1. Introduction
China’s economy entered an era of rapid development since its reform and opening in 1978. Consequently, most mineral resource-based cities (MRBC) have constructed an urban development model dependent upon the large-scale consumption of mineral resources. While efficiently promoting economic growth, this backward developmental model has caused significant damage to the environmental ecology of MRBC [
1,
2]. According to the “China City Statistical Yearbook” [
3] statistics, in Chinese MRBC, sulfur dioxide emissions in 2018 reached a total of 2.65 million tons, and the total amount of industrial wastewater discharge was as high as 3.1 billion tons. Moreover, differing interests and continuous disputes concerning environmental ecology between the residents of resource exploitation areas and the mineral enterprises may create socio-ecological problems. These conflicts of interest will eventually affect the healthy development of the entire national economy [
4]. To cope with this severe situation and smoothly promote the green transformation of MRBC, the central government issued a strategic policy document “National Resource-based City Sustainable Development Plan (2013–2020)” in 2013. The document clarifies the transformation goals of resource-based cities by 2020, that is, the historical legacy of resource depletion would have been basically solved, the capacity for sustainable development would have been significantly enhanced, and the transformation tasks would have been completed; the pattern of coordination between resource development, economic and social development and eco-environmental protection would have been formed; substantial progress would have been made in the transformation of economic development mode, and a long-term mechanism to promote the sustainable development of resource-based cities would have been established and improved [
5]. Subsequently, the National Development and Reform Commission successively promulgated the “guidelines on strengthening classification and guiding the cultivation of new drivers for the transformation and development of resource-based cities” [
6] and “notice on supporting the construction of the first batch of industrial transformation and upgrading demonstration zones in old industrial cities and resource-based cities” [
7] and other implementation opinions, and identified Anshan, Fushun, and Songyuan as pilot cities for transformation. Furthermore, many MRBC have combined the characteristics and advantages of regional development, through the creation of local regulations, e.g., opinions of Benxi City on promoting ecological development of ecological cities, 2019 and opinions on the implementation of green transformation development of industries in Taian City, 2017, to gradually explore and clarify the real path of green transformation. Consequently, the green transformation, as a new form of environmental protection, economic growth model, and the urban development model, has become an inevitable trend in the MRBC to achieve sustainable development.
As the MRBC green transformation strategy continues to advance, the academic research on MRBC green transformation evaluation also further develops. The content of this research primarily consists of the evaluation purpose, the sample characteristics, and the index selection. For example, Yuying et al. [
8] used panel data from 2006 to 2016 to construct an evaluation system of MRBC green transformation level by utilizing the Analytic Hierarchy Process and entropy weight method. In that study, the green transformation levels of 109 different MRBC in China were evaluated and analyzed. Although these studies provide valuable references for the sustainable development of MRBC, there are shortcomings in the research content, methods, and perspective. For example, the research on the evaluation of MRBC green transformation efficiency has not been included and valued, interference factors, such as the external environment of the research object, have not been eliminated, and a comprehensive evaluation index system has not been established and perfected. As different MRBC are in different stages of resource, social, and economic development, the contradictions and problems they face are varied and affected by different environmental factors. Thus, there is a significant variation in the green transformation efficiencies of each MRBC. Green transformation efficiency assessments can reflect the real effect of green transformation over time and help explore efficiency differences, influencing factors, existing problems, and their causes [
9,
10]. Therefore, it is necessary to improve the research on evaluations of MRBC green transformation and to specifically evaluate and analyze the efficiency of green transformation. These factors are particularly important for MRBC to improve green transformation methods promptly.
The empirical evaluation conducted in this study mainly involves the following three aspects of contributions. First, the evaluation system of MRBC green transformation efficiency was constructed based on six dimensions of economic growth, environmental protection, industrial transformation, energy conservation, mineral protection, and livelihood improvement. These dimensions improve the comprehensiveness and dynamics of the existing evaluation system. Second, the external environmental factors and statistical noise were eliminated by using the three-stage data envelopment analysis (DEA) method, and the comprehensive and real efficiencies of MRBC green transformation were evaluated and compared. Additionally, the influence of external environmental factors, including economic structure, energy consumption structure, and technological innovation level, was discussed to effectively improve the accuracy of efficiency evaluation results. Third, based on the decomposition of efficiency, we discuss the efficiency differences and the causes of the green transformation of Chinese MRBC in different regions and types. Additionally, we proposed different strategies for improving the efficiency of different groups, to benefit the formulation of targeted efficiency improvement measures. Overall, in this study, we explored methods to improve the efficiency of MRBC green transformation in China and suggested improvements for the efficiency of green transformation MRBC for other developing countries.
2. Literature Review
With the rise of urban green transformation research, the driving mechanisms of green transformation, influencing factors, and evaluations of the transformation have been receiving increasing attention.
Regarding the driving mechanisms of urban green transformation, Ji et al. [
11] believe that the environmental deterioration caused by bad behavior, such as excessive carbon emissions, restricts the sustainable development and utilization of urban natural resources. This breaks the balance between rapid economic development and environmental protection, and the urgent demand to solve this contradiction promotes the green transformation of cities. Qian et al. [
1] analyzed the existence of the resource curse effect by using a system generalized method of moments based on the panel data from 30 coal mine cities in China from 2005 to 2015. The results showed that the resource curse effect is the main driving force for cities to implement green transformation. Jian et al. [
12] analyzed the endowment of climate resources, urban transformation and development, and restructuring of the culture-economy-ecological geography chain in China’s Panzhihua City. The authors believed that the backward economy, significant pressure on the ecological environment, and depletion of natural resources are the key reasons for the green transformation of cities. Lin et al. [
13] found that extreme environmental pollution, particularly air pollution (considered a national emergency), has a significant negative impact on human health, industrial production, and social well-being. These impacts have prompted decision-makers to begin to advance the green transformation of cities, such as the implementation and expansion of The Clean Air Action measures. Additionally, Sun et al. [
14] indicate that China has proposed an inclusive green transformation strategy based on social, economic, and environmental coordination in response to global resource management, environmental demand, and economic growth slowdown. The consistency of developmental goals is the fundamental driving force for cities to achieve inclusive green transformation. In summary, the unsustainability of urban development is generally considered to be the key to driving cities to implement green transformation, primarily manifested in the contradiction between economic growth expectations and environmental pollution status. Important contributions have been made to the research on the driving mechanisms of urban green transformation; however, further research is required. For example, existing literature offers limited discussion regarding the protection of depleted minerals, improvement of people’s livelihood, and solving social and ecological problems caused by environmental pollution. There is still room for expansion.
Researchers have explored urban green transformation influence factors from a different perspective. Jin et al. [
15] found that the influence of macroeconomic uncertainty on green transformation in different cities varies, and its negative impact on developed and coastal cities is not evident; however, the inhibitory effect on underdeveloped cities is significant. Yuan et al. [
16] analyzed the spatial threshold effect of 272 prefecture-level cities in China from 2003 to 2014 using a spatial panel Durbin model and panel threshold regression model. They found that financial agglomerations have a considerable impact on promoting urban green transformation. Based on the logistic regression model, Zhao et al. [
10] estimated the determinants of green transformation efficiency in 286 cities of different sizes and different regions of China in 2013. They found that the regional effect and the population size effect are the most significant factors for transformation efficiency. Cheng et al. [
17] used the panel data of 194 prefecture-level cities in China from 2007 to 2016 and difference-in-differences models to discuss the impact of low-carbon city construction on urban green transformation. Their study showed that the construction of low-carbon cities has economies of scale and regional differences. Cities with larger scales, better infrastructure, and better technological foundations have a more pronounced positive impact on green transformation efficiency. Ma et al. [
9] constructed a green growth efficiency analysis database based on the remote sensing data and socio-economic data of 285 prefecture-level cities in China from 2005 to 2016. They found that the spatial difference had a significant impact on the efficiency of urban green transformation in China. Based on the panel data of 278 cities in China from 2011 to 2016, Lin et al. [
13] empirically analyzed the policy effect of clean air actions on urban green transformation by constructing a green transition indicator system. The results showed that clean air actions have a significant positive impact on the cities’ green transformation and show an increasing trend in efficiency over time, particularly for cities with high emission reduction targets and rich resource endowments. Sun et al. [
14] adopted a comprehensive directional distance function and slacks-based measure (DDF-SBM) model to make an inclusive green transformation of 285 cities in China from 2003 to 2015. The efficiency level was evaluated, and the authors found that technology and region are two important factors that affect the level of transformation. Among the two factors, the scope of technological change is the main obstacle in improving the level of transformation. In summary, researchers have analyzed the impact of different factors on green transformation based on the aspects of urban economy, finance, policy, technology, region, and scale, and have laid a good foundation for further research in this field. However, there are still some deficiencies. For example, there is no in-depth study regarding the influencing factors and their effects in the fields of industry and energy, culture and tradition, system and innovation, or other areas. Additionally, there is no cross over, mixing, or synthesis of factors in different fields and of different levels of research to obtain factors that jointly affect the performance, efficiency, and effectiveness of urban green transformation.
Given the urban green transformation evaluation, researchers have analyzed different transformation levels according to the evaluation purpose, sample characteristics, and index selection. Zhao et al. [
10] used a met frontier-DEA to analyze the green transformation capabilities of 286 sample cities in China in 2013 and evaluated their efficiency gaps. The authors found that cities of different sizes and in different regions have different green transformation efficiency statuses. Wang et al. [
18] adopted a multi-level evaluation method and entropy method to improve the human settlement environment and pollutant treatment and utilization, increase ecological benefits, and improve the ecological environment. A green transformation evaluation index system was constructed, and the green transformation level of nine cities in the Pearl River Delta in China was evaluated in 2015. The levels of green transformation in Shenzhen, Zhuhai, and Guangzhou were relatively high, whereas other cities lagged. Ma et al. [
9] used a technique for order of preference by similarity to ideal solution (TOPSIS) model and the super-efficient slack based measure (SBM) model to explore the efficiency of green transformation in 285 sample cities in China from 2005 to 2016. The results showed that the center of gravity of urban green transformation efficiency is shifting to the southwest; the comprehensive evaluation index of green transformation rises first and then falls, and that the regional difference is small, but gradually expanding. Simultaneously, the efficiency of green transformation decreases first and then rises, and the spatial difference is significant. Using the super-efficiency DEA epsilon-based measure model, Jin et al. [
15] incorporated macroeconomic uncertainty and high-level innovation into the urban green transformation performance analysis framework. They evaluated the green transformation efficiency and heterogeneity of 282 sample cities in China from 2005 to 2016. Sun et al. [
14] combined a DDF-SBM model and utilized a Luenberger indicator on panel data from 285 cities in China from 2003 to 2015. They analyzed the transformation efficiency and differences, and proposed countermeasures from both technical and regional aspects. In summary, we found that the existing research on the evaluation of urban green transformation has some shortcomings in terms of research objects, research methods, and research perspectives. In terms of sample selection for research objects in previous studies, main cities in the entire country or a certain region were focused upon, and there are few specialized studies on the green transformation evaluation of MRBC in a targeted manner. In terms of research methods, some of the more common ones are stochastic frontier analysis (SFA), TOPSIS model, met frontier-DEA, super-efficient SBM model, super-efficiency DEA epsilon-based measure model, and DDF-SBM model. However, none of these methods can remove disrupting factors such as the external environment of urban green transformation and statistical noise; therefore, the reliability and authenticity of the evaluation results are debatable. From a research perspective, the indicators selected in previous studies mainly focused on economic growth and environmental protection and lacked a comprehensive consideration of industrial transformation, energy conservation, mineral protection, and livelihood improvement. Most of these indicators are static indicators, showing that the evaluation index system is not yet perfect.
Based on the number and content of existing documents on urban green transformation, the evaluation of urban green transformation in recent years has received increasing attention from academic circles. Although most cities have made remarkable progress in implementing green transformation, there are significant differences and imbalances in the effectiveness of green transformation in different cities. According to the data of “TOP 50 report of Chinese Green Cities Index in 2019” [
19], only Zhangjiakou and Chengde are among the top 50 in China for 2019, and the remainder of the MRBC has not performed well. The evaluation of green transformation will help discover efficiency differences, influencing factors, existing problems, and their causes among different cities. This information will then provide advice, guidance, and decision-making references for cities to improve green transformation [
14,
18]. Given the shortcomings in the existing literature regarding the evaluation of urban green transformation, we provide exploratory amendments and attempt to make useful contributions to the research in this field. First of all, the sample selection of the research object has been specially set. This study is conducted with a focus on the research of green transformation efficiency evaluation for MRBC in China, and comparative evaluations are performed based on two features: area and city type. Furthermore, we strive to comprehensively analyze the efficiency, gaps, existing problems, and their causes in MRBC green transformation. Second, we use correct and suitable evaluation methods by utilizing the three-stage DEA method to measure and evaluate the efficiency of MRBC green transformation. Compared with other methods, the three-stage DEA method can eliminate interference factors such as the external environment and statistical noise and accurately calculate the relative efficiency value of each decision unit. Thus, the results are closer to the real values. Additionally, an expanded application field of the three-stage DEA method is introduced. Third, we optimize the evaluation index system. The evaluation index system not only includes economic growth and environmental protection, but also adds the relevant indexes, such as industrial transformation, mineral protection, and energy conservation, highlighting the characteristics of MRBC. Some indicators are set as dynamic indexes to comprehensively, accurately, and effectively present the efficiency of and differences between MRBC green transformations.
3. Methods
At present, the efficiency evaluation methods commonly used in the field of urban green transformation include: the SFA [
20], the global Malmquist-Luenberger productivity index [
21], the TOPSIS model [
9], the analytic hierarchy process and entropy method [
8], the multi-level evaluation method and entropy method [
18], and the DEA method [
15]. The DEA method, a non-parametric method, is more suitable to solve the “multi-input and multi-output” problem compared with other methods. Additionally, it can overcome defects such as function setting errors and limiting assumptions for overall distribution. Thus, the DEA method has received increasing attention and is widely used for efficiency evaluations in various fields. Since Charnes et al. proposed the Charnes, Cooper and Rhodes (CCR) model in 1978, a series of amendments and improvements have been made to the DEA method. Currently, the most widely used models are met frontier-DEA [
10], the super-efficient SBM [
9], super-efficiency DEA epsilon-based measure model [
15], DDF-SBM model [
14], and the three-stage DEA method [
22]. Compared with other improved DEA methods, the core purpose of the three-stage DEA method is to eliminate the external environment, statistical noise, and other interference factors, and to evaluate the relative efficiency of decision-making units more accurately. Therefore, in this study, we use the three-stage DEA method to evaluate the green transformation efficiency of MRBC in China (
Figure 1). In the first stage, the input-oriented Banker, Charnes and Cooper (BCC) model is used to calculate the comprehensive efficiency of green transformation of the MRBC under the influence of an external environment and statistical noise. In the second stage, based on the SFA, each input relaxation variable is divided into three different impact values: external environment, management inefficiency, and statistical noise. Concurrently, the external environmental impact value and statistical noise impact value are eliminated, that is, the second input variable value of each MRBC is adjusted. In the third stage, based on the adjusted input variables, the BCC model is used again to calculate the green transformation efficiency of MRBC to obtain the real efficiency evaluation value of each city.
3.1. First Stage: Comprehensive Efficiency Evaluation of MRBC Green Transformation
There are two types of DEA methods: input-oriented and output-oriented. Compared with output variables, input variables can better reflect the direction and controllability of the decision-making unit. Consequently, we use the DEA method based on input orientation to evaluate efficiency. Meanwhile, the BCC model can measure the scale efficiency (SE) and pure technical efficiency (PTE) of decision-making units (DMUs), whereas the CCR model cannot. Therefore, in this stage, the BCC model based on input orientation is used to calculate the green transformation efficiency of MRBC to obtain the comprehensive efficiency value of the MRBC green transformation. The equation is as follows:
where
and
represent the
-th input variable and the
-th output variable of the
-th DMU, respectively.
,
, and
represent the number of DMU, number of input, and number of output variables, respectively.
implies a
dimensional weight vector of the DMU.
represents the comprehensive efficiency of the green transformation of each DMU.
3.2. Second Stage: Calculate the Adjusted Secondary Input Value
In the second stage, the external impact value and the statistical noise impact value were eliminated. Here, by calculating the difference between the real input and the target input, we can find the relaxation value
of the input variables of the DMU. The relaxation value
consists of three parts: management inefficiency impact value, external environmental impact value, and statistical noise impact value. When it comes to the decomposition calculation of relaxation variables
, the related formulas based on the SFA are established as follows:
where
represents the environment variables,
, and
represents the number of environment variables.
is the coefficient of each environmental variable.
represents the function to measure the impact value of the external environment.
and
represent the statistical noise impact value and management inefficiency impact value of the
-th DMU, respectively.
obeys the distribution of
, whereas
obeys the distribution of
, and they are independent of each other.
The SFA is used because of the existence of the inefficiency-term, and this assumption can be verified by using a unilateral generalized likelihood ratio test where and . If the test results reject the original hypothesis, showing that exists, the SFA method can be used for estimation. If the test results accept the original assumption, that is, does not exist, ordinary least squares should replace the SFA for estimation.
To obtain the adjusted secondary input variable value of each DMU, the inefficiency-term
needs further decomposition calculation. The
calculation formula of conditional expectation [
23] is shown as follows:
where
,
,
, and
represent the evaluation value of
,
,
, and
, successively.
,
,
and
represent the density function and cumulative density function obeying standard normal distribution, successively. Please refer to the attached documents for specific operation processes [
24].
The conditional expectation of
can be calculated using the following formula (in which,
, which is the evaluation value of
, and can be calculated using the SFA):
Finally, we adjust the input variable
to the second input variable
. The formula is as follows:
where
represents the adjusted estimated value of each DMU under the most unfavorable external environment.
represents the adjusted estimated value of each DMU under the least favorable statistical noise condition. This calculation process can adjust all the DMUs to the same external conditions.
3.3. Third Stage: Real MRBC Green Transformation Efficiency Measurement
The adjusted secondary input variables and the original output variables are substituted into the BCC model based on input orientation. The real value of the green transformation efficiency of MRBC is calculated after excluding the impact of the external environment and statistical noise.