resilience. Control variables are systematically incorporated into the regression analysis to ensure the dependability of the benchmark results, as presented in Table 2. The results reveal a significant positive correlation, at the 1 % level, between the main explanatory variables and the outcome, regardless of the control variables included. This finding supports hypothesis H1 by demonstrating the favorable influence of the new urbanization pilot policy on urban economic resilience [62]. The implementation and advocacy of this policy can enhance cities' economic diversity and stability, contributing to bolstering economic resilience.Table 1 Descriptive statistics.

Table 1Variable	Obs	Mean	Std.dev.	Min	Max	
treat	4726	0.154042	0.361027	0	1	
UER	4726	0.437718	1.348430	−3.3443	6.0922	
fin	4726	2.023008	1.000595	0.0144	21.6035	
gov	4726	0.185551	0.102815	0.0426	1.5129	
info	4726	0.867563	0.786589	0.0296	10.1653	
green	4726	0.395223	0.123880	0.0039	3.8664	
open	4726	0.921902	3.967649	0.0018	30.5798	
innov	4726	1.071829	2.654987	−0.0644	44.5120	
is	4726	7.977202	0.429401	0.0000	9.0770	

Table 2 Benchmark regression results.

Table 2	(1)	(2)	(3)	(4)	(5)	
treat	0.1902***	0.1545***	0.1541***	0.1503***	0.1501***	
(3.6042)	(2.9283)	(2.9144)	(2.8361)	(2.8330)	
gov		−1.9728***	−1.9739***	−1.9666***	−1.9663***	
	(-6.8281)	(-6.8278)	(-6.8010)	(-6.7995)	
info			−0.0063	0.0021	0.0021	
		(-0.1211)	(0.0409)	(0.0405)	
green				−0.1434	−0.1443	
			(-1.1085)	(-1.1152)	
open					0.0020	
				(0.6381)	
_cons	0.5288***	0.7622***	0.7651***	0.8087***	0.8076***	
(10.3773)	(12.4656)	(11.6500)	(10.5611)	(10.5432)	
city/year	yes	yes	yes	yes	yes	
N	4726	4726	4726	4726	4726	
R2	0.607	0.611	0.611	0.611	0.611	
Note: *** represents significant at the 1 % level, ** represents significant at the 5 % level, * represents significant at the 10 % level, t-statistic in parentheses, same as the table below.

5.3 Robustness tests

5.3.1 Placebo test

To identify potential confounding variables in the benchmark regression findings, a placebo test is carried out in accordance with the methodology of Liu and Lu [63]. Random samples are drawn for the policy timing and treatment group, leading to the establishment of the policy year and a virtual treatment group. This process is reiterated 500 times to execute the benchmark regression. The kernel density plot illustrated in Fig. 2 showcases the distribution of the estimated coefficients on the x-axis and their corresponding P-values on the y-axis. The plot clearly illustrates that a majority of the estimated coefficients diverge from the original benchmark regression value of 0.1501. Moreover, a large proportion of the estimated P-values surpass the threshold of 0.1. Consequently, the robustness of the benchmark regression model in this study can be ascertained.Fig. 2 Placebo test.

Fig. 2

5.3.2 Bacon decomposition

Goodman-Bacon argued that in a multi-period Difference-in-Differences procedure [64], the assumption of uniform treatment for each individual exposed to a policy shock simultaneously is not fulfilled, potentially leading to erroneous outcomes. To address this issue, Goodman-Bacon introduced the Bacon decomposition method to evaluate the bias resulting from the assumption of homogeneous treatment. This method breaks down the overall average treatment effect into a weighted average treatment effect by utilizing samples from three distinct control groups, allowing for an examination of the contribution of the treatment group (new treatment vs. never treated group) to this effect. The outcomes of the Bacon decomposition analysis are detailed in Table 3. Specifically, column (1) of the table presents the regression results with control variables, while column (2) displays the regression outcomes without these controls. The table reveals that the average treatment effect weight for individuals who have never been exposed to the treatment, acting as the control group, is notably high at 95 % and 96 % in columns (1) and (2), respectively. These results bolster the reliability of the regression findings in the study.Table 3 Bacon decomposition.

Table 3	(1)	(2)	
Aggregate treatment effect	
Treat	0.150**(0.022)	0.190***(0.005)	
Bacon's Decomposition 1: Different Timing Points for Treatment Effects as Control Groups (Timing Groups)	
coefficient	−0.0656	0.3345	
Percentage of overall weight	0.0365	0.0356	
Bacon's Decomposition 2: Individuals never subjected to treatment effects as controls (Never vs Timing)	
coefficient	0.1990	0.1947	
Percentage of overall weight	0.9458	0.9644	
Bacon Decomposition 3: Between-group effects from control variables (Within)	
coefficient	−2.2970	/	
Percentage of overall weight	0.0176	/	

5.3.3 Other robustness tests

This study employs five methods to test the robustness of the previous benchmark regression conclusions.

5.3.3.1 Endogeneity test

The study addresses the issue of reverse causation between new urbanization and economic resilience by employing the instrumental variable method to test for endogeneity. Following Deng, Huang [65], this paper chooses the average geographic slope of each city as the instrumental variable for new urbanization. Cities with steeper slopes generally have more complex topography, which can hinder new urban development. However, the geographic slope itself does not directly relate to economic resilience, making it a suitable instrumental variable for the study. The average geographic slope of cities is transformed into panel data using the method by Nunn and Qian [66], which involves multiplying it with time dummy variables. The regression results are presented in column (1) of Table 4. The 2SLS estimation indicates a significantly positive coefficient of new urbanization on cities’ economic resilience. This finding is supported by passing both the non-identifiable test and the weak instrumental variable test, demonstrating the robustness of the regression results against endogeneity concerns.Table 4 Robustness test.

Table 4	(1)	(2)	(3)	(4)	(5)	(6)	
2SLS	Control High-dimensional fixed effects	Bilateral truncation	PSM-DID	Dual machine learning	Controlling the impact of the epidemic	
treat	1.6292***	0.0936*	0.1401***	0.1217*	0.1185**	0.2295***	
(6.4171)	(1.8811)	(2.6511)	(1.8278)	(2.3006)	(4.1912)	
gov	−6.1525***	−1.0487***	−2.8619***	−2.6129***		−2.4061***	
(-13.7813)	(-3.8031)	(-8.2451)	(-6.9317)		(-7.9653)	
info	0.2832***	−0.0060	−0.0033	0.0204		−0.0266	
(4.8780)	(-0.1285)	(-0.0446)	(0.3005)		(-0.3421)	
green	0.2840	−0.1687	0.2229	0.0570		−0.2046	
(1.4069)	(-1.6012)	(0.8452)	(0.1762)		(-1.6442)	
open	0.0037	0.0018	0.0021	0.0028		0.0014	
(0.7305)	(0.7131)	(0.6518)	(0.5379)		(0.4614)	
_cons	−0.4066	0.0936*	0.1401***	0.9203***	−0.0005	0.8931***	
(-1.1464)	(1.8811)	(2.6511)	(5.9213)	(-0.0406)	(11.3457)	
unidentifiable tests	389.482
(0.000)						
weak instrumental variable tests.	399.045
(16.38)						
city/year	yes	yes	yes	yes	yes	yes	
N	4726	4679	4726	3436	4726	3892	
R2	−0.011	0.797	0.613	0.626		0.260	
Note: P-values in parentheses for unidentifiable tests; 10 % threshold for weak instrumental variable tests in parentheses for weak instrumental variable tests.

5.3.3.2 Controlling for high-dimensional fixed effects

To consider the interconnectivity among cities within the same province, an extra analysis is performed to control for province fixed effects, while also incorporating individual effects. These results are detailed in column (2) of Table 4, displaying a coefficient of 0.0936 for the core explanatory variables. This value signifies a substantial positive association, reinforcing the credibility of the benchmark regression results.

5.3.3.3 Bilateral truncation processing

To address the influence of extreme values and outliers on the empirical results, the study utilizes bilateral truncation processing. This technique diminishes the impact of extreme value errors by excluding the top and bottom 1 % of the data in the sample. The findings are consistent with the benchmark regression, demonstrating a notable positive relationship in the coefficient of new urbanization.

5.3.3.4 PSM-DID model

The formulation of initial regulations for new urbanization may lack randomness, potentially resulting in sample self-selection bias. To mitigate this bias, the study replaces the regression method with the PSM-DID model in causal inference, revealing a robust positive impact of the key explanatory variables and reinforcing the validity of the earlier conclusions.

5.3.3.5 Dual machine learning

The integration of random forests and DID effectively alleviates the “curse of dimensionality” arising from redundant control variables and the bias caused by the absence of control variables. The empirical findings, shown in Table 4, column (5), reveal markedly positive coefficients for the main explanatory variables, consistent with the benchmark regression.

5.3.3.6 Controlling the impact of the epidemic

In light of the profound economic recession triggered by the COVID-19 pandemic on a global scale, this situation poses an unprecedented severe challenge to the stability of urban economic resilience. Consequently, to precisely disentangle the incidental disturbance factors arising from the pandemic period, this paper excludes data from 2020 to 2021 and reconstructs the regression analysis framework. The results indicate that even after the influence of the pandemic is eliminated, the baseline regression outcomes remain statistically significant, thereby demonstrating their robustness.

5.4 Heterogeneity analysis

In order to further elucidate the impact of new urbanization on the resilience of urban economies, this study is based on factors such as geographic location, urban scale, urban agglomeration, and urban economic development level to explore the differential impact of the pilot policies of new urbanization on the resilience of urban economies.

5.4.1 Geographic location heterogeneity

Compared to the central and western regions, the eastern region, as the core growth area of the Chinese economy, bears dense industrial clusters and human resources. These factors collectively promote its rapid development of new urbanization, leading to a significantly higher level of urbanization in the eastern region compared to the central and western regions. Therefore, the role played by the pilot policy of new urbanization in the economic resilience construction of eastern cities is likely to exhibit significant heterogeneity compared to the central and western regions. In this paper, referring to the research of Hao et al. [67], the samples are divided into eastern and central-western regions for regression analysis, and the regression results are shown in columns (1) and (2) of Table 5.Table 5 Heterogeneity test of geographic location and urban scale.

Table 5	(1)	(2)	(3)	(4)	(5)	
East	Midwest	Large	Medium	Small	
treat	0.2386***	0.1112	0.0419	0.0489	0.5541**	
(3.3910)	(1.5464)	(0.6979)	(0.5540)	(2.4182)	
gov	−0.5155	−2.6980***	−0.8331**	−4.5003***	−3.2348***	
(-1.2259)	(-7.1118)	(-2.0190)	(-8.1392)	(-3.9724)	
info	0.0355	−0.0878	−0.0130	−0.2508	0.2528	
(0.7336)	(-0.7143)	(-0.2635)	(-1.1607)	(0.8694)	
green	−0.2414**	0.2260	−0.2395*	0.9071**	−0.3223	
(-2.0281)	(0.7951)	(-1.8978)	(2.0618)	(-0.5828)	
open	0.0037	0.0014	0.0030	−0.0031	0.0056	
(0.9046)	(0.3251)	(0.7960)	(-0.5424)	(0.5429)	
_cons	0.6587***	0.8353***	0.6407***	0.7478***	1.2639***	
(7.0060)	(6.6217)	(7.1842)	(3.8280)	(4.7579)	
City/Year	yes	yes	yes	yes	yes	
N	1649	3077	2380	1683	799	
R2	0.735	0.565	0.657	0.564	0.558	

The coefficient of the pilot policy of new urbanization in the eastern region is 0.2386, which is significant at the 1 % level, while the coefficient of the pilot policy of new urbanization in the central and western regions is 0.2308, also significant at the 5 % level. It can be seen that the pilot policy of new urbanization in the central and western regions has a greater driving force on economic resilience. This is because, compared to the eastern region, the new urbanization in the central and western regions exerts a greater influence on regional industrial development and is more capable of attracting population concentration, thereby improving urban economic structure and enhancing urban economic resilience [68].

5.4.2 Urban scale heterogeneity

The influence of population size on urban development is paramount. Typically, cities with larger populations, leveraging abundant resource endowment and a well-structured industrial framework, tend to foster stable economic structures, thereby showcasing higher economic resilience. Conversely, cities with smaller populations often exhibit lower economic resilience. Hence, the impact of new urbanization pilot policies on economic resilience may vary significantly across cities of different scales. Drawing upon the research of Cai et al. [69], this study stratifies samples into large cities, medium-sized cities, and small cities for regression analysis, as presented in Table 5.

The coefficient for the new urbanization pilot policy in the eastern region is 0.2386, which is statistically significant at the 1 % level, whereas the coefficient for the new urbanization pilot policy in the central and western regions is 0.1112, but not statistically significant. This indicates that the impact of the new urbanization pilot policy on economic resilience in the central and western regions is notably limited. This disparity may be attributed to substantial differences in economic structure and industrial layout between the regions. The eastern region possesses a more developed and diversified economic structure with a greater abundance of advantageous industries and resources. In contrast, the industrial structure of the central and western regions is comparatively homogeneous, constrained by geographical conditions and resource endowments, thereby imposing certain limitations on their economic growth potential [70]. Consequently, the new urbanization pilot policy faces challenges in realizing its intended impacts in these regions.

5.4.3 Urban agglomeration heterogeneity

The urban agglomeration strategy is a pivotal element of China's regional coordinated development policy [71], playing a crucial role in mitigating regional economic disparities by designating a core city as a leader to drive the development of other cities within the agglomeration. This approach ultimately fosters balanced regional development. Urban agglomeration demonstrate a high level of urbanization, well-developed urban systems, and intricate industrial structures, making it challenging for additional urbanization efforts to bring about substantial changes to overall economic resilience. In non-urban agglomeration cities where urbanization is in its nascent stages, cities are at the early phases of growth, and in such instances, the process of urbanization can have a more pronounced impact, significantly enhancing the economic resilience of cities. Therefore, this study classifies the sample into urban agglomeration cities and non-urban agglomeration cities based on the respective urban agglomeration strategy. The regression results are exhibited in Columns (1) and (2) of Table 6.Table 6 Heterogeneity test of urban agglomeration and economic development level.

Table 6	(1)	(2)	(3)	(4)	
Urban agglomeration	Non-urban agglomeration	High	Low	
treat	0.0931*	0.3493**	0.1287	0.2357***	
(1.7440)	(2.0071)	(1.4031)	(2.6109)	
gov	−2.1052***	−1.8327***	−5.6588***	−1.8311***	
(-6.0168)	(-3.1902)	(-5.8734)	(-5.9654)	
info	0.0149	−0.2194	−0.0090	−0.0351	
(0.2980)	(-0.8757)	(-0.1442)	(-0.1711)	
green	−0.2136*	0.2697	−0.2051	0.2620	
(-1.6817)	(0.5836)	(-1.2573)	(1.0325)	
open	0.0019	0.0029	0.0015	0.0015	
(0.5644)	(0.3947)	(0.2511)	(0.4250)	
_cons	0.7867***	0.9150***	1.5491***	0.6616***	
(9.7957)	(4.1764)	(6.6702)	(5.7659)	
City/Year	yes	yes	yes	yes	
N	3655	1071	2363	2363	
R2	0.659	0.492	0.698	0.373	

The impact of new urbanization in urban agglomeration cities is noteworthy, with a coefficient of 0.0931, exhibiting statistical significance at the 10 % level. Conversely, the impact effect of new urbanization in non-urban agglomeration cities stands at 0.3493, signifying significance at the 5 % level. This disparity highlights a clear difference in the impact of new urbanization between urban agglomeration and non-urban agglomeration settings, showing that new urbanization in non-urban agglomeration has a more substantial positive influence on economic resilience. One potential explanation for this disparity could stem from the geographical challenges and weaker economic ties with neighboring cities that non-urban agglomeration cities face in contrast to urban agglomeration cities, leading to a less resilient regional economic structure [72]. New urbanization facilitates optimized resource allocation, enhances economic structure, and boosts the development potential of the city. Moreover, it bolsters the economic resilience of the city by enhancing its capacity to withstand risks.

5.4.4 Heterogeneity in levels of economic development

The economic development level of cities is an important factor influencing urban economic resilience. Generally, cities with higher economic development levels have more diversified and robust industrial structures, enabling them to demonstrate greater resistance to economic shocks and thus possess outstanding economic resilience. Conversely, cities with lower economic development levels, due to their relatively weak economic foundation, often lack sufficient resources and capacity to withstand external economic shocks, leading to weaker economic resilience. Therefore, the impact of the new urbanization policy on urban economic resilience varies among cities with different economic development levels. In this study, following the approach of Yang et al. [73], the sample is divided into high economic development level cities and low economic development level cities based on the median per capita GDP.

Regression analysis reveals that the coefficient for high economic development level cities is 0.1287, but it is not statistically significant. In contrast, the coefficient for low economic development level cities is 0.2357, and it is statistically significant at the 1 % level. This fully demonstrates the significant effect of the new urbanization policy in cities with low economic development levels, as it not only optimizes the economic structure of these cities to a great extent but also significantly enhances their economic resilience. In comparison, the new urbanization policy's further promotion effect on cities with high economic development levels is not as significant as the former, as they already possess a relatively robust foundation of economic resilience.

5.5 Mechanism analysis

This study explores the influence of new urbanization on economic resilience through an analysis of the impacts of technological innovation and industrial structure upgrading. The mediation effect model is utilized to assess this relationship, with the regression outcomes detailed in Table 7. The coefficient reflecting the impact of new urbanization on technological innovation is 0.6893, demonstrating statistical significance at the 1 % level. This finding indicates that the advancement of new urban regions fosters the concentration of innovative elements and resources, thereby boosting urban technological innovation and, ultimately, strengthening economic resilience. Additionally, heightened urbanization improves the urban economy's capacity to mitigate risks by facilitating the modernization of the industrial structure and the establishment of a robust industrial system.Table 7 Mechanism analysis results.

Table 7	(1)	(2)	
innov	is	
treat	0.6893***	0.0135**	
(8.8755)	(2.1054)	
gov	−3.1313***	−0.2844***	
(-7.3868)	(-8.1047)	
info	−2.5821***	0.0443***	
(-33.7055)	(6.9924)	
green	−1.3948***	0.0346**	
(-7.3543)	(2.2010)	
open	0.0033	−0.0007*	
(0.7199)	(-1.8778)	
_cons	2.1217***	7.7861***	
(18.8959)	(837.7390)	
city/year	yes	yes	
N	4726	4726	
R2	0.432	0.703	

6 Conclusions and recommendations for countermeasures

6.1 Conclusion

The new urbanization strategy holds a pivotal role in the 14th Five-Year Plan period. This research treats it as a quasi-natural experiment and employs the multi-period DID model and the mediating effect model to scrutinize the impact of new urbanization construction on economic resilience. The study yields the following conclusions.(1) The implementation of new urbanization policies significantly bolsters urban economic resilience. New urbanization has propelled urban economic expansion by fostering a varied and interconnected industrial ecosystem, thereby substantially enhancing urban economies' capacity to withstand external disruptions. In essence, communities with diverse industrial structures exhibit agility in adapting strategies, restoring stability, and sustaining robust economic growth amidst market fluctuations, resource constraints, or unforeseen economic challenges.

(2) Viewed from the perspectives of geographical location, urban scale, urban agglomeration, and urban economic development level, the impact of new urbanization on urban economic resilience displays significant regional disparities. Specifically, in terms of geographical positioning, the eastern region exhibits a more pronounced enhancement in economic resilience due to the new urbanization policy compared to the central and western regions, attributed primarily to the advanced industrial structure in the eastern region that facilitates the smooth implementation of the new urbanization policy. Concerning urban scale, the enhancement of economic resilience in small cities is notably prominent under the new urbanization, while the impact on large and medium-sized cities is less apparent, indicating the greater marginal effects of small cities in the new urbanization process. Disparities also exist between urban agglomerations and non-agglomerated cities, with non-agglomerated cities experiencing a stronger impetus from the new urbanization for enhancing economic resilience, possibly linked to their relatively weaker economic structures and the more significant economic structural improvements resulting from the new urbanization. Lastly, the level of economic development influences the role of the new urbanization policy in urban economic resilience, with cities at lower levels of economic development significantly improving economic resilience due to the new urbanization policy, while cities at higher levels of economic development benefit to a lesser extent as they already possess a strong economic resilience foundation.

(3) The mechanism test demonstrates that new urbanization improves urban economic resilience through technological innovation and industrial structure upgrading. Essentially, new urbanization boosts urban economic resilience by fostering technological innovation and upgrading industrial structures. Primarily, new urbanization propels the advancement of technical innovation, catalyzing ongoing urban economic growth. Technological innovation is pivotal in urban progress, elevating productivity, fostering balanced economic development, and ensuring the effective and sustainable operation of the urban economy. Additionally, the new urbanization process establishes a foundation for sustainable urban economic growth by modernizing the industrial sector. This upgrade signals a shift towards high-value-added industries with advanced technical content, showcasing cities' adaptability and strength in navigating external economic changes.

6.2 Countermeasures and recommendations

(1) Tailoring urbanization processes to local conditions to enhance urban economic resilience. In advancing the new urbanization, due consideration should be given to the unique characteristics of each city, necessitating the implementation of differentiated development strategies. For instance, bolstering the new urbanization construction in the eastern region, small cities, non-agglomerated cities, and cities with lower levels of economic development is imperative. This entails leveraging regional resource advantages, fostering region-specific industries, and achieving urban-industrial integration, thereby facilitating the smooth progression of regional new urbanization initiatives.

(2) The advancement of modern urbanization should prioritize enhancing the role of technological innovation in fortifying cities' economic resilience. The government must increase funding for scientific research and development, bolster support for crucial core technological advancements, and provide continuous incentives for urban economic advancement. It is also crucial to improve mechanisms for nurturing and attracting innovative talents to ensure a consistent supply of skilled individuals for urban innovative development. Moreover, creating an environment conducive to innovation, simplifying bureaucratic approval processes, strengthening intellectual property rights protection, and fostering an innovative and entrepreneurial culture are essential steps to boost the city's innovation capacity and economic resilience.

(3) Furthermore, there should be a continued focus on promoting industry diversification, establishing a supportive industrial ecosystem, and upgrading the industrial structure. Targeted industrial policies should be developed to attract investments, technology, and skilled professionals to emerging industries and key sectors to stimulate economic growth. Enhancing collaboration between industry, academia, and research institutions is crucial for translating research discoveries into practical applications, contributing to the formation of a modern and comprehensive industrial system that injects vitality into the city's economy.

6.3 Limitations and recommendations for future research

This paper uses the multi-period DID model and the mediating effect model to deeply explore the impact of new urbanization on urban economic resilience, but the above research still has the following deficiencies to be improved:

As the most basic administrative unit in China, the county is also one of the important carriers for the implementation of the new urbanization pilot policy. The above research only explores the policy effect of new urbanization on urban economic resilience from the urban level, while ignoring the impact of county urbanization pilot policy on county economic resilience. Therefore, the follow-up study can examine the policy effect of county new urbanization pilot policy on county economic resilience. The spatial spillover effect of new urbanization on urban economic resilience can enrich relevant research. (2) Generally speaking, cities that have achieved remarkable results in the construction of new urbanization can play an exemplary role and can drive neighboring cities to promote urbanization, which may have a spatial spillover effect on the economic resilience of surrounding cities.

CRediT authorship contribution statement

Fanglin Li: Writing – review & editing, Supervision, Resources, Funding acquisition, Conceptualization. Ziyu Diao: Writing – original draft, Visualization, Software, Methodology, Formal analysis.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This study is supported by the 10.13039/501100001809 National Natural Science Foundation of China under the "Analysis of Corporate Environmental Behavior and Reconstruction of Guiding Mechanisms for Reimbursable Use and Trading of Emission Rights" (71974081 ) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX24_3882 ).

1 China proposed for the first time in the 11th Five-Year Plan to take urban agglomerations as the main body of urbanization promotion, which also marked the beginning of China's new urbanization strategy, and 2006, as the opening year of the 11th Five-Year Plan, therefore this paper will use the data of Chinese cities from 2006 to 2022 as the research sample.
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