Spatial Characteristics and Influencing Factors of Green Development Progress Level of Private Enterprises in China: Based on Large Collection Surveys

Abstract

As the market subject of China’s economic development, private enterprises play an important role in fighting against pollution and solving ecological and environmental problems. It is especially important to evaluate the green development progress of Chinese private enterprises in the epidemic era. This paper conducts a questionnaire survey on 10,623 private enterprises in 31 provinces of China, and based on 6223 industrial survey results, it focuses on the production and operation status of private enterprises in terms of pollution reduction performance, energy saving, and carbon reduction intensity in order to construct a green development progress index. The spatial Moran index test and the spatial Durbin model are used to analyze the regional correlations and influencing factors of green development progress in China. The results show that the green development of enterprises with a main business income of more than 100 million CNY and key areas such as Fenwei Plain have improved significantly in 2021, especially with the increase in a private enterprise scale, the carbon reduction regime, the pollution abatement regime, and the pollution control manner, and because the investment, profitability, and pollution discharge of private enterprises is more significant. The indexes of the provinces in the southeast coastal area and the northeastern region of China are the highest and lowest, respectively, in terms of pollution, which is demonstrated by the spatial aggregation effect on the surrounding areas by Moran local index analysis. The urbanization level and government financial support for environmental protection are just two of the negative factors regarding this issue, while the economic development level and industrial structure are positive factors that have a spatial spillover effect.

1. Introduction

In its three-year-long fight against COVID-19, China posted outstanding results in economic development and epidemic control, reinforcing its status as a leading engine of the global economy. In 2021, the country’s gross domestic product reached 114.4 trillion CYP (18 trillion USD), bringing an average growth rate of 5.1%. The industrial added value of private enterprises above the specified size increased by 10.2% year-on-year, 6.5% higher than that in 2020, which was typically higher than that of state-owned holding enterprises, joint-stock enterprises, and foreign-funded enterprises, as well as funded enterprises in Hong Kong, Macao, and Taiwan in the same period. In an address to the United Nations General Assembly on 22 September 2020, it was stated that China aims to have carbon dioxide emissions peak before 2030 and aims to achieve carbon neutrality before 2060 [1]. As the main body of the development of the economy and the main force for China to fight the battle against pollution, enterprises are also the key to China’s green transformation and development and the eventual realization of carbon neutrality.

Low-carbon and a circular economy are the theoretical foundations for the green transformation and development of enterprises [2]. In 2003, the United Kingdom first proposed the concept of a low-carbon economy in a white paper titled, “Our Future Energy: Creating a Low-Carbon Economy” [3]. The aim outlined was to vigorously develop low-carbon technologies, products, and services based on energy technologies and institutional innovation in order to minimize economic dependence on fossil fuels. However, source control and reduction are the core of the circular economy theory and enhance resource utilization efficiency, including an industrial symbiosis combination of sharing resources and exchanging by-products [4].

The literature shows that private enterprises are an important force of green technology innovations that can foster green development [5,6,7]. Notably, in recent years, the COVID-19 epidemic has had varying degrees of impact on the production and operation of Chinese private enterprises, which are also facing new challenges in relation to green development that urgently need to be researched in various fields. Most of the questionnaire surveys on the green development of enterprises still remain in the empirical study of theories, and few investigations have appeared on private enterprises. In terms of the evaluation index of green development, most of the indicators involve regional data that only uses different theoretical methods to build the analytic model, and this may be different from the actual development of enterprises [8,9,10].

Based on the evaluation of green development performance, relevant research has analyzed the spatial differentiation of regional green development, and it has analyzed its influencing factors and spatial spillover with different methods. The influencing factors discussed include green finance, environmental regulation, city size, government financial support, industrial structure, economic development level, opening-up degree, and human capital level [11,12,13,14,15,16,17]. To replenish the shortcomings of these approaches, this paper conducts an investigation of policy implementation instead of theoretically and empirically trying to establish a multi-dimensional and comprehensive evaluation index of private enterprises, thereby grasping the actual situation of green development. The study also studies its spatial differences and influencing factors.

The contributions of the paper to the existing research are threefold. (1) A larger questionnaire sample size of 10,623 enterprises in 31 provinces across China is employed as a data source in the assessment of a green development progress index of private industrial enterprises rather than using statistical data. (2) A consideration of dynamic changes index construction and analysis of the innovations of the questionnaire data are the unique contributions of this paper to the existing research fields, taking into account both green progress and development progress, which supplement the theory of the green development of private enterprises. (3) Different from most of the existing literature based on provincial and prefecture-level city-level data, this paper makes a reasonable evaluation of the level of green development and progress from the perspective of enterprises, grasping the actual level of green development and its spatial differences in various provinces in the country. The influencing factors are also analyzed.

2. Literature Review

As a statistical and theoretical empirical research method, questionnaire surveys and structural equation modelling (SEM) are widely used to analyze the influencing factors of sustainable or green development for various sized private enterprises, multinational enterprises, and other manufacturing enterprises, especially in Vietnam, Pakistan, Egypt, Malaysia, China, and other developing countries, by collecting approximately 100–500 pieces of feedback from these enterprises. The research content mainly focuses on the mediating effect or the impact on corporate social responsibility, total quality management, technology or service innovation, knowledge management, supply chain management, and other indicators on the sustainable development of enterprises [18,19,20,21,22,23,24]. Basically, the conclusions of all of the research results have verified that the measures taken by enterprises to enhance environmental awareness, innovate green technology, and improve management efficiency have a significant positive impact on achieving sustainable development and achieving excellent financial performance (Table 1). In addition, there are some studies on the empirical analysis of the different factors affecting the sustainable development of enterprises [25]. For instance, based on the theory of the dynamic capability view and the theory of absorptive capacity, Khan et al. [26] propose that the knowledge absorption capacity of firms can help them to organize or utilize dynamic capabilities, such as big data analytics and digital platform capability, in order to enhance their agility and innovation performance. The existing literature examines the interrelationship between knowledge management (KM) enablers, KM processes, and GI by demonstrating the critical importance of green innovation (GI) for organizations in developing economies [27,28,29]. Accelerating the green development of industrial enterprises is the basis for curbing the serious pollution that has been caused by industrial growth and the excessive consumption of natural resources [30]. The quantification of the green development of industrial enterprises, such as index construction, is a tool for guiding the development process. The green development index has been applied for listed companies [31], manufacturing industries [32,33], or the regional industrial economy [34] in the literature, considering the waste gas, water, and residual emissions by the slack-based measure (SBM) or the Data Envelopment Analysis (DEA) model. The profitability and environmental management indicators of enterprises are also taken into account through regression, panel data, and other econometrics analysis methods [35,36].

Based on the measured regional green development performance or green development level, scholars have also carried out deeper research. One avenue is the spatial differentiation of regional green development [37,38], and the other is the factors that affect the level of regional green development and their spatial spillover effects [11,12,13,14,15,39]. For example, Zhao et al. [37] found that the green innovation efficiency in eastern, central, and western China is basically on the rise, but that there are differences in the upward trend, and this is based on the Tobit regression model to explore the impact of green finance and environmental regulation on green innovation efficiency in this region. Yu et al. [11] used the spatial Durbin model to analyze the influencing factors of green innovation efficiency. It was found that based on the spatial autocorrelation of resource-based cities, factors such as government financial support, industrial structure, and economic development all have a positive impact. The intensity of environmental regulation and the degree of opening to the outside world will also inhibit urban green innovation. Lu et al. [12] measured the development level of green intelligence in prefecture-level cities from 2005 to 2018 and explored both its temporal and its spatial evolution characteristics, and they used quantile regression tests and threshold regression to analyze its influencing factors and threshold effects. It was found that the level of opening to the outside world and the size of the city have a significant positive impact, while the level of economic development and the size of the government have a negative impact. The influence of the industrial structure, the financial development level, and the human capital level is heterogeneous. Li et al. [13] focused on the industrial green innovation efficiency of 30 provinces in China, and the spatial Durbin model (SDM) and the mediation effects test model were used to examine the direct impact, the spatial spillover effect, and the indirect effect of the digital economy on industrial green innovation efficiency. Regarding the transmission mechanism, Lyu et al. [14] established a mediation effect model to analyze the impact of environmental regulation on green innovation efficiency and the transmission mechanism. There are other studies that are similar to this, too.

Although the existing research has achieved plentiful outcomes, there are also some limitations that this paper aims to solve by innovative approaches.

① The research indicators of the questionnaire survey on the green development of enterprises mainly include financial, management, and institutional indicators by the SEM model, which is the hypothesis test that is used to verify the green development theory of enterprises. A single industry is often selected as the research sample, but a total of less than 500 firms is insufficient to reveal the overall situation of the enterprises. Therefore, a questionnaire survey was conducted on a total of 10,623 enterprises in 31 provinces across China in order to obtain abundant samples and to carry out a supplement on the theory of green development of private enterprises in this paper.

② In the construction of an enterprise’s green development or sustainable development index, pollutant emissions and financial indicators are selected in most of the literature that lack basic criteria, including energy consumption, carbon emissions, and resource utilization. Moreover, there are few studies that concentrate on the green assessment of private enterprises instead of the abundant research that has been conducted on manufacturing firms. The research period generally lags behind, too, due to the constraints of the statistical schedule. In addition, when many scholars choose green development evaluation indicators and their influencing factors, they often fail to take into account green and development progress, and they do not reflect the characteristics of green development level progress.

To remedy these shortcomings, a large amount of questionnaire samples are employed as data sources in the assessment of the green development progress index of private industrial enterprises rather than the statistical data, and the indicators of planning and management, production and operation, pollution abatement, energy conservation, and carbon reduction are all selected as parts of the index, which will serve as the regional value in the following spatial analysis by the bottom-up method. The data source expansion and the consideration of dynamic changes of index construction as well as analysis innovations of questionnaire data are the unique contributions of this paper to the existing research fields.

③ Most of the existing literature is based on provincial and prefecture-level city-level data, which may be different from the actual development of the enterprises. Therefore, on the one hand, this paper makes a reasonable evaluation of the level of green development and progress from the perspective of enterprises in order to grasp the actual level of green development and its spatial differences in various provinces in China. On the other hand, this paper further studies the relevant factors that affect the level of green development and progress, providing a reference for regional green development.

Table 1. Review of variables on questionnaire survey, index construction and influencing factor analysis of enterprise green development.

Classification	Variables/Indicators	Objectives	Period	Model	References
Questionnaire	Corporate responsibility, reputation, customer loyalty, big data analytical capabilities, environmental management system	Manufacturing firms	2019–2021	SEM	[2,19,20]
	Total quality management, corporate green performance	Manufacturing firms/large and medium-size	2020	SEM	[20,22]
	Green investment, green innovation	Small and medium-sized/medium- and large-sized manufacturing enterprises	2021	SEM/fsQCA	[18,24]
	Organizational green culture, organizational learning, knowledge management	Manufacturing firms/large and medium-size	2019–2021	SEM	[25,27]
	Green supply chain management, green information system	Manufacturing firms	2019–2021	PLS-SEM	[23,39]
Green development Index	Reduction of emissions, product innovation, and reduction of resource usage	351 European listed companies	2007–2015	Panel data regression	[31]
	Enhancement of living environment, treatment and utilization of pollutant, improvement of ecological efficiency, optimization of economic growth, and development of innovative potential	Nine cities in the Pearl River Delta (PRD)	2015	Multiple-evaluation and entropy method	[10]
	The number of employees, fixed asset net value, total energy consumption, fresh water consumption, value added, wastewater/gas/residual	Iron and steel industry enterprises	2010–2017	Malmquist–Luenberger index and an epsilon-based measure	[35]
	Greenhouse gas emissions, energy use, water withdrawals, hazardous waste generation, toxic releases	Manufacturing sectors		Input-Output Life Cycle Assessment (EIO-LCA) and Data Envelopment Analysis (DEA)	[32]
	Presence of board environmental committee, net income/total assets, Debt to Assets ratio, CO2 emissions	French listed company	2009–2014	Empirical test, regression, panel data	[36]
	The proportion of tertiary industry in GDP; actual utilized foreign investment: urban per GDP; the number of university students per 10,000 people; the ratio of current credit balance to regional GDP; the urban total population at the end of the year; the proportion of financial expenditure in the GDP	Panel data from 11 regions (9 provinces and two cities) in the Yangtze River Economic Belt (YREB)	2005–2018	The quantile regression model; Threshold Model	[12]
	The proportion of public expenditure on environmental protection in GDP; the proportion of completed industrial pollution investment in GDP; the ratio of tertiary industry output value to GDP; the ratio of electricity consumption to GDP; foreign direct investment	Panel data from 31 provinces	2011–2020	Tobit regression model	(Zhao et al., 2022) [37]
	The average assets of industrial enterprises above the designated size; the state-owned holding enterprises’ assets to total assets of industrial enterprises above designated size; the total imports and exports to GDP; the total postal and telecommunications services to GDP; the technology market turnover to GDP	Panel data of 30 Chinese provinces	2005–2019	the OLS panel regression model; the spatial Durbin model (SDM)	[38]

Table 1. Review of variables on questionnaire survey, index construction and influencing factor analysis of enterprise green development.

Classification	Variables/Indicators	Objectives	Period	Model	References
Questionnaire	Corporate responsibility, reputation, customer loyalty, big data analytical capabilities, environmental management system	Manufacturing firms	2019–2021	SEM	[2,19,20]
	Total quality management, corporate green performance	Manufacturing firms/large and medium-size	2020	SEM	[20,22]
	Green investment, green innovation	Small and medium-sized/medium- and large-sized manufacturing enterprises	2021	SEM/fsQCA	[18,24]
	Organizational green culture, organizational learning, knowledge management	Manufacturing firms/large and medium-size	2019–2021	SEM	[25,27]
	Green supply chain management, green information system	Manufacturing firms	2019–2021	PLS-SEM	[23,39]
Green development Index	Reduction of emissions, product innovation, and reduction of resource usage	351 European listed companies	2007–2015	Panel data regression	[31]
	Enhancement of living environment, treatment and utilization of pollutant, improvement of ecological efficiency, optimization of economic growth, and development of innovative potential	Nine cities in the Pearl River Delta (PRD)	2015	Multiple-evaluation and entropy method	[10]
	The number of employees, fixed asset net value, total energy consumption, fresh water consumption, value added, wastewater/gas/residual	Iron and steel industry enterprises	2010–2017	Malmquist–Luenberger index and an epsilon-based measure	[35]
	Greenhouse gas emissions, energy use, water withdrawals, hazardous waste generation, toxic releases	Manufacturing sectors		Input-Output Life Cycle Assessment (EIO-LCA) and Data Envelopment Analysis (DEA)	[32]
	Presence of board environmental committee, net income/total assets, Debt to Assets ratio, CO2 emissions	French listed company	2009–2014	Empirical test, regression, panel data	[36]
	The proportion of tertiary industry in GDP; actual utilized foreign investment: urban per GDP; the number of university students per 10,000 people; the ratio of current credit balance to regional GDP; the urban total population at the end of the year; the proportion of financial expenditure in the GDP	Panel data from 11 regions (9 provinces and two cities) in the Yangtze River Economic Belt (YREB)	2005–2018	The quantile regression model; Threshold Model	[12]
	The proportion of public expenditure on environmental protection in GDP; the proportion of completed industrial pollution investment in GDP; the ratio of tertiary industry output value to GDP; the ratio of electricity consumption to GDP; foreign direct investment	Panel data from 31 provinces	2011–2020	Tobit regression model	(Zhao et al., 2022) [37]
	The average assets of industrial enterprises above the designated size; the state-owned holding enterprises’ assets to total assets of industrial enterprises above designated size; the total imports and exports to GDP; the total postal and telecommunications services to GDP; the technology market turnover to GDP	Panel data of 30 Chinese provinces	2005–2019	the OLS panel regression model; the spatial Durbin model (SDM)	[38]

3. Methodology

3.1. Index Calculation

The private industrial enterprise evaluation index system based on the perspective of green development progress fully considers the theoretical and practical significance of the index in the design. Not only include the dimensions of green and low-carbon development such as financial management, planning system, pollution control, energy conservation and carbon reduction, and resource utilization, which also designs traditional financial indicators such as profitability, debt repayment, operation, and R&D investment. Meanwhile, it is necessary to focus on the relationship between indicators and the availability of data information in accordance with the following principles.

① Combination of integrity and importance. Integrity refers to the comprehensive performance level of the evaluation index system that can fully reflect the financial, pollution and carbon reduction dimensions of the private industrial enterprise. Importance means that the selected indicators should highlight the content of key evaluations and avoid redundancy.

② Combination of hierarchy and operability. Through comparative analysis, the contents affecting the green and low-carbon development of enterprises should be reasonably classified and unified on the grounds of evaluation purposes. Operability signifies that the selected indicators should be the daily statistics of enterprises in emission permits, annual reports, sustainable development reports and social responsibility reports to obtain the required data.

Weight is to reflect the criticality of each evaluation target relative to the overall. The expert scoring is combined with analytic hierarchy process method in the research to organically unify the qualitative and quantitative analysis. The judgement matrix is constructed through pairwise comparison and consistency based on the reasonable order of actual situation, which is the 9-digit scale method proposed by [40] as shown in

Table 2. Scoring basis and importance of judgment matrix.

Scale	Implication
1	Two factors are equally important
3	One factor is slightly more important than the other
5	One factor is obviously important than the other
7	One factor is mightily important than the other
9	One factor is extremely important than the other
2, 4, 6, 8	The median value of the above two adjacent judgments
reciprocal	The judgment of reciprocal factor i compared with j

.

Firstly, each column vector of E is normalized by Formular (1).

$$W_{ij}=\frac{e_{ij}}{{{\displaystyle \sum }}_{i=1}^n{e}_{ij}}$$

(1)

Then, the W_i is summed by rows and W_i = (w₁, w₂, w₃) is the feature vector.

$$W_i={\displaystyle \sum }_{j=1}^n{W}_i$$

(2)

The maximum eigenvalue are calculated by Formula (3).

$$\lambda =\frac{1}{n}{\displaystyle \sum }_{j=1}^n\frac{{\left(EW\right)}_i}{w_s}$$

(3)

$\mathsf{\lambda }$ is the approximate value of the maximum eigenvalue, and (EW)_i is the product of judgment matrix E and the eigenvector.

Finally, the consistency index (CI value) is calculated by Formula (4).

$$CI=\frac{\left(\lambda -n\right)}{\left(n-1\right)}$$

(4)

Based on the assignment of selected indicators and calculated weight, the composite index is obtained as follows:

$$X={\displaystyle \sum }_n{A}_i\times W_i$$

(5)

X is the green development progress index of private industrial enterprises, A_i is the detailed assignment of the indicator i, and W_i is the weight of the sub-criteria corresponding to the indicator i.

3.2. Spatial Moran’s I Test

Spatial Data Analysis is a method to explain spatial dependence and correlation related to location distribution. Moran’s Index is often used to measure spatial autocorrelation compared to the Geary’s and Getis-Ord index with considering the spatial weight. The Moran’s index is divided into global Moran’s index and local Moran’s index, which reflect global spatial correlation and local spatial correlation respectively. The global Moran’s index is calculated by Formula (6). When the value range of the index I is [−1,1], the value of −1 and 1 indicates a completely negative and positive correlation, and the value of 0 represents irrelevant and obeys random distribution.

$$I=\frac{n}{{{\displaystyle \sum }}_i{{\displaystyle \sum }}_j{W}_{ij}}\times \frac{{{\displaystyle \sum }}_i{{\displaystyle \sum }}_j{W}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{{{\displaystyle \sum }}_i{\left(x_i-\overline{x}\right)}^2}$$

(6)

where w_ij is the adjacency relationship between region i and j in the binary symmetric spatial weight matrix of W which is defined as Formulas (7) and (8). Particularly, since there are many methods to define the spatial weight matrix, in addition to the spatial adjacency matrix, the spatial inverse distance matrix (the element w_ij represents the reciprocal of the nearest road mileage between the provincial capital of the i region and the provincial capital of the j region), and the spatial inverse distance square matrix (the element w_ij represents the reciprocal of the square of the nearest road mileage between the provincial capital of the i region and the provincial capital of the j region) will be further used in the analysis of influencing factors.

$$W=\left(\begin{array}{ccc}{w}_{1,1}& \cdots & w_{1,n}\\ ⋮& \ddots & ⋮\\ w_{n,1}& \cdots & w_{n,n}\end{array}\right)$$

(7)

$$W_{i,j}=\left\{\begin{array}{c}1,when the distance between region i and j are less than d \\ 0, others\end{array}\right.$$

(8)

Although the global Moran’s index can disclose the agglomeration degree of the observed objects in geographical space, but the internal distribution characteristics are unrevealed that is solved by the local Moran’s index shown as follows.

$$I_i=\frac{n^2}{{{\displaystyle \sum }}_i{{\displaystyle \sum }}_j{W}_{i,j}}\times \frac{\left(x_i-\overline{x}\right){{\displaystyle \sum }}_i{W}_{i,j}\left(x_j-\overline{x}\right)}{{{\displaystyle \sum }}_j{\left(x_j-\overline{x}\right)}^2}$$

(9)

Moran’s index is tested by the normal distribution hypothesis through Z value shown as Formula (10). When the absolute value of Z are greater than or equal to 1.65, 1.96 and 2.58, which indicates the null hypothesis is rejected at the significant level of 10%, 5% and 1% respectively. Otherwise, the null hypothesis is accepted.

$$Z\left(I\right)=\frac{1-E\left(I\right)}{\sqrt{Var\left(I\right)}}~N\left(0,1\right)$$

(10)

where E is the expectation of I, Var is the variance of I.

3.3. Spatial Dubin Model

In order to further evaluate the influencing factors of the provincial green development progress level, the logarithm of the index of green development progress of private industrial enterprises was taken as the explained variable, relevant factors that may affect the level of green development progress are selected as explanatory variables to construct an OLS model (11), including economic development level, industrial structure, urbanization, foreign investment, power consumption, fiscal environmental protection expenditure. Due to the industrial linkages, technology spillovers and incremental spatial returns in the region, the green development of enterprises may also have spatial spillover effects [41]. Therefore, the Spatial Durbin Model (SDM) is further adopted to examine not only the lagging effect of the dependent variable, but also the spatial spillover effect of different explanatory variables on the progress of green development, while taking into account the complex spatial correlation and spatial dependence of the sample [42], the test results will be more robust. The spatial Dubin model (spatial Dubin model, SDM) was established as model (12):

$$Lngreen={\beta }_1+{\beta }_{2}X+\epsilon$$

(11)

$$Lngreen={\beta }_{1}WLngreen+{\beta }_{2}X+{\beta }_{3}WX+\epsilon$$

(12)

As above, lngreen is the explained variable, indicating the progress level of green development; X indicates a group of explanatory variables, including lnpergdp, ind_p, urban, foreign_p, lnelectricity, env_p, and the specific meanings are shown in the

Table 3. Main variables and implications.

Variable	Meaning of Variable
lngreen	The logarithm of the index of green development progress of private industrial enterprises
lnpergdp	The logarithm of per capital GDP
ind_p	The proportion of secondary industry value
urban	The proportion of the urban population in the permanent resident population
foreign_p	The ratio of foreign investment to regional GDP (USD/yuan)
lnelectricity	The logarithm of electricity consumption
env_p	The proportion of fiscal expenditure on environmental protection in GDP

. W represents the spatial weight matrix, which is substituted into the spatial inverse distance matrix, spatial inverse distance square matrix, and spatial adjacency matrix for inspection; ${\beta }_1$, ${\beta }_2$, and ${\beta }_3$ are coefficients; ε represents the random error term.

4. Evaluation of Green Development Progress Index

4.1. Fundamental Analysis of Questionnaires

In 2021, a total of 10,623 valid questionnaires of private enterprises were collected from 31 provinces in China with an effective rate of 99.23%, 58.6% industrial enterprises (6233), and 40% of the non-industrial interviewed enterprises, among which wholesale and retail, agriculture, forestry, animal husbandry, fishery, and construction accounted for larger proportions—respectively, 21.3%, 18.6%, and 12.8%. In regard to the progress and achievements of green development, the profitability and investing intensity of pollution control and energy conservation of high-energy-consuming industrial enterprises are higher than the average level of manufacturing sectors of 37.1% (about 5.5 and 4.8%) (see Figure 1). The systems of environmental information disclosure and risk management have been wildly practiced in industrial enterprises, with 84.1% across the country. The initiative of private enterprises to reduce carbon emissions has also been increased. In 2021, 35.1% of high-energy-consuming enterprises have prepared implementation plans for reaching a carbon peak in 2030 and achieving carbon neutrality in 2060. The specialist department for carbon reduction has been set up by 53.5% of firms, and 48.1% of the industrial firms are trying to apply green technologies in the production process.

However, the prominent problems faced by private enterprises in relation to green development are also identified. The effectiveness of pollution control and resource utilization have relatively low efficiency. In 2021, about 80% of industrial enterprises had not reduced waste gas, wastewater, and general solid waste emissions compared with last year, and 70% of industrial enterprises had a reuse rate of wastewater lower than 40%. The improvement trend of year-on-year was not shown in the indicators of the consumption of fossil fuels and clean energy, and 80% of industrial enterprises have not changed according to such indicators. It is noteworthy that there are many constraints for green and low-carbon development, reflected by the interviewed private enterprises: 64.2% of high-energy-consuming enterprises, 58.7% of industrial enterprises, and 46.7% of non-industrial enterprises report that high costs of pollution control and technical limitations are the biggest difficulties (see Figure 2). More than 30% of industrial enterprises have high financial risks of an asset-liability ratio of more than 60%, which may produce some resistance for enterprises to continue to invest in energy conservation and environmental protection.

4.2. Questionnaire Structure and Index System

The 10,623 enterprises that effectively filled in the questionnaire survey were distributed among various industries, and the absolute amount of pollution emissions and energy consumption of private enterprises in heavy industries, such as steel, non-ferrous, and chemical industries, was larger than that of light industries, such as textile and food manufacturing. Therefore, it is challenging to determine private industrial enterprises based on identical green assessment conditions, and the result is an unequal green development evaluation. Based on the three index selection principles mentioned above in Section 3.1, planning and management, production and operation, pollution abatement, energy conservation, and carbon reduction were selected as the dimensions of our questionnaire, and were further upgraded as 25 qualitative indicators with the intention of evaluating the green development progress of private industrial enterprises in 2021 compared with 2020 and solving the unequal assessment.

Our research also innovatively introduces different types of enterprise rating requirements in terms of index evaluation. For instance, the enterprises with different income scales or proportions of investment in pollution control will affect the scores of year-on-year changes in R&D or environmental protection investment, since the high proportion will hardly be improved. In terms of energy conservation and environmental protection investment scores, the energy conservation and investment accounts for the proportion of operating income scores are different because of enterprises, and the future investment ratio changes will then be more difficult, and the score should also be higher. Likewise, in terms of other indicators, such as pollution discharge and energy consumption, they were also subdivided according to the identical principles based on the data recorded by enterprises, as detailed in

Table 4. Evaluation index system and weight assignment table of the green development progress of private industrial enterprises.

Criteria	Sub-Criteria (Weight)	Abbreviation Codename	Qualitative Term	Quantitative Term	Detail Assignment	Maximum Assignment
Management planning	Carbon reduction regime (2.618%)	CRR	Preparation of ‘carbon peaking and carbon neutrality goals’ implementation scheme		50	50
			Establishment of energy conservation and carbon reduction agency or department		50	50
	Pollution abatement regime (1.895%)	PAR	Establishment of environmental information disclosure system		50	50
			Establishment of environmental risk prevention and control system or environmental emergency handling procedures		50	50
	Pollution control manner (7.458%)	PCM	Settlement of local industrial park		50	50
			Adoption of environmental protection third-party service		50	50
	Environmental publicity and education (1.428%)	EPE	Organization of environmental publicity education and training for employees		100	100
Production operation	Investment and profitability (10.805%)	IP	Year-on-year changes in the proportion of total profit and main business income	The main business income ≥ 20 million	Rising proportion: ≥50% (35), 30–50% (33), 15–30% (30), 5–15% (25); Float proportion: ±5% (20); Decreasing proportion: ≥50% (0), 30–50% (6), 15–30% (8), 5–15% (10)	35
			Year-on-year changes in the proportion of R&D investment and operating revenue	The proportion of R&D investment and operating revenue ≥ 5%	Rising proportion: ≥50% (20), 30–50% (19), 15–30% (18), 5–15% (17); Float proportion: ±5% (16); Decreasing proportion: ≥50% (0), 30–50% (6), 15–30% (8), 5–15% (10)	20
				The proportion of R&D investment and operating revenue < 5%	Rising proportion: ≥50% (20), 30–50% (18), 15–30% (15), 5–15% (10); Float proportion: ±5% (8); Decreasing proportion: ≥50% (0), 30–50% (2), 15–30% (3), 5–15% (5)
			The proportion of R&D personnel and total employees		0%(0); 1–3% (2); 3–5% (3); 5–10% (5); 10–15% (7); 15–20% (9); 20–40% (12); 40–60% (13); ≥60% (15)	15
			Assets-liability ratio		0–20% (20); 20–40% (22); 40–60% (30); 60–80% (15); 80–100% (5); 100% (0)	30
	Product management (5.192%)	PM	Satisfactory clean production in manufacturing process and equipment		50	50
			Application of green supply chain management system		50	50
Pollution Control	Governance input (7.458%)	GI	The proportion of industrial exhaust and wastewater treatment costs and total profit		0% (0); 0–1% (5); 1–3% (20); 3–5% (30); 5–10% (50); 10–30% (40); 30–60% (20); 60–80% (10); 80–100% (5); 100% (0)	50
			Year-on-year changes in the proportion of energy conservation and environmental protection investment and operating income	The proportion of energy conservation and environmental protection investment and operating income ≥ 10%	Rising proportion: ≥50% (35), 30–50% (40), 15–30% (50), 5–15% (40); Float proportion: ±5% (30); Decreasing proportion: ≥50% (0), 30–50% (5), 15–30% (15), 5–15% (25)	50
				The proportion of energy conservation and environmental protection investment and operating income 5–10%	Rising proportion: ≥50% (50), 30–50% (45), 15–30% (40), 5–15% (35); Float proportion: ±5% (30); Decreasing proportion: ≥50% (0), 30–50% (5), 15–30% (10), 5–15% (20)
				The proportion of energy conservation and environmental protection investment and operating income < 5%	Rising proportion: ≥50% (50), 30–50% (40), 15–30% (30), 5–15% (20); Float proportion: ±5% (10); Decreasing proportion: ≥50% (0), 30–50% (2), 15–30% (4), 5–15% (6);
	Pollution discharge (22.053%)	PD	Variation of industrial wastewater discharge	Industrial wastewater discharge < 100 tons	Rising proportion: ≥50% (0), 30–50% (5), 15–30% (8), 5–15% (10); Float proportion: ±5% (15); Decreasing proportion: ≥50% (25), 30–50% (24), 15–30% (22), 5–15% (20);	25
				Industrial wastewater discharge 100–1000 tons	Rising proportion: ≥50% (0), 30–50% (5), 15–30% (8), 5–15% (10); Float proportion: ±5% (15); Decreasing proportion: ≥50% (25), 30–50% (24), 15–30% (21), 5–15% (18);
				Industrial wastewater discharge ≥ 1000 tons	Rising proportion: ≥50% (0), 30–50% (4), 15–30% (6), 5–15% (8); Float proportion: ±5% (10); Decreasing proportion: ≥50% (25), 30–50% (22), 15–30% (20), 5–15% (15);
			Variation of industrial exhaust discharge	Industrial exhaust emissions < 1 million m3	Rising proportion: ≥50% (0), 30–50% (5), 15–30% (8), 5–15% (10); Float proportion: ±5% (15); Decreasing proportion: ≥50% (25), 30–50% (24), 15–30% (22), 5–15% (20);	25
				Industrial exhaust emissions 1–10 million m3	Rising proportion: ≥50% (0), 30–50% (5), 15–30% (8), 5–15% (10); Float proportion: ±5% (15); Decreasing proportion: ≥50% (25), 30–50% (24), 15–30% (21), 5–15% (18);
				Industrial exhaust emissions ≥ 10 million m3	Rising proportion: ≥50% (0), 30–50% (4), 15–30% (6), 5–15% (8); Float proportion: ±5% (10); Decreasing proportion: ≥50% (25), 30–50% (22), 15–30% (20), 5–15% (15);
			Variation of industrial solid waste generation	Industrial solid waste comprehensive utilization amount < 100 tons	Rising proportion: ≥50% (0), 30–50% (5), 15–30% (8), 5–15% (10); Float proportion: ±5% (15); Decreasing proportion: ≥50% (25), 30–50% (24), 15–30% (22), 5–15% (20);	25
				Industrial solid waste comprehensive utilization amount 100–1000 tons	Rising proportion: ≥50%( 0), 30–50% (5), 15–30% (8), 5–15% (10); Float proportion: ±5% (15); Decreasing proportion: ≥50% (25), 30–50% (24), 15–30% (21), 5–15% (18);
				Industrial solid waste comprehensive utilization amount 1000 tons	Rising proportion: ≥50% (0), 30–50% (4), 15–30% (6), 5–15% (8); Float proportion: ±5% (10); Decreasing proportion: ≥50% (25), 30–50% (22), 15–30% (20), 5–15% (15);
			Compliant disposal rate of hazardous waste		0% (0); 0–10% (3); 10–20% (5); 20–40% (7); 40–60% (12); 60–80% (17); 80–90% (20); 90–100% (25)	25
	Water resource utilization (3.675%)	WRU	Utilization ratio of wastewater		0% (0); 0–5% (5); 5–10% (10); 10–20% (20); 20–30% (30); 30–40% (40); 40–50% (50); 50–60% (60); 60–70% (70); 70–80% (80); 80–90% (90); 90–100% (100)	100
Energy conservation and carbon reduction	Energy consumption (22.053%)	EC	Variation of the proportion of fossil fuels consumption	Fossil fuels consumption < 100 tons	Rising proportion: ≥50% (0), 30–50% (10), 15–30% (15), 5–15% (20); Float proportion: ±5% (25); Decreasing proportion: ≥50% (50), 30–50% (45), 15–30% (40), 5–15% (30);	50
				Fossil fuels consumption ≥ 100 tons	Rising proportion: ≥50% (0), 30–50% (5), 15–30% (10), 5–15% (15); Float proportion: ±5% (20); Decreasing proportion: ≥50% (50), 30–50% (40), 15–30% (30), 5–15% (25);
			Variation of comprehensive energy consumption per unit product	Fossil fuels consumption < 100 tons	Rising proportion: ≥50% (0), 30–50% (10), 15–30% (15), 5–15% (20); Float proportion: ±5% (25); Decreasing proportion: ≥50% (50), 30–50% (45), 15–30% (40), 5–15% (30);	50
				Fossil fuels consumption ≥ 100 tons	Rising proportion: ≥50% (0), 30–50% (5), 15–30% (10), 5–15% (15); Float proportion: ±5% (20); Decreasing proportion: ≥50% (50), 30–50% (40), 15–30% (30), 5–15% (25);
	Carbon reduction (15.364%)	CR	Variation of carbon emission per unit product	Fossil fuels consumption < 100 tons	Rising proportion: ≥50% (0), 30–50% (10), 15–30% (15), 5–15% (25); Float proportion: ±5% (35); Decreasing proportion: ≥50% (65), 30–50% (60), 15–30% (55), 5–15% (45);	65
				Fossil fuels consumption ≥ 100 tons	Rising proportion: ≥50% (0), 30–50% (5), 15–30% (10), 5–15% (20); Float proportion: ±5% (30); Decreasing proportion: ≥50% (65), 30–50% (55), 15–30% (45), 5–15% (40);
			Application of low-carbon technology		35	35

.

The analytic hierarchy process method is applied to determine the weight of the secondary indicators of the green development progress index. The average value of the pairwise comparison judgment matrix of the sub-criteria by 11 experts and the weight calculation results are shown in

Table 5. The sub-criteria weight matrix of green development progress index by AHP.

Sub-Criteria	The Original Matrix by 11 Experts											The Calculation Matrix by AHP
	CRR	PAR	PCM	EPC	IP	PM	GI	PD	WRU	EC	CR	Feature Factor	Weight Value	Maximum Eigenvalue	CI Value
Carbon reduction regime	1	2	1/4	3	1/5	1/3	1/4	1/7	1/2	1/7	1/6	0.288	2.62%	11.505	0.050
Pollution abatement regime	1/2	1	1/5	2	1/6	1/4	1/5	1/8	1/3	1/8	1/7	0.208	1.90%
Pollution control manner	4	5	1	6	1/2	2	1	1/4	3	1/4	1/3	0.82	7.46%
Environmental publicity and education	1/3	1/2	1/6	1	1/7	1/5	1/6	1/9	1/4	1/9	1/8	0.157	1.43%
Investment and profitability	5	6	2	7	1	3	2	1/3	4	1/3	1/2	1.189	10.81%
Product management	3	4	1/2	5	1/3	1	1/2	1/5	2	1/5	1/4	0.571	5.19%
Governance input	4	5	1	6	1/2	2	1	1/4	3	1/4	1/3	0.82	7.46%
Pollution discharge	7	8	4	9	3	5	4	1	6	1	2	2.426	22.05%
Water resource utilization	2	3	1/3	4	1/4	1/2	1/3	1/6	1	1/6	1/5	0.404	3.68%
Energy consumption	7	8	4	9	3	5	4	1	6	1	2	2.426	22.05%
Carbon reduction	6	7	3	8	2	4	3	1/2	5	1/2	1	1.69	15.36%

. The maximum eigenvalue of the eigenvector of the weight matrix is 11.505, and the CI value is 0.050, which conforms to the consistency test. In general, pollution discharge (PD) and energy consumption (EC) are the principal indicators for judging the green development progress of private industrial enterprises, with weights of 22.05%. The other important elements are carbon reduction (CR) and investment and profitability (IP), and the index weights were 15.36% and 10.81%, respectively; posteriorly, pollution control manner (PCM) and governance input (GI) accounted for a weighting of 7.46%. The least significant indicators are carbon reduction regime (CRG), pollution abatement regime (PAR), environmental publicity and education (EPE), water resource utilization (WRU), and product management (PM), which basically conform to the theoretical principles of judging the green development of enterprises.

4.3. Index Calculation Results

In the current study, with the growth of the enterprise scale, the positive correlation in the carbon reduction regime, pollution abatement regime, pollution control manner, investment and profitability, and pollution discharge are all evident. However, energy consumption and carbon reduction express an opposite correlation. Among the enterprises with a main business income of 20 million to 100 million RMB, 81.0% of the private industrial enterprises have a green development progress index of lower than 60, which is 5.2% higher than the proportion of enterprises with a main business income of 100 million to 1 billion RMB, and 12% higher than enterprises with a main business income of more than 1 billion RMB. In the score range of index 60–70, the highest proportion of enterprises with a main business income of more than 1 billion CYP is 28.3%, which is about 10% higher than enterprises of other scales, indicating that the improvement in the green development level of Chinese private industrial enterprises still has plenty of space to improve.

In the critical areas of China’s economic development and ecological environment protection, the main business income of more than 1 billion industrial enterprises in Fenwei Plain, including some cities in Shanxi, Shaanxi, and Henan, has the highest level of green development and progress, with more than 30% of the enterprise index higher than 60. Among the industrial enterprises with a main business income of 20 million to 100 million RMB, the Pearl River Delta and the Yangtze River Economic Belt region index of more than 60 accounted for the highest proportion of 22.2% enterprises, while the Beijing-Tianjin-Hebei-Shandong-Henan, Fenwei Plain, Yangtze River Delta, Sichuan-Chongqing, Yellow River Basin, and other regions were lower than the national average. Among the industrial enterprises with a main business income of 100 million to 1 billion RMB, the proportion of enterprises with an index exceeding 60 in the Pearl River Delta region was the lowest at 19.4%. From the analysis of different industrial sectors, the green development progress of enterprises in the textile industry and chemical fiber manufacturing industries are better than other industries, and the proportion of enterprises with an index of more than 60 exceeds 50% in different intervals of main business income. While the index of enterprises of the mining industry and the power industry are below 60 for the pollution control of these industries, this was commanded previously, and all of the facilities were operated smoothly so that the green investment and progress were lower than average in 2021 (see Figure 3).

5. Empirical Results and Discussion

5.1. Moran’s I Test Results and Discussion

To reveal the spatially significant characteristics of the index of green development progress of private industrial enterprises in provincial areas of China in 2021, this paper employs the ARCGIS instrument to analyze the spatial correlation characteristics of the index in 31 provinces. The results show that the global Moran’s index is 0.189 > 0. The Z value of the significance results after iteration is 3.0669 and the p-value is lower than 0.01. Through the significance test at the 1% level, it shows that the green development progress level of private enterprises in 31 provinces affect each other in space.

Since the global Moran index is an overall measure of the level of green development progress of regional private industrial enterprises, it reflects the average degree of the relationship between this province and the neighboring provinces. For the purpose of determining its local spatial aggregation or abnormal relationship, the local Moran index is further applied to analyze its spatial pattern. As shown in Figure 4, 17 provinces with a p value greater than 0.1 are deemed insignificant in local spatial agglomeration, and the local spatial aggregation effect is revealed in the rest of the 14 provinces at a significance level of 0.05. Specifically, Heilongjiang, Jilin, and Liaoning provinces are included in the low clusters (LL), which is in the northeast region of China, and the high-high clusters (HH) cover Henan, Anhui, Jiangsu, Zhejiang, Fujian, Jiangxi, Hunan, and Guangdong provinces, mainly distributed among the middle and lower reaches of the Yangtze River and the southeast coastal areas of China. Only Hubei belongs to the low-high combination cluster (LH) where there is a low value of green development of regional private enterprises enclosed by high values of the southeast region.

It is apparent that the spatial clustering effect of the green development progress level index of private industrial enterprises in 31 provinces of China in 2021 is associated with the degree of economic development of the region. In the last decade, the northeastern region, including Heilongjiang, Jilin, and Liaoning, is the low region of China’s regional economic development, with lagging economic development rates, and the gap between this region and the developed southeast region is large. From 2010 to 2021, the disparity between total economic volume of the north area and the south area rapidly expanded from 14.4% to 29.6%, and the per capital GDP gap increased from 0.97% to 1.25%.

Similar features can also be found in other research regarding the level of green development of China’s industry. For example, Li et al. [43] found that the regional differentiation of China’s industrial green development level is highly distinct. The level of industrial green development in the southeastern region is significantly more advanced than that in the central, western, and northeastern regions. The primary cause of this is that the eastern region has a high level of economic development with additional inputs involving advanced production technology [44], a high energy utilization rate, and a leading industrial development degree. Therefore, in the process of promoting the green development level of private industrial enterprises of the future, the middle and lower reaches of the Yangtze River Economic Belt provinces should extend their influence to the northern and western regions of China in order to foster the coordinated green development of private industrial enterprises and the governance of environmental pollution.

5.2. Factors Analysis and Discussion

Based on the ordinary least square (OLS), the influence of each factor on the progress level of green development was analyzed in

Table 6. Influencing factor of green development progress level.

	OLS	W_distance	W_distance2	W_01
lnpergdp	0.169 **	0.190 ***	0.127 **	0.132 ***
	[0.075]	[0.049]	[0.051]	[0.048]
ind_p	0.164	0.500 *	0.653 ***	0.271
	[0.177]	[0.270]	[0.250]	[0.172]
Urban	−0.613 ***	−0.621 ***	−0.508 ***	−0.575 ***
	[0.214]	[0.148]	[0.163]	[0.176]
foreign_p	0.001	0.002	0.002	0.000
	[0.001]	[0.001]	[0.001]	[0.001]
lnelectricity	0.036 **	0.004	0.009	0.023
	[0.016]	[0.020]	[0.017]	[0.015]
env_p	−49.716 *	−75.130 **	−93.405 ***	−39.672
	[25.632]	[34.089]	[29.568]	[27.937]
w1x_lnpergdp		0.376 ***	0.351 *	0.049 *
		[0.097]	[0.208]	[0.027]
w1x_ind_p		0.858 **	1.307 **	0.235 *
		[0.415]	[0.553]	[0.130]
w1x_urban		−1.313 ***	−1.476 *	−0.191 **
		[0.318]	[0.779]	[0.090]
w1x_foreign_p		0.002	0.000	0.001
		[0.004]	[0.011]	[0.001]
w1x_lnelectricity		−0.086	−0.121	−0.005
		[0.054]	[0.083]	[0.011]
w1x_env_p		−132.001	−715.403 ***	−39.252 **
		[117.919]	[263.120]	[15.968]
R-squared	0.536	0.665	0.707	0.663
Adjustment R2	0.420	0.471	0.537	0.467

Note: ***, **, and * are significant at 1%, 5%, and 10%, respectively, standard errors in brackets.

. Results show that per capital GDP, urbanization rate, electricity consumption, and fiscal environmental protection expenditure have statistical significance and an impact on the level of provincial green development progress. The linear regression result based on the OLS of classical statistics is shown in Table 6, which assumed the spatial stationarity of the elements in the data fitting process. Because the research scale is spatial units, the bias of the model results may be produced from the differences in geospatial weights. When the estimation deviations exist, an optimized model should participate in the analysis and the comparison. Green development progress has had an appreciable impact on provincial areas according to the research conclusions of the spatial autocorrelation test of the Moran index, which need to be taken into account in the optimized models to study the influencing factors of both the region and the adjacent regions. Therefore, the spatial Durbin model (SDM) is further employed as the optimized model [45].

The results of SDM are basically consistent with the OLS analysis results (Table 6). The Adjustment R² of SDM is 0.471 larger than 0.42 of OLS, displaying the advance of explanatory power. The per capital GDP (0.190) and the proportion of secondary industry (0.500) have a significant positive effect on the provincial green development progress, while urbanization (−0.621) and the proportion of fiscal environmental protection expenditure (−75.130) have a significant negative impact on green development progress. The motivation for developing a green economy in a region will be stronger with the strength of urban economic development, which brings with it a high level of green development progress in private enterprises [17]. The government will also attach importance to environmental governance following the expansion of the manufacturing sectors. On the contrary, the boost of urbanization will have a crowding-out effect on the green development of industries through the rapid development of the real estate business. If the proportion of fiscal environmental protection expenditures maintain a stable growth, the dependency of firms on the government will be largely improved, leading to a low level of green development progress.

From the view of the spatial impact on selected influencing factors, the regression coefficients of the spatial lag items of GDP per capital and industrial structure are 0.376 and 0.858, respectively, which are statistically significant, implying that the improvement of the economic and industrial development in adjacent provinces will accelerate the green development progress of the province itself. However, the regression coefficients of spatial lag items of urbanization and fiscal environmental protection expenditure negatively manifesting the two factors would not significantly promote green development progress in adjacent provinces. Economic growth is conducive to driving the development of surrounding areas owing to the spatial spillover effects, which also produce the phenomenon of industrial agglomeration and simultaneously show the shared progress of green development [46], and no mutual positive impacts on provinces have been proved due to the fact that urbanization and fiscal environmental protection expenditures are endogenous.

For the purpose of testing the reliability of the above results, the construction method of the spatial weight matrix is also changed for robustness analysis. The spatial inverse distance square matrix (W_distance²) and the spatial adjacency matrix (W_01) are used to replace the spatial inverse distance matrix for analysis. The results are basically consistent with those shown in Table 6, signifying that the analysis of the influencing factors is relatively robust.

6. Conclusions and Suggestion

6.1. Conclusions

The green development progress level of private industrial enterprises was evaluated from four dimensions of management planning leadership, production and operation status, pollution control effect and energy saving, and carbon reduction intensity, based on a large sample of 10,623 enterprises in 31 provinces across China in 2021. Spatial differences in various provinces in the country and the influencing factors have also been further analyzed.

Three main conclusions are drawn from the analysis. (1) The positive spatial correlation between the green development progress index in 31 provinces of China has been proved. In particular, the middle and lower reaches of the Yangtze River and the southeast coastal areas of China were presented as high-high clusters, and the northeast region was shown as a low-low cluster, which was similar to the economic development features. The index also performed a positive relationship to the scale of enterprises that was the larger of the enterprise scale, and the index of green development progress possessed a higher value embodied in profitability, pollution control, and carbon reduction indicators. (2) Economic development, industrial structure, urbanization, and government financial support for environmental protection were the crucial factors affecting green development progress. Specifically, per capital GDP and the proportion of secondary industry had a substantial positive impact, while urbanization and the proportion of fiscal expenditure on environmental protection had a significant negative effect. (3) Improvement of the economic and industrial development of the adjoining provinces will accelerate the green development of the region. While indicators of urbanization and fiscal environmental protection expenditure appear, though, no mutual positive influence will be seen between the regions.

6.2. Suggestion

(1) In the process of promoting the green development level of private industrial enterprises of the future, the middle and lower reaches of the Yangtze River Economic Belt provinces are supposed to extend its influence to the northern and western regions of China in order to foster the coordinated green development of private industrial enterprises and the governance of environmental pollution.

(2) The impact of economic development for the green development level in local and adjoining regions should be focused, and local governments need to balance urbanization, environmental fiscal expenditure, and green development progress, reducing the dependency of firms on the government.

The evaluation index of the green development progress of private enterprises was analyzed by cross-sectional time series data, which required a long time series data accumulation to further analyze the econometric, regression, and mechanism effects. In the future, the research team will keep expanding the scope and quantity of private enterprises involved in the questionnaire survey in order to improve the data accuracy, and they will strengthen the systematic and scientific evaluation of the index in order to provide more comparable results for other researches based on statistical data. Empirical cases to supplement the green development law and theory for private enterprises will also be obtained.