# Study on Measurement and Drivers of Inclusive Green Efficiency in China

Authors: Yanbo Shen; Shuangshuang Fan; Lipeng Liu
Journal: Journal of Sensors
 (2022)

---

## Abstract

The stagnation of growth, huge income gap, and excessive ecological environment degradation are the three issues worldwide. As China entered the stage of high-quality development, it slowed down the economic growth speed and the government paid more attention to social harmony and environment protection. Inclusive green growth efficiency(IGGE) is used to measure the quality of economic development and the degree of coordination among economic, social, and natural systems. Under this background, this paper employs the Data Envelop Analysis (DEA) model to measure IGGE from 2006 to 2019 in 30 provinces of China. Moreover, the temporal and spatial evolution characteristics are revealed and the key drivers of IGGE are explored by the spatial econometric model. The results indicate that the level of provincial IGGI of China has an upward trend in the statistical period and the characteristics of spatial agglomeration have been enhanced. For the drivers of IGGI, the level of opening-up, human capital, and innovation have significant impact on IGGI in China, while other elements are not very important. Finally, some policy suggestions were proposed to promote IGGE in China.


---

## Body

## 1. Introduction

China’s economy has made great achievements in the past 40 years, but it also has paid huge environmental and social costs. On the one hand, the extensive growth mode with high investment, high consumption, high pollution, and low benefit has caused resource depletion, environmental pollution, and ecological destruction. On the other hand, the development opportunities brought by economic growth are not equally distributed, and the fruits of growth are not fairly shared, which leads to the widening income gap and the aggravation of social unfairness [1, 2]. Therefore, the report of the 19th National Congress pointed out that it is necessary to promote green development, regional coordination, and achievement sharing, and the country should shift from “unbalanced distribution” to “common prosperity,” as well as accelerate the construction of ecological civilization and shift from “high carbon growth” to “green development” [3]. As China has shifted from high-speed growth to high-quality development stage, how to achieve high-efficiency, opportunity fair distribution, and green development with the goal of meeting people’s growing needs for a better life has become a realistic problem to be solved urgently [4]. In March 2021, the 14th Five-Year Plan and the outline of the long-term goal for 2035 reemphasized to focus on solving the problem of insufficient development imbalance; narrowing the gap between urban and rural areas; improving the level of coconstruction, governance, and sharing of people’s livelihood and well-being; and promoting the harmonious coexistence between man and nature. It also pointed out that an action plan for peaking carbon emissions before 2030 should be formulated, and efforts should be made to achieve carbon neutrality before 2060, so as to reverse the lack of “greening” and the lack of “inclusiveness” in the traditional growth mode.The definition of inclusive green growth (IGG) originated from the initial concept framework of IGG put forward for the first time at the Rio +20 Summit in 2012, which is aimed at combining inclusive and green economy [5]. At present, there is no clear conclusion on how to define inclusive green growth in academic circles. One of the mainstream viewpoints is that IGG should pay attention to inclusive growth representing “contemporary welfare” and green growth representing “future generations’ welfare” at the same time, including sustainable and effective economic growth in the utilization of resources, and minimize the negative impact on the environment, emphasizing the importance of social equity in the distribution of economic and environmental benefits, aiming at improving social welfare [6]. The second category is from the perspective of development economics, which is defined as “sustainable development economics.” This perspective mainly discusses the possibility of combining sociolect-economic improvement with environmental protection and emphasizes how to use natural resources reasonably and efficiently and coordinate environmental externalities and inclusive sustainable development of economic growth [7, 8]. The core goal of IGG is to promote the greening and inclusive transformation of economic growth, which provides a new opportunity and direction for China’s current economic transformation. Its concept covers the concepts of coordination, sharing, and green and can effectively measure the coordination degree of efficiency and fairness, as well as the relationship among economy, society, and environment, which is a pathway to meet the sustainable development goal.However, there is no consistent conclusion on how to measure IGG, especially for developing countries like China, which restricts the ability of the government to formulate targeted policies to improve IGG. To bridge this gap, we employ the superefficiency DEA model, select the input and output indicators related to IGG based on China’s national conditions, and calculate the IGGE of various provinces in China from 2006 to 2019 from the perspective of efficiency. Further, this paper reveals the spatial agglomeration characteristics of IGGE in the statistical period through exploratory spatial analysis technology. On this basis, we build a spatial econometric model to demonstrate the key drivers of China’s IGGE. Then, we put forward some policy suggestions to improve China’s IGGE and meet the sustainable development goal in the future. The main contributions of this study are as follows:(1)
China’s economic system, social system, and ecosystem are seriously unbalanced, so it is extraordinary urgent to realize IGG in China. We reconstructed the conceptual framework of IGG in line with China’s current situation and selected relevant input and output variables to measure the level of IGGE in China’s provincial regions. Different from the traditional measurement method of constructing the index system, DEA method not only considers the result level of IGG but also considers the process of forming the result and uses the idea of linear programming to solve it. Therefore, our research enriches the research results in the field of IGG in China(2)
Based on the verification of the spatial correlation of IGGE in Chinese provinces, this paper introduces a nest of spatial econometric model to capture the driving factors of IGGE in China. Following the classical view proposed by Elhorst, if we ignore the spatial correlation between variables, the estimation results of classical econometric models (such as ordinary least square method) may be seriously biased. In order to solve this concern, we build different econometric models such as spatial Durbin and spatial lag models, respectively, and determine the optimal model by comparing the performance of each model. Then, among the possible independent variables, we found the most significant variable affecting IGGE in China(3)
According to the previous empirical results, we summarize the full text and improve a series of policy suggestions to improve IGGE according to China’s national conditions. On the one hand, these suggestions have important contribution value for guiding China’s decision-making authorities to formulate scientific and accurate IGGE and reduce the uncertainty of future policy implementation. On the other hand, there are many emerging economies like China, which are accompanied by serious income gap expansion and ecoenvironmental pollution in the process of rapid economic take-off. The reason may be due to extensive development planning in these countries. Therefore, this paper also provides reference for these countries, so as to promote the realization of sustainable development goals worldwideThe remaining chapters of this paper are arranged as follows: in Section2, literature review, we combed the literature related to IGG. The next section is the Section 3, methods and data, which introduces the construction of DEA model, exploratory space technology, and data sources, followed by the Section 4, which reveals the temporal and spatial differentiation of inclusive green efficiency in China. Furthermore, Section 5 is the construction of the spatial econometric model. Finally, Section 6 is the conclusions and policy recommendations.


## 2. Literature Review

IGG is a new concept combining inclusive growth and green development. To understand the connotation and extension of inclusive green growth, the key is to accurately grasp the definition of these two constituent elements [9]. Inclusive growth was first proposed by the Asian Development Bank in its report “inclusive growth: towards a prosperous Asia” in 2007 [10], and its conceptual connotation includes equality of opportunity [11], social inclusion, empowerment and security [12], poverty reduction, and employment increase [13]. Inclusive growth not only emphasizes the creation of employment opportunities and other development opportunities through economic growth but also emphasizes the equality of development opportunities. The importance of equal opportunities for all lies in the intrinsic value and instrumental role of opportunities. The intrinsic value of opportunity is based on the belief that equal opportunity is a basic human right. The instrumental role of opportunity comes from that equal use of opportunity will increase growth potential, while inequality of opportunity will reduce growth potential and make growth unsustainable. Subsequently, scholars and international organizations published articles on the understanding of inclusive growth. For instance, Ngepah believes that propoor growth is the prototype of the concept of inclusive growth. While for economic growth, we should pay attention to the absolute improvement of the income of the poor and prevent the aggravation of unfair social opportunities [14]. Similarly, Sissons et al. took British cities as an example and proposed to enhance inclusiveness to low-income and poor people by empowering urban marginalized groups [15], while Gupta and Pouw believe that inclusive growth involves addressing inequality, ensuring the satisfaction of basic needs, safeguarding human rights, and equitable distribution of resources from local to global. Different countries ensure the wide enjoyment of water, food, health services, housing, justice, and basic political rights among members of society in accordance with their own development priorities and based on the principle of promoting human well-being [16]. In terms of international organizations, the World Bank (WB) defines inclusive growth as a series of processes to improve the fairness of individual and group participation in social distribution and enhance the ability, opportunity, and dignity of vulnerable groups to participate in society [17]. It regards inclusive growth as the primary social moral obligation and a means to promote economic growth. UN Habitat emphasizes that better opportunities should be provided for people living on the edge of society [18]. Although the understanding of the connotation of inclusive growth is not unified, they generally emphasize the concept of “people-oriented”; that is, all social groups participate in the process of economic growth and benefit from the results of economic growth, focusing on “social participation” and “result sharing.” The dimension of social participation includes that social members enjoy fair employment and entrepreneurship opportunities, and achievement sharing means that people share the fruits of economic prosperity and eliminate income poverty and social welfare inequality.The concept of green growth was first put forward at the fifth Asia Pacific Regional Conference on environment and development in 2005. It is defined as an environmentally sustainable economic process to promote low carbon and benefit all members of society [19]. The definition of the concept of green development is mainly divided into three categories: the first type of research combines ecology and economy from the perspective of “resource efficiency,” emphasizes the decoupling between economic growth and resource use, makes efficient use of natural resources, and reduces environmental pollution while maintaining economic growth [20, 21]. However, the potential rebound effect of green development based on the concept of efficiency will weaken the expectation of absolute reduction of resource use. The second view, based on the dimension of “green process,” puts forward that green development refers to reducing emissions at the production and demand ends through green technology innovation of cleaner production and supply chain [22, 23]; this definition focuses on the greening of the value chain creation process at the industrial level but ignores the contribution of social members in green development. The third view is based on “harmonious symbiosis.” Green development is a coordinated development process with the value orientation of harmony between man and nature, based on the carrying capacity of natural resources and ecological environment, with the important characteristics of resource conservation, environmental friendliness, and ecological protection, and the ultimate goal of realizing the harmonious coexistence of economy, society, and ecology and sharing green welfare [8, 24]. This view is based on the theory of “green water and green mountains are golden mountains and silver mountains” under the background of China’s high-quality development, focusing on coordinating the relationship between economy, society, and ecology, thus reflecting the connotation characteristics and practical orientation of China’s green development at present.Based on the above concepts of inclusive growth and green growth, the concept of IGG was first put forward at the “Rio +20” summit in 2012. It advocates the combination of economic inclusiveness and greening, aiming to ensure sustained economic growth and promote social equity and resource and environment improvement. It is an inevitable choice for building ecological civilization and ensuring and improving people’s livelihood [25]. According to different emphases, the concept of IGG can be divided into two categories. One is from the perspective of development economics, which regards inclusive green growth as a way of sustainable development, that is, to give consideration to the interests of contemporary people and future generations [26]. For example, World Bank (2012) defined IGG as an environment-friendly and socially inclusive economic growth mode and an important way to achieve sustainable development [27]. The United Nations Environment Programme (UNEP) believes that IGG includes three pillars: society, economy, and environment, and promotes its realization based on the principles of inclusiveness, equity, and sustainable development [28]. The International Monetary Fund (IMF) believes that IGG is a model aimed at achieving sustainable development by coordinating the interests of developing countries. In 2015, changing our world-2030 agenda for sustainable development adopted by the United Nations Summit on sustainable development became the central theme in the field of global environment and development. Countries around the world formulated core development strategies based on the idea of inclusive green growth. In terms of scholars’ research, Halkos et al. believe that sustainable development goals can be achieved through economic inclusiveness and green industrial transformation [29]. Ge et al. pointed out that IGG is a sustainable development mode that pursues economic growth, social equity, people’s livelihood welfare, achievement sharing, energy conservation, and environmental protection, as well as the comprehensive coordination of economy, society, resources, and environment [30]. The definition of IGG from the perspective of development economy emphasizes that economic development is the direction, narrowing the social gap and realizing shared development achievements are the path, and environmental protection is the key strategy. IGG not only emphasizes economic growth and social people’s livelihood but also requires continuous improvement of ecological benefits in the development process, taking into account the coordination of economic interests, social equity, environmental protection, and so on.Another definition is based on the viewpoint of welfare economics, which holds that improving welfare is the main purpose of economic growth. A typical study is that Berkhout and others believe that IGG is an economic growth model that takes into account the welfare of social poor groups (inclusive) and future generations (green) [6]. Verheggen et al. advocated that IGG should focus on the game between inclusiveness, greenness, and economic growth and pay attention to future generations while enjoying current welfare [31]. Kessler and Slingerland believe that IGG is aimed at improving social welfare, emphasizing environmental greening and social inclusiveness [32]. Mandle et al. believe that IGG involves improving human well-being through the optimization of ecosystems and the protection and restoration of natural assets such as land, water, and biodiversity [33]. In China, there is a lack of research on the connotation of relevant theories. Schoneveld and Zoomers took the sub-Saharan Africa area as the research object and proposed an IGG framework based on the inclusive social security system and ensuring the improvement of environmental quality [34].By combing and summarizing the above literature, this paper holds that IGG, through the coordinated development of economic, social, and natural systems, drives economic growth, reduces the gap between the rich and the poor, realizes the sharing of development achievements, protects the ecological environment, and ensures the interests of future generations while improving the well-being of contemporary mankind. IGG is of great significance for China to build a harmonious society. On the one hand, a harmonious society needs China’s economy to maintain moderate and sustainable growth in order to create a large number of jobs and other development opportunities. On the other hand, rapid growth does not necessarily guarantee the fair sharing of opportunities and the protection of the ecological environment. Therefore, we must deeply explore how to improve the development level of IGG in China.


## 3. Methods and Data

### 3.1. Construction of Index System to Evaluate the IGGE

DEA model is a typical input-output analysis method and is one of the most commonly used models for efficiency evaluation due to its advantages of few evaluation indexes, accurate evaluation results, and less loss of original index information [35]. DEA models mainly include traditional DEA model, three-stage DEA model, superefficiency DEA model, undesirable output (SBM) model, and Malmquist index [36]. Existing evaluation index system of green development efficiency and inclusive efficiency in building, the input index from the perspective of classical economics, and more elements such as land, capital, labor force, output indicators both have significant difference in that the green development efficiency increases GDP as desirable output indicators and the pollution of the environment as undesirable output [37]. The expected output index of inclusive efficiency is basically the same as that of green efficiency, but the nonexpected output selects indicators such as urban-rural income ratio, the Gini coefficient, or number of poor people to represent social “noninclusive” factors from the perspective of social nonexpected output. This paper argues that IGGE should consider both greenness and inclusiveness. Therefore, social inequality should be fully considered as an indicator of inclusive degree in the unintended output of IGGE. Based on this idea, this paper follows scientific and systematic principles and constructs an inclusive green efficiency evaluation index system, as shown in Table 1.Table 1
Evaluation system of inclusive green efficiency.
Classification of indicatorGeneral indicatorSpecific indicatorInput indicatorLabor inputNumber of employees and individual businesses (unit: 10,000)Capital inputInvestment in fixed assets (unit: ten thousand yuan)Energy inputStandard coal (ten thousand tons)Output indicatorEconomic system outputGross regional product (100 million yuan)Social system outputUrban-rural income ratioUrban-rural consumption ratioEnvironmental system outputCarbon emission (million tons)Industrial sulfur dioxide (ten thousand tons)Waste water (ten thousand tons)General industrial solid waste (ten thousand tons)As can be seen from Table1, the input index includes labor input, capital input, and energy input. The sum of employed personnel and private personnel in each province at the end of the year is calculated. Capital investment is characterized by fixed assets, taking the year 2000 as the base year, follow the formulaKit=Kit−11−δ+Iit to carry out the deduction calculation. In terms of energy input, because China is dominated by petrochemical resources, standard coal is selected to depict it.The output index covers three major systems: economic, social, and environmental. For the desirable output of the economic system, the variable we use is the per capita GDP of the sample city. Considering the impact of inflation, we deflate it based on 2011. And the output of the social system is characterized by urban-rural income ratio and urban-rural consumption ratio, which are the undesirable outputs of the social system. Due to the main inequality in China’s current society which is reflected in the gap between urban and rural areas, the greater the difference between urban and rural areas, the more serious the social inequality will be. In terms of the output of the environmental system, carbon dioxide emissions, industrial waste water emissions, industrial sulfur dioxide emissions, general industrial solid waste, and other indicators are represented. The larger these indicators are, the more serious the environmental pollution is, so they are all undesirable outputs.The data used in the indicator system in Table1 are mainly from China Urban Statistical Yearbook, China Statistical Yearbook, China Population and Employment Statistical Yearbook, China Energy Statistical Yearbook, China Environmental Statistical Yearbook, etc. The statistical period is from 2005 to 2019. Due to the serious trend of the data in Tibet, we deleted the data and finally obtained the data of 30 provinces, autonomous regions, and municipalities in China. In addition, for the missing data of some sample cities, we have adopted the linear difference method for fill them, which has ensured that our sample data is a balanced panel data.


### 3.2. Construction of DEA Model

By referring to other literatures [30, 38–40], because DEA method can solve the problem of inconsistent units of various input and output elements in inclusive green efficiency and it does not need to consider specific production functions and estimate weights and parameters in advance, therefore, the SBM-DEA model is used as the evaluation model of inclusive green efficiency in China’s provinces. In order to fit the actual situation, this paper introduces unexpected output into super-SBM model for comprehensive measurement, which more truly reflects the nature of IGGE in China’s provinces. The model assumes that the production system has n decision-making units, and each decision-making unit contains three input-output variables, namely, input, expected output and, unexpected output. The specific calculation formula is
(1)minδ∗=1+1/m∑m=1MSmx/Xjmt1−1/l+h∑l=1LSly/yjll+∑h=1HShb/bjht(2)s.t.xjmt≥∑j=1.k≠0nλjtxjmt+smxyjlt≥∑j=1.j≠knλjtxjlt−slxbjht≥∑j=1.k≠0nλjtbjht+shbλjt≥0,smx≥0,sly≥0,j=1,⋯,nAmong them,δ∗ stands for IGGE, xt, j, and yt, which, respectively, represent the input, expected output, and unexpected output value of DMUj in the period t. And m,l,hindicate the number of input, expected output, and unexpected output elements, respectively. At the same time, smx, sly, and shb are relaxation vectors of input, expected output, and unexpected output, whereas λ is the weight vector of the decision unit.


### 3.3. Exploratory Spatial Data Analysis (ESDA)

This paper mainly uses the global spatial autocorrelation and local spatial autocorrelation analysis methods to identify the agglomeration of IGGE in various provinces in China. The global spatial autocorrelation generally uses the global MoranI coefficient to reflect the distribution effect of regional units. The specific calculation formula is as follows:
(3)I=n∑t=1n∑j=1nXi−X¯Xj−X¯∑i=1nXi−X¯2∑i=1n∑j=1nWij,whereas n means the number of spatial units in the study area, and Xi,Xj represents the observed values of region i and region j, respectively. X− represents the average value of object x; Wij is the spatial weight matrix. I∈−1,1, at a given significance level; if I>0, it means that the regional green development level space presents an agglomeration trend; on the contrary, it means that the regional green development level space presents differences; the greater the absolute value of I, the stronger the spatial correlation.Although the above method analyzed the distribution effect of China’s provinces within the time limit, it cannot distinguish the high- and low-value agglomeration of different spatial provinces in the region. The local spatial autocorrelation can effectively identify the spatial dependence and heterogeneity of inclusive green efficiency. The specific calculation formula is as follows:(4)Gi∗d=nYi−Y¯∑i=1nwijYj−Y¯∑i=1nYi−Y¯2,where Gi∗d is the local Jerry index; Wij is the spatial weight matrix; and Xi and Xj represent the green development level of region I and region J. For Gi∗d, z test shall be conducted. If z is significantly positive, it indicates that the green development level space presents a high-value agglomeration area. If z is significantly negative, it is a low-value agglomeration area. Therefore, the green development level can be divided into four categories: high and high agglomeration, high and low agglomeration, low and high agglomeration, and low and low agglomeration.


## 3.1. Construction of Index System to Evaluate the IGGE

DEA model is a typical input-output analysis method and is one of the most commonly used models for efficiency evaluation due to its advantages of few evaluation indexes, accurate evaluation results, and less loss of original index information [35]. DEA models mainly include traditional DEA model, three-stage DEA model, superefficiency DEA model, undesirable output (SBM) model, and Malmquist index [36]. Existing evaluation index system of green development efficiency and inclusive efficiency in building, the input index from the perspective of classical economics, and more elements such as land, capital, labor force, output indicators both have significant difference in that the green development efficiency increases GDP as desirable output indicators and the pollution of the environment as undesirable output [37]. The expected output index of inclusive efficiency is basically the same as that of green efficiency, but the nonexpected output selects indicators such as urban-rural income ratio, the Gini coefficient, or number of poor people to represent social “noninclusive” factors from the perspective of social nonexpected output. This paper argues that IGGE should consider both greenness and inclusiveness. Therefore, social inequality should be fully considered as an indicator of inclusive degree in the unintended output of IGGE. Based on this idea, this paper follows scientific and systematic principles and constructs an inclusive green efficiency evaluation index system, as shown in Table 1.Table 1
Evaluation system of inclusive green efficiency.
Classification of indicatorGeneral indicatorSpecific indicatorInput indicatorLabor inputNumber of employees and individual businesses (unit: 10,000)Capital inputInvestment in fixed assets (unit: ten thousand yuan)Energy inputStandard coal (ten thousand tons)Output indicatorEconomic system outputGross regional product (100 million yuan)Social system outputUrban-rural income ratioUrban-rural consumption ratioEnvironmental system outputCarbon emission (million tons)Industrial sulfur dioxide (ten thousand tons)Waste water (ten thousand tons)General industrial solid waste (ten thousand tons)As can be seen from Table1, the input index includes labor input, capital input, and energy input. The sum of employed personnel and private personnel in each province at the end of the year is calculated. Capital investment is characterized by fixed assets, taking the year 2000 as the base year, follow the formulaKit=Kit−11−δ+Iit to carry out the deduction calculation. In terms of energy input, because China is dominated by petrochemical resources, standard coal is selected to depict it.The output index covers three major systems: economic, social, and environmental. For the desirable output of the economic system, the variable we use is the per capita GDP of the sample city. Considering the impact of inflation, we deflate it based on 2011. And the output of the social system is characterized by urban-rural income ratio and urban-rural consumption ratio, which are the undesirable outputs of the social system. Due to the main inequality in China’s current society which is reflected in the gap between urban and rural areas, the greater the difference between urban and rural areas, the more serious the social inequality will be. In terms of the output of the environmental system, carbon dioxide emissions, industrial waste water emissions, industrial sulfur dioxide emissions, general industrial solid waste, and other indicators are represented. The larger these indicators are, the more serious the environmental pollution is, so they are all undesirable outputs.The data used in the indicator system in Table1 are mainly from China Urban Statistical Yearbook, China Statistical Yearbook, China Population and Employment Statistical Yearbook, China Energy Statistical Yearbook, China Environmental Statistical Yearbook, etc. The statistical period is from 2005 to 2019. Due to the serious trend of the data in Tibet, we deleted the data and finally obtained the data of 30 provinces, autonomous regions, and municipalities in China. In addition, for the missing data of some sample cities, we have adopted the linear difference method for fill them, which has ensured that our sample data is a balanced panel data.


## 3.2. Construction of DEA Model

By referring to other literatures [30, 38–40], because DEA method can solve the problem of inconsistent units of various input and output elements in inclusive green efficiency and it does not need to consider specific production functions and estimate weights and parameters in advance, therefore, the SBM-DEA model is used as the evaluation model of inclusive green efficiency in China’s provinces. In order to fit the actual situation, this paper introduces unexpected output into super-SBM model for comprehensive measurement, which more truly reflects the nature of IGGE in China’s provinces. The model assumes that the production system has n decision-making units, and each decision-making unit contains three input-output variables, namely, input, expected output and, unexpected output. The specific calculation formula is
(1)minδ∗=1+1/m∑m=1MSmx/Xjmt1−1/l+h∑l=1LSly/yjll+∑h=1HShb/bjht(2)s.t.xjmt≥∑j=1.k≠0nλjtxjmt+smxyjlt≥∑j=1.j≠knλjtxjlt−slxbjht≥∑j=1.k≠0nλjtbjht+shbλjt≥0,smx≥0,sly≥0,j=1,⋯,nAmong them,δ∗ stands for IGGE, xt, j, and yt, which, respectively, represent the input, expected output, and unexpected output value of DMUj in the period t. And m,l,hindicate the number of input, expected output, and unexpected output elements, respectively. At the same time, smx, sly, and shb are relaxation vectors of input, expected output, and unexpected output, whereas λ is the weight vector of the decision unit.


## 3.3. Exploratory Spatial Data Analysis (ESDA)

This paper mainly uses the global spatial autocorrelation and local spatial autocorrelation analysis methods to identify the agglomeration of IGGE in various provinces in China. The global spatial autocorrelation generally uses the global MoranI coefficient to reflect the distribution effect of regional units. The specific calculation formula is as follows:
(3)I=n∑t=1n∑j=1nXi−X¯Xj−X¯∑i=1nXi−X¯2∑i=1n∑j=1nWij,whereas n means the number of spatial units in the study area, and Xi,Xj represents the observed values of region i and region j, respectively. X− represents the average value of object x; Wij is the spatial weight matrix. I∈−1,1, at a given significance level; if I>0, it means that the regional green development level space presents an agglomeration trend; on the contrary, it means that the regional green development level space presents differences; the greater the absolute value of I, the stronger the spatial correlation.Although the above method analyzed the distribution effect of China’s provinces within the time limit, it cannot distinguish the high- and low-value agglomeration of different spatial provinces in the region. The local spatial autocorrelation can effectively identify the spatial dependence and heterogeneity of inclusive green efficiency. The specific calculation formula is as follows:(4)Gi∗d=nYi−Y¯∑i=1nwijYj−Y¯∑i=1nYi−Y¯2,where Gi∗d is the local Jerry index; Wij is the spatial weight matrix; and Xi and Xj represent the green development level of region I and region J. For Gi∗d, z test shall be conducted. If z is significantly positive, it indicates that the green development level space presents a high-value agglomeration area. If z is significantly negative, it is a low-value agglomeration area. Therefore, the green development level can be divided into four categories: high and high agglomeration, high and low agglomeration, low and high agglomeration, and low and low agglomeration.


## 4. Temporal and Spatial Differentiation of IGGE

### 4.1. Horizontal Time Series Characteristics of IGGE

We calculate the IGGE of 30 provinces in China from the year of 2006 to 2019 through formula (1), and the trend of the average value of each year is shown in Figure 1. As it can be seen from Figure 1, China’s inclusive green efficiency showed a slow fluctuating upward trend from 2006 to 2019. From 2006 to 2008, the increase was relatively slow, which may be due to the impact of the global economic crisis, which hampers the economy growth in China; from 2011 to 2014, the increase trend was significant; after 2014, as China’s economic development entered the new normal stage, the growth rate was flat in the region, but the overall trend increased.Figure 1
The average trend of inclusive green efficiency in China.Figure2 reports the dynamic evolution characteristics of IGGE over the years. We selected four typical years: 2006, 2010, 2015, and 2019. The IGGE of each year is divided into four levels: poor, average, good, and excellent. From Figure 2, we can see that the level division standards of each year show an upward trend, indicating that the IGGE of China’s provinces increased year by year from 2006 to 2009. The standard of excellent-level provinces has increased from 0.338 or more in 2006 to 1.152 or more, the classification standard of good grade has evolved from 0.267 or more and 0.338 or less in 2006 to 0.754 or more and 1.152 or less in 2009, and the standard of poor-level provinces has evolved from less than 0.229 in 2006 to less than 0.460 in 2019. It also shows the characteristics of spatial agglomeration; the level of southeast provinces is obviously better than that of central and western regions. The possible reason is that the southeast provinces have a good economic foundation and business environment, and the effect of environmental governance is remarkable. In addition, due to the “trickle-down effect” of economic growth, regional economic growth may drive the surrounding areas, so the overall IGGE level of southeast cities is high.Figure 2
Dynamic evolution trend of inclusive green efficiency from 2006 to 2019.


### 4.2. Spatial Differentiation of IGGE in China’s Provinces

According to formula (3) and formula (4), we calculate the global Moran I index from 2006 to 2009 (Table 2). It can be seen from Table 2 and Figure 3 that the IGGE in 2011 is significant at the level of 0.01, which shows that there is obvious spatial dependence among all provinces in China in 2011. Moran’s I index is greater than 0 within the research time limit, which indicates that there is a spatial agglomeration phenomenon in areas with IGGE development level close to each other, that is, areas with higher green development level. It is close to other cities with higher development level, while cities with lower IGGE development level tend to be close to each other.Table 2
Moran’sI index in the years 2006-2019.
YearMoran’sI indexZ valueP value20060.3402.2720.00420070.3462.4740.00320080.3402.6540.00420090.3472.8540.00320100.3512.3880.00420110.3172.9250.01020120.2903.1360.01520130.2853.6340.01620140.2743.7170.01820150.3814.0180.00220160.3823.7120.00220170.3813.3750.00220180.3803.2710.00220190.3813.8550.002Figure 3
Moran’sI from the year 2006 to 2019.The spatial weight matrix is used to draw the Moran scatter diagram in which the abscissa is the standardized attribute value of the IGGE horizontal spatial unit, and the ordinate is the standardized spatial lag value determined by the spatial connection matrix. The four quadrants of the scatter chart can be divided into four types: HH (high concentration) region, LH (low and high concentration) region, LL (low and low concentration) region, and HL (high and low concentration) region. The local spatial autocorrelation test of IGGE of 30 provinces in China in 2006, 2010, 2015, and 2019 was calculated. And the result shows that most evaluation units in the four years have obvious spatial positive correlation, reflecting that our provincial IGGE has obvious spatial club convergence characteristics. Within the research time, HH is concentrated in southeast coastal areas, such as Shanghai, Jiangsu, and Zhejiang, and LL is concentrated in western provinces, such as Qinghai, Gansu, and Xinjiang. The provinces in the Yangtze River economic belt show obvious polarization characteristics: the LL agglomeration characteristics in the Yangtze River Delta are significant, and the development level in the upper reaches of the Yangtze River is poor. Due to the insufficient development endowment of the western region, its long-term IGGE is in the cold spot area, which may further fall into the abyss of deterioration. It is an area that needs to be improved. Figure3 presents Moran’s I from the year 2006 to 2019.


## 4.1. Horizontal Time Series Characteristics of IGGE

We calculate the IGGE of 30 provinces in China from the year of 2006 to 2019 through formula (1), and the trend of the average value of each year is shown in Figure 1. As it can be seen from Figure 1, China’s inclusive green efficiency showed a slow fluctuating upward trend from 2006 to 2019. From 2006 to 2008, the increase was relatively slow, which may be due to the impact of the global economic crisis, which hampers the economy growth in China; from 2011 to 2014, the increase trend was significant; after 2014, as China’s economic development entered the new normal stage, the growth rate was flat in the region, but the overall trend increased.Figure 1
The average trend of inclusive green efficiency in China.Figure2 reports the dynamic evolution characteristics of IGGE over the years. We selected four typical years: 2006, 2010, 2015, and 2019. The IGGE of each year is divided into four levels: poor, average, good, and excellent. From Figure 2, we can see that the level division standards of each year show an upward trend, indicating that the IGGE of China’s provinces increased year by year from 2006 to 2009. The standard of excellent-level provinces has increased from 0.338 or more in 2006 to 1.152 or more, the classification standard of good grade has evolved from 0.267 or more and 0.338 or less in 2006 to 0.754 or more and 1.152 or less in 2009, and the standard of poor-level provinces has evolved from less than 0.229 in 2006 to less than 0.460 in 2019. It also shows the characteristics of spatial agglomeration; the level of southeast provinces is obviously better than that of central and western regions. The possible reason is that the southeast provinces have a good economic foundation and business environment, and the effect of environmental governance is remarkable. In addition, due to the “trickle-down effect” of economic growth, regional economic growth may drive the surrounding areas, so the overall IGGE level of southeast cities is high.Figure 2
Dynamic evolution trend of inclusive green efficiency from 2006 to 2019.


## 4.2. Spatial Differentiation of IGGE in China’s Provinces

According to formula (3) and formula (4), we calculate the global Moran I index from 2006 to 2009 (Table 2). It can be seen from Table 2 and Figure 3 that the IGGE in 2011 is significant at the level of 0.01, which shows that there is obvious spatial dependence among all provinces in China in 2011. Moran’s I index is greater than 0 within the research time limit, which indicates that there is a spatial agglomeration phenomenon in areas with IGGE development level close to each other, that is, areas with higher green development level. It is close to other cities with higher development level, while cities with lower IGGE development level tend to be close to each other.Table 2
Moran’sI index in the years 2006-2019.
YearMoran’sI indexZ valueP value20060.3402.2720.00420070.3462.4740.00320080.3402.6540.00420090.3472.8540.00320100.3512.3880.00420110.3172.9250.01020120.2903.1360.01520130.2853.6340.01620140.2743.7170.01820150.3814.0180.00220160.3823.7120.00220170.3813.3750.00220180.3803.2710.00220190.3813.8550.002Figure 3
Moran’sI from the year 2006 to 2019.The spatial weight matrix is used to draw the Moran scatter diagram in which the abscissa is the standardized attribute value of the IGGE horizontal spatial unit, and the ordinate is the standardized spatial lag value determined by the spatial connection matrix. The four quadrants of the scatter chart can be divided into four types: HH (high concentration) region, LH (low and high concentration) region, LL (low and low concentration) region, and HL (high and low concentration) region. The local spatial autocorrelation test of IGGE of 30 provinces in China in 2006, 2010, 2015, and 2019 was calculated. And the result shows that most evaluation units in the four years have obvious spatial positive correlation, reflecting that our provincial IGGE has obvious spatial club convergence characteristics. Within the research time, HH is concentrated in southeast coastal areas, such as Shanghai, Jiangsu, and Zhejiang, and LL is concentrated in western provinces, such as Qinghai, Gansu, and Xinjiang. The provinces in the Yangtze River economic belt show obvious polarization characteristics: the LL agglomeration characteristics in the Yangtze River Delta are significant, and the development level in the upper reaches of the Yangtze River is poor. Due to the insufficient development endowment of the western region, its long-term IGGE is in the cold spot area, which may further fall into the abyss of deterioration. It is an area that needs to be improved. Figure3 presents Moran’s I from the year 2006 to 2019.


## 5. Construction of the Spatial Econometric Model

Based on the above analysis, the IGGE among provinces has significant spatial agglomeration characteristics, and we find that it is more suitable to use the spatial econometric model to conduct regression for exploring factors affecting IGGE among provinces in China. Additionally, as mentioned above, the traditional research often ignores the spatial dependence and geographical spillover effect between regions, which will lead to serious errors in the measurement and estimation results. Therefore, this paper constructs spatial Durbin model (SDM), spatial lag model (SLM) and spatial error model (SEM) to explore the influencing factors of IGGE in China. The models are shown as follows:(5)IGGIit=a+BTXit+ρ∑j=1NwijYjt+∑k=1KθK∑j=1Kwijxkjt+φit,IGGIit=a+BTXit+ρ∑j=1NwijYjt+εit,IGGIit=a+BTXit+λ∑j=1Nwijφjt+εit,where our dependent variable is denoted by IGGIit and i and t mean the city and year, respectively. And Xit represents a couple of independent variables in this study. Wit represents the elements in the spatial weight matrix W. It should be pointed out that the setting of spatial weight matrix has a great impact on the results of measurement estimation. This paper considers whether provinces are close to each other to construct a spatial weight matrix. If two provinces are adjacent, they are represented by 1 in the matrix. If they are not adjacent, they are represented by 0 in the matrix.After model selection and spatial weight calculation, six possible independent variables on IGGE are selected, which are as follows: industrial structure (IDS), calculated by the proportion of the added value of the secondary industry in the regional GDP; the extent of government intervention (GI), which is the ratio of public expenditure to gross regional product; the level of opening-up (OP), calculated by dividing the amount of foreign capital actually utilized by GDP; human capital (HC), which is represented by the number of students in colleges and universities; the level of financial development (FD), calculated by the balance of deposits and loans of financial institutions; and the innovation level (IL), calculated by the amount of invention patents in the city. The data of these variables come fromThe Statistical Yearbook of China, and their statistical description regression results are shown in Tables 3 and 4, respectively.Table 3
The statistical description of variables.
VariablesAmountMinMaxMeanStd. deviationMedianIDS3002.66090.97047.62111.13047.930GI30025.3218367.179120.671547.15398.146OP3007112308256372954.669179810.37516452.500HCl3000.070.4560.1480.2150.091FD300568.23199287.4581373.9295967.48033502IL3000.325279.4786.20817.7271.026IGGE3000.2891.2360.4940.2330.437Table 4
Estimation results of spatial econometric panel models.
(1)(2)(3)(4)(5)(6)(7)OLSSDMSDMSDMSARSARSARIDS-0.027∗ (0.013)0.005 (0.020)0.023 (0.018)0.021 (0.019)0.020 (0.017)0.025∗ (0.014)0.035∗∗∗ (0.016)GI-0.012 (0.013)-0.037∗ (0.022)-0.040 (0.029)-0.036∗ (0.018)0.001 (0.016)-0.046∗∗ (0.019)-0.052∗∗∗ (0.015)OP0.123∗∗∗ (0.021)0.155∗∗∗ (0.023)0.148∗∗∗ (0.047)0.128∗∗∗ (0.039)0.139∗∗∗ (0.047)0.138∗∗∗ (0.051)0.117∗∗∗ (0.036)HC0.147∗∗∗ (0.013)0.221∗∗∗ (0.067)0.029∗∗ (0.036)0.186∗∗∗ (0.055)0.357∗∗∗ (0.076)0.113∗∗∗ (0.030)0.207∗∗∗ (0.056)FD0.066∗∗∗ (0.016)-0.007 (0.019)0.023 (0.017)-0.012 (0.018)0.018 (0.017)0.021 (0.014)-0.016 (0.017)IL0.438∗∗∗ (0.032)0.060 (0.072)0.065 (0.067)0.315∗∗∗ (0.091)0.157∗∗ (0.061)0.332∗∗∗ (0.067)0.134∗∗ (0.059)Constant-0.000 (0.011)ρ0.245∗∗∗ (0.064)0.258∗∗∗ (0.085)0.253∗∗∗ (0.056)0.358∗∗∗ (0.077)0.263∗∗∗ (0.080)0.279∗∗∗ (0.057)Wald-lag47.49∗∗∗ [0.000]10.26∗∗ [0.012]15.43∗∗ [0.015]LR-lag174.26∗∗∗ [0.000]63.38∗∗∗ [0.000]41.61∗∗∗ [0.000]LM-lag465.20∗∗∗ [0.000]374.28∗∗∗ [0.000]152.86∗∗∗ [0.000]12.02∗∗∗ [0.000]R-LM-lag152.82∗∗∗ [0.000]378.18∗∗∗ [0.000]57.97∗∗∗ [0.000]44.78∗∗∗ [0.000]W∗XNoYesYesYesNoNoNoCity fixed effectsNoYesNoYesYesNoYesYear fixed effectsNoNoYesYesNoYesYesAdjustedR20.3930.3720.3990.4580.4470.3990.317σ20.2690.4360.2080.1970.2020.256Log-likelihood-2503.94-3270.03-1753.79-3291.07-2301.722973.59Notes: the parameters are estimated by the bias-corrected maximum likelihood estimator developed by Lee and Yu (2010) when controlling for two-way fixed effects. The coefficients ofW∗X, city fixed effects, and year fixed effects are not reported to save space; ∗, ∗∗, and ∗∗∗ represent the 10%, 5%, and 1% significance levels, respectively. Robust standard errors, clustered at the city level, are reported in parentheses. P values in brackets.Table4 reports the results of spatial econometric regression for each influencing factor. The first column is the regression result of ordinary least square method, and the second to seventh columns are the regression result of the spatial econometric model. Among them, the model with the best maximum likelihood estimation and goodness of fit is the SDM with fixed individual effect and fixed time effect. Therefore, next, we will analyze the results based on this model. As can be seen from Table 4, the coefficient of industrial structure is positive, but not significant. This shows that the upgrading degree of China’s industrial structure is not enough to support a high level of IGGE, so it still needs to be further improved. Subsequently, we can see that the impact coefficient of government intervention on IGGE is negative, which is significant at the significance level of 10%. It shows that the government’s intervention in the economy has not well promoted its greening and inclusiveness. The possible reason is that the economic championship launched by the government for economic performance only pursues the improvement of GDP but ignores the performance of other aspects. Then, it reveals that the coefficient of opening-up is positive, which is significant at the significance level of 1%. This shows that the implementation of the opening-up strategy is conducive to China’s IGGE. It fully illustrates the “halo effect” brought by introducing foreign investment. The possible reason is that the introduction of foreign investment can promote China’s employment and prosper the local economy. By learning foreign advanced production technology and management experience, we can improve the green level. Furthermore, we can see that the coefficient of the variable of human capital level is positive at the significance level of 1%, and the coefficient is greater than that of opening-up. It shows that human capital plays an important role in promoting regional IGGE. On the one hand, the promotion of human capital will drive entrepreneurship and increase high-quality employment opportunities, so as to reduce the occurrence of poverty and narrow the income gap. On the one hand, human capital will positively affect environmental awareness and green technology innovation, so as to promote the process of green development. Subsequently, the impact of traditional financial development on IGGI is negative and not significant. It shows that traditional finance has not achieved “inclusiveness” and still only serves regional economic growth, without taking into account social equity and environmental protection. Finally, the influence coefficient of innovation level is significant at the significance level of 1%, and its coefficient is the largest among all independent variables. It reveals the irreplaceable role of innovation in driving IGGE. There is no doubt that innovation will drive technological progress, improve production efficiency, promote industrial leap, and increase national income. In addition, innovation reduces energy consumption and carbon emissions through the development of new technologies, so as to achieve a win-win situation of the systems of economy, society, and ecology.


## 6. Conclusion and Suggestion

China has entered a new period of “high-quality development,” which means to slow down the speed of economy and focus on improving the level a comprehensive development to meet the sustainable goal in 2030. Under this background, this paper systematically concludes the definition of IGG in China and uses the DEA method to evaluate the IGG efficiency of 30 provinces in China during 2006-2019. The results show that the level of IGGE has increased year by year, and the provinces located in the eastern region present better performance than those in the central and western region. Then, the spatial econometric model was constructed to explore the drivers of IGGE in China. We found that the intervention of government in the economy plays a negative role in IGGE, while opening up, human capital, and innovation have positive influence in improving the level of IGGE. Based on the above conclusion, we propose some suggestion to promote IGGE in China as follows.To begin with, the government should slow down the direct intervention in local economy, releasing the market mechanism in supplying and demand, and stimulating the dynamic activities in economy and play the supervisor and coordinator role but not the operator. At the same time, due to the significant confluence of opening up to IGGE, some measure should be taken to attract high-tech and low pollution foreign enterprises to invest, so as to promote the employment and economic prosperity. Additionally, because the important contribution of human capital and innovation and these drivers are connected to each other, a cooperative relationship among the universities, the enterprises and the research institutions should be formed. The universities located in the core to transfer high-quality human capital, and the enterprises can absorb the knowledge and skill from the talent, to maintain a sustainable life power and energy of innovation. Besides that, the excellent research results from institution should increase the speed to transfer their study into actual innovation. Finally, due to the gap among the western, central, and eastern regions of IGGE in China, different regions should take some measure to slow down the gap, such as learning form the eastern region, as well as cultivating their core competitive capabilities, to upgrade the industrial structure and so on.



---

*Source: 3162465-2022-06-18.xml*