Spatial–Temporal Evolution and Influential Factors of Eco-Efficiency in Chinese Urban Agglomerations

Abstract

Against the background of a high-quality development philosophy, the realization of the coordinated development of the economy, environment, and resources is particularly important. This study adopts the super-efficiency slacks-based measure (SBM) model to evaluate the eco-efficiency of 208 cities in 19 urban agglomerations in China from 2006 to 2020, and the kernel density estimation and spatial econometric specifications are combined to reveal the spatial–temporal evolution. Finally, Tobit regression is used to analyze the driving factors of the eco-efficiency of urban agglomerations in China. The main results can be summarized as follows: (1) The eco-efficiency of Chinese urban agglomerations is generally low, and the differences in eco-efficiency between urban agglomerations are obvious, with different trends of change. (2) In terms of the time series, the sample period shows a “steadily rising” trend followed by a “fluctuating downward” trend. From the results of the kernel density estimation, the internal difference in the overall eco-efficiency of urban agglomerations shows the trend of a small decline followed by a gradual increase. (3) From the spatial point of view, the eco-efficiency of urban agglomerations decreased from the coast to the inland areas, and there was a “cluster effect”. The overall eco-efficiency of urban agglomerations shows a trend of spatial aggregation. (4) From the perspective of influencing factors, fiscal expenditure, opening-up level, and population density have a significant negative correlation with the eco-efficiency of urban agglomerations, while science and technology investment, industrial structure, and urbanization level have a significant positive correlation with the eco-efficiency of urban agglomerations. The research in this paper provides guidance for the coordinated development of urban agglomerations and the formulation of environmental policies.

1. Introduction

Since its reform and opening up, China’s urbanization construction and city development have made remarkable achievements, with China’s urbanization rate increasing from 10.64% in 1949 to 65.22% in 2022, and the main form of urbanization of the “central city-metropolitan area-city cluster” being initially formed. Urban agglomeration is the highest form of spatial organization for the mature development of cities, which is the objective reflection of economic development and industrial agglomeration and is also the main body of the new urbanization and new economic growth pole in China, which is of great significance to the high-quality development of China’s economy. However, the rapid urbanization process has also brought huge ecological pressure and caused serious environmental problems in cities. Influenced by the long-term, high-intensity development and construction and rough developments by human beings, urban agglomerations are facing important problems, such as rapid resource consumption and serious ecological and environmental damage. Therefore, the sustainable development of resources and the environment is facing serious challenges. Compared with cities, the contradiction between urbanization and environmental protection in urban agglomerations is more prominent and serious [1]. Reducing the environmental cost and energy losses in the process of economic development and improving the ecological efficiency of urban agglomerations has become an important topic of current research. In order to alleviate these problems, the report of the 20th Party Congress in 2022 pointed out that “China’s modernization is a modernization in which human beings live in harmony with nature” and made the construction of ecological civilization an important strategic task in the new period. The progress of ecological civilization, a green and innovative form of discourse in contemporary China, has become an important part of the political thought and governance strategy of the Communist Party of China (CPC) and the government [2].

Eco-efficiency is an important indicator of sustainable development, which reflects the comprehensive efficiency of the systems of economy, environment, and resources and is an inherent requirement for the coordinated development of new forms of urbanization and ecology, as well as an important basis for evaluating the effectiveness of the construction of an urban ecological civilization. An in-depth study of the evolutionary characteristics and driving factors of the eco-efficiency of Chinese urban agglomerations is of great significance in promoting green development during the process of urbanization and realizing the sustainable development of urban agglomerations. What was the overall eco-efficiency of China’s urban agglomerations during the 15 years from the 11th Five-Year Plan to the 13th Five-Year Plan? What are the characteristics of eco-efficiency with changes in time and space, and what are its driving factors? This paper intends to investigate these questions.

This paper presents an in-depth study on the spatial and temporal evolution and influencing factors of the eco-efficiency of the nineteen urban agglomerations classified in the 14th Five-Year Plan of China from a holistic and global perspective. The super-SBM model is adopted to measure the eco-efficiency, the efficiency of the decision-making units located on the frontier at the same time are further compared, and the spatial distribution and spatial aggregation of urban efficiency within urban agglomerations are outlined using GIS software-ArcMap 10.8 to visualize the data. In addition, the 15-year sample period of this paper coincides with the 11th, 12th, and 13th Five-Year Plan periods of China, which enhances the depth and breadth of the study.

The rest of the paper is structured as follows: Section 2 describes the literature related to eco-efficiency and explains the shortcomings of the current research and the main contributions of this paper; Section 3 introduces the eco-efficiency index evaluation system constructed in this paper and clarifies the study area and data sources; Section 4 reports the experimental results of this paper, focusing on three aspects, namely eco-efficiency measurement, characteristics of spatial and temporal changes in eco-efficiency, and driving factors of eco-efficiency in Chinese urban agglomerations; Section 5 reports the experimental results of this paper, which focus on the measurement of eco-efficiency in Chinese urban agglomerations, the spatial and temporal characteristics of eco-efficiency, and the drivers of eco-efficiency. The research framework is presented in Figure 1.

2. Literature Review

2.1. Meaning of Eco-Efficiency

As the emphasis on sustainability and environmental change issues has increased over the past decade, the concept of eco-efficiency has attracted the attention of a growing number of scholars. In 1990, the concept of eco-efficiency was first proposed by Schaltegger and Krähenbühl [3], incorporating the three systems of economy, resources, and environment into the indicator evaluation system, which is an important indicator of resource consumption and economic development. WBCSN further defines this as the balance between the needs of human activities and the maximum carrying capacity of the environment, and elaborates on it from a business perspective, defining it as “the ability to provide competitively priced products and services that meet human needs and improve the quality of life”. In 1998, the World Organization for Economic Cooperation and Development (OECD) extended the concept to governments, industrial enterprises and other businesses, noting that eco-efficiency is an input–output process. In the past, eco-efficiency has been used to study microenterprises by scholars such as Jiang [4] and Mirmozaffari [5], who investigated the impact of eco-efficiency improvements in product prices and firm competitiveness. The study of micro-aspects struggles to effectively address the strong externalities of environmental pollution [6]. Therefore, most of the current domestic and international research on eco-efficiency is focused on macro-areas, and the content is mainly regional and industrial.

2.2. Measurement of Eco-Efficiency

Research on eco-efficiency at home and abroad mainly focuses on the evaluation methodology [7], measurement [8,9], spatial and temporal distribution [10,11], temporal evolution [12,13], and influencing factors [14,15,16]. In terms of measurement methods, current research on measuring eco-efficiency includes models such as the data life cycle method [17], ecological footprint method [18], data envelopment analysis [19], stochastic frontier method [20], and hierarchical analysis method [21]. Among them, data envelopment analysis (DEA) can endogenize the weights of evaluation indicators without the need to set a specific function form. It can effectively avoid using human interference and is the most widely used method. Mirmozaffari [5] combined a novel data envelopment analysis (DEA) approach in the first step of optimization with an innovative clustering algorithm in the second step of machine learning to compare 24 cement companies from five developing countries from 2014 to 2019, and Zhang [6] used a window DEA model to analyze the spatio-temporal pattern of the industrial eco-efficiency and spatial spillover effects between provinces in China. However, the nature of the traditional DEA model is radial, ignoring the non-radial slack variables, which means that the calculation results are high in order to take into account the non-desired output indicators. Tone [22] proposed an SBM model of non-desired output in 2001, which not only effectively avoids the bias caused by radial and angular metrics but also considers the influence of non-desired output in the production process, and can more effectively respond to the nature of the efficiency. However, the SBM model results can only be between 0 and 1, and it is not possible to simultaneously compare the decision-making units that are on the frontier. Therefore, Tone [23] proposed the super-efficient SBM model, which effectively solves the problems of slack measure and not being able to compare the decision-making units, meaning that the efficiency value can be more than 1. The model is now widely used; for example, Zhang [24] took 1766 counties as the research object and used the super-efficiency SBM model to explore the overall characteristics, differential characteristics, dynamic evolution characteristics, and spatio-temporal patterns of eco-efficiency in Chinese counties, and Liu [25] took generalized agriculture as the research object and adopted the super-SBM model to measure agro-ecological efficiency (AEE). Following previous studies, this paper adopts the super-efficient SBM model to measure eco-efficiency.

The research scales mainly focus on countries [26], regions [27,28], provinces [29], urban agglomerations [30,31,32,33,34,35], cities [36,37], and counties [38]. In existing studies, most of the research scales are interprovincial or urban, and relatively few studies use urban agglomerations as scales. Most of the few studies on urban agglomerations only selected representative urban agglomerations for study; for example, Xue [30] measured the eco-efficiency (EE) of 64 cities in the Beijing–Tianjin–Hebei metropolitan area, the Yangtze River Delta, the Pearl River Delta, and the Chengdu–Chongqing Economic Zone from 2006 to 2019 using the efficiency SBM model with non-expected outputs, and Yu [32] used the SE-U-SBM method based on a sequential DEA to measure the eco-efficiency of Chinese city clusters and examined regional differences using Dagum’s Gini coefficient and its decomposition. Shi [33] used a two-phase DEA model to measure the eco-efficiency of four major city clusters in China’s eastern coastal area as the study object. Zhang [35] measured the eco-efficiency of 99 representative cities in five major city clusters in China using the super-efficient SBM model with non-expected outputs to reveal the spatial and temporal evolution of the eco-efficiency of the five city clusters. Our study object is to measure and analyze the eco-efficiency of the urban agglomerations in the past 15 years. Studying only some of the developed areas of the city clusters would not provide a complete and objective response to the level of eco-efficiency in China’s city clusters; therefore, this paper will include all 19 city clusters in the 14th Five-Year Plan as the research object and conduct a side-by-side comparison, so that a more comprehensive response to the eco-efficiency of China’s city clusters can be achieved.

2.3. The Influencing Mechanism of Eco-Efficiency

A further exploration of the factors influencing eco-efficiency has a positive effect, improving eco-efficiency. Previous research on the factors influencing eco-efficiency mainly used the Tobit regression model [39,40], OLS regression [41,42], stochastic frontier method [43], geodetector [44], STIRPAT model, and spatial measurement model [45]. Since the measured eco-efficiency are restricted data, the panel Tobit model has been widely used in research on the influencing factors of eco-efficiency. Based on the previous research results, this paper adopted the panel Tobit model with random effects. Regarding the selection of influencing factors, most existing studies on the influencing factors of eco-efficiency consider the level of economic development, urbanization level, degree of openness to the outside world, environmental regulation, and industrial structure. Xue [34] used the STIRPAT model combined with fixed effects based on the extended STIRPAT model. This was combined with fixed-effects Tobit regression to explore the influencing factors of eco-efficiency in four major urban agglomerations, and the study shows that economically developed urban agglomerations are dominated by labor-intensive products, heavy industries, and heavy-polluting exports. This industrial structure restricts the optimization of industries, which has a negative impact on eco-efficiency. He [39] used a random-effects panel Tobit model to separately analyze the impact of tourism efficiency on the Yangtze River Economic Belt from the national and regional perspective to analyze the influencing factors of wellbeing-based eco-efficiency, indicating that regional differences are the main driving factors of eco-efficiency differences. Wang [45] used the panel Tobit model to explore the impact of five factors, such as economic growth, urbanization, and environmental regulation, on the efficiency of tourism in the Yangtze River Economic Belt. With reference to the previous research results, this paper intends to explore the impact of seven aspects of eco-efficiency, such as the level of economic development, industrial structure, scientific and technological level, urbanization level, and the degree of opening up to the outside world of the city.

A review of the literature reveals that a wealth of research has been conducted on eco-efficiency, but the following shortcomings remain: in terms of research scale, most of the existing studies were conducted in provincial areas, making it difficult to reflect the differences in efficiency within regions; in terms of research area, most of the current studies on eco-efficiency focused on local areas or selected a few representative urban agglomerations as research objects, and the sample period is relatively short, lacking a large sample of overall research; in terms of research method, the traditional SBM model cannot further compare the efficiency of decision-making units on a simultaneous frontier; and in terms of indicator selection, most of the studies only consider the industrial “three wastes” for undesirable output, ignoring the impact of other pollutants. In addition, most studies on urban agglomerations were conducted focusing on urban agglomerations as a whole, and few scholars have used GIS software to show the efficiency level and spatial aggregation status of cities within urban agglomerations.

Looking at the existing studies, this paper mainly expands on the following three aspects. First, compared with existing studies that only measure the eco-efficiency of some representative city clusters, this paper measures the eco-efficiency of the 19 city clusters delineated in China’s 14th Five-Year Plan from a global and holistic perspective in order to comprehensively portray the eco-efficiency of China’s city clusters. Secondly, the sample period selected for this paper is the 15-year period from 2006 to 2020, which is a long timespan and coincides with the three Five-Year Plans ranging from China’s 11th Five-Year Plan to the 13th Five-Year Plan, so that the eco-efficiency of each urban agglomeration in the past 15 years can be compared with the eco-efficiency of the environmental management measures taken in the past. The analysis combines the eco-efficiency level of each urban agglomeration in the past 15 years with the environmental management policies that were adopted to provide a reference for the formulation of future environmental policies, which increases the depth and breadth of the article. Thirdly, based on the spatial perspective, GIS software is used to visualize the data and further explore the spatial aggregation of urban efficiency within the city clusters, which provides a reference for improvements in eco-efficiency from the perspective of regional synergy. Fourth, a panel Tobit model is constructed to further explore the influencing factors of eco-efficiency in China’s urban agglomerations and to provide a basis for the formulation of policies to promote eco-efficiency improvements in China’s urban agglomerations.

3. Materials and Methods

3.1. Research Methods

3.1.1. Super-Efficiency SBM-DEA Model

The data envelopment analysis (DEA) model is a widely used, non-parametric method to evaluate the relative efficiency of multi-input and multi-output decision units for the measurement of eco-efficiency. However, traditional DEA models mostly belong to radial and angular measures and lack consideration of input–output slack, while SBM models put slack variables into the objective function, which can more effectively evaluate the efficiency problem under non-desired output. The SBM model based on undesirable output was first proposed by Tone (2001) [22], which overcomes the radial problem of traditional DEA methods, but this model cannot horizontally compare the efficiency of decision-making units located on the frontier. Therefore, K. Tone (2002) [23] proposed the super-SBM model, which further compares the decision-making units on the frontier. The model is constructed as follows.

$$Min\rho =\frac{1+\frac{1}{m}{\displaystyle {\sum }_{i=1}^m\frac{s_i^{-}}{X_{ik}}}}{1-\frac{1}{q_1+q_2}\left({\displaystyle {\sum }_{r=1}^{q_1}\frac{s_r^{+}}{y_{rk}}+{\displaystyle {\sum }_{t=1}^{q_2}\frac{s_t^{b-}}{b_{tk}}}}\right)}$$

$$s.t{\displaystyle \sum _{j=1,j\ne k}^n{x}_{ij}{\lambda }_j}+s_i^{-}\le x_{ik}$$

$${\displaystyle \sum _{j=1,j\ne k}^n{y}_{rj}{\lambda }_j}-s_r^{+}\ge y_{rk}$$

(1)

$${\displaystyle \sum _{j=1,j\ne k}^n{b}_{tj}{\lambda }_j+s_t^{b-}\le b_{tk}}$$

$$1-\frac{1}{q_1+q_2}\left({\displaystyle \sum _{r=1}^{q_1}\frac{s_r^{+}}{y_{rk}}+{\displaystyle \sum _{t=1}^{q_2}\frac{s_t^{b-}}{b_{tk}}}}\right)>0$$

$$\lambda ,s^{-},s^{+}\ge 0$$

$$i=1,2,\dots ,m;r=1,2,\dots ,q_1;t=1,2,\dots ,q_2,j=1,2,\dots ,n\left(j\ne k\right)$$

In the equation, ρ represents the eco-efficiency of each city; m represents the number of input indicators; q₁ and q₂ represent the number of desired outputs and undesired outputs, respectively; x_ik, y_rk, and b_tk are the i-th input factor, the r-th desired output, and the t-th undesired output of the k-th city, respectively; $s_i^{-}$, $s_r^{+}$, and $s_t^{b-}$ are the slack variables for the inputs, the desired outputs, and the undesired outputs, respectively; and λ_j is the weights.

3.1.2. Kernel Density Estimation

Kernel density estimation is a non-parametric method used to estimate the probability density function. The probability distribution curve of the eco-efficiency of urban agglomerations is obtained by fitting the kernel density function. The dynamic evolution characteristics of the eco-efficiency of urban agglomerations are judged by the distribution pattern, location, peak value, and ductility of the curve. Given its weak model dependence and excellent statistical properties, the method has been extensively used in the exploration of the spatial distribution imbalance problem. The calculation formula of the kernel density estimation method is as follows:

$$f(x)=\frac{1}{nh}{\displaystyle \sum _{i=1}^{n}K(\frac{x-x_i}{h}})$$

(2)

In this form, f(x) is the kernel density estimate, x is the variable, x_i is the marker point, n represents the number of samples, K(x) represents the Gaussian kernel density function, and h represents the bandwidth. In general, the wider the broadband, the smoother the density function and the lower the estimated precision.

3.1.3. Spatial Econometric Specifications

Spatial autocorrelation is a measure of the degree of clustering of attribute values of spatial units [35]. Global spatial autocorrelation is used to measure the correlation degree, distribution pattern, and saliency of spatial objects in a certain spatial region. The global Moran‘s I index is

$$I=\frac{n{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{W}_{ij}\left|x_i-x\right|\left|x_j-x\right|}}}{{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{W}_{ij}{\displaystyle \sum _{i=q}^n{\left|x_j-x\right|}^2}}}}$$

(3)

In the formula, I is the global Moran index; x_i, x_j are the observations in regions i and j, respectively; x is the mean value of each region; and W_ij is the spatial relationship measure of cells i and j (1 for adjacent, 0 for non-adjacent). The value of Moran’s I is within the range [−1, 1]: a value greater than 0 means positive spatial autocorrelation, a value less than 0 means negative spatial autocorrelation, and a value equal to 0 means spatial uncorrelation. At the same time, the significance level of spatial autocorrelation was tested using the standardized statistic Z.

$$Z=\frac{I-E(I)}{\sqrt{Var(I)}}$$

(4)

Local Indicators of Spatial Association (LISA) is a localized form of Moran’s I index. It is used to test the agglomeration and disagglomeration effects in local areas to fully reflect the trend of regional economic spatial differences and reveal the degree of spatial autocorrelation between local areas and each spatial unit and its neighboring units. The expression is

$$I_i=\frac{X_i-X}{S}{\displaystyle \sum _{j=1}^N{w}_{ij}^{-}}\left(X_j-\overline{X}\right)$$

(5)

$$S=\frac{{\displaystyle \sum _{j=1,j\ne i}^N{X}_j^2}}{N-1}-{\overline{X}}^2$$

If I_i is positive, this means that there is a spatial agglomeration of similar values (high–high or low–low) around the regional unit, and if I_i is negative, it means that there is a spatial agglomeration of non-similar values (high–low or low–high) around the regional unit.

3.1.4. Tobit Regression

Considering that the variables measured by the super-efficient SBM are restricted variables, if the regression is performed using the ordinary OLS model, the problem of parameter estimation bias will occur due to the incomplete presentation of the data. Therefore, this paper used a panel Tobit model with random effects for the regression-specific formula as follows.

$$Y_{ip}^{*}=\{\begin{array}{c}\alpha +\beta X_{ip}+{\mu }_{ip},Y_{ip}^{*}>0\\ 0,Y_{ip}^{*}\le 0\end{array}$$

(6)

In Equation (6), i denotes the city, p denotes the year, and Y^∗ is the restricted dependent variable (eco-efficiency of city i in year p, as measured by the super-efficient SBM model). X_ip is the main influencing factor of eco-efficiency, α is a constant term, β is the regression coefficient vector, and µ is stochastic disturbance.

3.2. Indicator Selection

3.2.1. Input and Output

Referring to the experience of existing studies and combining this with the characteristics of urban agglomerations, the evaluation system of eco-efficiency indicators was constructed using the input–output theory framework used in economics (

Table 1. Input–output indicator system of eco-efficiency.

Category	Indicator Name	Variable	Unit
Input indicators	Labor input	Number of employees at the year-end	Ten thousand people
	Capital input	Fixed-asset investment	CNY 100 million
	Land resource input	Urban construction land area	Ten thousand ha
	Energy consumption	Energy consumption	Ten thousand tons
	Water consumption	Annual water consumption	Ten thousand tons
Desired output indicators	Economic benefit	GDP	CNY 100 million
	Financial revenue	Government budget revenues	CNY 100 million
Undesired output indicators	Environmental pollution	Industrial wastewater emissions	Ten thousand tons
		Industrial soot emissions	Ten thousand tons
		Industrial SO2 emissions	Ten thousand tons
		PM2.5 concentration	mg/m3

).

(1)

Input indicators

Input indicators include labor input, capital input, land resource input, energy consumption, and water resource consumption. For fixed assets, referring to Xu and Deng [46], the volume of fixed-asset investment is used to measure the capital investment of each city. Due to the availability of municipal data, the total energy consumption of the whole society, including natural gas and liquefied petroleum gas, is converted into tons of standard coal to approximate the energy consumption of urban agglomerations.

(2)

Undesirable output

In urban economic activities certain pollutants are generated, which cause a burden on the urban environment. It is presumed that the lower the undesired output generated by production activities, the better. In addition to traditional industrial wastewater, industrial sulfur dioxide, and industrial smoke, the undesired output indicators were selected in this paper considering the fact that PM_2.5 has become an important air quality indicator of public concern in recent years and that urban agglomerations are gathering places with a high PM_2.5 concentration [2]. Accordingly, the PM_2.5 concentration was selected to reflect the air pollution in urban agglomerations.

(3)

Expected output

Desired output indicators are the economic benefits generated from the economic activities of a city. In this paper, GDP and government budget revenue are used as the expected outputs, which reflect the level of economic development and the level of government financial power in a region, respectively.

3.2.2. Influential Factors on Eco-Efficiency

With the efficiency result measured by the super-efficiency SBM as the explained variable and the environmental factor as the explanatory variable, Tobit regression was carried out to explore the influencing factors of the ecological efficiency of urban agglomeration. Referring to existing studies [35,38,47], this paper mainly considers eight aspects, and the specific influencing factors were selected as shown in

Table 2. Influential factors of eco-efficiency.

Variable	Influential Factors	Index	Unit
Independent variable	Economic development level (X1)	GDP per capita	CNY million
	Industrial structure (X2)	Proportion of secondary industry in GDP	-
	Technology input (X3)	Science and technology expenditures	CNY million
	Urbanization level (X4)	Urbanization rate	%
	Degree of openness (X5)	Total imports and exports	CNY million
	Population density (X6)	Total population/municipal district area	Person/Ha
	Government support (X7)	Proportion of government expenditure in GDP	CNY million
	Urban afforestation (X9)	Park green area	Ten thousand ha
Dependent variable	Ecological efficiency (Y)

.

3.3. Data and Variables

The evaluation index of eco-efficiency and the influential factors for eco-efficiency are presented here to illustrate the measurement framework. The original data were mostly collected from the China City Statistical Yearbook (2007–2021) and China City Construction Statistical Yearbook (2006–2020), as well as statistical bulletins from each city. The PM_2.5 concentration data were calculated by the global average annual PM_2.5 concentration provided by the Center for Economic Data and Application of Columbia University, USA. A few missing values were filled in by linear interpolation using Stata15.0 software.

3.4. Research Area and Timespan

Due to the fact that partial urban agglomeration data struggle to comprehensively and objectively reflect the current situation of the development of eco-efficiency in China’s urban agglomerations, to measure the eco-efficiency of Chinese urban agglomerations from a global perspective and to increase the credibility of the study, the research area of this paper is divided into 19 national urban agglomerations, as defined in the Fourteenth Five-Year Plan of National Economic and Social Development of the People’s Republic of China and Outline of Vision 2035, excluding the very few cities with missing data and including 208 prefecture-level cities in total.

The nineteen urban agglomerations combine the Beijing–Tianjin–Hebei urban agglomeration, Yangtze River Delta urban agglomeration, Pearl River Delta urban agglomeration, Shandong Peninsula urban agglomeration, Yue–Min–Zhe coastal urban agglomeration, Mid-Southern Liaoning urban agglomeration, Harbin–Changzhou urban agglomeration, Middle Yangtze River urban agglomeration, Central Plains urban agglomeration, Central Shanxi urban agglomeration, Guanzhong Plain urban agglomeration, Hohhot-Baotou-Ordos-Yulin urban agglomeration, Chengdu–Chongqing urban agglomeration, Beibu Gulf urban agglomeration, Central Guizhou urban agglomeration, Central Yunnan urban agglomeration, Lanzhou–Xining urban agglomeration, Ningxia Yanhaung urban agglomeration, and Northern Tianshan Mountain urban agglomeration. According to the location and development of each city in the urban agglomeration, the included cities were divided into three regions: the eastern region, the central region, and the western region. According to the geographical location, development status, and resource endowment of each city, the 19 urban agglomerations were divided into eastern, central, and western urban agglomerations for the study, and the specific location division and included cities are shown in Appendix A.

The sample period selected for this paper is 2006–2020, a total of fifteen years, which coincides with China’s eleventh, twelfth, and thirteenth five-year planning periods. As a socialist country, China’s five-year plan/planning system is one of the most important means by which the Chinese government manages economic and social development. China’s five-year planning system is unique and comprehensive, and it is not confined to the economic sphere. The five-year plan mainly includes plans for major national construction projects, the distribution of productive forces, and important proportional relationships in the national economy, and it sets the goals and directions for the national economy’s development. The Chinese Government continues to plan for social development, social change, and structural adjustment through the implementation of Five-Year Plans in order to advance the modernization of the country step by step.

In this paper, the 15 years from the 11th Five-Year Plan to the 13th Five-Year Plan was selected as the sample period. China has attached great importance to ecological protection in the past fifteen years, and during the 11^th Five-Year Plan period China first put forward the new concept and strategy of “ecological civilization construction”. During the Twelfth Five-Year Plan period, the “construction of ecological civilization” was incorporated into the overall layout of the country’s “Five-in-One”. During the Thirteenth Five-Year Plan period, China put forward the five development concepts of innovation, coordination, green, openness, and sharing. Taking 2006–2020 as the sample period, through the measurement and analysis of China’s macro-data during the three Five-Year Plans and summarizing the effectiveness and shortcomings of China’s previous policies regarding ecological construction and ecological environment improvements, a reference is provided for the improvement in the ecological environment and the formulation of future policies. Since 2006, China has incorporated city clusters into the spatial body of the country’s new urbanization, and the country repeatedly proposed city clusters as China’s new economic growth poles during the Eleventh Five-Year Plan, Twelfth Five-Year Plan, and Thirteenth Five-Year Plan periods. During this period, the development pattern centering on city clusters was formally formed, coinciding with the research topic of this paper.

4. Results and Discussion

4.1. Overall Analysis of the Eco-Efficiency of Urban Agglomerations in China

Based on the super-efficiency SBM model with undesired outputs, this study used software-MATLAB R2021a to calculate eco-efficiency in the 208 cities of 19 urban agglomerations between 2006 and 2020. As the timespan of this paper is exactly 15 years, spanning the 11th, 12th, and 13th Five-Year Plans, the average values of each 5-year period were analyzed separately and the mean value was analyzed for every 5 years; the results are shown in Figure 2.

The results show that the average eco-efficiency of urban agglomerations in China from 2006 to 2020 was between 0.418 and 0.631, with an average of 0.544, indicating that the overall ecological efficiency of China‘s urban agglomerations is at a low level, and there is still much room for improvement at the frontier. The eco-efficiency level of different urban agglomerations is quite different: the ecological efficiency of the Hohhot-Baotou-Ordos-Yulin urban agglomeration, Pearl River Delta urban agglomeration, Yangtze River Delta urban agglomeration, and Shandong Peninsula urban agglomeration is relatively high, while the eco-efficiency of the Ningxia Yanhuang urban agglomeration and Lanzhou–Xining urban agglomeration is relatively low. Further analysis of the data reveals that the eco-efficiency of 10 out of 19 urban agglomerations is below 0.5, and most of these urban agglomerations are located in the central and western parts of China. The urban agglomerations in the west of China are more typical of the Ningxia Yanhuang urban agglomeration and Northern Tianshan Mountain urban agglomeration, which have very fragile ecological environments, comparatively less-developed socio-economies, and relatively large populations of poor people. Although the State has adopted various forms of environmental management, such as returning farmland to forests and grasslands, planting trees, soil erosion control, and desertification control, the results of this management have remained unsatisfactory from the ecological viewpoint of the local governments and their performance. In the future, western urban agglomerations should be given more favorable policies and the performance appraisal system of local governments should be improved to promote the ecological efficiency of western urban agglomerations. The central urban agglomerations, including the Central Plains urban agglomeration and the Central Shanxi urban agglomeration, are mostly in the early stage of industrialization, with a relatively sloppy economic development and the transference of polluting enterprises from developed regions. This has led to low levels of eco-efficiency in these urban agglomerations. In the future, these urban agglomerations should accelerate their industrial upgrades and eliminate backward production capacity in order to promote improvements in eco-efficiency.

Based on the trend of the mean eco-efficiency values of each urban agglomeration in the three five-year plan periods, the 19 urban agglomerations were classified into four types. The first type is the “upward trend” type in the 11th, 12th, and 13th Five-Year Plans, which can be illustrated by the symbol of “/”. This type of urban agglomeration has only two urban agglomerations: the Beijing–Tianjin–Hebei urban agglomeration and Central Plains urban agglomeration. The Beijing–Tianjin–Hebei urban agglomeration is located in the Bohai Rim of China, which is China’s “capital economic circle”. As the two core cities of urban agglomeration, Beijing and Tianjin have had a high level of eco-efficiency in the past 15 years due to the political advantages of policy orientation, strong environmental governance, and technological advantages of high-tech agglomeration. However, cities in Hebei Province have long contained industrial enterprises with high levels of energy consumption, causing serious pollution in the capital, and the eco-efficiency of most cities is at a low level. In response to environmental pollution in the Beijing–Tianjin–Hebei urban agglomeration, during the Twelfth Five-Year Plan period, the State issued the Implementing Rules for Implementing the Action Plan for Prevention and Control of Air Pollution in Beijing–Tianjin–Hebei and the Surrounding Areas, proposing that efforts to prevent and control air pollution in Beijing–Tianjin–Hebei and the neighboring areas should be intensified and that ambient air quality should be effectively improved. During the 13th Five-Year Plan period, the State put forward the Beijing–Tianjin–Hebei Cooperative Development Strategy, which proposes to strengthen cooperation in ecological and environmental protection in the Beijing–Tianjin–Hebei region and to improve cooperation mechanisms in the areas of protective forest construction, water resource protection, water environment management, and the use of clean energy on the basis of establishing a collaborative mechanism for the prevention and control of atmospheric pollution, which has already been initiated. With the support of stronger environmental policies, the cities of the Beijing–Tianjin–Hebei urban agglomeration have been optimizing the division of labor in the inter-regional industrial chain, combating heavily polluting enterprises, strengthening the driving role of developed cities in the past 15 years, and thus promoting the ecological efficiency of the urban agglomeration and causing it to show an upward trend in the three Five-Year Plans. The Central Plains urban agglomeration, with Zhengzhou as its core, is located in the east-central part of China and the lower reaches of the Yellow River. Its improved eco-efficiency is mainly related to China’s strategy for the rise of central China, which has continued to be pushed forward in the past three Five-Year Plans, promoting the Central Plains urban agglomeration’s continuous adjustment of its industrial structure, optimization of its resource allocation, and improvement in its eco-environment. The second type is the “upward first and then downward” type in the 11th, 12th, and 13th Five-Year Plans, which can be illustrated by the symbol of “^”, which is more typical of the Yangtze River Delta urban agglomeration, the Shandong Peninsula urban agglomeration, and urban agglomerations in the middle reaches of the Yangtze River. The eco-efficiency of these city clusters increased during the 12th Five-Year Plan period but decreased during the 13th Five-Year Plan period. Located in the impact plain before the Yangtze River enters the sea, the Yangtze River Delta (YRD) city cluster is one of the most active regions in China in terms of economic development, with the highest degree of openness and the strongest capacity for innovation. In the past Five-Year Plans the Yangtze River Delta city cluster has received national attention many times, being recognized in the State Council’s approval of the Regional Plan for the Yangtze River Delta Region in 2010 and in the issuance of the State Council’s Guiding Opinions on Promoting the Development of the Yangtze River Economic Belt by Relying on the Golden Waterway in 2014. The introduction of these policies has contributed to the improvement in the eco-efficiency of the Yangtze River Delta city cluster during the Twelfth Five-Year Plan period. However, due to the high development intensity and high levels of resource consumption in economic development in the region, there are some prominent problems in the ecological environment, such as the unstable effect of water environment quality improvements, high resource and energy consumption, obvious regional air pollution characterized by PM_2.5 and ozone (O₃), and large ecological differences between regions. The environmental quality of some cities does not yet match the level of economic and social development. Although the Outline of the Plan for the Integrated Development of the Yangtze River Delta issued in 2019 proposes that the Yangtze River Delta urban agglomeration should prioritize ecology and put the protection and restoration of the ecological environment at the top of the list, it still failed to prevent the Yangtze River Delta urban agglomeration’s ecological efficiency from declining following the rapid economic development that occurred during the 13th Five-Year Plan period. The Shandong Peninsula urban agglomeration is located at the mouth of the lower reaches of the Yellow River. The urban agglomeration has a high level of economic development, a strong industrial base, and a favorable geographic location, and its eco-efficiency has always been at a high level. However, the eco-efficiency of the Shandong Peninsula city cluster declined during the 13th Five-Year Plan period, probably due to the impact of the old and new kinetic energy conversion policy that was first implemented by the Shandong Peninsula urban agglomeration in the country, which caused the economy of the urban agglomeration to suffer a short-term setback and led to a decline in eco-efficiency. The third type is the “downward first and then upward” type, which can be illustrated by the symbol “v”, such as the Central Shanxi urban agglomeration, Central Guizhou urban agglomeration, and Ningxia Yanhuang Urban agglomeration. The ecological efficiency of these urban agglomerations decreased during the 12th Five-Year Plan period but improved during the 13th Five-Year Plan period due to the increased emphasis on environmental protection. It is worth noting that urban agglomerations of this type are mainly located in the central and western regions of China, and most of these urban agglomerations started late and did not attract much attention from the State at first. In the case of the Qianzhong urban agglomeration, which has the most obvious trend, there were not many policies regarding the development of this urban agglomeration at first, and it was not until 2017 that the Development Plan of Qianzhong urban agglomeration was formally issued and published, which put forward the construction of a new economic growth pole in the western region, an advanced demonstration area of new urbanization with mountainous characteristics, a new inland open-economy highway, and a green ecological and livable urban agglomeration. The implementation of this policy has promoted improvements in the ecological efficiency of this urban agglomeration. The fourth type is the “downward” type in the 11th, 12th, and 13th Five-Year Plans, which can be illustrated by the symbol of “\”, such as the Pearl River Delta urban agglomeration, Hohhot-Baotou-Ordos-Yulin urban agglomeration, and Beibu Gulf urban agglomeration. The Pearl River Delta urban agglomeration is the gateway to the outside world for southern China and an important engine for China’s economic development. The rapid economic development of this urban agglomeration has previously been accompanied by outstanding environmental problems, with the problem of atmospheric pollution being particularly serious, and although a series of contingency plans have been introduced to deal with the problem of atmospheric pollution, the eco-efficiency of this urban agglomeration continued to show a continuous downward trend over the past three Five-Year Plans. The Hohhot-Baotou-Ordos-Yulin urban agglomeration is a newly developed urban agglomeration in the northwest of China and is an important energy exporter in China. However, the urban agglomeration started late, and the core cities have not yet established a close cooperation mechanism. The ecological environment of the Hohhot-Baotou-Ordos-Yulin city cluster is very fragile, and the resource and environmental problems are becoming more and more prominent while the city rapidly expands. In addition, this urban agglomeration has not been emphasized for a long time, which has led to the continuous decline in its ecological efficiency. Among the four types, the “/”-type urban agglomerations occupy the smallest proportion, and only two urban agglomerations show a continuous upward trend. This indicates that most urban agglomerations have not paid enough attention to the ecological environment in the development process in the past 15 years, while “^”-type urban agglomerations occupy the largest proportion of the total, with 9 of 19 urban agglomerations showing such a trend, accounting for 47.37%. This indicates that the efficiency of most urban agglomerations improved during the 12th Five-Year Plan period and declined during the 13th Five-Year Plan period. Economic development was not correlated with the protection of the ecological environment and tended to develop extensively.

4.2. Temporal Evolution Pattern of the Eco-Efficiency of Urban Agglomerations in China

The results of the eco-efficiency measurements of eastern, central, western, and overall urban agglomerations were averaged and plotted as a line graph in years (Figure 3) in order to explore the time-series evolution characteristics of urban agglomerations.

From a regional perspective, the eco-efficiency of Chinese urban agglomerations shows a pattern of “eastern urban agglomerations > central urban agglomerations > western urban agglomerations”, which is consistent with the traditional economic development pattern. Eastern urban agglomerations are much higher than the average value of urban agglomerations, while western urban agglomerations are much lower than the average value of urban agglomerations, and the eco-efficiency level of central urban agglomerations is similar to the average value of urban agglomerations. In terms of trends, the overall trend in the eco-efficiency averages of the eastern, central, and western urban agglomerations is consistent with that of the national urban agglomerations.

The trend in the eco-efficiency of urban agglomerations in China during the sample period can be divided into two stages: “steady rise” (2006–2012) and “fluctuating decline” (2013–2020). Eco-efficiency steadily increased from 2006 to 2012, and the overall mean value increased from 0.533 in 2006 to 0.631 in 2012, with a growth rate of 18.4%. During this period, China’s rapid economic growth was accompanied by a gradual increase in the degree of attention being paid to pollution prevention and ecological environment management. For example, the idea of building an ecological civilization was first put forward in the 17th National Congress, and targets were set for energy conservation and consumption reductions during the 11th Five-Year Plan period, which led to a great degree of improvement in ecological environment quality in that period. From 2013 to 2020, the overall eco-efficiency of China’s urban agglomerations fluctuated and decreased from 0.596 in 2013 to 0.473 in 2020. On the one hand, China’s economic development entered a new normal in this period, with economic growth shifting from high-speed to medium–high-speed, but resource consumption maintained a high rate of growth and there was little harmonization between eco-protection and economic development. On the other hand, with the massive influx of people into cities during the previous period of high-speed urbanization, the ecological problems of various urban agglomerations have become more and more prominent, leading to a series of problems, such as overpopulation, rough land expansion, water shortages, and serious air pollution. The slowdown of the economy and the increasingly serious environmental problems led to a fluctuating downward trend in the ecological efficiency of urban agglomerations from 2012 to 2020.

The kernel density estimation method was further applied to study the peak eco-efficiency of Chinese urban agglomerations and the offset changes during the sample period and to analyze the dynamic evolution characteristics of the eco-efficiency of Chinese urban agglomerations. The years 2006, 2009, 2011, 2013, 2015, 2017, and 2020 were selected as representative years to estimate the kernel density of the eco-efficiency of urban agglomerations in general and urban agglomerations in the east, middle, and west of China through the stata15.0 software, as well as to draw kernel density maps. Through the comparison of eco-efficiency in different periods, the dynamic change characteristics of the eco-efficiency of Chinese urban agglomerations over time can be studied more closely (Figure 4, Figure 5, Figure 6 and Figure 7).

The following is an analysis of the characteristics of the nucleus density curves of the urban agglomerations in general and in the east, central, and west from three perspectives: location, polarization characteristics, and distribution patterns.

(1)

Dynamic evolutionary characteristics of the overall eco-efficiency of urban agglomerations:

①

Distribution position: from 2006 to 2013, the eco-efficiency kernel density curve of China’s urban agglomerations shows a rightward shift, while from 2013 to 2020 it shows a leftward shift, indicating that the eco-efficiency of China’s urban agglomerations generally rose and then fell during the sample period.

②

Polarization characteristics: the overall eco-efficiency kernel density curve of urban agglomerations shows a double-peaked trend of one main and one secondary, with the first peak efficiency value clustering around 0.4 and the second peak efficiency value clustering around 1.1. This indicates that the eco-efficiency of urban agglomerations in China varies significantly from region to region, showing a two-tier differentiation trend, and that the proportion of cities with low efficiency values is high, showing a low value.

③

Distribution pattern: the main peak in the nuclear density curve from 2006 to 2015 tends to decrease in height and increase in width, while the main peak increases in height and decreases in width from 2015 to 2020, indicating that the internal differences in the overall eco-efficiency of urban agglomerations tend to slightly decrease and then gradually increase.

(2)

Dynamic evolutionary characteristics of eco-efficiency in regional urban agglomerations:

①

Distribution position: the change in the main peak of the nuclear density curve of each regional urban agglomeration is generally consistent with the overall urban agglomeration, showing different degrees of rightward and then leftward shifts, indicating that the eco-efficiency of each regional urban agglomeration shows different degrees of upward and then downward trends.

②

Polarization characteristics: the eastern urban agglomerations show the most pronounced double-peaked pattern, with the height of the second wave of the eastern urban agglomerations being closer to the height of the first wave than the Chinese urban agglomerations as a whole, indicating that the eastern urban agglomerations have a relatively large proportion of high-value clusters. The central urban agglomerations also show a double-peaked pattern of one main and one secondary peak, but the height of the second wave is relatively low, indicating that the central urban agglomerations contain a relatively small proportion of cities with high-value aggregation. The western urban agglomeration shows a double-peaked trend from 2006 to 2013, while the second wave gradually disappears from 2013 to 2020, evolving towards a single-peaked trend of low-value agglomeration.

③

Distribution pattern: the changes in the height and bandwidth of the main peaks in the eastern, central, and western urban agglomerations are broadly consistent with the overall trends in urban agglomerations, suggesting that the internal differences in the eco-efficiency of each regional urban agglomeration all show a tendency of slightly decreasing and then gradually increasing.

4.3. Spatial Evolution Pattern of the Eco-Efficiency in Urban Agglomerations in China

4.3.1. Spatial Distribution of the Eco-Efficiency of Urban Agglomerations in China

In order to better understand the spatial distribution pattern of ecological efficiency of urban agglomerations in China, the efficiency of cities within urban agglomerations was divided into five levels according to the natural-breaks method: high, medium–high, medium, medium–low, and low–low eco-efficiency for 2006, 2013, and 2020. This was spatially visualized using ArcMap10.8. Figure 8, Figure 9 and Figure 10 show the evolution of the spatial pattern of eco-efficiency.

In terms of spatial differentiation, the eco-efficiency of urban agglomerations in China decreases from coastal areas to inland areas as a the whole, but with the passage of time, the ecological efficiency of urban agglomerations in central and western China also gradually improves, which is mainly related to the implementation of the strategy of the rise of the central region and the development of the western region. As can be seen from the figure, the relationship between eco-efficiency and economic development level is very close, and there is an obvious spatial spillover effect. Shen Weiteng and other scholars call this phenomenon the “cluster effect” [28]; that is, economically developed regions are more likely to communicate and cooperate with neighboring regions with a similar economic development level. For an urban agglomeration with a higher level of economic development, there is often close communication within the urban agglomeration and between adjacent urban agglomerations. In the process of communication and cooperation, policy formulation, environmental protection concepts, and science and technology are constantly innovating in the process of exchange and cooperation. Finally, ecological efficiency is rapidly improved. However, if underdeveloped areas want to exchange and cooperate with developed areas, they need to pay a higher price, which is not conducive to the development of the region and the improvement in eco-efficiency level. For example, the eco-efficiency of the Pearl River Delta urban agglomeration is relatively high as a the whole, with Guangzhou and Shenzhen as the core developed cities, both of which have an eco-efficiency above 1. While developing themselves, the two cities actively promote the development of surrounding cities and spread advanced environmental protection concepts, thus promoting the overall efficiency of the Pearl River Delta urban agglomeration. In contrast, the eco-efficiency of each core city in the western urban agglomerations of the Lanzhou–Xining City agglomeration and Ningxia Yanhuang urban agglomeration is below 0.5. It is difficult for these cities to take advantage of the high-quality resources in developed regions, thus restricting the improvement in eco-efficiency.

4.3.2. Global Spatial Autocorrelation Analysis of the Eco-Efficiency of Urban Agglomerations in China

The global Moran’s I provides a whole measurement of autocorrelation and represents a formal indication of the linear correlation between a unit and its adjacent units. In order to further explore the spatial aggregation and differentiation characteristics of urban agglomerations in China, Arcmap10.8 software was used to calculate the global Moran’s I index of urban agglomerations in China from 2006 to 2020 (

Table 3. Overall Moran’s I of ecological efficiency in urban agglomerations in China.

Year	Moran’s I	Z-Value	p-Value
2006	0.258738	7.840211	0.000000
2007	0.268505	8.126275	0.000000
2008	0.180171	5.739512	0.000000
2009	0.263412	7.973411	0.000000
2010	0.265535	8.027882	0.000000
2011	0.260806	7.893990	0.000000
2012	0.252563	7.640991	0.000000
2013	0.157284	4.821853	0.000001
2014	0.200794	6.118566	0.000000
2015	0.265154	8.008573	0.000000
2016	0.170293	5.202458	0.000000
2017	0.270865	8.243287	0.000000
2018	0.15265	4.686708	0.000003
2019	0.185024	5.647019	0.000000
2020	0.132376	4.098242	0.000042

).

From 2006 to 2020, the global Moran’s I index values of Chinese urban agglomerations were between 0.132376 and 0.270865, all of which are positive and pass the significance test at the p-value < 0.01 level, indicating that the overall ecological efficiency of Chinese urban agglomerations presents a positive spatial correlation. That is, the ecological efficiency between cities presents an aggregation trend (high–high aggregation or low–low aggregation). The aggregate effect of urban agglomerations in China showed a fluctuating downward trend in the global Moran index from 2006 to 2020, indicating that the agglomeration of urban agglomerations in China showed a fluctuating trend during the sample period and that the spatial dependence between cities declined with the passage of time. In conclusion, the efficiencies of urban agglomerations in China influence each other, showing a trend of fluctuating aggregation, which is suitable for further analyses of spatial aggregation characteristics obtained by the local spatial autocorrelation method.

4.3.3. Local Spatial Autocorrelation Analysis of Eco-Efficiency in Urban Agglomerations in China

The global Moran’s I index does not indicate the location of specific areas. The characteristics of the association between the spatial agglomeration of surrounding areas and local agglomeration characteristics need to be investigated. Based on the sectional ecological efficiency data of 2006, 2013, and 2020, the spatial aggregation index of the eco-efficiency of Chinese urban agglomerations was further measured and Lisa aggregation maps were drawn, as shown in Figure 11, Figure 12 and Figure 13. These were divided into four types: high–high, high–low, low–high, and low–low.

Overall, there are far more high–high and low–low cities than low–high and high–low cities, showing a clear spatial clubbing phenomenon, and more low–high cities than high–low cities. Low–low cities account for the largest share at all three time points, indicating that a large proportion of China’s regions still need to improve their urban eco-efficiency. The aggregation effect of urban eco-efficiency tends to diminish over time.

As can be seen from the figure, different cities within the same urban agglomeration exhibit different agglomerations. The following analysis therefore splits the clusters and analyzes them from the perspective of the cities, which are the internal elements that make up the clusters:

(1)

Cities that exhibit high–high agglomeration are more eco-efficient in their own right, and the cities around them are also more eco-efficient. Cities of this type are mainly located in the Yangtze River Delta urban agglomeration, the Pearl River Delta urban agglomeration, the Yue-Min-Zhe coastal urban agglomeration, and the Shandong Peninsula urban agglomeration. These cities are mainly located in the eastern coastal region of China. On the one hand, these cities are economically developed, have strong financial resources to invest in environmental management, introduce advanced ecological management technologies, have a reasonable industrial structure, and have sound environmental protection policies. These cities also have well-developed transportation networks and communication technologies and are closely connected to the surrounding cities, giving full play to the role of radiation between cities. The proportion of cities in the high–high category has gradually increased over the past three years, indicating that more and more cities in China are gradually moving towards a high-quality development path that prioritizes ecology.

(2)

The cities that show a low–high concentration are those that are less eco-efficient on their own but have higher eco-efficiency levels when grouped with neighboring cities. Typical cities that fall into this category include Huzhou in the Yangtze River Delta urban agglomeration, Rizhao in the Shandong Peninsula urban agglomeration, and Putian and Longyan in the Yue–Min–Zhe coastal urban agglomeration. The trend over time is towards the concentration of cities in the eastern urban agglomeration. The low–high-type cities are adjacent to cities with high eco-efficiency but have not been able to effectively drive the efficiency of these cities. These cities should give full play to the geopolitical advantages of their proximity to high-efficiency cities, increase exchanges and interactions with high-efficiency cities, and make good use of the spillover effects of neighboring high-efficiency cities in order to promote the improvement in their own eco-efficiency.

(3)

Cities that exhibit low–low agglomeration are less eco-efficient in their own right and less efficient in terms of their neighboring cities. These cities are mainly located in the Central Plains urban agglomeration, the Chengdu–Chongqing urban agglomeration, the Central Plains urban agglomeration, the Mid-Southern Liaoning urban agglomeration, the Lanzhou–Xining urban agglomeration, and the Ningxia Yanhuang urban agglomeration. Most cities of this type are located in the northeastern part of China and in the urban agglomerations in the central and western parts of the country. These cities have a relatively low level of economic development, a fragile ecological environment, and inadequate use of resources. In addition, these cities are spatially distant from the high-efficiency cities, and due to their location, it is difficult for them to receive the radiation drive from high-efficiency cities, thus limiting their eco-efficiency.

(4)

The cities with high–low agglomeration have individual high eco-efficiency but low eco-efficiency in terms of neighboring cities, such as Changsha in the Middle Yangtze River urban agglomeration; Zhengzhou, Zhoukou, and Liaocheng in the Central Plains urban agglomeration; and Shenyang in the Mid-Southern Liaoning urban agglomeration. Specifically, most of these cities are provincial capitals in the mid-western and northeastern regions, and their development levels are dominant compared to those of their neighboring cities. These cities are prone to imbalances with their neighbors in the development process and should strengthen cooperation and exchange with these cities to give full play to their driving role.

4.4. Analysis on Influencing Factors of the Eco-Efficiency of Urban Agglomerations in China

Tobit regression was conducted on the influencing factors of eco-efficiency of urban agglomerations in China from 2006 to 2020, and the results are shown in

Table 4. Tobit regression results.

Y	Regression Coefficient	Standard Error	Z Value	p > |z|
GDP per capita	−0.0001714	0.0001321	−1.30	0.195
Proportion of government expenditure in GDP	−0.5445673 ***	0.0758429	−7.18	0.000
Science and technology expenditures	0.001452 ***	0.0004295	3.38	0.001
Total imports and exports	−0.0349323 **	0.0163357	−2.14	0.032
Population density	−0.0000603 *	0.000031	−1.94	0.052
Proportion of tertiary industry in GDP	0.0013151 ***	0.0004622	2.85	0.004
Urbanization rate	0.0028271 ***	0.0006418	4.40	0.000
Park green area	−4.75 × 10−6	4.50 × 10−6	−1.05	0.292
Regression intercept	0.4388666	0.050911	8.62	0.000

*** represents passing the test with a significance level of 1%, ** represents passing the test with a significance level of 5%, and * represents passing the test with a significance level of 10%.

.

As can be seen from Table 4, among the eight influencing factors, only GDP per capita and park green area did not pass the significance test, which indicates that the influencing factors selected in this paper are reasonable. The specific analysis of the six variables that passed the significance test is as follows.

The regression coefficient of the proportion of government expenditure in GDP was negative, passed the significance test at the 1% level, and had the largest value of all the variables, indicating that excessive government expenditure is detrimental to improvements in eco-efficiency. Further analysis of the structure of government expenditure in China reveals that most government revenues are invested in public cumulative expenditure and public consumption expenditure, such as infrastructure construction, education expenditure, and social security, with less investment in environmental protection, and the transfer function funds are not used efficiently, which has a limiting effect on ecological protection.

The regression coefficient for science and technology expenditure is positive and passes the significance test at the 1% level. The development of science and technology can promote rapid economic growth and improve the efficiency of resource use, thus promoting the improvement in ecological efficiency. Technological innovations in environmental protection are conducive to reducing energy consumption in the production process, improving the treatment of pollutants, and reducing energy input and the three industrial wastes of undesired output. Scientific and technological innovation in environmental protection can help reduce energy consumption in production, improve the treatment of pollutants, reduce energy input and the emission of industrial wastes with undesired output, and reduce resource consumption and ecological pressure per unit of output, thus promoting the improvement in eco-efficiency.

The coefficient of total imports and exports is negative and passes the significance test at the 5% level, indicating that the degree of openness of urban agglomerations has a negative effect on the ecological environment. There is no consensus among scholars on the impact of the degree of opening up on eco-efficiency. Some scholars believe that opening up is conducive to attracting excellent talents and advanced technology and improving the competitiveness of enterprises, and thus has a positive effect on eco-efficiency [16,48]. Some scholars also point out that developed regions will transfer high-energy-consuming and high-polluting industries to less developed regions that are labor intensive and have more relaxed environmental regulations, thus causing great damage to the ecological environment in these regions. The results of this study show that opening up to foreign investment is not conducive to improving the eco-efficiency of urban agglomerations, which, to a certain extent, confirms the “pollution paradise hypothesis” and is consistent with the findings of Shi [33] and Zhong [49]. This further suggests that special attention should be paid to the quality of the foreign investments being considered, and that the ecological damage and excessive use of energy by foreign enterprises should not be neglected due to the excessive pursuit of economic efficiency, which leads to a decline in eco-efficiency.

The regression coefficient for population density is negative and passes the significance test at the 10% level. With the development of cities, especially for some large and medium-sized cities, the growing population and the excessive in-migration of people from the surrounding areas often place huge ecological pressure on the city, exceeding its environmental carrying capacity, causing an excessive use of resources, and thus reducing the eco-efficiency of the city.

The regression coefficient for the proportion of tertiary industry in GDP is positive and passes the significance test at the 1% level, indicating that an active industrial restructuring is required to improve eco-efficiency. The transformation of leading industries from resource-intensive industries to innovation-driven and eco-friendly industries can help to reduce pollutant emissions. In addition, high-end service industries tend to attract more highly skilled personnel, which is conducive to the formation of technology spillover and talent-gathering effects, thus promoting an improvement in eco-efficiency.

The coefficient of urbanization level regarding eco-efficiency is positive and passes the significance test at the 1% level. Economically developed central cities tend to bring more employment opportunities, thus attracting a large influx of labor, which is conducive to the formation of economies of scale and agglomeration effects. They also tend to promote the sharing of infrastructure and the promotion of the division of labor, thus driving rapid economic growth and promoting the improvement in eco-efficiency in urban agglomerations.

5. Conclusions, Policy Implications, and Discussion

5.1. Conclusions

This article focuses on the core issue of the eco-efficiency of urban agglomerations in China from 2006 to 2020 and uses the kernel density estimation method, spatial autocorrelation, and Tobit regression models to analyze the spatial and temporal evolution of eco-efficiency and the influencing factors of the 19 urban agglomerations classified under the 14th Five-Year Plan. Our conclusions are as follows:

(1)

Overall, the eco-efficiency of China urban agglomerations is still at a low-to-medium level: the eco-efficiency of the Hohhot-Baotou-Ordos-Yulin urban agglomeration, the Pearl River Delta urban agglomeration, the Yangtze River Delta urban agglomeration, and the Shandong Peninsula urban agglomeration is relatively high, while the eco-efficiency of the Ningxia Yanhuang urban agglomeration and the Lanzhou–Xining urban agglomeration is relatively low.

(2)

From a temporal perspective, China’s urban agglomerations are divided into two stages: “steady rise” and “fluctuating decline”. The overall urban agglomerations, as well as the eastern, central, and western urban agglomerations, all show a double-peaked trend, with the second main peak in the eastern urban agglomerations being higher than the overall urban agglomerations and the second main peak in the central urban agglomerations being lower than the overall urban agglomerations. The second main peak in the western urban agglomeration gradually disappears, evolving towards a single peak. The changes in the height and bandwidth of the main peaks in the eastern, central, and western urban agglomerations are generally consistent with the overall trend of the urban agglomerations, and the internal differences in the eco-efficiency of each regional urban agglomeration all show a trend of first slightly decreasing and then gradually increasing.

(3)

In terms of spatial distribution, the eco-efficiency of China’s urban agglomerations shows a decreasing trend from coastal to inland areas, and their eco-efficiency is closely related to the level of economic development. The global Moran index of the eco-efficiency of Chinese urban agglomerations is all positive, indicating that the eco-efficiency of urban agglomerations shows a clustering trend. From the Lisa clustering diagram, there are far more high–high and low–low cities than low–high and high–low cities in China’s urban agglomerations, showing an obvious spatial clubbing phenomenon. Among them, most cities in the eastern urban agglomerations, such as the Yangtze River Delta urban agglomeration and the Pearl River Delta urban agglomeration, show significant high–high clustering, while most cities in urban agglomerations located in the inland areas, such as the Ningxia Yanhuang urban agglomeration and the Lanzhou–Xining urban agglomeration, show significant low–low clustering.

(4)

The regression results of the influencing factors, fiscal expenditure, level of openness to the outside world, and population density show significant negative correlations with the eco-efficiency of urban agglomerations, while investment in science and technology, industrial structure, and urbanization level show significant positive correlations with the eco-efficiency of urban agglomerations.

According to the efficiency measurement results, the following five suggestions are put forward:

(1)

Give full play to the synergy effect and radiation-driven effect between different urban agglomerations and between different cities. Cities within the same urban agglomeration should establish a cooperative relationship in order to complement the strengths, weaknesses, and advantages of each city. The construction of information and transportation networks between city clusters should be strengthened to reduce the cost of the spatial spillover of talent and technology.

(2)

The formulation of ecological and environmental protection policies should be continuous and phased, and a long-term environmental governance mechanism should be established. The assessment of local government performance should not be based on GDP as a single indicator but should also include pollution control and ecological protection within the assessment mechanism. In addition, the environmental assessment index system should be refined to prompt local governments to take ecological and environmental factors into account when making decisions and transform the traditional goal of economic growth into green and coordinated development.

(3)

The industrial structure should be actively adjusted to promote the development of environmentally friendly industries, support the development of clean energy industries, and accelerate the transformation from secondary to tertiary industries. For the secondary industries, it is necessary to actively promote the transformation and upgrading of traditional industrial enterprises, phase out old infrastructure that is more harmful to the environment, and change the crude mode of production and operation.

(4)

Environmental regulations on foreign investment should be strengthened and stricter and more effective resource and environmental policies should be formulated. If foreign investors are blindly pursuing economic benefits while ignoring the damage to the ecological environment, they will eventually become users of “pollution refuges” in developed regions. Therefore, the government should strengthen its scrutiny and supervision of foreign investment to attract quality foreign investment and strictly prohibit foreign enterprises that pollute the environment.

(5)

From the perspective of input and output, reducing resource consumption and pollutant emissions while achieving a high level of output is an effective way to boost eco-efficiency. This can be achieved by adjusting the ratio of inputs of human resources, land resources, water resources, and energy; optimizing the allocation of resources and achieving the efficient use of resources; accelerating the replacement of traditional energy with clean energy; and reducing the pollutant emissions per unit of energy, thus promoting the improvement in eco-efficiency.

5.2. Limitations and Future Scope of the Study

This study provides new ideas for the high-quality development and eco-efficiency of urban agglomerations, but there are still some limitations. In terms of the sample, this study only examined prefecture-level cities in 19 urban agglomerations. County-level cities included in urban agglomerations were not included in the sample due to the limited amount of available data. Further research could select more levels of sample cities. Meanwhile, in addition to the non-desired outputs selected in this paper, pollutants such as carbon dioxide emissions and nitrogen oxide emissions could also be included in the index evaluation system to make it more complete. In addition, in terms of the analysis of influencing factors, the Tobit model is only an attempt to identify the model that leads to the least deviation in the regression results. In the future, the regression results of multiple regression models can be compared and analyzed to conduct a more comprehensive study on the influencing factors of eco-efficiency.