1. Introduction
In the 21st century, with the rapid economic development, China’s energy consumption has been rising rapidly. As a result of the massive increase in energy consumption, the air pollution problem has become more serious. According to a report by the International Energy Agency [
1], as shown in
Figure 1, up to 99% of sulfur dioxide (SO
2) and oxynitride (NOx) emissions and up to 85% of fine particulate matter (PM
2.5) emissions are attributable to energy production and use [
1].
The sulfur dioxide (SO
2), nitrogen oxides (NOx), and soot (DS) in China’s air pollutants mainly come from the burning of fossil fuels. In China’s fossil energy consumption, coal is the most important energy source. Therefore, an important cause of air pollution in China is the burning of coal as a fuel [
2]. Coal combustion produces a large amount of SO
2, NOx, DS, which increases the concentration of air pollutants. Studies have shown that an increase in the concentration of air pollutants will significantly reduce the health of residents [
3]. Both short-term and long-term income elasticity show that sulfur oxide emissions have a significant positive impact on health expenditures [
4]. Moreover, previous studies have found that air pollution has a strong spatial spillover effect, and air pollution has a strong mutual influence in geographically similar cities or regions [
5,
6,
7,
8].
Therefore, to reduce air pollution, it is necessary to reduce the proportion of coal consumption in fossil energy consumption, and thus the SO
2, NOx, and DS emissions during the energy consumption process are reduced. At this stage, to reduce the proportion of coal consumption, replacing fossil fuel combustion with clean renewable energy is generally considered to be the best solution [
9]. Therefore, increasing the proportion of renewable energy consumption to replace part of the fossil energy consumption is a very suitable solution to the problem of air pollution. However, the problem is that the price, stability, and technological maturity of renewable energy at this stage are worse than those of fossil energy. Obviously, this hinders the consumption of renewable energy. Therefore, if we want to increase the proportion of renewable energy consumption on the consumer side, we must strengthen the green innovation of renewable energy. When the price, stability, and technological maturity of renewable energy are comparable to fossil energy, the proportion of renewable energy consumption will increase. As a result, the proportion of coal consumption will drop, and the air pollution problem will be alleviated.
However, existing research focuses on the impact of energy consumption and economic and social factors on air pollutant emissions. Some scientists have separately studied the impact of renewable energy green innovation on air pollution and the impact of fossil energy consumption on air pollution. However, there are not many scientists who have combined renewable energy green innovation and fossil energy consumption to conduct research. Among researchers, spatial effects have not received enough attention.
To improve the research in this area, this article fully considers the spatial effect. Using spatial econometric models, we investigated the temporal and spatial evolution characteristics, spatial correlation, and spatial aggregation effects of air pollution in China. This article also focuses on the impact of renewable energy green innovation and fossil energy consumption on air pollution emissions.
The rest of this research is organized as follows.
Section 2 is a literature review of related research and the contributions of this research.
Section 3 introduces the variables, models, data sources, and methods of this study.
Section 4 presents the results and discussion of spatial exploration analysis.
Section 5 shows the empirical results of the spatial panel model. Conclusions and policy recommendations can be found in
Section 6.
2. Literature Review
In recent years, the increasing energy consumption and increasingly unreasonable energy consumption structure have aggravated the air pollution problem, attracting the attention of many scientists [
10,
11,
12].
Among the influencing factors of air pollution, fossil energy consumption is an important incentive [
13,
14,
15]. Research shows that 75% of global greenhouse gas emissions, 66% of nitrogen oxide emissions, and most of the emissions of particulate matter (PM
2.5) come from the energy sector [
16] with oil, coal, and natural gas energy as the main energy consumption resources. Coal consumption has become the main type of energy consumption in the industrial sector and thermal power plants [
17]. Considering the situation, China relies on thermal power generation, and thermal power plants emit a large amount of SO
2 and NOx, which are considered to be the main reason for China’s sulfur dioxide emissions [
18]. Electricity consumption is also considered as non-clean energy consumption [
19]. Zhang, S. et al. (2015) [
20] found that the cement industry is China’s second-largest energy-consuming industry and the main source of carbon dioxide and air pollutants. The cement industry accounts for 7% of China’s total energy consumption and 15% of CO
2, accounting for 21% of PM, 4% of SO
2, and 10% of NOx. Kanada et al. (2013) [
21] found that the consumption of energy fossils is an important source of SO
2 in major cities. Yuan, J. et al. (2017) [
22] found that due to the large-scale utilization of high-carbon fossil energy, many critical air pollutants (CAPs) and greenhouse gases (GHGs) are emitted, which has led to increasingly serious problems of global climate change and local air pollution.
The renewable energy green innovation that this article focuses on is considered to play an important role in alleviating air pollution [
23,
24]. This is because the green innovation of renewable energy can promote the consumption and application of clean and renewable energy, replace fossil energy, and reduce air pollution [
25]. Boudri, J. et al. (2002) [
26] studied the potential of China and India in using renewable energy and their cost-effectiveness in reducing air pollution in Asia. It is found that increasing the use of renewable energy can reduce the cost of controlling sulfur dioxide emission by 17%–35% in China, and reduce the cost of controlling sulfur dioxide emission by more than two-thirds in India. Alvarez et al. (2017) [
27] confirmed the positive impact of the energy innovation process on air pollution and pointed out that renewable energy can help improve air quality. Xie et al. (2018) [
28] concluded through scenario analysis that improvements in renewable energy have always been more effective than taxation in reducing carbon dioxide and air pollutant emissions. Zhu et al. (2019) [
29] found that technological innovations in renewable energy help reduce the concentration of nitrogen oxides (NOx) and respirable suspended particulates (PM
10). Obviously, increasing the proportion of clean energy consumption and reducing fossil energy consumption are very effective ways to control air pollution and achieve green development [
24,
30].
Most of the previous literature separately studied the impact of fossil energy consumption or renewable energy green innovation on air pollution. Previous scientists rarely integrated renewable energy green innovation and fossil energy consumption to study their impact on air pollution. There are not many scientists studying the alternative relationship between fossil energy consumption and renewable energy. There is not enough attention on the spatial correlation and spatial overflow between the three. However, as small as individual cities, as large as individual provinces or regions, the discharge of pollutants has spatial spillover and correlation effects. Xie et al. (2019) [
21] found that PM
2.5 pollutants have strong spatial spillover characteristics. Zhao et al. (2018) [
31] discussed the temporal trends and spatial differences of air pollution in five hotspots in China, as well as the impact of macro-influencing factors on four pollutants, and found that there is a spatial spillover phenomenon of particulate matter across the country. Zeng, et al. (2019) [
32] found that provincial renewable energy policies have a positive impact on the reduction of SO
2 and PM
2.5, and that the energy policy of a province will affect the pollutant emissions of neighboring provinces. Li et al. (2020) [
33] found that air pollution emissions have a significant agglomeration effect, and the spatial accumulation pattern of air pollution emissions is similar to that of fossil energy consumption. The proportion of clean energy consumption and the allocation of energy and labor factors have suppressed air pollution emissions.
Therefore, this article will fully explore how renewable energy innovation affects air pollution, organically integrate the substitution relationship between renewable energy and fossil energy, and fully consider the spatial spillover and related effects of renewable energy. Furthermore, it will explore the linkage mechanism and interaction relationship among renewable energy green innovation, fossil energy consumption, and air pollution. Specifically, the contribution of this research will lie in the following aspects. First, we use non-spatial models and spatial measurement models to study the impact of renewable energy green innovation and fossil energy consumption on air pollution. Second, we fully consider the spatial correlation and spatial spillover effects of renewable energy green innovation and fossil energy consumption, and quantify their impact on air pollution. Third, we use visualization methods to show the temporal and spatial characteristics of renewable energy green innovation, fossil energy consumption, and air pollution. Fourth, we expand the STIRPAT (stochastic impacts by regression on population, affluence, and technology) model to quantify the impact of renewable energy green innovation, fossil energy consumption, environmental regulations, industrial structure, population, GDP (Gross Domestic Product), and other factors on air pollution.
The framework of this study is shown in
Figure 2. Among them, REGI (renewable energy green innovation) has a positive effect on RE (renewable energy). RE has a negative effect on FEC (fossil energy consumption) and AP (air pollution). FEC has a positive effect on AP.
3. Methodology
3.1. Variables and Data
3.1.1. Explained Variable
Air pollution: The most representative variable of air pollution is air pollutant emission. In previous studies, the concentration of PM2.5 was generally used as a proxy variable to measure the degree of air pollution. However, PM2.5 cannot make an objective and comprehensive evaluation of air pollution. Therefore, the pollutant indicators for measuring air pollution in this article mainly include sulfur dioxide (SO2), oxynitride (NOx), and dust and smoke (DS). In addition, considering that each pollutant has its own limitations, this article reduces the dimensions of these indicators to obtain a new indicator—comprehensive pollution (CP). Consequently, an objective and comprehensive assessment of air pollution is made.
This article draws on the method of Liu et al. (2015) [
34] for index dimensionality reduction. First, the three environmental output indicators are subjected to a unified dimensionality reduction process by factor analysis. After the Bartlett sphere test, the statistic value is 56.077, the significance probability is 0.000, and the Kaiser–Meyer–Olkin (KMO) value is 0.748. Therefore, the null hypothesis that the indicators are not correlated is rejected. It is suitable for factor analysis. At the same time, the corresponding weight of each indicator is calculated through the factor score matrix and the variance contribution rate of the common factor. As a result, the weights of sulfur dioxide, nitrogen oxides, and dust and smoke indicators were 24%, 49%, and 27%, respectively. Combining the weights of the three types of pollution indicators, the comprehensive pollution (CP) can be calculated, and the formula is as follows:
Among them: is the weight of each pollutant, is the pollutant component.
3.1.2. Explanatory Variables
Main Explanatory Variable
(1). Renewable Energy Green Innovation (REGI): The article selects renewable energy patents as a proxy variable for green innovation. Refer to Zhu, Y et al. (2019) [
29] to select the International Patent Classification (IPC) code shown as
Table 1 to represent the renewable type. The patent has an impact on the corresponding technology from the date of application. Therefore, previous studies mostly counted the number of patents from the date of patent application. This article refers to the practice of Wang ban-ban et al. (2019) [
35]; count the number of renewable energy green patents based on the date of application.
Hypothesis 1a. Renewable energy green innovation will reduce comprehensive pollution (CP) emissions.
Hypothesis 1b. Renewable energy green innovation will reduce sulfur dioxide (SO2) emissions.
Hypothesis 1c. Renewable energy green innovation will reduce oxynitride (NOx) emissions.
Hypothesis 1d. Renewable energy green innovation will reduce dust and smoke (DS) emissions.
(2). Fossil energy consumption (FEC): China’s rapid development has led to a large consumption of energy, especially fossil fuel consumption. The main cause of pollution is the consumption of fossil fuels, and coal consumption accounts for more than 50% of China’s fossil energy consumption [
36]. Therefore, coal consumption and pollutant emissions are closely related, so this paper selects coal consumption to represent fossil energy consumption.
Hypothesis 2a. Fossil energy consumption (FEC) will increase comprehensive pollution (CP) emissions.
Hypothesis 2b. Fossil energy consumption (FEC) will increase sulfur dioxide (SO2) emissions.
Hypothesis 2c. Fossil energy consumption (FEC) will increase oxynitride (NOx) emissions.
Hypothesis 2d. Fossil energy consumption (FEC) will increase dust and smoke (DS) emissions.
Control Variable
- (1)
Environmental Regulation (ER): There are many ways to measure the intensity of environmental regulation, considering China’s environmental pollution control policies. This article refers to the practice of Zhu Y et al. (2019) [
29] and selects the number of environmental punishment cases as a proxy variable for the intensity of environmental regulation. To a certain extent, environmental regulations will restrain the emission of micro-subjects.
- (2)
Industrial Structure (IS): Select the proportion of the secondary industry as a proxy variable. The study of Hao et al. (2016) [
37] shows that the correlation coefficient between the proportion of secondary industry and the amount of pollutant emissions is positive. Therefore, this article assumes that there is a positive correlation between industrial structure and air pollution.
- (3)
Gross Domestic Product (GDP): Due to the difference between nominal GDP and real GDP, this article is based on the GDP of each province and municipality directly under the central government in 2000. By calculating the GDP deflator, the constant price GDP is calculated.
- (4)
Population (POP): There is a direct link between population size and pollutant emissions. An increase in population will significantly increase energy consumption and pollutant emissions.
3.1.3. Variable Descriptive Statistics
The descriptive statistics of all variables in this article are shown in
Table 2.
3.1.4. Data Resources
The emission data of air pollutants and the data of environmental regulations come from the China Environmental Yearbook. Energy consumption data comes from China Energy Statistical Yearbook. The GDP and population data come from the China Statistical Yearbook. Patent data comes from the wisdom bud patent database.
3.2. Spatial Autocorrelation Test
3.2.1. Global Correlation Index
Here, this article calculates the global spatial correlation according to the global Moran index:
Among them: represent spatial and geographic units i and j, and i ≠ j. represents the spatial weight matrix; represents the average value of each province and municipality. represents the variance; represents the similarity between spatial units i and j; n represents the number.
3.2.2. Local Correlation Index
However, the global correlation index cannot measure the local correlation, so the local Moran index needs to be quoted:
- (1)
H–H: area units with high observation values are surrounded by high-value areas.
- (2)
H–L: area units with high observation values are surrounded by areas with low values.
- (3)
L–L: area units with low observation values are surrounded by low-value areas.
- (4)
L–H: area units with low observation values are surrounded by high-value areas.
Whether it is global spatial autocorrelation or local spatial autocorrelation, the establishment of the spatial weight matrix is very important. This article selects the spatial adjacency weight matrix.
The spatial adjacency weight matrix is a spatial weight matrix that reflects the spatial adjacency relationship. It can be set that there is a significant mutual influence relationship between the adjacent areas, and the non-adjacent areas have no significant interaction. The spatial adjacency weight matrix can more closely reflect the spatial relationship of the development indicators of each region. Therefore, this paper introduces the spatial adjacency weight matrix to make the spatial relationship of development indicators specific.
3.3. Spatial Econometric Model
Based on the above regression model, we set a general provincial pollutant emission regression model.
The IPAT (Impact, Population, Affluence, Technology) model is used to explore the complex social dynamics generated by environmental problems. The original IPAT model was proposed by the famous American demographers Ehrlich and Holdren in 1971. The model believes that environmental pressure (impact) is the product of population (population), affluence (affluence), and technology (technology) [
38]. Dietz et al. (2004) [
39,
40,
41] expressed the IPAT model in a stochastic form to estimate the environmental impact of population, wealth, and technological level. A STIRPAT model (stochastic impacts by regression on population, affluence, and technology) was proposed. The specific expression of the STIRPAT model is:
Among them, a is a constant term; b, c, and d are exponential terms of P, A, and T, respectively; and e is an error term.
Take the logarithm of the left and right sides of the equation:
Applied to this article, the following formula can be obtained:
However, the general regression model does not consider the spatial influence between variables, and the spatial economic model incorporates the spatial influence on the basis of the general regression model [
39]. The spatial lag model (SLM) can be expressed as:
where Y is the vector of the dependent variable; X represents an explanatory variable matrix; W is the spatial weight matrix; Wy is the vector of the spatial lagging dependent variable;
is the spatial regression coefficient, reflecting the spatial autocorrelation relationship of the dependent variable;
is a parameter vector, reflecting the influence of explanatory variables on dependent variables; and
is a vector of disturbance terms.
By distinguishing the spatially related error ε and the spatially independent error μ, the spatial error model (SEM) can be expressed as [
42]:
Among them, is the spatial autocorrelation coefficient on the error term, reflecting the influence of the residual of the nearby area on the residual of the area; μ is the interference term of a vector. The values of other variables and parameters are the same as the SLM formula.
For SLM and SEM, specific panel data estimation methods are used, namely: fixed effects and random effects are calculated in this study. In addition, to estimate the spatial spillover effects of each region, this study examines the direct and indirect effects of explanatory variables.
As a result of the mutual influence among air pollution, innovation factors, and energy factors in different regions. When measuring their influence from a spatial perspective, the spatial measurement model is generally used. For the sake of generality, this article adopts the spatial Durbin model (SDM), which is the general form of the spatial lag model (SLM) and the spatial error model (SEM), and its expression is:
Among them, is the explained variable, is the explanatory variable, c is the constant term, is the spatial autoregressive coefficient, and are the coefficients to be estimated, and is the residual term. is the influence of the regional independent variable on the dependent variable. is the spatial lag term, which means that the explanatory variable of each spatial unit (i = 1 …, n) is at time t (t = 1 …, T) the dependent variable composed of observations. is an independent and identically distributed random error term; and represent spatial and temporal effects, respectively. This paper constructs spatial variables: W* dependent variables describe the spatial spillover effects of pollutant emissions, renewable energy green innovation, and fossil energy consumption. W is the spatial weight, which indicates the degree of correlation and mutual influence between various spatial elements.
3.4. Direct and Indirect Spatial Impact
In the spatial econometric model, the independent variable usually has an indirect effect on the dependent variable in the surrounding area (spatial spillover). We estimated direct, indirect, and total spatial effects based on estimated spatial regression coefficients [
43,
44]. We further quantified the spatial spillover effects of renewable energy green innovation, energy consumption, and other socio-economic indicators on air pollution. Determine the direct and indirect spatial effects according to the determined spatial correlation coefficient ρ, as shown in the formula below:
where X represents the explanatory variable,
represents the constant vector, and α is the parameter of the intercept term.
The partial derivative differential equation matrix of the explained variable to the Kth independent variable is:
The above Equation (12) defines the average value of the sum of the elements of the right matrix as a direct effect. The average value of the sum of all row and column elements of off-diagonal elements is an indirect effect, reflecting the influence of other regional independent variables on regional dependent variables.