The Impact of Resource Endowment on Provincial Green Development: An Empirical Analysis from China

Abstract

Indepth research on the impact of energy endowment on provincial green development provides a new perspective for achieving high-quality provincial economic development. Based on 30 provinces’ panel data in China from 2009 to 2020, this study first constructs an index system to analyze energy endowment and provincial green development; then it explores the mechanism by which energy endowment affects provincial green development, including the moderating role of environmental regulation and the mediating role of energy efficiency. The results show that energy endowment can significantly curb provincial green development when environmental regulations are not taken into account. Moreover, environmental regulation has moderated the process of energy endowment affecting provincial green development. Energy efficiency has mediated the process of energy endowment affecting provincial green development. From a geographical viewpoint, the moderating effect of environmental regulation has regional heterogeneity but the mediating effect of energy efficiency does not. The conclusions of this research may supply applicable recommendations for provincial green development in China.

1. Introduction

Presently, China has entered the high-quality development stage. Adhering to the concept of prioritizing ecology and embracing provincial green development are the bases for realizing great development goals of ecological-civilization construction and a beautiful China. While China’s industrialization and urbanization are advancing rapidly, the extensive development mode has resulted in low energy and resource transformation efficiency, resource shortages, and intensified contradictions between people and land. Therefore, promoting ecological civilization construction by improving resource utilization efficiency has turned into an urgent matter for green development in China. Given the implementation of the Environmental Protection Law in China, different areas have formulated differentiated policies, such as the applicable tax amount for environmental protection, which means that China is taking a new road of regional coordination and sustainable development of “ecological priority and green development.” Therefore, green development is a major livelihood project and an essential way to alter the manner of economic development and boost high-quality economic expansion. Resource endowment, as a necessary element to realize economic development, also carries a heavy environmental “burden” and easily falls into the “trap” of resource advantage. Therefore, it is vital to correctly understand the connection between resource endowments and provincial green development and explore their mechanisms of influence to promote high-quality economic development.

Several complex factors affect provincial green development. We can offer an empirical foundation for drawing up reasonable policies for promoting provincial green development by examining the influence of relevant factors on green development and accurately identifying key factors. However, owing to the availability of statistical data and other reasons, the current academic circle mainly focuses on the efficiency of green development [1], spatial-temporal models and predominant factors of green development efficiency [2,3], the life cycle of green development [4], influential factors of green development, such as the digital economy [5], carbon neutrality [6], the market integration of urban agglomeration [7], progress in green technology [8], transition to pollution control [9], clean air action policy [10] and environmental management [11]. Some scholars have also studied the level of green development and mode from the regional perspective [12,13], countries along the Belt and Road [14], and different income groups [15]. Few researchers have investigated the influence on provincial green development from the resource-endowment perspective. The high-carbon characteristic of “more coal, less oil and less gas” in China constrains the transformation of green development.

In summary, economic development and ecological environmental protection in the limited space of organic coordination and benign interaction aim on enhancing the efficiency of energy utilization and minimize pollutant emissions by balancing economic output. It is biased to analyze research on the green-development effect of resource endowment from a single perspective, which leads to unstable conclusions. Given the continuous deepening of green development, academic circles have made excellent achievements in continuous exploration. However, they have neglected to examine the impact of resource endowment on provincial green development from the perspective of different energy abundances, the moderating role of environmental regulation, and the mediating role of energy efficiency. Does resource endowment affect provincial green development? Does the intensity of environmental regulation moderate the effect of resource endowment on provincial green development? In different energy-rich areas, the regulatory effect of environmental regulation has regional heterogeneity. Does energy efficiency mediate the impact of resource endowment on provincial green development? This study is based on the energy abundance of 30 provinces in China, which are divided into energy abundance and energy poverty areas. This is done by using the entropy and TOPSIS method to measure provincial green development, the resources endowment effect on provincial green-development heterogeneity, and by studying the impacts of environmental regulation intensity in resource endowment of the regulating role in the development of green energy and the energy efficiency intermediary role. This research assists in opening the “black box” of resource endowment affecting provincial green development and provides suggestions for improving green development.

The following aspects mainly indicate the empirical compositions of this study. Firstly, regarding regional resource abundance, this study reveals the effect of different resource endowments on provincial green development, enriches research on the relationship between resource endowment and provincial green development, and deepens the research on the existing resource curse theory. Secondly, this study introduces the advantage theory of resource endowment and tests the mediating role of “energy efficiency” between resource endowment and provincial green development. It expands the channel for resource endowment to play its value and reveals the “theoretical black box” between resource endowment and provincial green development. Thirdly, when examining the moderating effect of environmental regulation intensity on the effect of resource endowment on provincial green development, the theoretical extension of the impact process of resource endowment on provincial green development can be improved, which provides a reference for subsequent research in related fields. At the practical level, this study helps us have a deeper understanding of provincial green development, which has a certain reference significance for policy-making departments to further regulate resource-based enterprises’ behavior and realize ecological civilization.

The rest of this paper is structured as follows. The second portion presents the research hypotheses of this study. The third portion includes model construction, variable measurement, and data source. The fourth portion consists of the data analysis and discussion of the results. The last portion summarizes the current research, discusses study limitations, and recommends possibilities for future studies.

2. Research Hypothesis

2.1. The Indirect Impact of Resource Endowment on Provincial Green Development

The resource endowment theory (H-O) was developed by Ohlin [16] based on Heckschers’ basic argument and later expanded by Samuelson [17] by incorporating factor price into the H-O theory and forming the H-O-S theorem. This theory emphasizes that under a free-trade environment, if resource endowment is regarded as an N factor of production, then resource-based cities are the main producers of resource-based products. They tend to transfer carbon-intensive products in trade, whereas nonresource-based regions are the opposite.

Resource endowment determines a region’s comparative advantage. China is the first developing country, and its economic development is constrained by resource endowment. First, take the energy price into consideration; owing to the comparative advantages of energy supply and using cost in energy-rich areas, there is a shortage and deficiency of willingness to improve energy efficiency in this region. Compared with energy-poor areas, energy-rich areas have lower efficiency of energy utilization, which leads to higher energy utilization. Large-scale development and extensive utilization will inevitably increase carbon emissions in neighborhoods and affect economic development. Second, from the perspective of the “crowding out effect”, energy-rich areas will hinder the advancement of local technologies, which may bring on low energy efficiency and further increase energy consumption per unit of output value, and, ultimately, increase carbon emissions in provinces. As Yu et al. [18] state, resource endowment is significantly positively correlated with carbon emissions. Provincial green development is a new concept and goal of China’s high-quality development in the new era. It is necessary to reduce carbon emission intensity for the Chinese government’s ecological civilization construction under the background of global warming. Finally, regions with abundant natural resources “crowd out” manufacturing [19], human capital, and technological progress [20]. These are the main drivers of provincial green development. Hence, the first hypothesis is pointed out as follows.

Hypothesis 1: 

Resource endowment negatively impacts provincial green development.

2.2. The Moderating Role of Environmental Regulation

Serving as a basic approach for addressing environmental problems, environmental regulation can practically enhance the standard of the ecological environment conversely. In contrast, environmental regulation should minimize the implementation cost of regulators and regulate objects to achieve effective environmental governance. Traditional neoclassical environmental economists generally deem that it is costly to effectively protect the environment. Environmental regulation will cause a heavy economic burden on regulated enterprises, affect social output, and negatively impact the economic development of a country or region. The neoclassical school believes that environmental regulation raises the cost of enterprise compliance, and enterprises need to pay pollution charges for environmental pollution during their production process. Therefore, the financial burden on enterprises is increased, and the resources for green innovation are crowded out by enterprises [21].

In high-intensity environmental-regulation areas, policies of environmental regulation in effect can reduce rent-seeking and corruption and promote green development. Firstly, the intervention of industrial policy provides an internal incentive for the adjustment of the industrial structure [22], driving the adjustment of industrial structure. Secondly, strict environmental regulations enhance consumers’ awareness of green consumption and green consumption preferences, realize the transformation from polluting consumer products to green consumer products, and derive green demand. Finally, environmental regulation brings significant cost pressure or economic incentives to enterprises and directly induces their ecological innovation. The improvement of ecological innovation capacity will enhance the capacity to clean up and manage the ecological environment and enhance the capacity for ecological and environmental management. This will continuously promote the adjustment and transformation of industrial structures and provide strong scientific and technological aid for the construction of an ecological civilization. In contrast, in regions with weak environmental-regulation intensity, although the initial weak environmental regulation provides a good institutional environment for promoting the progress of pollution-control technology, it will negatively affect the progression of production technology and make the overall technological progress show a downward trend. Therefore, weak environmental regulations may impact the development of a green economy. Therefore, effective government actions can restrain or even eliminate the “resource curse” phenomenon, and energy-rich areas can reduce carbon emissions through effective environmental regulation. Hence, the second hypothesis is pointed out as follows:

Hypothesis 2: 

Environmental regulation weakens the impact of resource endowment on provincial green development.

2.3. The Mediating Role of Energy Efficiency

In 1979, the World Energy Council defined “energy saving” as “improving the efficiency of energy consumption by taking economically, technically, environmentally and socially feasible measures.” Since the connotations of energy saving and efficiency are the same, energy efficiency is exploited to substitute energy savings. Some domestic scholars define energy efficiency as the contribution of energy consumption to sustainably developing the economy, environment, and society [23].

Currently, most scholars at home and abroad analyze the relationship between resource endowment and energy efficiency from two aspects: “resource curse” and “endowment advantage.” There is no consistent conclusion about the relationship between the two, which depends on the sum of technological and structural effects. In terms of the technology effect, compared with energy-poor areas, people in energy-rich areas lack the motivation for technological innovation, thus reducing the efficiency of resource utilization. In energy-poor areas, people actively innovate and explore new modes of production and ways of life through technological progress while seeking new technologies and materials to replace increasingly scarce resources. Resource endowment can make technology inefficient and thus reduce energy efficiency. In terms of the structural effect, the agglomeration of resource endowment advantage in spatial distribution is beneficial to the formation of the energy industry agglomeration area. Compared with other industries, the energy industry has economies of scale effect. Areas with resource endowment can determine the industrial structure of provinces according to their own advantages and use economies of scale effect formed by the industry to boost the efficiency of the energy industry. Therefore, the more abundant the mineral resources a region has, the more coal it produces, the higher the percentage of mining workers in the entire population, and the lower its energy efficiency is. The resource curse is related to economic growth and energy efficiency. In accordance with Ying et al. [24], energy endowment causes a significantly negative impact on energy efficiency. That is, a resource curse exists in energy efficiency.

In energy-rich areas, energy development and utilization are the main industries. In contrast, other industries shrink, and the industrial structure is single, resulting in increased energy consumption and low energy efficiency, which increases the growth of carbon emissions and affects provincial green development. However, owing to the “resource curse” effect, the slow economic development catalyzes a slow augment in energy consumption, which relatively inhibits the increase of carbon emissions. Compared to energy-rich areas, provincial green development can be better promoted in energy-poor areas. As stated by Mahapatra et al. [25] and Nam and Jin [26], this can help reduce pollution emissions and improve energy efficiency. Therefore, improving energy efficiency can improve regional environmental quality, strengthen people’s green welfare, and promote the transformation of development modes while also promoting economic efficiency. Reducing pollution emissions per unit of energy also means reducing emissions of sulfur dioxide, carbon dioxide, nitrogen oxide, soot, and other environmental pollution, promoting the transformation of the economy to the direction of “low pollution, low emissions” and promoting green development. According to Li and Xu [27], provinces with higher resource abundance tend to have lower levels of green economic growth, and resources are a “curse” to achieving green economic growth. Hence, the third hypothesis is reached as follows.

Hypothesis 3: 

Energy efficiency mediates the impact of resource endowment on provincial green development.

3. Methodology and Variables

3.1. Methodology

This study inspects the impact of resource endowment on provincial green development. It selects industrial structure, human capital level, foreign direct investment, population density, energy consumption structure, and infrastructure construction as control variables. With the ideas provided by this research, the static panel data are produced on the basis of the above elaboration:

$$GR{E}_{it}=\alpha +{\beta }_{1}lnR{E}_{it}+{\beta }_{2}Xcontro{l}_{it}+m_i+e_{it}$$

(1)

where GRE_it denotes the level of green development of province i in year t. An interaction term is introduced into the benchmark model. The impact of environmental regulation and provincial green development is estimated by multiplying them through decentralized processing to verify the moderating effect of environmental regulation on resource endowment and provincial green development. The regulatory effect model is constructed as follows:

$$GR{E}_{it}={\alpha }_0+{\alpha }_{1}lnR{E}_{it}+{\alpha }_{2}E{R}_{it}+{\alpha }_{3}lnR{E}_{it}*E{R}_{it}+{\alpha }_{4}Xcontro{l}_{it}+e_{it}$$

(2)

According to existing studies and the above analysis, resource endowment may affect GRE through energy efficiency. Therefore, to test how resource endowment influences provincial green development, this study examines whether energy efficiency serves a critical mediating role in the impact of resource endowment on provincial green development. In this study, the energy-efficiency factor is a mediator variable for further tests and the functional relationship of intermediary variables is determined by successive regression coefficients and the model is set as follows:

$$E{F}_{it}={\gamma }_0+{\gamma }_{1}lnR{E}_{it}+m_i+e_{it}$$

(3)

$$GR{E}_{it}={\gamma }_0+{\gamma }_{1}lnR{E}_{it}+{\gamma }_{3}E{F}_{it}+m_i+e_{it}$$

(4)

where EF_it is the intermediary variable that indicates the energy efficiency of province i in year t. Resource endowment has an overall effect on provincial green development if coefficient β₁ is significantly positive. Resource endowment importantly affects energy efficiency if the coefficient γ₁ is significantly positive. The coefficient γ₂ represents the direct effect of energy efficiency on the high-quality development of the provincial economy after controlling for the mediating variable. Energy efficiency has a complete intermediary effect if coefficient γ₂ is insignificant. The difference and similarity between the estimated values of γ₁ × γ₃ and γ₂ must be observed if coefficient γ₂ is significant.

Provincial green development in different provinces is not interdependent, and green development in one province may be affected by provincial green development in other provinces. Therefore, it may lead to the wrong setting of the model to ignore the spatial correlation associated with provincial green development. Based on this, spatial econometric analysis technology, which considers the spatial correlation of economic activities, is used to examine the connection between resource endowment and provincial green development. To obtain the spatial econometric model with the best fitting effect and to study if varied manners of spatial econometric models can be converted into each other, OLS-[SAR and SEM]-SAC-SDM is followed to set and test the model. The weight matrix W_ij is usually determined according to the adjacency of the space elements. The corresponding element in the weight matrix is 1 if the two regions are adjacent. Otherwise, it is 0. However, some scholars believe that the geographical-proximity matrix is not sufficient to fully reflect the objective association between provinces [28].

In a spatial econometric model with a spatial lag term, the regression coefficient is not sufficient enough to represent the relationship between the independent and dependent variables. LeSage and Pace [29] divided the influences of independent variables on dependent variables in the spatial econometric model into direct effects, indirect effects, and total effects, according to the varied perspectives and extent of spatial effects. Later, LeSage and Pace [29] found that partial differential equations can compensate for the defects of point estimation in explaining spatial effects. It effectively explains the impact of random shocks on various variables to measure the direct, spatial spillover, and the total effects correctly of independent variables on dependent variables in the spatial econometric model. The calculation formula is as follows:

Convert the general form of the SDM schema to:

$$\begin{array}{l}(I_n-\delta W)Y={\tau }_n{{\beta }^{\prime }}_0+\beta W+\beta WX+\epsilon \\ P(W)={(I_n-\delta W)}^{-1},Q_m(W)=P(W)(I_n{\beta }_m+{\theta }_{m}W)\end{array}$$

(5)

The above formula can be transformed into:

$$Y={\displaystyle \sum _{m=1}^k{Q}_m(W)}{X}_m+P(W)I_n{\beta }_0^{\prime }+p(W)\epsilon$$

(6)

Upon transforming the above equation into matrix form, we can obtain:

$$\left[\begin{array}{c}{Y}_1\\ Y_2\\ Y_3\\ \hspace{0.33em}⋮\\ \hspace{0.33em}⋮\\ Y_n\end{array}\right]={\displaystyle \sum _{m=1}^k\left(\begin{array}{cccc}{Q}_m{\left(W\right)}_{11}& Q_m{\left(W\right)}_{12}& \cdots & Q_m{\left(W\right)}_{1n}\\ Q_m{\left(W\right)}_{21}& Q_m{\left(W\right)}_{22}& \cdots & Q_m{\left(W\right)}_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ Q_m{\left(W\right)}_{(n-1)1}& Q_m{\left(W\right)}_{(n-1)2}& \cdots & Q_m{\left(W\right)}_{(n-1)n}\\ Q_m{\left(W\right)}_{n1}& Q_m{\left(W\right)}_{n2}& \cdots & Q_m{\left(W\right)}_{nn}\end{array}\right)}\left(\begin{array}{l}{X}_{1m}\\ X_{1m}\\ X_{1m}\\ \hspace{0.33em}\hspace{0.33em}⋮\\ X_{nm}\end{array}\right)+P(W)({\tau }_n{\beta }_0^{\prime }+\epsilon )$$

(7)

where m = 1, 2, 3, …, and k represent the explanatory variable. The first matrix to the right of the equal sign refers to the partial differential matrix demonstrated by LeSage and Pace [29]. The components on the diagonal display the average impact of the change in the X_ik variable in a specific spatial unit on the dependent variable of the unit, namely, the spatial spillover effect, direct effect, indirect effect, and total effect, which can be recorded as follows:

$$\begin{array}{l}direct=\frac{\partial Y_i}{\partial X_{im}}=Q_m{\left(W\right)}_{ii}\\ indirect=\frac{\partial Y_i}{\partial X_{jm}}=Q_m{\left(W\right)}_{ij}\\ total=Q_m{\left(W\right)}_{ii}+Q_m{\left(W\right)}_{ij}\end{array}$$

(8)

3.2. Variable Measurement

In this paper, five categories of variables are set: explanatory variables, explained variables, moderating variables, mediating variables, and control variables, and the meaning, metrics, and description of each variable are as follows:

(1)

Measurement of explanatory variables

The explanatory variable is resource endowment. Presently, there is no unified method for accurately measuring resource endowment at home and abroad. In terms of the viewpoints of Li and Xu [27], this study adopts mining practitioners to measure resource endowment. This index considers the coal, oil, natural gas, metal, nonmineral mining, and selection industries directly connected to raw materials matters and can thoroughly evaluate the economic dependence on natural resources, avoiding “measuring” the areas with highly developed regions as relatively poor resources. Provinces with more than 50% resource abundance are defined as “energy-rich areas,” while those with less than 50% are defined as “energy-poor areas.”

(2)

Measurement of explained variables

The primary explained variable is provincial green development. Green development significantly ensures the sustainable development of the regional economy. This requires strengthening environmental governance, saving, and efficiently utilizing resources in a well-rounded way, effectively resolving resource and environmental constraints, and promoting low-carbon development. Combined with existing research results and the connotations of green development based on Wei and Li [30], six indicators are selected to evaluate provincial green development: forest coverage, nature reserve coverage, built-up area greening coverage, sewage effluent, disposal of unusable gas, and disposal of useless solid waste. The former three indicators are used to measure provincial green environmental protection, and the latter three indicators indicate how the growth of the economy and society affects the environment. The entropy weight TOPSIS method is used to measure provincial green development in this study. The key to this approach is to use the entropy weight method to assign a weight value to each measure index depending on standardization and then to use the TOPSIS method to rank the provincial green development of each province quantitatively. The index weight value of the entropy weight method depends on the volume of information illustrated by the variation degree of each measure of index data, which minimizes the interference from human beings in the index weight assignment. The TOPSIS method quantifies the ranking by comparing the relative distance between each measurement object and the best and worst schemes, which benefits from the simple calculation and reasonable results. The entropy TOPSIS method merges the benefits of entropy weight and the TOPSIS method, making provincial green-development measurement more objective and reasonable. The step-by-step guidelines are the following:

① To eliminate the inconsistency of different measure indexes in magnitude and dimension, the range method is used to standardize each measure index X_ij in the provincial green development measurement system:

$$Y_{ij}=\left\{\begin{array}{l}\frac{X_{ij}-\min (X_{ij})}{\max (X_{ij})-\min (X_{ij})}\\ \frac{\max (X_{ij})-X_{ij}}{\max (X_{ij})-\min (X_{ij})}\end{array}\right.$$

(9)

where i represents the province and j represents the measure index. X_ij and Y_ij represent the original and standardized the measurement of provincial green-development values, respectively, and Max(X_ij) and min(X_ij) represents the maximum and minimum values of X_ij, respectively.

② This study calculates the information entropy X_ij of each measurement index Y_ij in the measurement of the provincial green development system as follows:

$$E_j=\ln \frac{1}{n}{\displaystyle \sum _{i=1}^n[(Y_{ij}/{\displaystyle \sum _{i=1}^n{Y}_{ij}})\ln (Y_{ij}/{\displaystyle \sum _{i=1}^n{Y}_{ij}})]}$$

(10)

③ This study calculates the weight W_j of each measurement index Y_ij in the measurement of provincial green development.

$$W_j=(1-E_j)/{\displaystyle \sum _{j=1}^m(1-E_j)}$$

(11)

④ This study constructs the weighted matrix R of the measurement of provincial green-development indicators:

$$R=(r_{ij})n*m$$

(12)

where, r_ij = W_ij * Y_ij.

⑤ This study determines the best and worst schemes according to the weighting matrix R:

$$Q_j^{+}=(\max r_{i1},\max r_{i2},\cdots ,\max r_{im})$$

(13)

$$Q_j^{-}=(\min r_{i1},\min r_{i2},\cdots ,\min r_{im})$$

(14)

⑥ This study calculates the sum of the Euclidean distances between each measure scheme, the best scheme, and the worst scheme.

$$d_i^{+}=\sqrt{{\displaystyle \sum _{j-1}^m{(Q_j^{+}-r_{ij})}^2}}$$

(15)

$$d_i^{-}=\sqrt{{\displaystyle \sum _{j-1}^m{(Q_j^{-}-r_{ij})}^2}}$$

(16)

⑦ This study calculates the relative proximity C_i of each measurement scheme to the ideal scheme.

$$C_i=\frac{d_i^{-}}{d_i^{+}+d_i^{-}}$$

(17)

The relative proximity of C_i is between 0 and 1, and the larger the C_i value, the better the provincial green development. In contrast, provincial green development is worse.

(3)

Measurement of moderating variable

Environmental regulation serves as a moderating variable. Intellectuals at home and abroad evaluate environmental regulation from the perspective of environmental regulation policies or regulatory agencies’ inspection of enterprises’ emissions [31] and changes in pollution emissions [32]. Relevant data on the potency of environmental regulation are difficult to obtain, so it is difficult to measure this variable. Based on the treatment method of Song and Wang [33], this study uses the amount of contribution in environmental contamination treatment to measure the potency of environmental regulation.

(4)

Measurement of mediating variables

Energy efficiency serves as a mediating variable. The GDP of each province is taken as the desired output variable. In this study, GDP is used as an effective output to measure the economic growth level of every province and city. The GDP deflator is converted to the actual level of GDP in terms of the year 2000 to minimize the impact of price fluctuations. The undesired output variables are carbon dioxide, sulfur dioxide, nitrogen oxide, and smoke (powder) dust emissions. The input variables are energy consumption, employment number, and capital stock. Among them, the employment number takes the mean of the beginning and end of the year. For capital stock, the year 2000 is taken as the base period and accounts for the capital stock of each province. The “perpetual inventory method” measures the fixed capital asset of provinces and cities in China.

(5)

Measurement of control variables

Population density. The population size determines the entire region’s consumption and pollutant emission. In accordance with the viewpoint of Herzog et al. [34], this study embraces population density as the measurement index of population size.

Industrial structure. The deviation in industrial structure among provinces and the difference in the level of total-factor energy efficiency (TFEE) of industries will affect the provincial performance of TFEE. Compared with tertiary industries with low energy consumption and high output, the development of secondary industries with high energy consumption and low output will consume more energy and the TFEE of secondary industries will be lower than that of tertiary industries. Therefore, this study selects the industrial structure index by calculating the rate of value-added secondary industry to GDP.

Energy consumption structure. The difference in the energy consumption structure of each province will inevitably lead to a difference in TFEE, and further affect it. Therefore, this study selects the percentage of coal in energy consumption as the index of energy consumption structure.

Foreign direct investment. Conversely, foreign direct investment can improve total-factor energy efficiency by directly introducing advanced foreign technology. In contrast, foreign direct investment can also change total-factor energy efficiency through technology spillover. This study uses foreign direct investment (FDI) as a measure.

Human capital. Natural resources provide a sustainable source of wealth, reducing people’s demand for the transfer of existing capital to the future; thus, resource development activities may lead to a decline in education investment returns and the requirement of educational quality. Therefore, this study adopts Li et al. ’s [28] viewpoint to measure the level of human capital by years of education per capita.

Infrastructure construction. Improving public infrastructure can reduce transportation and transaction costs between regions. This is advantageous for foreign exchange, information communication, and the streamlining of production factors, driving the spread of technology knowledge, improving the efficiency of resource allocation and efficient use of elements, and thus contributing to the improvement of overall social and economic efficiency. Based on Bai and Bian [35], this study uses the length of long-distance optical cables per square kilometer to characterize the provincial infrastructure environment. The index system is presented in

Table 1. Qualitative description of each variable.

Variable Types	Variable Symbol	Mean	Metrics and Descriptions
Explained variable	GRE	Provincial green development	It is estimated by the TOPSIS entropy weight method
Explanatory variable	RE	Resources endowment	Extractive workers
Moderating variable	ER	Environmental regulation	Investment in environmental pollution control
Mediating variable	EF	Energy efficiency	Input	Undesired output
				The number of jobs
				The capital stock
			Expect output	The provincial GDP
			Undesired output	Carbon dioxide emissions
				Sulfur dioxide emissions
				Nitrogen oxide emissions
				Smoke (powder) dust emission
Control variables	POP	The population density	The population density
	IS	The industrial structure	The proportion of the added value of the secondary industry in the GDP
	CS	Energy consumption structure	Share of coal in energy consumption
	FDI	Foreign direct investment	Foreign direct investment
	HC	Level of human capital	Years of education per capita
	IC	Infrastructure construction	Area per square kilometer long-distance optical cable line length

.

In this study, the descriptive statistics of resource endowment, provincial green development, environmental regulation, energy efficiency, and related control variables are calculated using Stata15.0, involving average, standard deviation, and minimum and maximum values, as shown in

Table 2. Descriptive statistical analysis.

Variable	Mean	Standard Deviation	Min	Max
GRE	0.411	0.099	0.219	0.574
RE	2.09	1.789	−4.342	4.635
ER	5.27	0.866	2.510	7.256
EF	0.898	0.203	0.382	1.180
IS	0.454	0.084	0.186	0.590
HC	2.176	0.092	1.932	2.487
FDI	14.607	1.669	7.990	16.932
POP	7.858	0.426	6.639	8.669
IC	0.182	0.130	0.022	0.878
CS	0.943	0.418	0.016	2.446

.

Before regressing the econometric model, statistically investigating the correlation between explanatory variables is necessary. Therefore, the Pearson correlation coefficient is adopted in this study to examine the correlation and significance of all variables. The particular test results are presented in

Table 3. Correlation test analysis.

	RE	GRE	IS	lnHC	lnFDI	POP	IC	CS	ER	EF
RE	1	−0.35 *	0.28 *	−0.00	0.07	0.14 *	−0.06	0.62 *	0.32 *	0.11
GRE	−0.30 *	1	−0.12 *	−0.13 *	−0.22 *	0.06	−0.46 *	−0.42 *	−0.39 *	−0.22 *
IS	0.42 *	−0.21 *	1	−0.05	0.09	−0.04	−0.07	0.35 *	0.11	−0.00
lnHC	−0.14 *	−0.11	−0.31 *	1	0.43 *	−0.01	0.33 *	−0.09	0.29 *	0.19 *
lnFDI	0.13 *	−0.19 *	0.04	0.48 *	1	−0.03	0.72 *	−0.29 *	0.64 *	−0.24 *
POP	0.08	0.06	0.05	−0.11	−0.01	1	−0.08	0.06	−0.19 *	0.03
IC	−0.35 *	−0.36 *	−0.19 *	0.48 *	0.50 *	0.06	1	−0.18 *	0.43 *	−0.00
CS	0.52 *	−0.44 *	0.38 *	−0.20 *	−0.21 *	−0.00	−0.27 *	1	0.09	0.49 *
ER	0.45 *	−0.39 *	0.13 *	0.33 *	0.65 *	−0.13 *	0.24 *	0.06	1	−0.05
EF	0.21 *	−0.20 *	0.04	0.21 *	−0.16 *	−0.01	0.14 *	0.42 *	0.04	1

* denote p < 0.1.

.

3.3. Data

In this study, 30 provincial-level administrative regions are selected as the research objects from 2009 to 2020, which are not included due to the different statistical standards of Taiwan province, Hongkong, and Macau. All data are obtained from the China Statistical Yearbook, China Energy Statistical Yearbook, China Environmental Statistical Yearbook, China Provincial Statistical Yearbook, and the Provincial Statistical Yearbook. In the actual regression process, to reduce the order of magnitude and eliminate heteroscedasticity, the five variables of resource endowment, environmental regulation intensity, human capital level, foreign direct investment, and population density are treated by logarithm. The list of the 30 provincial administrative units studied in this paper is shown in

Table 4. Research Subjects: List of Provincial Administrations.

Administrative District Type	Administrative District Name
Province	Hebei	Shanxi	Heilongjang	Jilin	Liaoning
	Zhejiang	Anhui	Fujian	Jiangxi	Shandong
	Hubei	Hunan	Guangdong	Hainan	Sichuan
	Yunnan	Shaanxi	Gansu	Qinghai	Guizhou
	Jiangsu	Henan
Autonomous regions	Inner Mongolia	Guangxi	Ningxia	Xinjiang
Municipality	Beijing	Shanghai	Tianjin	Chongqing

.

4. Results and Discussion

4.1. Econometric Model

Utilizing the F test and the Hausman test to select among fixed-effects, random-effects, and mixed estimation models is vital when analyzing the panel data. This research runs these tests on a regression model of the influence of RE on GRE. First, it can be seen from the Hausman test that chi2 (8) = 15.8, prob > chi2 = 0.045; thus, a random-effects model works for the test. The results of the heteroscedasticity test of panel data show that chi2 (30) = 12657.90, Prob > chi2 = 0.000, indicating the existence of heteroscedasticity. Meanwhile, the sequence-correlation test results of panel data show that F (1,29) = 9.903, Prob > F = 0.0038, rejecting the null hypothesis, indicating the existence of sequence correlation. Due to the existence of sequence correlation and heteroscedasticity, based on the Hausman test, chi2 (7) = 2.41, Prob > chi2 = 0.9340, the random effects is believed to be appropriate. The feasible generalized least-square method (FGLS) can correct the heteroscedasticity and sequence correlation caused by the cross-section data, and panel correction standard error (PCSE) is an alternative method of FGLS, which can more accurately display the regression estimation of the panel data. Therefore, this study adopts PCSE for hypothesis testing.

4.2. Result Analysis of Direct Effect and Moderation Effect

The test results for the direct effects of resource endowment on provincial green development and the moderation effects of environmental regulation are displayed in

Table 5. The direct effect of resource endowment on provincial green development and the moderation effect of environmental regulation.

	Model 1	Model 2	Model 3	Model 4
Constant	0.323	0.318	0.295	−0.076
IS	0.129	−0.081	−0.067	0.397
lnHC	−0.093	0.008	0.009	−0.174
lnFDI	−0.003	0.006*	0.014 ***	0.018 ***
POP	0.024 ***	0.030 ***	0.016 **	0.012 *
IC	−0.384 ***	−0.491 ***	−0.456 ***	−0.565 ***
CS	−0.129 ***	−0.096 ***	−0.099 ***	−0.088 ***
RE		−0.018 ***	−0.010 ***	−0.024 ***
ER			−0.034 ***	−0.044 ***
RE*ER				−0.019 ***
R2	0.816	0.464	0.544	0.642
Wald	952.63	1889.01	2123.21	2103.44

***, **, and * denote p < 0.01, p < 0.05, and p < 0.1, respectively.

. According to the estimation of model 2, without considering environmental regulation factors, resource endowment is tested to be negatively correlated with provincial green development (β = −0.018, p < 0.01), and hypothesis H1 passes the test. In accordance with the estimation results of model 4, the regression coefficient of the product of resource endowment and environmental-regulation intensity is significantly negative (β = −0.019, p < 0.01) after introducing environmental-regulation factors. This indicates a negative association between resource endowment and provincial green development, and the cross term is negatively associated with provincial green development (β = −0.019, p < 0.01). Therefore, environmental regulation weakens the influence of resource endowment on regional development.

4.3. Result Analysis of the Mediation Effect of Energy Efficiency

The test results for the intermediary effect of energy efficiency are shown in

Table 6. The mediation role of energy efficiency.

Variable	Model 1	Model 2	Model 3	Model 4
		RE	EF	RE
Constant	0.323	0.318	0.248	0.292
IS	0.129	−0.081	1.415 **	−0.231
lnHC	−0.093	0.008	−0.645 ***	0.076
lnFDI	−0.003	0.006 *	−0.048 ***	0.011 ***
POP	0.024 ***	0.030 ***	−0.017	0.032 ***
IC	−0.384 ***	−0.491 ***	0.830 ***	−0.579 ***
CS	−0.129 ***	−0.096 **	0.179 ***	−0.115 ***
RE		−0.018 ***	0.036 ***	−0.021 ***
EF				0.106 ***
R2	0.464	0.514	0.347	0.545
Wald	1889.01	2746.66	2133.79	2990.23

***, **, and * denote p < 0.01, p < 0.05, and p < 0.1, respectively.

. According to model 2, resource endowment negatively impacts provincial green development (β = −0.018, p < 0.01). In model 3, resource endowment plays a significantly positive impact on energy efficiency (β = 0.036, p < 0.01). Further observation of model 4 shows that energy efficiency (β = 0.106, p < 0.01) and resource endowment (β = −0.021, p < 0.01) are statistically significant to provincial green development. In other words, the result indicates that resource endowment negatively correlates to provincial green development, while energy efficiency positively correlates to provincial green development. The above results indicate that energy efficiency partially mediates the impact of resource endowment on provincial green development, which verifies H3.

4.4. Spatial Effect Test and Heterogeneity Test

The date-of-estimation results indicated that the coefficients of the SDM, SAR, and SEM spatial econometric models are all significantly negative in

Table 7. Panel econometric regression results of resource endowment on provincial green development space.

	SDM	SAR	SEM
_cons	0.354 *	0.175	0.353 **
RE	−0.005 **	−0.007 *	−0.009
IS	−0.097 *	−0.019	−0.025
lnHC	0.025	0.063	0.058
lnFDI	−0.000	−0.003	−0.002
POP	−0.008	0.000	−0.000
IC	−0.009	−0.083	−0.057
CS	−0.047 **	−0.026	−0.032 **
	300	300	300
	0.235	0.090	0.073
Wx
RE	−0.011 *	/	/
IS	−0.007	/	/
lnHC	0.018	/	/
lnFDI	0.002	/	/
POP	−0.009	/	/
IC	−0.344 **	/	/
CS	0.075 **	/	/
rho	0.409 ***	0.416 ***
lambda			0.417 ***
R-sq	0.235	0.090	0.072
Log-L	656.504	645.414	641.668

***, **, and * denote p < 0.01, p < 0.05, and p < 0.1, respectively.

. This indicates that the impact of resource endowment on provincial green development will be weighted by provincial green development of other provinces. The results show that the horizontal term and spatial interaction coefficient of resource endowment in the SDM model significantly influence provincial green development.

However, the regression coefficient of the SDM model failed to straightforwardly reveal how independent variables influence dependent variables. Therefore, it is necessary to calculate the direct effect, spatial spillover effect, and total effect. The results are presented in

Table 8. Direct effect, spatial spillover effect, and the total effect of the SDM model.

LR_Direct		LR_Indirect		LR_Total
RE	−0.008	lnRE	−0.005	lnRE	−0.012
IS	−0.023	IS	−0.011	IS	−0.034
lnHC	0.075	lnHC	0.051	lnHC	0.126
lnFDI	−0.003	lnFDI	−0.002	lnFDI	−0.005
POP	−0.001	POP	−0.001	POP	−0.002
IC	−0.087	IC	−0.058	IC	−0.145
CS	−0.029	CS	−0.020	CS	−0.0492

. As seen in Table 8, the direct and spatial spillover effects of resource endowment are significantly negative, demonstrating that resource endowment is to take not only a direct but also a considerable effect and inhibitory effect on provincial green development caused by the spatial spillover effect. Upon observing the spatial spillover growth effect and the total effect of resource endowment, it obviously displays that the spatial spillover growth effect of resource endowment accounts for over 30% of the whole effect. This further verifies the impact of the spatial effect of resource endowment on provincial green development.

According to the classification criteria proposed above, in this study, the resource endowment of energy-rich and energy-poor areas are used to regress provincial green development. The regression results are presented in

Table 9. Regional heterogeneity test of resource endowment on provincial green development and the moderation effect of environmental regulation.

	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12
	Energy-rich areas				Energy-poor areas
Constant	0.175	−0.270	−0.200	−0.146	−0.051	−0.118	−0.138	−0.275
IS	0.209	0.530	0.333	0.315	0.990 ***	0.993 **	1.036 **	1.095 **
lnHC	−0.204	−0.335	−0.211	−0.202	−0.324 **	−0.309 **	−0.323 **	−0.364 **
lnFDI	−0.002	0.006	0.026 ***	0.024 ***	−0.001	0.003	0.001	0.006
POP	0.036 ***	0.047 ***	0.021 *	0.020 *	−0.008	−0.007	−0.005	−0.002
IC	−0.993 ***	−0.976 ***	−0.876 ***	−0.874 ***	−0.259***	−0.318 ***	−0.322 ***	−0.423 ***
CS	−0.122 ***	−0.094 ***	−0.081 ***	−0.085 **	−0.128***	−0.111 ***	−0.110 ***	−0.111 ***
RE		−0.039 ***	−0.030 ***	−0.027 ***		−0.012 ***	−0.014 ***	−0.024 ***
ER			−0.058 ***	−0.050 ***			0.006	−0.015
RE*ER				−0.007				−0.012 ***
Observations	160	160	160	160	140	140	140	140
R-squared	0.631	0.672	0.724	0.725	0.489	0.535	0.536	0.552
Number of id	16	16	16	16	14	14	14	14
Wald	361.82	504.29	545.57	592.43	2988.00	3221.19	2847.40	1739.63

***, **, and * denote p < 0.01, p < 0.05, and p < 0.1, respectively.

. In energy-rich areas, according to model 6, the impact of resource endowment on provincial green development is overwhelmingly negative (β = −0.039, p < 0.01). After the introduction of environmental regulation and the cross term, there still exists a negative correlation between resource endowment and provincial green development (β = −0.027, p < 0.01), indicating that the environmental regulation weakens the impact of resource endowment on provincial green development. However, the cross-term and provincial green development are not significant. In energy-poor areas, the regression results of model 10 show that the regression coefficient of resource endowment on provincial green development is negative (β = −0.012, p < 0.01), indicating that resource endowment inhibits provincial green development in energy-poor areas. After the introduction of environmental regulation and the cross term, the resource endowment is found to be negatively correlated with provincial green development (β = −0.024, p < 0.05), indicating that environmental regulation weakens the negative relationship between resource endowment and provincial green development. Therefore, resource endowment negatively impacts green economic development, and provinces with high resource abundance have a lower level of green economic development than those with low resource abundance.

The mediating results of energy efficiency are shown in

Table 10. Regional heterogeneity test of resource endowment on provincial green development and the moderation effect of environmental regulation.

Variable	Energy-Rich Areas				Energy-Poor Areas
	Model5	Model6	Model7	Model8	Model9	Model10	Model11	Model12
		RE	EF	RE		RE	EF	RE
Constant	0.175	−0.270	−1.157	−0.188	−0.051	−0.118	−3.465 **	0.209
IS	0.209	0.530	4.347 *	0.225	0.990 **	0.993 *	4.372 **	0.580
lnHC	−0.204	−0.335	−2.052 *	−0.190	−0.324 *	−0.309 *	−1.769 ***	−0.142
lnFDI	−0.002	0.006	−0.064 ***	0.011 *	−0.001	0.003	−0.036 ***	0.006
POP	0.036 ***	0.047 ***	−0.117 ***	0.055 ***	−0.008	−0.007	0.143 ***	−0.020 *
IC	−0.993 ***	−0.976 ***	0.443 **	−1.007 ***	−0.259 ***	−0.318 ***	1.037 ***	−0.416 ***
CS	−0.122 ***	−0.094 ***	0.245 ***	−0.112 ***	−0.128 ***	−0.111 ***	0.274 ***	−0.136 ***
RE		−0.039 ***	−0.027 *	−0.037 ***		−0.012 ***	0.075 ***	−0.019 ***
EF				0.070 **				0.094 ***
R2	0.631	0.672	0.516	0.680	0.489	0.535	0.605	0.560
Wald	361.82	504.29	2550.09	540.08	2988.00	3221.19	971.84	1638.87

***, **, and * denote p < 0.01, p < 0.05, and p < 0.1, respectively.

. In energy-rich areas, it can be seen from model 6 that resource endowment negatively affects provincial green development (β = −0.039, p < 0.01). In model 7, resource endowment significantly affects energy efficiency (β = −0.027, p < 0.05). Further observation of model 8 shows that resource endowment (β = −0.037, p < 0.01) and energy efficiency (β = 0.070, p < 0.01) both significantly affect provincial green development. Resource endowment negatively affects provincial green development, while energy efficiency positively affects provincial green development. The above results indicate that energy efficiency partially mediates the impact of resource endowment on provincial green development. In energy-poor areas, it can be observed from model 10 that resource endowment significantly affects provincial green development in a negative way (β = −0.012, p < 0.01). In model 11, resource endowment significantly affects energy efficiency (β = 0.075, p < 0.01). Further observation of model 12 shows that resource endowment (β = −0.019, p < 0.01) and energy efficiency (β = 0.094, p < 0.01) both play a significant role in provincial green development. The above research results indicate that energy efficiency partially mediates the impact of resource endowment on provincial green development in energy-poor areas.

4.5. Discussion

The above empirical results indicate that environmental regulation weakens the impact of resource endowment on regional development, and energy efficiency partially mediates the impact of resource endowment on provincial green development in energy-poor areas.

Specifically, resource endowment negatively correlates to provincial green development, while energy efficiency positively correlates to provincial green development, which indicates that energy efficiency can partially mediate the effect of resource endowment on provincial green development. This is consistent with the general view that improving energy efficiency is conducive to promoting the development of resource-based regions [25,26]. And the empirical results of this paper more accurately suggest that energy efficiency plays a partially mediating role in the influence of resource endowment on the level of green development.

The influence of resource endowment on provincial green development will be weighted by provincial green development of other provinces, indicating that the spatial effect of resource endowment affects provincial green development. Auty’s viewpoint explains this precisely [36]. That is, the carbon emissions of resource-based regions and economic growth have a “gain out, damage in” carbon-emissions profit and loss deviation phenomenon, resulting in the green growth transition facing a certain degree of the “natural resource curse” problem.

Environmental regulation weakens the negative relationship between resource endowment and provincial green development. This is because implementing environmental regulation policies in areas with high environmental regulation intensity can effectively reduce rent-seeking and corruption and promote green development. Therefore, resource endowment negatively impacts green economic development, and provinces with high resource abundance have a lower level of green economic development than those with low resource abundance.

5. Conclusions and Prospects

5.1. Conclusions

This study inspects how RE affected provincial GRE, further analyzes the dimensional and regional heterogeneity of the impact, and examines the effect of the moderator variable ER and the mediator variable EF based on 30 provinces’ panel data in China from 2009 to 2020. The conclusions of this study are threefold. Firstly, RE can curb provincial GRE, and there is regional heterogeneity. Secondly, ER positively moderates the process of how RE affects GRE, as the degree of ER weakens the impact of RE on GRE. Thirdly, EF mediates the process of RE affecting GRE as RE fosters the advancement of GRE by affecting EF.

5.2. Policy Recommendations

Firstly, resource-based cities should accelerate the alteration of resource endowment advantages to change them into other factor endowment advantages, breakthrough path locks, and realize green development. The government will actively develop the oil and gas industry. Compared with coal resources, oil and natural gas have a higher calorific value and less pollution and have the advantage of economic cost and environmental protection. Importance should be attached to the development and utilization of renewable energy sources. The development of renewable energy has reduced the dependence on fossil fuels and promoted green development. Secondly, governmental agencies need to consolidate the construction of an environmental supervision system to ensure that laws are available for governing the environment. The government should ensure the implementation of environmental regulations, execute environmental management rooted in local circumstances, and make environmental policies more operational. To enhance the level of environmental regulation, it is critical to constantly improve and optimize the structural factors in China’s economy and deepen the structural reform of energy consumption. The government should inspire enterprises to technologically innovate under the premise of strictly abiding by environmental regulatory policies. Thirdly, energy-based cities should focus on energy efficiency and build a bridge in which resource endowment affects high-quality economic development. The government guides and encourages the rational flow of regional resources and improves energy efficiency and establishes the information-sharing mechanism of energy utilization technology between resource-based cities and nonresource-based cities. It is urgent for resource-based cities to learn advanced energy technology and management concepts of nonresource-based cities to realize common improvements in energy efficiency.

5.3. Limitations and Future Research

There are two notable deficiencies in this research that are likely to be discussed in the future. First, in analyzing the influence of resource endowment on green development, this study considers environmental regulation and energy efficiency in investigating the relationship. There are many other influencing factors in the green development process, including the “amplification” variables and “bridge” variables of development. However, in reality, many factors affect provincial green development, and more decisive factors can be included in future research for an indepth analysis of their influence. Second, this study only explores the impact of resource endowment on provincial green development from a macro perspective. From a microscopic viewpoint, the transformation of resource-based enterprises plays a critical role in the progress of resource-based cities. Some enterprise-level factors will also play a role in the impact of resource endowment on urban green development. Enterprise-level data can be used to investigate the role of environmental regulation and energy efficiency in the impact of resource endowment on urban green development.