Research Articles

The impact of economic development on urban livability: Evidence from 40 large and medium-sized cities of China

  • WANG Yi , 1 ,
  • MIAO Zhuanying 1 ,
  • LU Yuqi , 2, * ,
  • ZHU Yingming 1
  • 1. Research Base of Jiangsu Industrial Cluster, School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
  • 2. School of Geography, Nanjing Normal University, Nanjing 210023, China
*Lu Yuqi (1963-), PhD and Professor, specialized in spatial structure and regional development. E-mail:

Wang Yi (1989-), PhD and Associate Professor, specialized in human settlements and regional development. E-mail:

Received date: 2023-07-05

  Accepted date: 2023-08-10

  Online published: 2023-10-08

Supported by

National Natural Science Foundation of China(41901205)

Natural Science Foundation of Jiangsu Province(BK20190482)


Under the background that economy and urbanization of China are gradually entering the stage of high-quality development, clarifying the influence of economic development on urban livability is of significant academic and practical value. In this paper, regarded as one “factor”, livability was introduced into the research framework of production function, and a theoretical model of the impact of economic development on urban livability was established. Based on the panel data of 40 cities in China from 2005 to 2019, the System GMM, panel threshold model and other methods were further adopted to carry out an empirical analysis. The results show that: (1) The livability level of large and medium-sized cities in China from 2005 to 2019 has been rising generally, but they present obvious characteristics of dimensional and spatial differentiation. (2) In general, economic development has an inhibiting effect on the improvement of urban livability, but this logical effect shows obvious heterogeneity in different time periods and diverse city scales. This inhibitory effect is more significant for the cities before entering the new normal phase of economy, and large-scale municipalities and economically-developed provincial capitals (namely Class-A cities). (3) There are significant threshold effects in the impact of economic development on urban livability, where the threshold variables are income level and economic development. With the increase of city dwellers’ income, this effect presents an inverted N-shaped nonlinear feature. When the development of economy makes the average wage of employees between 60,000 and 80,000 yuan, economic development can significantly improve urban livability. Also, there is a significant single threshold inhibitory effect when economic development is taken as a threshold variable. However, its negative impact shows a law of diminishing marginal efficiency. In addition, a similar threshold effect is found in smaller-scale Class-B cities. The findings of this research can provide some insights for urban planners and policymakers in both China and vast developing countries to understand better the relationship between economic development and urban livability. Finally, according to the research findings, we proposed the corresponding policy enlightenment from both “macro guidance” and “micro action”.

Cite this article

WANG Yi , MIAO Zhuanying , LU Yuqi , ZHU Yingming . The impact of economic development on urban livability: Evidence from 40 large and medium-sized cities of China[J]. Journal of Geographical Sciences, 2023 , 33(9) : 1767 -1790 . DOI: 10.1007/s11442-023-2152-4

1 Introduction

Since the reform and opening up in 1978, China’s economy has developed vigorously and the level of urbanization has been continuously improved. However, the rapid development of economy and urbanization has brought a series of challenges to urban development. Problems such as population expansion, traffic congestion, environmental pollution, and housing shortage have frequently emerged, which have generated a profound impact on the urban human settlements and people’s living conditions (Fang, 2014; Fu and Zhang, 2017; Zhou et al., 2019). At the same time, there is a growing demand for a higher quality living environment with their income level increasing. In order to solve these “urban diseases” and better meet the people’s growing demand for a better environment, the Central Urban Work Conference of 2015 raised “urban livability” to an unprecedented strategic height, and clearly pointed out that it is necessary to “improve the livability of urban development”. Livability, low carbon and ecology have become the core concepts of promoting high-quality urban development and important goals of beautiful city construction (Zhan et al., 2018).
Urban livability belongs to the research category of human settlements. Since the Greek scholar Doxiadis proposed the discipline of human settlements in the 1950s (Wu, 2001), scholars have conducted much research around urban human settlements or livable cities, and achieved fruitful results. The existing relevant research can be roughly divided into theoretical exploration perspective, objective environment supply perspective and subjective environment perception perspective. The research from the theoretical perspective focuses on the systematic refinement and summary of the concept, connotation, theoretical basis, evaluation criteria, construction guidelines and framework of livable cities or livability from a qualitative perspective (Blomquist et al., 1988; Zhang, 2007; Li et al., 2008; Zhang et al., 2016). Although the definition and evaluation criteria for livability have not yet formed an absolutely unified understanding currently (Zhang, 2016; Mesimäki et al., 2017), the theoretical basis or concept that should be followed in the construction of livable cities has basically formed a consensus in the academic and practical circles. The theories and ideas such as pastoral city theory, ecological city theory, comfort development theory, smart growth theory, new urbanism, and human settlement theory (Howard, 1989; Clark et al., 2002; Register, 2002; Zhang and Wang, 2012; Zhang, 2016) have provided effective guidance for the urban construction and development of various countries and regions, and the resulting core framework and model of livable city construction (Zhang, 2016), urban space design and layout strategies (Chen et al., 2015; Wey and Huang, 2018) have been put into practice in various places. In general, although there are differences in the connotation and emphasis of livability from the perspectives of geography, architecture, planning, environmental studies and other disciplines, they are all based on the problems faced by urban development in different periods and the higher-level needs of people. With the development of the times, the conceptual connotation and theoretical thoughts of livability will continue to expand and extend.
The research from the perspective of objective environment supply focuses on the measurement and evaluation of livability at the level of urban entity composition, usually based on multi-source heterogeneous data such as socio-economic data, land cover data, POI data, etc., and carries out the construction of urban livability evaluation index system and its comprehensive integrated evaluation research on different spatial scales or different spatial units, with natural environment pleasantness, social environment harmony and public service convenience as the main characteristics (Wang, 2010; Chen et al., 2015; Jia and Gu, 2017; Tang et al., 2017; Paul and Sen, 2018), revealing the spatial differentiation characteristics and space-time evolution laws of livability in different regions (Li and Jin, 2012; Li et al., 2017; Ghasemi et al., 2018; Yang et al., 2018; Ghasemi et al., 2019). It also discussed the correlation or coupling coordination between economic development (Xiong et al., 2007), urbanization process (Li et al., 2004; Wang et al., 2017), real estate development (Zhang et al., 2014), tourism development (Liu et al., 2016; Liu et al., 2017), residents’ health (Cao et al., 2019) and urban livability. With the advent of the era of big data, new data such as high-score remote sensing data, street view data, and traffic big data, as well as matching artificial intelligence, machine learning and other technical means, are constantly expanding the research perspective in the field of livability and innovating their research methods (Huang et al., 2018; Fu et al., 2019; Wang et al., 2019; Kourtit et al., 2021).
The research from the perspective of subjective environment perception is often based on the needs of urban residents’ human settlement environment elements, using micro-level multi-sample survey data, and establishing evaluation index system and models to evaluate urban livability or residents’ satisfaction with urban living environment (Stanislav and Chin, 2019; Paul, 2020; Liu et al., 2021), and explore its spatial differentiation characteristics and the demand characteristics of different resident groups (Chen et al., 2008; Wu et al., 2013). On this basis, the perceptual factors and personal socioeconomic attributes of different dimensions are used as explanatory variables to further explore and analyze the influence mechanism of livability or satisfaction (Mahmoudi et al., 2015; Dang et al., 2016; Zhan et al., 2018). In addition, more and more attention has been paid to issues such as environmental satisfaction perception and behavioral intention (Zhan et al., 2014; Zhan et al., 2017), residential demand preference and reality misalignment (Chen et al., 2013), and job-housing space mismatch (Fan et al., 2014). In general, the research from the subjective perspective starts from people’s subjective feelings, which makes up for the shortcomings that objective data are difficult to reflect the psychological needs of residents, but it is difficult to explore time series due to the limitation of sample size. A small number of scholars try to combine subjective evaluation with objective analysis to accurately outline the livability level of cities and their influencing factors (Chen et al., 2017), but limited by differences in data structure and form, the research methods and depth need to be expanded.
In summary, the above research results provide a useful theoretical reference for the planning, construction and management of livable cities, and also lay an important theoretical foundation and ideological source for this paper. However, most of the existing research focuses on the theme of urban livability or urban human settlement, and carries out the “indicator-evaluation” paradigm research from a subjective or objective perspective, but the comprehensive study of combining urban livability with other factors has not received enough attention. A small number of studies mainly use correlation analysis, coupled models and the like to reveal the degree of correlation or coordination between urban livability and other factors (Xiong et al., 2007; Yu and Wang, 2014). In terms of research depth, it is only found that there is a correlation between them, or a certain factor has an impact on livability, but a systematic analysis of the influence mechanism of urban livability has not yet been formed. In terms of research conclusions, there are certain differences in the direction and degree of influence of the same factor on urban livability. Taking economic factors as an example, some scholars believe that economic prosperity is a vital guarantee for residents’ material life and an important basic condition for the construction of livable cities, and we should incorporate economic factors into the urban livability evaluation system (Li and Jin, 2012; Jia and Gu, 2017). However, some scholars point out that the livability evaluation should not overemphasize the importance of urban economic development, but should highlight the focus on “people”, and believe that economic factors should not be included in the evaluation system (Chen et al., 2008; Chen et al., 2017; Zhan et al., 2018). Other scholars have found that economic development has a negative impact on the improvement of urban livability (Mouratidis, 2020). In addition, comparing the comprehensive economic competitiveness and livable competitiveness ranking of cities, some cities with strong economic competitiveness also have higher livability indexes, such as Hong Kong; However, some economically competitive cities have low scores for livability, such as Shanghai1(1From the “17th Report on China’s Urban Competitiveness” released by the National Academy of Economic Strategy, CASS in 2019. In the report, economic competitiveness and livable competitiveness are the two sub-items of urban competitiveness, and the evaluation of livable competitiveness is mainly carried out from seven dimensions: economy, society, ecology, residence, education, medical care and infrastructure. According to the report, Hong Kong ranked second in the 2019 ranking of China’s cities in terms of economic competitiveness and first in the ranking of livable competitiveness; Shanghai ranks third in the city's economic competitiveness, while its livability competitiveness does not make it into the top ten.). All these lead to unclear specific policy significance of relevant research, thus difficult to provide clear operational opinions for solving practical problems.
In essence, this requires sorting out two questions. First of all, it is necessary to answer whether economic development should be included in the evaluation system of urban livability. Livable city or urban livability is a relative concept, but also a dynamic goal. A city suitable for human habitation and life can be considered a livable city. In reality, there is no absolute livable city or unlivable city; Different cities have differences in geographical environment, history, culture and functional orientation, so it is difficult to determine a unified standard to judge whether it is livable. From this point of view, it is difficult to conclude whether economic factors should be included in the livability evaluation system. The focus of urban livability discussed in this paper lies in the living environment closely related to the vital interests of residents within a single city, which is the unity of pleasant natural ecological environment and harmonious social and human environment. Although economic factors can well support the construction of livable cities, the degree of economic development of cities does not necessarily match their livable comfort, and economically developed cities often face great pressures, such as the high cost of housing and living, which in turn poses challenges to urban livability. Therefore, this paper does not consider the economic benefits and other possible economic pressures of each city when evaluating its livability. Secondly, we also need to think deeply about whether economic development has an impact on urban livability. If so, what is the influence mechanism and how strong is the impact? Does this effect present spatial heterogeneity or threshold interval effects? The solution of the above problems will be an innovation and breakthrough in the study of livable cities, which will help deepen the comprehensive understanding of urban livability, and also promote the improvement of the scientific method system and theoretical connotation of human settlements. Unfortunately, there is little literature that answers these questions from a theoretical and empirical perspective.
In view of this, based on the panel data of 40 cities in China from 2005 to 2019, using mathematical statistics, systematic GMM and threshold regression model, this paper aims to integrate the above index variables into the same system for research and analysis. And on the basis of revealing the characteristics of the time-series evolution of livability of major cities in China, this paper focuses on the influence direction, intensity and threshold value of economic development on urban livability. In this way, it can provide theoretical basis and decision-making support for the construction of livable cities, the economic structure adjustment, industrial layout and development strategy of cities in China in the new era.

2 Theoretical frameworks

Based on the traditional analysis framework of production function, this paper introduces urban livability as a “factor” into the production function, and constructs a new model of urban high-quality development function, so as to explore the influence of economic development level on urban livability. In the new era, China’s economic development is changing from a high-speed growth stage to a high-quality development stage. Correspondingly, China’s urban development has entered a critical transition period from low-quality development to high-quality development (Fang, 2019). High-quality urban development is generally understood as innovation-driven, synchronously creating a more efficient and active economic environment, a fairer and more harmonious social environment, a greener and healthier natural environment, and a more convenient and comfortable living environment for residents (Zhang et al., 2019). The elements of the living environment here are the core part of the livability described herein; In addition, the livability of this paper also involves some factors in the social and natural environment, such as unemployment rate, temperature and other factors. These elements are collectively referred to as environmental livability features, which are the explanatory variables (urban livability) in this paper. As for how to separate the correlated factors from the social and natural environment, because it does not affect the derivation of the theoretical model, it will not be described here. Therefore, based on the basic form of the Cobb-Douglas function, the urban high-quality development function can be set as:
D t = A t × f E t , S t , H t = A t × E t α × S t β × H t θ
where D is the comprehensive level of high-quality urban development (which can be regarded as the total output of urban development); A, E, S, and H 2(2 According to the setting of the context variables, the livable environment should be expressed in HB, but for the sake of concise and easy reading of the formula, this article only uses H to characterize, and the two have the same meaning.)respectively represent scientific and technological innovation, economic development, social progress and environmental livability indicators; t is the time section; α, β and θ are the elastic coefficients of the above indicators on the comprehensive development level of cities, and their values are all within the [0,1] range.
In the process of wealth creation and development, cities will pay a price, that is the so-called “development cost”, which generally includes the economic cost for urban development and construction, the social cost for the stability and harmony of the city itself, and the environmental cost for maintaining urban development and continuous expansion (Wang and Luo, 2007). The “environment” here mainly involves the natural ecological environment and the living environment, and its connotation and category are very close to the livability defined in this paper, so this part can be understood as the construction cost of environmental livability. Therefore, the total cost of urban development can be roughly expressed as:
C t = ω E t + γ S t + φ H t
where ω is the economic cost coefficient; γ is the social cost coefficient; φ is the cost coefficient of environmentally livable construction, then the total benefit of urban development Pt is:
P t = D t C t = A t × E t α × S t β × H t θ ω E t γ S t φ H t
From equation (3), the partial derivative of the income function with respect to urban livability can be obtained as follows:
P t H t = θ × A t × E t α × S t β × H t θ 1 φ
Generally speaking, maximizing the profit of urban development is our goal, and its basic requirement is to make the first-order partial derivative equal to zero, then there are:
φ = θ × A t × E t α × S t β × H t θ 1
Taking the natural logarithm on both sides of equation (5) yields:
ln φ = ln θ + ln A t + α ln E t + β ln S t + θ 1 ln H t
Further collation yields:
ln H t = α 1 θ ln E t + β 1 θ ln S t + 1 1 θ ln θ + 1 1 θ ln A t + 1 θ 1 ln φ
The key coefficient in this paper is α, which can be understood as the elastic coefficient of economic development to the total output of urban development, that is, the “contribution” of economic factors to the high-quality urban development. The value of this coefficient is affected and constrained by multiple factors, such as industrial structure, income distribution, innovation drive, market mechanism, etc. (Wei and Li, 2018). Among these factors, the impact of residents’ income level on urban livability is extremely obvious (Paul, 2020). As income levels increase, residents are more capable of choosing cities or neighborhoods with better livability, that is, income levels have a contributing effect to livability (α > 0); However, it should be noted that the increase in income may make people have higher requirements for livability characterized by pleasant natural environment and comfortable human environment (α < 0). Therefore, in theory, income levels have both positive and negative effects on urban livability. In addition, with the continuous expansion of the urban population and its increasing demand for housing, urban housing prices have become an important variable affecting regional economic development, and have also become a factor in livability evaluation and attracting talents (Yang and Pan, 2020). Existing studies have shown that when using objective data, there is a positive correlation between urban housing prices and urban livability (α > 0), but based on residents’ subjective perception, the higher the urban housing price, the lower the residents’ satisfaction with the living environment (α <0) (Zhang et al., 2016). Based on this, equation (8) is used to characterize the value of the α coefficient:
α t = α W t , Z t
where W represents income level; Z represents the price of housing.
Bringing Equation (8) into Style (7) yields:
ln H t = α W t , Z t 1 θ ln E t + β 1 θ ln S t + 1 1 θ ln θ + 1 1 θ ln A t + 1 θ 1 ln φ
The information that can be interpreted from Equation (9) is that the impact of economic development on urban livability is also affected by factors such as residents’ income level and housing price. From the perspective of influence form, since the specific expansion of α function in Equation (8) is unknown, it is not possible to determine how factors such as income level and housing price affect the coefficient of contribution of livable construction to urban development. So, it can only be subjectively guessed. On the one hand, if α is a simple multiplication of W and Z, then an OLS model can be formed by taking the natural logarithm expansion of equation (9); On the other hand, if α is a stage function, and its specific value is controlled by the interval where the values of W and Z are located, that is, it is not a simple multiplication, nor a simple monotonically increasing or decreasing function, but is likely to be a nonlinear relationship, it needs to be analyzed by discontinuous threshold effect regression (Xiao, 2016).

3 Research methods and data sources

3.1 Evaluation index system for urban livability

Based on the research objectives of this paper and the basic connotation of urban livability (Blomquist et al., 1988; Zhang, 2007; Li et al., 2008; Zhang et al., 2016; Zhan et al., 2018), combining the existing relevant research results(Wang, 2010; Li and Jin, 2012; Chen et al., 2017; Jia and Gu, 2017; Li et al., 2017; Tang et al., 2017; Arpan and Joy, 2018; Ghasemi et al., 2018; Yang et al., 2018; Ghasemi et al., 2019) and relevant experts’ opinions, the systemic, representative, comparable and operable principles are followed to construct a urban livability evaluation index system (Figure 1) consisting of 5 dimensions and 29 specific indicators, including public service accessibility, natural environment pleasantness, humanity environment comfort, urban safety and environmental health. In the figure, the values in the outermost circle are the index weights calculated by the entropy weight method, and the positive and negative signs in the inner ring represent the index directions (Fang et al., 2019).
Figure 1 Evaluation index system diagram for urban livability

3.2 Variable design and metrics

Explained variable: urban livability (HB). Based on the evaluation index system constructed above and its weights (Figure 1), the livability index of each sample city was calculated and used as the explanatory variable in this study.
Core explanatory variable: level of economic development (EG). Referring to related studies (Zhang and Xia, 2020), this paper uses labor productivity per capita to measure economic development. Labor productivity is the core indicator of productivity development, and it reflects the efficiency of economic output and the nature of economic development better comparing with traditional indicators such as GDP per capita.
Control variables: credit scale (L), expressed as the year-end yuan (RMB) loans balance of each city; science and technology level (SI), expressed as the share of science and technology expenditure in GDP of each city; industrial structure (SU), measured by the share of tertiary sector divided by the share of secondary sector; fiscal adequacy (F), fiscal adequacy measures the fiscal pressure of local governments, expressed as the difference between general public budget revenue and general public expenditure of each city; economic openness (OP), measured by the ratio of FDI to GDP of a city; square of GDP per capita (AG), the Kuznets curve shows that economic development affects income distribution and that the city’s income distribution status will in turn affect the city’s livability. In addition, the non-linear relationship between economic development and environmental pollution (Shao and Jin, 2016).
Threshold variables: income level (IC), measured by the average wage of urban workers on the job; and house price (HP), measured by the average sales price of commercial houses in the city.

3.3 System generalized method of moments regression

Based on the above model derivation, we can first assume that α in Equation (8) is a simple multiplication of W and Z. Considering income level and housing price as control variables and introducing the lag, constant and residual terms, the following benchmark regression model is established:
ln H B i t = α 0 + α 1 ln H B i ( t 1 ) + α 2 ln E G i t + C V i t + ε i , t
where HB represents urban livability, EG represents economic development, CV represents control variables, and ε is a random disturbance term. Considering the possible lagged effects of economic development and other factors on urban livability, the data of the previous period (t-1) of urban livability are introduced so that the impact of endogeneity on the empirical results can be better eliminated. The treatment of endogeneity is also not limited to this, as the System Generalized Method of Moments (System GMM) is used in this study for the empirical estimation model (10). This is a parameter estimation method formed when the actual parameters of the model satisfy certain moment conditions, and is a generalization of the moment estimation method. Compared with traditional econometric methods, System GMM relaxes many assumptions, such as heteroskedasticity of random error terms, autocorrelation, and correlation among explanatory variables, etc. By combining the horizontal regression equation and the difference equation for estimation, it can effectively solve or exclude dynamic panel bias and endogeneity (Hansen, 1999; Biresselioglu et al., 2016).

3.4 Panel thresholds regression

The GMM regression is used to confirm the direct effect of economic development on urban livability. On this basis, we would like to further investigate whether the extent of the effect of economic development on urban livability is controlled by the interval in which the income level of residents and the housing price are taken. That is, assuming that α in (8) is a stage function, we would like to investigate: Does the effect of economic development on urban livability vary with changes in income levels or housing prices? Is there an optimal interval? Traditional test methods are based on constructing interaction terms for testing, but it is difficult to probe the specific threshold level; also, when dealing with nonlinear problems, if a priori assumptions are made on the data based on a certain indicator, and thus converting the nonlinearity of the data into linearity, this is likely to lead to biased model setting (Xiao, 2016). Therefore, in terms of scientificity, the threshold model test is one of the most concise ways to deal with the nonlinear model. In this paper, we establish a threshold panel data model to detect the threshold effect of income level (IC) and housing price (HP) in the association between economic development and urban livability. The specific model is shown as follows:
ln H B i t = μ + α 1 ln E G i t I I C i t γ 1 + α 2 ln E G i t I γ 1 < I C i t γ 2 + . . . + α n ln E G i t I γ n 1 < I C i t γ n + α n + 1 ln E G i t I I C i t > γ n + β n X + ε i t
ln H B i t = μ + α 1 ln E G i t I H P i t γ 1 + α 2 ln E G i t I γ 1 < H P i t γ 2 + . . . + α n ln E G i t I γ n 1 < H P i t γ n + α n + 1 ln E G i t I H P i t > γ n + β n X + ε i t
where μ and ε denote the constant and disturbance terms, respectively, i and t are cities and years, γ is the threshold to be estimated, α and β are the coefficients, I(·) is the indicative function, and X is a set of control variables that have an impact on urban livability. The rest of the variables have the same meaning as in the previous section.

3.5 Regions studied and data sources

In this paper, 40 large and medium-sized cities in China are selected as the research sample3(3Beijing, Tianjin, Shanghai, Chongqing, Harbin, Changchun, Shenyang, Shijiazhuang, Jinan, Nanjing, Hangzhou, Fuzhou, Guangzhou, Haikou, Nanning, Taiyuan, Zhengzhou, Wuhan, Changsha, Hefei, Nanchang, Hohhot, Yinchuan, Xi'an, Chengdu, Guiyang, Kunming, Lanzhou, Xining, Urumqi, Lhasa, Dalian, Weihai, Qingdao, Suzhou, Ningbo, Xiamen, Zhuhai, Shenzhen, Sanya.), including 4 municipalities directly under the central government, 27 provincial capitals and 9 recognized livable cities. We chose them for the following two main reasons. First, these cities have relatively complete and highly available data; second, these cities are the most representative cities in different regions of China, representing the highest level of economic and social development in China, and are also frequently chosen case sites for empirical studies of livability, so they are suitable for the topic of this study.
The data involved in the study are mainly derived from the 2006-2020 China City Statistical Yearbook, China Statistical Yearbook, China Meteorological Yearbook, China Tourism Statistical Yearbook, China Labor Statistical Yearbook, statistical yearbooks of each city, and relevant data published by platforms including China Economic Database and China Meteorological Science Data Sharing Service Network4(4 We also winsorize the extreme values, and individual missing indicator data are supplemented by interpolation.

4 Analysis of results

4.1 Sequential evolution characteristics of urban livability in China

In general, the livability of large and medium-sized cities in China showed an increasing trend from 2005 to 2019, as the mean value of the overall livability index rises from 0.2684 at the start of the period to 0.3202 at the end of the period, with a relative increase of 19.31% (Figure 2). The main reason is that since the 21st century, China has promoted a shift in urban development objectives from quantitative to qualitative, and the shift in urban development dynamics from terrestrial harmony to human-land harmony, which has resulted in the continual upgrading of the quality of urban ecological livability. However, we should also see that the current livability is not yet very high, and there remains a considerable distance between it and a habitable city with which the inhabitants are satisfied.
Figure 2 The change trend of the urban livability index from 2005 to 2019 in China
Urban safety dimension has long been at the end of the spectrum in terms of the livability component, and it has not improved much in the last 15 years. Furthermore, with changes in the international and domestic environment over the past few years, conflicts and risks of various kinds are deeply interwoven and layered, and the new challenges that result have to some extent offset progress in other aspects of urban security construction. The environmental healthiness dimension has also been in a slow growth trend for a long time, rising from 0.0442 to 0.0507, with an average annual growth rate of only 0.99%. Nevertheless, under the direction of high-quality economic development and industrial transformation and upgrading, the continued improvement of environmental healthiness is expected. In relative terms, livability levels of such three dimensions of the natural environment pleasantness, human environment comfort and public service accessibility have improved more rapidly, all with an average annual growth rate of more than 1.6 percentage points.
As for the spatial differentiation of livability, urban livability shows the characteristics that eastern coastal cities are better than central and western cities. Despite the former’s high population density and pressure on resources and the environment, in addition, these cities pay more attention to the construction of the human living environment than other areas. On the other hand, many cities in the northwestern and southwestern regions of China have a highly restrictive and constrained natural environmental context and insufficient urban habitat construction, resulting in their significantly lower habitat quality.

4.2 The influencing mechanism of economic development on urban livability

4.2.1 Descriptive statistics and smoothness test

To ensure the quality of the regression results, we first diagnosed all variables for cointegration. Apparently, all regression quantities in the model are free of covariance, as their variance inflation factors (VIF) are less than 10 (Biresselioglu et al., 2016). In addition, to make the non-stationary series smooth, some of the variables were logged or differenced in this paper, and then a unit root test was performed on the panel data using the Fisher-PP method. The results are shown in Table 1, and the variables are smooth under the horizontal series, which ensures the validity of the regression results.
Table 1 Descriptive statistics and validity tests
Variable Definition Observation Mean Std. dev Minimum Maximum VIF Fisher-PP
LnHB Logarithm of urban livability 600 -1.430 0.324 -2.241 -0.353 - 100.06**
LnEG Logarithm of economic development 600 17.887 1.114 14.643 20.232 3.16 244.05***
L Credit scale 600 4.110 3.505 0.153 17.176 1.91 402.22***
SI Science and technology level 600 3.456 0.435 2.408 4.399 1.54 522.32***
DF Difference in fiscal adequacy 600 -22.917 94.565 -647.209 232.062 2.13 681.84***
DLnSU Difference in logarithm of industrial structure 600 0.037 0.109 -0.341 0.429 2.59 510.32***
LnAG Logarithm of the square of GDP per capita 600 22.459 0.975 19.913 24.299 1.77 229.07***
LnOP Logarithm of economic openness 600 -3.606 1.051 -10.407 -1.678 1.42 108.24***
LnHP Logarithm of house price 600 8.886 0.591 7.634 10.432 1.98 378.63***
LnIC Logarithm of income level 600 10.806 0.495 9.750 11.781 3.01 700.18***

Note: Figures in () are the z-values of the coefficients. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. The same as in the following table.

4.2.2 Analysis of System GMM regression results

Table 2 shows the results of estimating the System GMM. Sargan test indicates that all estimation results do not suffer from over identification of instrumental variables, and the instrumental variables are valid. The companion probabilities from the AR(1) and AR(2) statistics indicate that the model does not suffer from serial correlation at the second order, which further indicates that the results of the System GMM regression show good robustness.
Table 2 The estimation results of System GMM
Variables Dependent variable: Logarithm of urban livability (LnHB)
Entire sample Entire sample Before entering the new normal (2005-2014) After entering the new normal (2015-2019) Class-A cities Class-B cities
Lagged LnHB 0.403*** 0.496*** 0.279*** 0.856*** 1.499** 0.425***
LnEG 0.019* -0.104** -0.043 0.028* -1.378** -0.093*
CS 0.038** 0.058* 0.007 1.527* 0.000
SI 0.076* 0.282** 0.064* 3.243** -0.006*
LnAG 0.039* -0.036 0.067 0.367 0.055*
DF -0.017* -0.015 -0.004 -0.105** -0.160
DLnSU -0.148 0.295 -2.239*** -1.608 -2.024
LnOP 0.027 -0.062 0.065* 0.102** -0.096
_cons -0.924*** -1.842*** -1.549** 2.158** -29.931** 0.206***
AR(1) 0.000 0.000 0.000 0.062 0.098 0.000
AR(2) 0.427 0.214 0.915 0.819 0.249 0.160
Sargan Test 410.000 447.910 262.240 27.940 139.350 316.74
WALD Test 10913.12*** 9765.45*** 1356.66*** 5673.21** 9938.45** 2367.81***
N 600 600 400 200 195 405

Note: The values reported for AR(1) and AR(2) are the p-values for the null hypothesis of no one-order and second-order serial correlation the first-differenced residuals.

As can be seen in Table 2, in addition to the impact of the city’s livability by its own past stock, economic development has a positive contribution to the improvement of urban livability, with a 0.019 unit increase for every 1 unit increase in per capita labor productivity. This coefficient changes from positive to negative (-0.104**) when the control variables enter the model, indicating that economic development is not conducive to improving urban habitability.
The effects of each of the control variables indicate that industrial structure does not significantly affect urban livability, and only in the regression results of a sub-sample does industrial structure have some inhibiting effect on urban habitability, presumably because even though industrial structure upgrading has improved economic efficiency, there is still a mismatch between the quality of the workforce and industrial structure upgrading in many places after entering the new normal, and the employment market’s bias toward high-skilled labor the employment market has become more and more obvious in favor of high-skilled labor (Song and Li, 2019), which makes the employment pressure in cities generally increase, thus reducing urban livability. This indicator of industrial structure is further characterized by the ratio of the output value of tertiary industry to that of secondary industry. While the proportion of tertiary industry is increasing year on year, the traditional middle and lower end industries within the tertiary industry still account for a large proportion in many cities, which is also not conducive to improved livability. There is a significant positive contribution of the credit scale to urban livability. To some extent, the credit scale reflects the level of ability of city residents to pay for housing, and typically the larger the scale of credit, the higher the population’s daily consumption and ability to pay for housing, which indirectly increases the livability of the city. The estimated coefficient on the level of technology is significantly positive, indicating that science and technology have a significantly positive effect on increasing livability. In recent years, many new technologies such as artificial intelligence technology, big data technology, and rainfall simulation technology have played a significant role in the construction of safe cities, smart cities, and sponge towns, as well as in the improvement of the quality of urban habitats. Fiscal adequacy has a negative effect on urban livability, likely due to financial pressure, some local governments often support local tax expenditures and urban construction through the transfer of land, and gradually fall into the “land-finance” situation. Urban total factor productivity is significantly negatively affected by land finance, and also causes housing prices to rise, leading to distortionary effects on urban resource allocation and causing wage levels to deviate from benchmarks of labor productivity (Li et al., 2020), all of which have a negative impact on urban habitability. There was no significant positive effect of economic openness on the livability of the whole sample city, but in both sub-sample regression results, economic openness has a significantly positive effect on urban livability. Perhaps because of the increased focus of these samples on bringing clean, environmentally friendly firms or foreign investment as environmental regulatory standards improve, helping to improve the quality of the local environment.
Given that China’s economic development has moved into a new normal over the sample period, economic development after 2014 is more focused on quality and the optimization and upgrading of the economic structure in comparison to the previous year, and changed from factor and investment driven to innovation driven, so we further split the sample into two sub-samples to analyze the impact of economic development on urban livability at different stages of development. Regression results show (Table 2) that prior to entry into the new economic norm, urban livability was not significantly affected by economic development, likely because we paid more attention to the extent and quantity of economic development in the past, but paid little attention to the ecological and environmental impacts caused by it, and the construction of urban habitat environment lagged behind so that the sustaining effect of economic development on the construction of habitable cities was outweighed by the negative environmental externalities brought about by it. After entering the new normal stage, however, the effect of economic development on urban livability changes from negative to positive, though the coefficient is small, but it is significant. The main reason for this is that our country has gradually shifted from pursuing the quantity of economic growth to pursuing the quality of economic development and has promoted the reshaping of urban form and the valorization of values with the concept of ecological priority and green development to create a new realm of “livable city” building.
In addition, this paper analyzes differences in the performance of the above effects across cities of various sizes. Using SPSS 26.0 software, based on the average values of indicators such as total GDP and the size of the population over the sample period, we cluster 40 cities into two categories of city samples. Class-A cities includes Shenzhen, Chongqing, Hangzhou, Shanghai, Jinan, Chengdu, Tianjin, Nanjing, Xi’an, Wuhan, Guangzhou and Beijing, which basically belong to municipalities directly under the central government and economically developed provincial capitals, while the other 27 cities belong to Class-B cities. With the relevant indicators from both sample types estimated (see the last two columns of Table 2), economic development can be found to have a significant negative effect on urban livability in Class-A cities, with an estimated coefficient of -1.378, indicating that there is no unavoidable relationship between high urban livability and the size of the city, and the effect of scale is not significant, and the inhibiting effect of economic development on urban livability is stronger in cities that are economically developed and larger. It also means that in the new era we need to further promote the direction of urban development away from population and economic growth and towards a focus on the connotation and quality of urban development and pay more attention to creating and building a pleasant ecological space, a convenient living space, and a harmonious social space. In the case of Class-B cities, the degree to which economic development influences urban livability is relatively low, which may be a result of the small population sizes of these cities and the relatively high per capita possession of some of the habitat resources. These cities are also mostly tourist oriented, which are superior to other cities in terms of environmental building and beautification, and the development of tourism has contributed to improvements in local public service facilities as well as transport and travel conditions (Liu et al., 2017), leading to increased urban livability.

4.2.3 Analysis of heterogeneous threshold effects

It is important to note that the interaction between the two key variables is only judged by the GMM estimate, which is somewhat partial. In order to deal with heterogeneity in the size of existing cities in the aforementioned “economic development-urban livability” problem, we further investigated this phenomenon using the threshold method. Bootstrap tests of self-sampling thresholds were performed on the basis of stata16.0 statistical software to determine the existence and number of thresholds. We found that the double-threshold effect of income level (IC) passed the test of significance (Table 3), implying that income level has a double-threshold effect; whereas all three types of threshold effects of housing price (HP) have failed the significance test, i.e., there is no threshold as a result. One possible reason for this is that, in addition to wages and housing prices, urban livability is also an important factor affecting the level of labor force utility, and urban livability has a significant and positive correlation with the price of housing (as their Pearson correlation coefficient is 0.4502), and because the inhibiting effect of high housing prices on urban livability is partially offset by positive livability along other dimensions, and Chinese urban residents, in turn, are concerned with urban habitat and urban quality of life, so the relative increase in urban livability thus has a smoothing effect on the high price of housing (Lin et al., 2019).
Table 3 Results of threshold effect significance test for the entire sample
Models F value P value Number of BS 1% critical value 5% critical value 10% critical value
IC Single threshold 23.95 0.059 500 51.126 41.890 37.518
Double threshold 30.52 0.064 500 38.026 31.572 27.692
Triple threshold 10.42 0.898 500 59.890 46.987 41.628
HP Single threshold 9.86 0.198 500 16.863 14.236 11.820
Double threshold 3.88 0.946 500 16.834 14.887 13.033
Triple threshold 3.70 0.966 500 24.954 19.860 16.208
EG Single threshold 25.96 0.036 500 28.962 24.857 21.545
Double threshold 9.27 0.290 500 18.330 14.137 12.624
Triple threshold 6.88 0.952 500 37.812 31.348 28.071
Interestingly, however, when we conducted the threshold test, we found that the extent of the effect of economic development (the core explanatory variable EG) on urban livability was also controlled by the interval in which economic development itself was located, as shown in Table 3, where economic development also had a significant single threshold effect. This implies that the effect of economic development on urban livability varies with its own level of development, and the core explanatory variable itself as a threshold variable is a point that is often overlooked in many studies. Therefore, based on the above threshold effect test, this paper further analyzes the nonlinear effect of economic development itself on urban livability using a panel threshold model.
The results of the threshold regressions are presented in Table 4, and there is a significant threshold effect of economic development on the livability of cities, and the effect exhibits a more significant “inverted-N” threshold feature as the income level of the urban dwellers continues to rise. Specifically, the double thresholds of income level (IC) are 61, 083 and 86, 422, and economic development has a significant positive effect on urban livability when the average wage of employees is higher than 61, 083 yuan and lower than 86, 422 yuan, while economic development does not significantly affect urban livability when the average wage of employees is in the other range. When we think of livability as a commodity, for the lower income group, livability is a higher level of demand and a higher price, because people’s demand for livability is lower, economic development at this point does not significantly enhance urban livability. Once people’s incomes reach a high level, their demand begins to shift from “subsistence” to “quality”, and they expect to have an improved ecological environment, utilities and living conditions. As it stands, the promotive effect of economic development on urban livability cannot fully meet the new demands of residents for a better life, and thus economic development does not have a significant impact on urban livability.
Table 4 Threshold regression results for the entire samples
Parameters Estimated value t statistic Confidence interval
(1) α1: LnIC≤11.020 (IC ≤ 61083) 0.088 1.25 (-0.051, 0.226)
α2: 11.020 < LnIC≤11.367 (61083 < IC ≤ 86422) 0.143** 2.06 (0.007, 0.280)
α3: 11.367 < LnIC (86422 < IC) 0.073 1.09 (-0.058, 0.204)
L 0.208*** 3.90 (0.103, 0.313)
DLnSU -0.131 -1.46 (-0.308, 0.045)
LnAG -0.117*** -3.50 (-0.183, -0.051)
LnOP 0.031 0.62 (-0.052, 0.103)
SI 0.012* 1.21 (0.098, 0.121)
DF 0.022 0.69 (-0.041, 0.085)
_cons 0.338 0.57 (-0.836, 1.513)
R2 0.465
F statistic 6.54
(2) α1: LnEG ≤ 3.269 (EG ≤ 26.285) -0.182** -2.51 (-0.324, -0.039)
α2: 3.269 < LnHP (EG >26.285) -0.129* -1.88 (-0.264, 0.006)
L 0.313*** 5.79 (0.207, 0.419)
DLnSU -0.104 -1.18 (-0.278, 0.070)
LnAG -0.134*** -4.23 (-0.196, -0.072)
LnOP -0.076 -1.31 (-0.042, 0.085)
SI 0.128** 2.52 (0.227, 0.028)
DF -0.012 -1.15 (-0.033, 0.009)
_cons -0.403 -0.75 (-1.464, 0.658)
R2 0.680
F statistic 11.00

Note: The robust standard error calculation was adopted in this paper. In the threshold significance test, the number of Bootstrap is 500. The following table is the same.

The effect of economic development itself (EG) on urban livability is also nonlinear, showing a significant single threshold suppression effect with a single threshold value of 26.285. In the two intervals divided by the single threshold, when EG is below 26.285, the effect coefficient is -0.182 at the 5% significance level, indicating that lower levels of economic development have a negative suppressive effect on urban livability; when EG is above 26.285, the coefficient of influence is -0.129 at the 10% significance level, which also shows a negative inhibitory effect. However, it is important to see that the inhibitory effect diminishes significantly with economic development, i.e., the negative effect of economic development on urban livability shows a non-linear characteristic of diminishing marginal efficiency. This is mainly because when urban economic development reaches a certain stage, the focus of urban development will gradually shift from production to living and consumption, and will pay more attention to improving urban functions, improving residents’ quality of life, and protecting the ecological environment (Wang and Luo, 2007), and the crowding-out effect of economic construction on the construction of livable cities will be weakened. As China’s economy enters the stage of high-quality development, it is reasonable to believe that the driving effect of economic development on urban livability will gradually show a “U-shaped” relationship of suppression followed by promotion, and the two will eventually achieve a positive interaction and a high degree of coordination. This is also reflected in the GMM regression results, i.e., the regression coefficient of squared GDP per capita (AG) is significantly positive (namely 0.039).
Figures 3-5 show the trend of the “likelihood ratio” series LR1( ) as a function of the threshold value when “income level” and “economic development” are the threshold variables, respectively. They are visual representations of the threshold model estimates and confidence intervals. Obviously, we find that the LR values corresponding to these threshold estimates are much smaller than the threshold value of 7.35 (at 95% confidence level, indi-cated by dashed lines in the graph), so these threshold estimates are accurate and valid.
Figure 3 The first estimated threshold of income level in LR ( )
Figure 4 The second estimated threshold of income level in LR ( )
Figure 5 The estimated threshold of economic development in LR ( )
In this paper, threshold regressions were also conducted for the samples of Class-A cities and Class-B cities combined with the two threshold variables mentioned above, and the test results are shown in Table 5. The results show that for Class-A cities, both income level (IC) and economic development (EG) failed the threshold effect test, so the threshold regression model could not be constructed. This indicates that for the larger cities, there is no threshold effect characteristic between economic development and urban livability. However, in Class-B cities, there is a double threshold effect on income level and a single threshold effect on economic development. The regression results are shown in Table 6. We find that the regression results of income level as a threshold variable are basically consistent with the results in Table 4, i.e., the effect of economic development on urban livability is non-linear with the change of income level, and only when economic development makes people’s average income in a certain range, economic development only when economic development makes people’s average income in a certain range can the livability of cities be improved. The regression results of economic development as a threshold variable differ from those in Table 4, with the inhibitory effect of economic development on urban livability becoming more pronounced in smaller cities than in the national sample. This is likely because some of the cities in Class-B cities are still relatively limited in economic strength at this stage and would still devote more resource elements to economic construction under the same conditions, while supporting urban habitat construction insufficiently, or even having a crowding-out effect on their resource allocation. However, we should also see that the crowding-out effect of economic construction on the construction of livable cities will gradually diminish with the development of economy.
Table 5 Results of threshold effect significance test for diverse subsamples
Samples Models F value P value Number of BS 1% critical value 5% critical value 10% critical value
IC Single threshold 9.14 0.590 500 26.722 22.267 20.022
Double threshold 5.72 0.698 500 24.147 19.132 15.890
Triple threshold 4.72 0.882 500 44.325 31.653 26.624
EG Single threshold 8.95 0.304 500 20.653 15.632 13.278
Double threshold 7.08 0.226 500 17.035 11.613 9.181
Triple threshold 2.74 0.884 500 32.327 18.646 15.122
IC Single threshold 30.86 0.039 500 51.653 44.924 39.519
Double threshold 43.1 0.000 500 31.309 23.745 21.317
Triple threshold 12.93 0.998 500 53.008 45.202 42.157
EG Single threshold 21.55 0.034 500 25.015 20.541 18.838
Double threshold 7.51 0.526 500 17.190 14.337 12.579
Triple threshold 8.17 0.986 500 37.026 32.045 30.118
Table 6 Threshold regression results for Class-B cities
Parameters Estimated value t statistic Confidence interval
(1) α1: LnIC≤10.962 (IC≤57642) 0.083 1.22 (-0.052, 0.224)
α2: 10.962 < LnIC≤11.321 (57642<IC≤825) 0.134** 2.01 (0.007, 0.282)
α3: 11.321 < LnIC (82573<IC) 0.068 1.03 (-0.056, 0.202)
L 0.211*** 3.94 (0.106, 0.317)
DLnSU -0.125 -1.47 (-0.303, 0.042)
LnAG -0.117*** -3.58 (-0.181, -0.057)
LnOP -0.058 0.93 (0.154, 0.024)
SI 0.014 0.23 (-0.096, 0.124)
DF 0.027 0.64 (-0.042, 0.086)
_cons 0.328 0.51 (-0.833, 1.517)
R2 0.458
F statistic 6.51
(2) α1: LnEG≤3.156 (EG≤23.476) -0.202** -2.34 (-0.371, -0.032)
α2: 3.269 < LnEG (EG >23.476) -0.146* -1.78 (-0.308, 0.015)
L 0.287*** 5.17 (0.178, 0.396)
DLnSU -0.147 -1.56 (-0.333, 0.039)
LnAG -0.132*** -3.66 (-0.202, -0.061)
LnOP -0.062 0.86 (-0.141, -0.035)
SI 0.100* 1.72 (0.115, 0.215)
DF 0.014 0.40 (-0.053, 0.080)
_cons -1.787*** -0.65 (-1.676, -0.844)
R2 0.532
F statistic 6.18

5 Conclusions and discussion

5.1 Conclusions

Based on the “Cobb-Douglas production function” model, this paper introduces livability as a “factor” into the production function, constructs a theoretical model to analyze the effect of economic development on urban livability, and empirically investigates the effect based on the panel data of 40 large and medium-sized cities in China from 2005 to 2019, on the basis of revealing the time-series evolution of livability in major cities in China, using System GMM model, fixed-effects panel threshold model and other econometric methods. The main findings are as follows:
First, the livability level of large and medium-sized cities in China generally showed an upward trend from 2005 to 2019, but there were differences in the growth trends of different dimensions, with urban safety and environmental healthiness in a long-term stagnant or slow-growing trend, while the natural environment pleasantness, human environment comfort and public service accessibility had a relatively fast promotion. In terms of spatial distribution, urban livability also generally shows the characteristics that eastern coastal cities outperforming central and western cities.
Second, the empirical results show that economic development has a negative inhibitory effect on the improvement of urban livability, which is in line with existing studies (Xiong et al., 2007; Yu and Wang, 2014). After dividing the sample into different subsamples, the above “economy grows - city becomes livable” logic shows significant heterogeneity across time and city scales. Before entering the new normal phase of economy, economic development did not significantly affect urban livability, but after entering the new normal phase, economic development significantly contributed to the improvement of urban livability. There is no inevitable relationship between urban livability and city scale. In larger municipalities directly under the central government and economically developed provincial capitals (Class-A cities), economic development has a stronger inhibitory effect on urban livability, while for smaller Class-B cities, the degree of influence of economic development on urban livability is relatively small.
Third, there is a threshold effect in the influence of economic development on urban livability, and the effect shows a more significant “inverted-N” threshold as the income level of residents increases. When the average wage of employees is between 60,000 and 80,000 yuan, economic development can improve urban livability; while in other ranges, economic development does not significantly affect the urban livability. In addition, the impact of economic development itself on urban livability also exhibits a significant single threshold suppression effect, but its negative effect shows a law of diminishing marginal efficiency as the economy develops. For cities of different scales, there is no significant threshold effect in the regression results for the sample data of larger municipalities directly under the central government and provincial capitals; in other smaller cities the income levels that produce a threshold effect are basically consistent with the national sample average, and the inhibitory effect of economic development itself on the livability of such cities becomes more and more obvious. However, with the development of the economy, this inhibitory effect also shows a trend of diminishing marginal efficiency.

5.2 Discussion

Although the level of economic development shows a certain inhibitory effect and heterogeneous threshold effect on urban livability, this is not a problem of economic development itself, there is no doubt that high-quality economic development can provide a solid material foundation for the construction of livable cities. What is problematic is that the model of our urban development and construction in the past needs to be optimized and improved. This paper, combined with the findings of the empirical research, proposes to break through the gap between economic development and environmental livability at two levels: “macro guidelines” and “micro actions”:
For the macro guidelines, many cities have basically achieved the goal of “making the economy and population bigger” in the high-speed growth stage, and “improving the quality of development” has become the leading direction of urban development in the new era. “Livability” is the eternal pursuit of the ideal urban living environment, and it is also the rightful meaning of high-quality urban development. Based on the continuous consolidation of the foundation of economic development, we should internalize livability as a hard constraint and new wealth of urban development, and focus on promoting the benign adaptation of urban economy and population, resource and environmental endowments. Urban development, especially those economically developed high-income central cities and mega-cities, should pay more attention to the value of life and human-oriented, and to increasing the supply of ecological green, civilized and harmonious beautiful environment, strive to create a new modern city where urban development ascends and environmental harmony and livability complement each other, strive to materialize the results of high-quality urban development into high-quality life experience, and fully manifest the original mission of “Better City, Better Life”.
For the micro actions, firstly, the future construction of livable cities should pay extra attention to the improvement of urban safety and environmental health, and should adhere to the principle of “adapting to local conditions” and avoid a “one-size-fits-all” policy-making approach. Secondly, the rapid development of the tertiary industry and the increase of its proportion should not be the only way to solve the problem of environmental livability, but to upgrade and optimize the city’s industrial structure by improving the quality of workers and promoting the service industry to the middle and high end of the value chain, as well as guaranteeing wages that match labor productivity. Also, it is still the trend to promote high-quality urban development with high level of openness, but we should pay attention to the optimization of the structure of foreign investment introduction, especially in the cities of central and western regions, to promote foreign investment and foreign trade to high-tech and clean type, so as to break out of the dilemma of “Pollution Haven Hypothesis” and realize the “Pollution Halo Hypothesis”. Finally, we should make efforts to build a set of regular medical examination and evaluation mechanism and evaluation index system suitable for Chinese cities, and promote the implementation of the urban medical examination and evaluation system in the country, so as to take a timely and comprehensive pulse of “urban diseases” and crack the urban development problems.
In general, this study has enriched and broadened the theoretical perspective and methodological system of urban livability research to a certain extent. However, due to realistic dilemmas such as the limited availability of research data, there are still some areas to be strengthened: on the one hand, livability is a multidimensional concept, and the connotation of urban livability may not be fully interpreted and comprehensively measured due to the constraints of data availability and measurability when constructing the evaluation index system. On the other hand, due to the problem of missing data in many cities, we only selected a sample of 40 cities with more complete data in our empirical study, so the results obtained may have some limitations. However, the 40 large and medium-sized cities can basically provide a general picture of China’s economic development and the process of building livable cities. With the gradual improvement of national and local statistics, the author will further deepen these shortcomings. In addition, this paper also finds that the impact of economic development on livability is heterogeneous depending on city scale, so it is worthwhile to further explore how to determine the optimal scale of urban development under the livability perspective.
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