Urban-Rural Integration and Green Development

How Does Livelihood Public Service Affect Urban Green Development Efficiency? Evidence from 281 Cities in China

  • MENG Xia ,
  • DING Tao , *
  • School of Economics and Management, China University of Geosciences, Wuhan 430074, China
*DING Tao, E-mail:

MENG Xia, E-mail:

Received date: 2023-08-06

  Accepted date: 2023-10-30

  Online published: 2024-03-14

Supported by

The National Social Science Foundation of China(19BJY010)


In the new stage of China’s economic development, significant transformations have occurred in the mode, emphasis and speed of urban development. Enhancing the level of livelihood public service and promoting the green development of cities have emerged as the prevailing themes of contemporary urban development. Based on a theoretical model analysis, using the panel data of 281 cities in China from 2007 to 2020, this paper adopts the fixed-effect model for empirical analysis. The study revealed the following four main points. (1) The provision of livelihood public services can effectively enhance the efficiency of urban green development. After conducting a series of robustness tests, this conclusion is still valid. (2) The provision of livelihood public services only fosters the green transformation of non-resource-based cities, while its impact on resource-based cities remains non-significant. (3) The mechanism test demonstrated that livelihood public services promote green development efficiency mainly through population, innovation and producer services agglomeration. (4) The threshold effect test showed that there are differences in the nonlinear relationship between the livelihood public service level and urban green development efficiency under different threshold variables.

Cite this article

MENG Xia , DING Tao . How Does Livelihood Public Service Affect Urban Green Development Efficiency? Evidence from 281 Cities in China[J]. Journal of Resources and Ecology, 2024 , 15(2) : 304 -316 . DOI: 10.5814/j.issn.1674-764x.2024.02.006

1 Introduction

Over the past four decades of reform and opening up, China has witnessed rapid economic development, steady improvement in the urbanization level, and continuous expansion of the urban scale. However, such swift development and urbanization processes have led to an alarming consumption of resources and emission of pollutants, adversely affecting the ecological environment. China’s extensive development model is the main reason for the severe damage to the environment (Wang et al., 2023). The destruction of the ecological environment and the deterioration of air quality have had negative impacts on residents’ health and quality of life (Meng et al., 2023). In 2022, to foster the green development of the economy and society, the Chinese government proposed expediting the transition towards green models of development, promoting low-carbon and high- tech industries. Green development constitutes a crucial link in the pursuit of high-quality development. Embracing the concept of green development and augmenting the efficiency of resource utilization across all facets emerge as requirements for attaining high-quality economic advancement (Shao et al., 2022). Hence, in the process of regional governance, it is necessary to account for both environmental protection and economic growth currently. Urban green development efficiency serves as a valid measure for evaluating the endeavors of local governments in environmental governance and economic growth (He and An, 2019). Accordingly, the key to accomplishing high-quality economic development lies in the improvement of green development efficiency.
A prerequisite for cities to attain superior economic progress lies in the modernization of social governance. In recent times, the provision of public services has emerged as a pivotal mechanism by which local governments execute their urban governance responsibilities. This provisioning has played an exceedingly crucial role in promoting urban economic growth, enhancing the populace’s welfare, and facilitating the reallocation of factor resources. The provision of high-level and efficient public services stands as a significant indicator of high-quality urban development and directly reflects the government’s capacity to govern. Local governments’ public service provision can be broadly categorized into infrastructure and livelihood domains. While research has validated the influence of infrastructure public services on urban economic growth and green transformation, there is a dearth of effective exploration pertaining to the economic and environmental ramifications of livelihood public services. Livelihood public services bear a closer connection to people’s well-being and play a particularly substantial role in resource reallocation. So, one crucial question is: Can the factor mobility and agglomeration caused by the provision of livelihood public services promote the green development of the urban economy? This issue demands further study. Consequently, building upon existing research, this paper explores whether the provision of livelihood public services can enhance the efficiency of green development, facilitating urban high-quality advancements from both theoretical and empirical perspectives, while examining its specific mechanism of influence. Ultimately, the findings lead to a set of academic suggestions for livelihood public service supply and urban green transformation.

2 Literature review and theoretical analysis

2.1 Literature review and theoretical basis

Numerous scholars have delved into the pivotal role of public services in impacting economic development through empirical analysis. However, there are still debates on whether public services foster economic growth. The prevailing consensus among scholars suggests that the government’s fiscal expenditures allocated to the provision of public services serve as a catalyst for local economic development, acting as a potent driving force behind the attainment of high-quality economic progress (Hu, 2018; Li, 2020; Li, 2022). The existing literature has not only investigated the impact of infrastructure public service supply represented by transportation facilities on regional economic growth (Zhang, 2012; Zhang et al., 2018; Tang et al., 2021), but it has also scrutinized how public service expenditures in the livelihood category, represented by education, social security, and health care, contribute to economic growth (Monteiro and Turnovsky, 2008; Wang and Alvi, 2011; Chu et al., 2020). Fiscal expenditure is the main means for the government to provide public services, and it is also the direct expression of the level of public service supply. For example, Gupta et al. (2005) took 39 low-income countries in the 1990s as samples to assess the impact of public expenditure policies and their composition on economic growth. Their analysis found that public expenditure is closely related to higher economic growth in both the short and long term. Among them, productive public expenditure plays a more significant role in promoting economic growth. Iimi (2005) explored whether decentralization would lead to the effective provision of local public services and stimulate economic development based on the cross-country data from 1997 to 2001, and the results confirmed such positive effects. Phiri and Mbaleki (2022) took South Africa as an example to analyze the impact of fiscal expenditure on labor productivity. They found that fiscal spending on health, recreation and public safety has significant positive effects on labor productivity and economic development in both the long and short term, while the remaining spending items have no significant effect on productivity.
The current research on how public services contribute to economic growth has essentially focused on two main aspects. Firstly, public services are the core competitiveness of cities in attracting population, capital, and other factors (Deng et al., 2021). Due to the inadequacy of basic public services’ equalization in China, people tend to migrate across regions to pursue better public services (Xia and Lu, 2015). Cities with high-level public services can attract more people, and their population agglomeration and human capital level are higher, which are vital factors for promoting economic development. These cities also tend to be attractive to capital, knowledge, and other resources, leading to greater economic opportunities. The augmentation of the urban public service level exerts a favorable influence on the agglomeration of local production factors (Liu, 2021), thus forming an agglomeration effect that promotes the innovation activities of local enterprises, technological progress, labor productivity improvement, wider boundaries of production activities, and overall regional economic development (Sun et al., 2018; Liu et al., 2022; Wei et al., 2023). Secondly, government expenditure on public services helps to stimulate household consumption and expand domestic demand (Chen and Wei, 2017; Wang et al., 2018). Government expenditure on education, medical services, social security, and other areas enhances the residents’ welfare, lowers their living costs, and raises their consumption level. Government public spending on infrastructure, including transportation, water conservancy, urban and rural power grids, and other essential facilities, expands the domestic demand, increases employment rates, and promotes regional economic growth. It is worth highlighting that despite the consensus among many scholars regarding the beneficial influence of public services on the economy, some contradictory findings suggest that the provision of such services may potentially impede local economic growth. Furthermore, empirical studies indicate that various categories of public expenditure yield heterogeneous impacts on economic growth. For example, government public expenditures on cultural and educational costs, maintenance costs, and other aspects can have an inhibitory effect on economic growth (Fu and Shen, 2006; Kou and Zhou, 2007). Additionally, the government incurs adjustment costs during the public expenditure process, which weakens its driving force on economic growth (Zhuang and Zou, 2003).
Regarding the impact of public services on the green development of the economy, different scholars conducted research from different perspectives. Some scholars have studied the impact of public service provision on the green development of the economy mainly from the perspective of public financial expenditure. For example, Lin and Zhu (2019) explored the impact of governmental public financial expenditure on green economic growth, and found that fiscal R&D spending and education spending primarily promote green economic growth through technological activities and human-capital intensive activities. Fang et al. (2024) argued that green fiscal spending is conducive to improving green total factor energy efficiency and promoting green urban development. Chen et al. (2023) found that fiscal expenditure on science and technology can promote carbon emission reduction through digital innovation and green technological progress, and promote green and low-carbon development. Some other scholars took high-speed rail as an example to discuss the impact of infrastructural public services on the efficiency of urban green development, and they have unanimously agreed that the opening of high-speed rail can promote the improvement of urban green development efficiency (Peng and Wang, 2019; Ran et al., 2020; Wang et al., 2021). High-speed rail affects urban green development efficiency mainly by influencing factor flow and agglomeration, resource reallocation, industrial restructuring, technological innovation, and alleviating factor distortion.
However, there is a shortage of literature concerning the impact of livelihood public services on the efficiency of urban green development. The circulation of population, capital, and other factors resulting from the provision of livelihood public services constitutes the fundamental factor behind its influence on regional economic growth and green development, as well as the primary catalyst for the agglomeration effect. The augmentation of urban public service supply for livelihoods attracts the inflow of population, capital, and other factors, instigating a reallocation of resources. The movement of the population is driven by the desire for improved public services such as healthcare and education. The influx of population contributes to the expansion of the urban populace and a greater number of high-level talents, enhancing the levels of human capital and fostering a conducive environment and foundation for enterprise innovation. Consequently, population agglomeration and innovation agglomeration effects ensue, thereby fostering heightened enterprise productivity and innovation in green technologies. The provision of public services for livelihoods serves as an embodiment of a city’s soft power and core competitiveness, representing an indispensable factor in attracting capital inflows, particularly those of high quality. Capital inflow then brings more local enterprises and more advanced production technology, thereby expanding the scale of urban production. The influx of population, capital, technology, and other factors constitutes the bedrock for producer services agglomeration. This not only fosters the aggregation of population and innovation but also encourages the agglomeration of producer services. Producer services are characterized as being knowledge-intensive, technology-intensive, capital-intensive, and talent-intensive. Not only do they provide specialized services to cities, but they also encourage the diffusion and overflow of knowledge and technology by reducing the transaction costs, communication costs, and production costs of manufacturing industries, and ultimately promoting urban green development (Zeng et al., 2019; Huang and Guo, 2020). Producer services and the manufacturing industry are interconnected, with the advancement of the manufacturing sector predominantly achieved through the agglomeration of producer services. This agglomeration brings in human capital and knowledge capital for the manufacturing industry, which is conducive to deepening the industrial division of labor and cooperation, propelling technological innovation, and facilitating the application of advanced production technologies that prioritize energy conservation and emission reduction in enterprise operations. Consequently, this leads to the upgrading of manufacturing industries, improved energy utilization efficiency, enhanced enterprise productivity, and reduced energy waste and pollution emissions throughout the production process (Liu et al., 2017). Ultimately, this situation promotes the advancement of green development efficiency within cities.

2.2 Theoretical model

(1) Corporate decision making
According to the literature review above, public service plays an important role in promoting regional economic growth. Meanwhile, to analyze the influence of livelihood public services on the efficiency of urban green development, this study introduced both government public service expenditure and green total factor productivity into the enterprise production function and constructed a Cobb-Douglas type production function:
Y = G D E × L α × K β × G θ
G = G 1 + G 2
where Y is the economic output level; G is government public service expenditure; G1 is livelihood public service expenditure; G2 is infrastructure public service expenditure; GDE is urban green development efficiency, which is represented by green total factor productivity; L denotes labor factor input; K denotes capital factor input; α is the output elasticity of the labor factor; β is the output elasticity of the capital factor; θ is the output elasticity of government public expenditure; and 0<α, β, θ<1.
Now, the first-order optimality conditions can be calculated for the enterprise’s production based on the CD-type production function:
Y L = α × G D E × L α 1 × K β × G θ = w
Y K = β × G D E × L α × K β 1 × G θ = r
Y G 1 = θ × G D E × L α × K β × G θ 1
where w is the urban wage level, which represents the labor factor price; and r is the level of interest rate, which represents the price of capital factors.
(2) Government decision making
Referring to the study of Zhang et al. (2022), this study assumed that the government mainly pursues two aspects of effectiveness in the decision-making process of fiscal expenditure. The first aspect is that public service expenditure can promote the GDP growth of the area, and government officials can accumulate political achievements and obtain the possibility of political promotion. The second aspect is government consumption spending. Therefore, this study set the utility function of local government in the following form:
U = λ × Y ( G ) + U ( C )
where U is the utility level of local government; C is the consumption expenditure of the local government; U(C) is the utility of government consumption expenditure; and λ is the magnitude of the utility generated by the possible political promotion opportunities brought by the GDP growth of local government jurisdictions relative to the marginal utility from consumption expenditure.
Here, the budget constraint of the local government was set as:
G + C = G 1 + G 2 + C = η × τ × Y + T
where η is the share of the total tax revenue of the region under the area of the local government that belongs to the local government; 1-η is the share of the total tax revenue belonging to the central government; τ is the tax rate of the local government levied on enterprises, which is set as a constant here; and T is the transfer payment of the central government to the local government.
According to the utility function and budget constraint of the local government, the optimal solution for the local government’s livelihood public service expenditure was calculated as:
Y G 1 = U ( C ) λ + η × τ × U ( C )
where U C is the first derivative of U C, representing the marginal utility of government consumption expenditure, and U C > 0.
(3) Market equilibrium
The decision-making process of government expenditure and enterprise production can be regarded as a sequential game. The local government will decide on public service expenditure first, and it also fully understands the decision-making pattern of enterprises. So, the local government will fully consider the impact on enterprise production in the decision-making process. Therefore, when solving the equilibrium solution, the first-order optimal condition of the enterprise’s production on livelihood public service expenditure can be brought into the optimal solution of the government’s livelihood public service expenditure, and the following optimal condition can be obtained as:
Y G 1 = θ × G D E × L α × K β × G θ 1 = U ( C ) λ + η × τ × U ( C )
(4) Comparative static analysis
Regarding the influence of labor factor, capital factor and livelihood public services on the efficiency of urban green development, the calculation results can be obtained according to equations (3), (4) and (9) above:
d G D E d L = ( 1 α ) × w α × L α × K β × G θ > 0
d G D E d K = ( 1 β ) × r β × L α × K β × G θ > 0
d G D E d G 1 = 1 θ × L α × K β × λ × U ( C ) × G 1 θ × η × τ × Y G 1 1 + U ( C ) × G θ × ( 1 θ ) × λ + η × τ × U ( C ) λ + η × τ × U ( C ) 2 > 0
where, U C is the second derivative of U C, and U C < 0. Equations (10), (11), and (12) show that when the supply of livelihood public services increases, the green development efficiency of cities also increases. Meanwhile, the enhancement in the level of labor and capital factors also contributes to greater green development efficiency.
By constructing the aforementioned model and solving it, this study theoretically substantiates the significant influences of livelihood-oriented public services, population, and capital on urban green development. This forms the foundation for subsequent theoretical hypotheses and empirical examinations. On one hand, the model confirmed the direct facilitative role of livelihood public service provision in enhancing urban green development efficiency, thereby offering theoretical support for the baseline regression results in later sections. On the other hand, regarding the indirect mechanism through which livelihood public services affect urban green development efficiency, this theoretical model can serve as a valid basis for the mechanism test in subsequent analyses. The results obtained from the model’s solution validated the positive contributions of population and capital factors to urban green development. Furthermore, the supply of livelihood public services can significantly promote the redistribution of resource elements between regions, facilitating the flows of population, capital, and knowledge across cities and fostering the generation of agglomeration effects. Thus, based on this model, this study posits that the agglomeration effects brought about by the provision of livelihood public services are the primary pathway for enhancing urban green development efficiency. Figure 1 illustrates the theoretical framework of this study.
Fig. 1 Theoretical framework
Based on the above theoretical analysis, the following two hypotheses are proposed:
Hypothesis 1: The improvement of the livelihood public service level contributes to the improvement of urban green development efficiency.
Hypothesis 2: The agglomeration effect resulting from the supply of livelihood public services is the main way to drive the improvement of urban green development efficiency.

3 Method

3.1 Model setting

Based on the above theoretical analysis, the following econometric model was constructed for empirical analysis in order to specifically study the impact of the supply of livelihood public services on urban green development efficiency:
G D E i t = α 0 + α 1 p o p l i f e i t + β X i t + μ i + γ t + ε i t
The indices i and t denote cities and years, respectively. GDEit is the explained variable, which indicates urban green development efficiency. poplifeit is the core explanatory variable, which indicates the level of urban livelihood public services. Xit is a series of control variables that affect the green development efficiency of cities. μi is the city fixed effect, γt is the year fixed effect, and εit is a random error.

3.2 Variable description

(1) Explained variable
In this study, the green total factor productivity index was used to indicate urban green development efficiency (GDE). Referring to Liu and Xin (2018), we selected the GML index based on the SBM directional distance function to measure the green total factor productivity. The relevant input-output indicators are as follows. Capital input: With reference to Zhang et al. (2004), the fixed capital stock was calculated using the perpetual inventory method with the formula: Kt=It/Pt+(1-δt)Kt-1, where Kt denotes the fixed capital stock in the current period, It denotes the total nominal fixed capital formation in the current period, Pt denotes the fixed asset investment price index, and δt denotes the depreciation rate. In the selection of the depreciation rate, the capital depreciation rate was set at 10.96% and deflated by 2006 as the base period. Labor input: The number of individual employees at the end of the year in the city was selected to denote labor input. Energy input: The city's coal consumption was selected to denote energy input. The output indicators mainly include desired output and undesired output. The desired output was expressed by the city's real GDP at constant prices in 2006. The undesired output included industrial soot emissions, industrial wastewater emissions and sulfur dioxide emissions. According to the above indicators, this study used matlab software to measure the GML index of 281 cities. With reference to Peng and Wang (2019), the GML index was cumulatively multiplied with 2006 as the base period to obtain the value of the dependent variable green total factor productivity.
(2) Core explanatory variables
The core explanatory variable is urban livelihood public service, which is represented in this study by the per capita expenditure of livelihood public service (poplife). In order to more comprehensively investigate the impact of varied livelihood public services on urban green development efficiency, this study adopted per capita education expenditure, per capita medical and health expenditure, and per capita social security and employment expenditure to denote urban education level (education), medical level (medical), and social security level (security), respectively.
(3) Control variables
In this study, the levels of economic development, local government competition, industrial structure, foreign direct investment, R&D investment, infrastructure level and degree of financial development were selected as control variables. Economic development level was expressed by real per capita GDP. Local government competition, referring to Qian et al. (2011), was calculated as follows: local government competition degree = (local fiscal income - local fiscal expenditure)/local fiscal revenue. Industrial structure was measured by the proportion of tertiary industry output level to total regional output level. R&D input was measured by the proportion of science and technology expenditure to local fiscal expenditure. Foreign direct investment was calculated as the ratio of actual foreign capital used to the city’s year-end GDP. The level of infrastructure was measured by per capita road area. The level of financial development was calculated as the ratio of loan balance of financial institutions to GDP at the end of the year.
(4) Mediating variable
The mediating variable in this study is the agglomeration effect, which can be specifically divided into population agglomeration, innovation agglomeration and producer services agglomeration. Population agglomeration (popagg) is expressed by urban population density. Innovation agglomeration (invagg) is expressed by the number of patents per square kilometer. Referring to Liu and Zhang (2021), producer services agglomeration (seragg) was measured by the locational entropy index, and its calculation formula is:
s e r a g g i = E i , s / E i E s / E
where, Ei,s is the employment number of producer services in city i, Ei is the total employment number of city i, Es is the national employment number of producer services, and E is the total employment number of the country.
The data used in this study were mainly obtained from China City Statistical Yearbook (2007-2021), China Statistical Yearbook (2007-2021), China City Construction Statistical Yearbook (2007-2021), the official websites of EPS DATA, and prefectural-level city governments. Table 1 shows the descriptive statistics of the main variables.
Table 1 Descriptive statistics
Variable Definition Obs. Mean Std.D. Min Max
GDE Green development efficiency 3934 0.9982 0.0171 0.8910 1.2870
poplife Livelihood public service level 3934 0.2970 0.2122 0.0285 2.3378
education Education level 3934 0.1326 0.1047 0.0141 1.3150
medical Medical level 3934 0.0647 0.0491 0.0001 0.6818
security Social security level 3934 0.0997 0.0786 0.0017 0.7446
popagg Population agglomeration 3934 0.8400 0.3455 0.0308 2.4294
invagg Innovation agglomeration 3934 0.2932 0.5217 0.0001 4.7218
seragg Producer services agglomeration 3934 0.7938 0.3154 0.0845 4.2904
lnpgdp The logarithm of per capita GDP 3934 10.3506 0.6641 8.0171 12.3429
gov Local government competition 3934 -1.8845 1.8661 -17.3985 0.3512
is Industrial structure 3934 0.4016 0.0996 0.0858 0.8387
rd R&D input 3934 0.0158 0.0156 0.0006 0.2068
fdi Foreign direct investment 3934 0.0181 0.0188 0.0000 0.1984
lnproad The logarithm of per capita road area 3934 1.1040 0.9121 -1.9059 4.3816
finance Financial development level 3934 0.9351 0.5993 0.0753 9.6221

4 Empirical results and analysis

4.1 Benchmark regression results

Table 2 reports the benchmark regression results. Column (1) shows that the influence coefficient of livelihood public service on urban green development efficiency is 0.0505, which is significantly positive at the 1% level. This indicates that an increase in the local governments' livelihood public expenditure and the improvement of livelihood public service level can significantly improve the efficiency of urban green development, thus verifying hypothesis1. In this study, livelihood public service expenditure is divided into education expenditure, medical expenditure and social security expenditure. Columns (2), (3) and (4) report the individual impacts of education level, medical level and social security level on urban green development efficiency, with regression coefficients of 0.0506, 0.1334 and 0.1225, respectively. All of them are significantly positive at the 1% level, this indicating that education, medical services and social security, as sub-indicators of livelihood public service, can all promote urban green development.
Table 2 Benchmark regression results
Variable (1) GDE (2) GDE (3) GDE (4) GDE (5) GDE (2007-2012) (6) GDE (2013-2020)
poplife 0.0505*** -0.0074 0.0812***
(0.0032) (0.0059) (0.0061)
education 0.0506***
medical 0.1334***
security 0.1225***
lnpgdp -0.0021 -0.0058** -0.0060** -0.0033 0.0037 -0.0008
(0.0024) (0.0024) (0.0024) (0.0024) (0.0035) (0.0043)
gov 0.0002 0.0000 0.0001 0.0006** 0.0005 0.0012**
(0.0003) (0.0003) (0.0003) (0.0003) (0.0003) (0.0005)
is 0.0100 0.0024 0.0097 0.0078 0.0047 -0.0059
(0.0068) (0.0069) (0.0069) (0.0067) (0.0090) (0.0102)
rd -0.0948*** -0.0748*** -0.0913*** -0.0360 -0.0997** -0.0290
(0.0262) (0.0270) (0.0268) (0.0257) (0.0397) (0.0358)
fdi -0.0411** -0.0625*** -0.0617*** -0.0358* -0.0284 -0.1390***
(0.0191) (0.0195) (0.0193) (0.0190) (0.0226) (0.0310)
lnproad -0.0011 -0.0014* -0.0014* -0.0011 0.0001 -0.0017
(0.0008) (0.0008) (0.0008) (0.0008) (0.0009) (0.0013)
finance -0.0016** -0.0013* -0.0012 -0.0022*** 0.0010 -0.0002
(0.0007) (0.0008) (0.0008) (0.0007) (0.0009) (0.0010)
_cons 1.0064*** 1.0557*** 1.0534*** 1.0230*** 0.9614*** 0.9861***
(0.0258) (0.0262) (0.0258) (0.0252) (0.0362) (0.0463)
Control variables Yes Yes Yes Yes Yes Yes
City fixed effect Yes Yes Yes Yes Yes Yes
Year fixed effect Yes Yes Yes Yes Yes Yes
N 3934 3934 3934 3934 1686 2248
Adjusted R2 0.4437 0.4169 0.4256 0.4535 0.3419 0.6220

Note: The values above the parentheses are the estimated coefficients for each variable. The standard errors are in parentheses. *, ** and *** indicate statistical significance at the 10%, 5% and 1% levels, respectivley. The same below.

Furthermore, given that China's development model has transitioned from an extensive phase to a new phase of high-quality development, regressions were conducted on the samples in different time periods. In 2012, the Chinese government made strategic decisions regarding ecological civilization construction to address the environmental pollution issues caused by extensive development. The promotion of a green and low-carbon transformation in the economic development model became one of the key themes in China’s economic development process. Therefore, taking 2012 as a time reference point, the samples were divided into two periods: 2007-2012 and 2013-2020, for the regression analysis. During the period of 2007-2012, the coefficient of the influence of livelihood public services on urban green development efficiency did not pass the significance test. However, during the period of 2013-2020, the level of livelihood public services significantly improved urban green development efficiency. Possible reasons for this can be attributed to the fact that before 2012, our country’s development model mainly focused on extensive development characterized by high pollution, emissions, and energy consumption, prioritizing the pursuit of higher GDP while neglecting environmental protection. Correspondingly, the structure and expenditure preference of government public service expenditures were more inclined towards infrastructure construction conducive to promoting economic growth. Consequently, during this period, the impact of livelihood public services on green development was minimal due to the local government expenditure bias and GDP competition. Comparing the regression coefficients of livelihood public services in Columns (1) and (6), the coefficient during the period of 2013-2020 is 0.0812, which is greater than the overall coefficient of 0.0505. This indicates that with the implementation of our country’s ecological civilization construction strategy in 2012, local governments have attached greater importance to environmental protection and green development in their economic development efforts. Meanwhile, there has been a gradual adjustment in the government’s public expenditure structure. Therefore, compared to the overall sample period, the green effect due to livelihood public services is more notable during the period of 2013-2020.

4.2 Heterogeneity analysis

China boasts a vast territory, with significant differences in geographical environments, resource endowments, and levels of economic development among its various cities. Cities with different resource endowments exhibit considerable disparities in their reliance on resources and industrial layouts during the process of economic development. The degree of dependence on resources and the industrial structure of a city are key factors that influence its green transformation.To further investigate the impact of livelihood public service provision on the green development of different types of cities, this study divided Chinese cities into resource-based and non-resource-based categories based on the “National Sustainable Development Plan for Resource- Based Cities (2013-2020)” issued by the State Council in 2013, and then conducted a heterogeneity test. Resource- based cities play a crucial role in ensuring the energy supply and industrial development in China, while non-resource- based cities tend to focus more on the development of the tertiary sector. Table 3 presents the results of the heterogeneity test, which indicate that livelihood public service provision only promotes the green transformation of non-resource-based cities and it has no significant impact on the green transformation of resource-based cities. One possible reason is that resource-based cities rely heavily on traditional industries and resources for their economic development. Currently, most resource-based cities in China are facing a series of problems such as difficulties in transforming their development models, insufficient industrial upgrading, slow economic growth, delayed urban development and population loss, which make it difficult to bring the green effect of livelihood public service supply into play. This situation makes it difficult for the green effects of livelihood public service provision to come into play. The reliance of resource-based cities on traditional industries impedes the development of high-tech industries. Due to the inherent limitations of traditional industries, compared to high-tech enterprises, the technological innovation level of traditional industrial enterprises is limited, making it challenging for them to serve as the leaders in technology and consequently unable to drive the green transformation of the resource-based cities.
Table 3 Heterogeneity analysis
Variable (1)
Non-resource-based cities
Resource-based cities
poplife 0.0640***
Control variables Yes Yes
City fixed effect Yes Yes
Year fixed effect Yes Yes
N 2352 1582
Adjusted R2 0.4493 0.4918

4.3 Endogeneity test

Considering the benchmark regression equation (9), there may be endogeneity problems that bias the OLS estimation results. The two potential endogeneity problems are mainly the omitted variable problem and the reverse causality problem. First, although baseline regression equation (9) takes into account enough control variables, the problem of omitted variables may still exist. The omitted variables may be correlated with the residuals, which may lead to endogeneity problems. Second, a plausible inverse causal connection could exist between livelihood public services and the efficiency of urban green development. To address these endogeneity concerns, the instrumental variables approach has proven to be a widely-used and effective methodology. Referring to Li (2020), this study adopted the number of urban road miles as an instrumental variable for the two- stage least squares (2SLS) regression. Table 4 presents the outcomes of the instrumental variable regression. This study also conducted the under-identification test and weak identification test, whose results indicate that the instrumental variable does not have under-identification, weak identification and over-identification problems. After considering the possible endogeneity, the results from the second-stage regression reveal a persistent significantly positive coefficient for livelihood public services, and the reliability of the benchmark regression results is verified.
Table 4 Endogeneity test
Variable First-stage
poplife 0.0940***
iv -0.0977***
Control variables Yes Yes
City fixed effect Yes Yes
Year fixed effect Yes Yes
Unidentifiable test
(Anderson LM statistics)
Weak identification test
(Cragg-Donald Wald F statistics)
N 3934 3934

Note: iv represents instrumental variable. Values in brackets [ ] are P-values. Values in braces { } are critical values for the Stock-Yogo weak identification test at the 10% level.

4.4 Robustness test

To further enhance the credibility in the empirical analysis findings above, this study carried out robustness tests encompassing four key aspects. First, the indicator of the proportion of livelihood public expenditure in local fiscal expenditure (popliferate) was used to replace the original core explanatory variables. Second, the core explanatory variable was lagged by one period for the regression. Third, the explained variable was replaced. In this study, based on the super-efficient SBM model, the green total factor productivity was measured again using the Globe-Malmquist-Luenberger (GML) index. Fourth, considering that municipalities directly under the central government have their own unique political, economic, and locational advantages compared with other general cities, this study conducted the regression analysis again after excluding those municipalities directly under the central government and adjusting the overall city sample. According to the results in Table 5, all the coefficients are still significantly positive, so Hypothesis 1 is once again verified.
Table 5 Robustness test
Variable (1)
Replacing the core explanatory variable
Lagging one period
Replacing the explained variable
Excluding the municipalities
popliferate 0.0184***
poplife 0.0582***
Control variables Yes Yes Yes Yes
City fixed effect Yes Yes Yes Yes
Year fixed effect Yes Yes Yes Yes
N 3934 3653 3934 3878
Adjusted R2 0.4079 0.4753 0.4962 0.4749

5 Discussion

5.1 Mechanism test

According to the theoretical analysis in the previous section, the supply of livelihood public services mainly affects the efficiency of urban green development through population agglomeration, innovation agglomeration and producer services agglomeration. Therefore, this section will test its specific mechanism of influence.
Table 6 presents the findings pertaining to the examinations of the population agglomeration mechanism. Based on the regression outcomes depicted in column (1), a city’s attractiveness to the population is directly proportional to the provision of its livelihood public services. In column (2), the regression coefficient of population agglomeration on urban green development efficiency was determined to be 0.0049, signifying that heightened population agglomeration fosters increased levels of human capital and talent concentration, subsequently promoting the enhancement of urban green development efficiency. Column (3) incorporates both livelihood public services and population agglomeration into a unified framework for simultaneous regression. The outcomes affirm that both livelihood public services and population agglomeration yield significant positive contributions to urban green development efficiency, with regression coefficients of 0.0502 and 0.0036, respectively.
Table 6 Mechanism test: Population agglomeration
Variable (1)
poplife 0.0901***
popagg 0.0049***
Control variables Yes Yes Yes
City fixed effect Yes Yes Yes
Year fixed effect Yes Yes Yes
N 3934 3934 3934
Adjusted R2 0.8741 0.4075 0.4442
Table 7 presents the findings pertaining to the examination of the innovation agglomeration mechanism. As depicted in column (1), a noteworthy positive correlation exists between livelihood public services and innovation agglomeration. This observation signifies that the provision of livelihood public services facilitates the influx of population, capital, and other influencing elements, thereby serving as an effective catalyst for enterprise innovation activities and elevating the level of innovation agglomeration within the cities. Moving to column (2), the regression coefficient between innovation agglomeration and urban green development efficiency was determined to be 0.0136. This finding indicates that a heightened level of innovation agglomeration corresponds to enterprise productivity enhancement and green technology innovation, consequently resulting in elevated green development efficiency. In column (3), livelihood public services and innovation agglomeration are included in the same framework for the regression. The results show that both livelihood public services and innovation agglomeration have a significant positive contribution to urban green development.
Table 7 Mechanism test: Innovation agglomeration
Variable (1)
poplife 1.0814***
invagg 0.0136***
Control variables Yes Yes Yes
City fixed effect Yes Yes Yes
Year fixed effect Yes Yes Yes
N 3934 3934 3934
Adjusted R2 0.9275 0.4218 0.4453
Table 8 presents the findings pertaining to the mechanism of agglomeration in productive service industries. The data in column (1), indicate that the augmentation in the provision of livelihood public services significantly fosters the agglomeration of producer services. An elevated level of supply in these local government livelihood public services proves advantageous in attracting inflows of populace, capital, technology, and other pivotal factors. The producer service industry is knowledge-intensive, technology-intensive, capital-intensive, and talent-intensive, so the factor inflows brought by the livelihood public service supply can directly stimulate the local producer services agglomeration. The regression outcome displayed in column (2) indicates a noteworthy positive impact of producer services agglomeration on the efficiency of urban green development. The amalgamation of producer services not only effectively strengthens the quality and growth efficiency of urban economic progress but also serves as a catalyst for urban green development. Moving to column (3), the regression analysis results demonstrate that both livelihood public services and producer services agglomeration remarkably accelerate the realization of urban green development goals with regression coefficients of 0.0495 and 0.0078, respectively.
Table 8 Mechanism test: Production services agglomeration
Variable (1)
poplife 0.1300***
seragg 0.0091***
Control variables Yes Yes Yes
City fixed effect Yes Yes Yes
Year fixed effect Yes Yes Yes
N 3934 3934 3934
Adjusted R2 0.7770 0.4125 0.4482
The results of the above mechanism test verify hypothesis 1.

5.2 Nonlinear effects test

Considering the possible nonlinear effect of livelihood public services on urban green development efficiency, the following threshold panel model was constructed to test it.
G D E i t = α 0 + α 1 × p o p l i f e i t × I t h r γ + α 2 × p o p l i f e i t × I t h r > γ + β × X i t + μ i + γ t + ε i t
where, I(·) is an indicative function. If the condition in parentheses is true, it is assigned a value of 1, otherwise it is assigned a value of 0. γ is the threshold value to be estimated. t h ris the threshold variable. The explained variable and the core explanatory variable were selected as threshold variables, respectively, to test the threshold effect between livelihood public service level and urban green development efficiency.
Table 9 presents the regression outcomes concerning the threshold effect. Based on the findings in column (1), when the green development efficiency serves as the threshold variable, the coefficient indicating the impact of livelihood public services on urban green development efficiency is insignificant when the green development efficiency falls below 1.0256. However, as the threshold variable surpasses 1.0256, the amplification of livelihood public service provisions substantially advances urban green development. This shift can be attributed to several reasons: Firstly, when cities exhibit a low level of green development efficiency, their economic growth primarily relies on an extensive development approach. In order to meet the local GDP growth targets, local governments may readily embrace labor-intensive and environmentally detrimental enterprises. Simultaneously, to attract inward investment, these governments may opt to increase their allocations of fiscal expenditures towards infrastructure development, thereby resulting in a crowding-out effect on livelihood public expenditures. This skewed distribution of public expenditures impedes the progress of urban green development.
Table 9 Threshold effect regression results
Variable Threshold variables and values
GDE (1.0256)
poplife (0.6330)
p o p l i f e × I t h r γ -0.0018
p o p l i f e × I t h r > γ 0.1000***
Control variables Yes Yes
City fixed effect Yes Yes
Year fixed effect Yes Yes
N 3934 3934
Adjusted R2 0.5635 0.1108
The results presented in column (2) demonstrate that when considering the level of livelihood public services as the threshold variable, the estimated coefficient pertaining to livelihood public services amounts to 0.0151 when the threshold variable falls below 0.633. Subsequently, the estimated coefficient rises to 0.0376 when the threshold variable exceeds 0.633. This shift suggests that as the level of livelihood public services escalates, its impact on urban green development becomes more pronounced. Several plausible rationales may be behind this phenomenon. When the level of urban livelihood public services remains low, the structure of public expenditure may exhibit greater distortions. Local governments tend to allocate their public expenditures disproportionately towards infrastructure public services, thereby impeding the gradual improvement in the level of livelihood public services and weakening their impetus for urban green development. However, with sustained economic progress and a shift in the local government’s development mindset, the distortion in the structure of public expenditure is likely to subside, leading to a potential increase in the proportion of expenditure on livelihood public services. Consequently, the promotional effect of livelihood public services on urban green development will be further amplified.

6 Conclusions and policy suggestions

6.1 Conclusions

Elucidating the causal relationship between the level of urban public services and the efficacy of green development bears immense practical significance in advancing China’s pursuit of high-quality economic growth. Based on the theoretical model analysis, this study empirically analyzed the impact of the level of livelihood public services on the efficiency of green development and its mechanism of influence using panel data of 281 cities from 2007 to 2020. Previous studies have explored the path of urban green transformation from different aspects, such as government fiscal expenditure, green fiscal expenditure and infrastructure construction, but there is a lack of comprehensive and in-depth research on how government livelihood public expenditure affects urban green development. Therefore, compared with previous studies, this study investigated the impact of livelihood public service supply on urban green development efficiency from both theoretical and empirical dimensions. It mainly focused on the mechanism of livelihood public services affecting the efficiency of green development for theoretical elaboration and empirical testing. Finally, it clarifies the intrinsic mechanism by which livelihood public services influences green development efficiency, and enriches the theoretical connotation of urban green development. In terms of research perspectives, this study examined the impacts of education level, medical level and social security level on urban green development efficiency while studying how the level of livelihood public services affects green development efficiency. In the mechanism testing part, this study then analyzed the specific impact of livelihood public service provision on urban green development efficiency from the perspective of the agglomeration effect.
The research conclusions can be summarized in four main points. 1) The provision of livelihood public services exerts a significant positive influence on urban green development efficiency, and this conclusion still holds after a series of robustness tests. Notably, advancements in educational, medical, and social security provisions within the realm of livelihood public services all contribute to the promotion of urban green development. In addition, with the progression of economic development, increased efforts from the government in environmental protection, and adjustments in the structure of public expenditure, the green effect of livelihood public services has become more pronounced. 2) The findings derived from the heterogeneity analysis based on city type reveal that livelihood public services make a substantial contribution to enhancing the efficiency of green development in non-resource-based cities, whereas their impact on green progress in resource-based cities is not statistically significant. 3) The results of the mechanism test demonstrate that livelihood public services predominantly influence green development efficiency through the agglomeration effect. This is exemplified by the fact that such services facilitate green and low-carbon city construction by fostering the agglomeration of population, innovation, and producer services. 4) The threshold effect test results indicate that the role of livelihood public services in promoting urban green development can be effectively harnessed only when the threshold value for green development efficiency is surpassed. Additionally, when the level of livelihood public services becomes the threshold variable, their positive contribution to enhancing urban green development efficiency is further augmented with an increase in the supply of such services.

6.2 Policy suggestions

(1) The adequate provision of high-quality public services is a crucial prerequisite for fostering economic transformation and achieving green development. To this end, local authorities should proactively increase the supply of livelihood-related public services, so as to infuse impetus into the attainment of urban green development objectives. Livelihood public services mainly promote urban green development through the reallocation effect of resource factors. Accordingly, the Chinese government should expedite the reform of the household registration system and the construction of a unified national market, reduce local protectionism, eliminate barriers between regions due to rigid administrative divisions, and provide institutional guarantees for the free flow of population, capital, knowledge and other factors. This, in turn, would furnish an effective guarantee for livelihood public services to propel the green and low-carbon transformation of cities.
(2) Local governments should adopt some approaches to facilitate the green transformation of resource-based cities. On the one hand, they should actively eliminate outdated production capacity and increase financial expenditures to promote technological innovation in traditional industrial enterprises. Moreover, it is imperative to enhance the business environment, strengthen intellectual property protection, and provide ample guarantees in order to attract high-tech enterprises and high-quality capital. On the other hand, vigorous efforts should be made to develop the tertiary sector and gradually reduce the resource dependency of resource-based cities. Furthermore, traditional enterprises should intensify their reform efforts, improve labor skills through training, enhance human capital, and boost labor productivity. The application of digital technology should be encouraged to facilitate the deep integration of digital technology with traditional industrial production, thereby promoting the digitization and intelligent transformation of enterprises.
(3) Local governments should focus on enhancing the local population, innovation and producer services agglomeration levels in accordance with the resource reallocation role of the livelihood public service supply. Relevant measures may include optimizing the business environment for enterprises, reducing their operating costs, strengthening the protection of intellectual property rights, stimulating their innovative vitality, cultivating a number of leading high-tech enterprises with green and low-carbon attributes, promoting green technological progress and the application and promotion of clean production technologies, as well as fostering new momentum for urban green development.
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