**3. Results**

#### *3.1. Data Description*

The PM2.5 concentrations data used in the study were extracted from the global data set provided by Van Donkelaar, Martin, Brauer and Boys [28]. In his study, sample points outside North America and Europe had precision with a correlation coe fficient of 0.81 and a slope of 0.68. However, given the regional di fferences, the precision of the data involved in the study in Liaoning Province was ye<sup>t</sup> to be verified.

Only in 2013 did the monitoring of particulate matter begin in various cities of China. Among them, cities in Liaoning Province started to have stable and continuous monitoring data from May 2014. Therefore, we selected the 76 regulatory stations that monitored PM2.5 values in 2015 for verification, and the correlation coefficient was 0.7 (Figure 3). Additionally, Peng, Chen, Lü, Liu and Wu [29] compared 45 sample points values from published studies and the corresponding remote-sensing values in China, with 78.7% correlation. Therefore, it is reasonable to believe that the data can reflect the variation of PM2.5 concentrations in the region and can be used for the following analysis.

The PM2.5 concentration, GDPPC, UIS and IND of fourteen cities in Liaoning Province from 2000 to 2015 were selected; the descriptive statistics are summarized in Table 1.


**Table 1.** Description of the panel data from 2000 to 2015.

**Figure 3.** Scatter plot of regulatory stations that monitored PM2.5 concentrations and remote-sensed PM2.5 concentrations. Dashed red lines represent a 95% confidence interval of the fitting line.

Since 2000, PM2.5 concentration has been on the rise in fourteen cities in Liaoning Province, except for a temporary decrease from 2009 to 2012, and after 2014, the concentration also weakened (Figure 4). Increasing trends also occurred in the GDPPC and UIS, but after 2013, the economic growth of most cities slowed down or even declined. The changes of UIS in fourteen cities were basically stable, and most cities showed faster increasing trends after 2009. Regarding IND, the proportions in all cities decreased after 2012, indicating a characteristic of industrial transformation, or that the contribution of industrialization to economic growth has declined.

**Figure 4.** Data of PM2.5 concentrations (**A**), GDP per capita (GDPPC) (**B**), proportion of urban impervious surface area (UIS) (**C**) and the value added by industry as a percentage of GDP (IND) (**D**) of fourteen cities in the panel that changed over the time series from 2000 to 2015.3.2. Panel Unit Root Test Results.

#### *3.2. Panel Unit Root Test Results*

The results (Table 2) showed that not all the variables in the panel were stationary at the levels; however, the four variables were basically stationary at the first difference. Therefore, we can reject the null hypothesis and assume the panel variables were stationary at the first difference.


**Table 2.** Panel unit root test results.

Note: Significance: \* 0.1, \*\* 0.05, \*\*\* 0.01.

#### *3.3. Panel Cointegration Test Results*

The results (Table 3) showed that six statistics could significantly reject the null hypothesis that there was no cointegration relationship; that is, a long-term stable cointegration relationship between PM2.5 concentration and explanatory variables existed in our panel data.


**Table 3.** Panel cointegration test results using the Pedroni methods.

Note: Significance: \* 0.1, \*\* 0.05, \*\*\* 0.01.

#### *3.4. Panel Fully Modified Least Squares (FMOLS) Regression Results*

The results are shown in Table 4, indicating that economic growth, urbanization and industrialization all had long-term positive effects on changes in PM2.5 concentrations in the sixteen years.


**Table 4.** Panel fully modified least squares regression results.

R<sup>2</sup> = 0.492128, Adj. R<sup>2</sup> = 0.487221; Significance: \* 0.1, \*\* 0.05, \*\*\* 0.01.

#### *3.5. Panel Granger Causality Test Results*

Table 5 showed that all the coe fficients of ECT (-1) of variables were significant; that is, bidirectional and long-term causal relationships existed between both variables in the panel. According to the χ2-Wald statistics, bidirectional short-term causal relationships between PM2.5 concentrations and GDPPC were found in the structure. In addition, one-way short-term causalities were found from IND to PM2.5 concentrations and UIS, from GDPPC to IND and UIS and from PM2.5 concentrations to UIS. A more visual and clearer figure is shown (Figure 5) based on the above results.


**Table 5.** Panel Granger causality test results.

Significance: \* 0.1, \*\* 0.05, \*\*\* 0.01.

**Figure 5.** Diagram of the causal relationships between PM2.5 concentrations, GDP per capita (GDPPC), the proportion of urban impervious surface area (UIS) and the value added by industry as a percentage of GDP (IND).

In the panel, all socioeconomic variables caused the variations of PM2.5 concentrations in Liaoning Province, especially economic growth, which not only influenced changes in pollutant concentrations in the long and short term but also a ffected the changes in industrialization and urbanization in the long and short term. Additionally, industrialization directly caused changes in pollutant concentrations in the long and short run and caused variations in urbanization in the short and long term (Figure 5).

#### *3.6. Variance Decomposition and Impulse Response Analysis Results*

The results of the variance decomposition analysis in Table 6 compared the contribution of each variable to the changes in PM2.5 concentration. In the panel, the variances of PM2.5 concentration were mostly explained by its own standard shock (80.95%) in the 16-year period, while the contributions from the GDPPC, IND and UIS to the PM2.5 concentration were 9.20%, 9.56% and 0.29%, respectively.


**Table 6.** Variance decomposition analysis results of pm2.5 concentrations in the panel.

The impulse responses result presented in Figure 6 showed that the responses of the PM2.5 concentration to itself decreased because of shocks from decreasing UIS and IND in the first two years. Then, from the fifth year, the response of the PM2.5 concentration continued to decrease because of shocks from decreasing GDPPC and decreasing IND in the latest seven years.

**Figure 6.** Results of the impulse response of *ln*PM2.5 to Cholesky one S.D. innovations of the variables.

## **4. Discussion**

#### *4.1. The Analysis of Relationships between PM2.5 and Socio-Economic Development in Liaoning Province*

Studies have shown that changes in fine particulate pollution concentrations in China are influenced by natural factors and human activities [42,43]. Therefore, to explore the impacts of urban socioeconomic factors on PM2.5 concentrations, we selected three indicators: urban GDP per capita, the proportion of urban impervious surface area and the value added by industry as a percentage of GDP, representing economic growth, urbanization and industrialization, respectively, which were assumed to be the most significant socioeconomic factors affecting PM2.5 concentrations in China. In our results, all selected socioeconomic variables were long-term causalities of the changes of PM2.5 concentrations, and economic growth and industrialization also significantly affected the variations in PM2.5 concentrations in the short term. The variance decomposition results showed that industrialization was the determinate factor affecting PM2.5 concentration variations in Liaoning Province, which was basically the same with the results found by Li, Fang, Wang and Sun [22], but only five cities in Liaoning Province were included in their industry-oriented panel, and the study period and indicators were different from ours. This further confirmed the attribute of Liaoning Province as a socio-economic mode of industry-oriented development.

Liaoning Province is an area in Northeast China where cities characterized by heavy industry are concentrated. Equipment manufacturing, the coal industry, the metallurgy industry and commodity production are the strengths of Liaoning Province [44]. For a long time period, heavy industry had been the main driving force of economic growth of most cities in Liaoning Province, promoting the rapid urbanization process. The concentrating population and developing economies would also motivate the urban industrial activities [45]. However, with the popularization and development of technology, the pressure of market competition increases. As a result, the supply of products in Liaoning Province far exceeds the market demand, and the problem of overcapacity is becoming increasingly serious [44]. Following the third scientific and technological revolution, the new science and technology industry, represented by electronics, computers, biological engineering, etc., seriously impacted traditional industries, resulting in a decline in the proportion of primary and secondary industries and leading to the rise of emerging industries such as the internet industry. However, in Liaoning Province, the tertiary industry only accounted for 38.7% of GDP in 2013, 5.8% less than the national average [46]. In 2015, the tertiary industry as a percentage of GDP rose to 46.06%, with major growth, basically equal to the national average. The slowdown in economic growth (Figure 4B) and the increase in the proportion of the tertiary industry indicated that adhering to the transformation of economic structure and industrial structure is a policy with both opportunities and difficulties. However, in recent years, the PM2.5 concentration has declined (Figure 4A), further proving the validity of industrial structure transformation.

#### *4.2. The Analysis of Environmental Kuznets Curve (EKC)*

Although industrialization contributed the most to the PM2.5 concentration changes in the sixteen years in Liaoning Province, the contribution of economic growth dominated a longer period (Table 6). Moreover, some relationships between the economic growth and PM2.5 concentration changes were also noteworthy, such as the feedback effects in the Granger causality test (Figure 5) and fluctuations in the impulse response of shocks (Figure 6). Therefore, we constructed a regression model based on the Environmental Kuznets Curve (EKC) theory to study the relationship between economic growth and PM2.5 pollution. Grossman and Krueger [47] found that an inverted U-shaped relationship existed between economic growth and environmental pollution [48]. With a low level of economic development in a country or region, the degree of environmental pollution is relatively low, and with an improved economic level, the degree of environmental pollution intensifies. However, when economic development reaches a certain level, that is to say, reaches an "inflection point", environmental quality gradually improves thenceforth with the increase in income.

Our result of the EKC regression between GDPPC and PM2.5 is shown in Figure 7. According to the model equation, when the GDPPC was equal to CNY 74.8 thousand, the pollution reached the inflection point, and a decreasing trend appeared. Referring to the panel data, we found that the data of GDPPC higher than the turning point mainly appeared in the later periods of the time series, and the value added by industry as a percentage of GDP declined. The EKC result further proved that economic growth did not always increase PM2.5 concentrations in Liaoning Province, suggesting that changing economic growth mode was a correct choice for pollution control.

**Figure 7.** Scatter plot and Environmental Kuznets Curve (EKC) fitting line between PM2.5 concentrations and GDP per capita.

#### *4.3. Implications for Regional Air Pollution Management*

Through the study on the relationships between socioeconomic factors and PM2.5 concentration changes in Liaoning Province from 2000 to 2015, we found that the industrialization and economic growth were the main causes affecting the PM2.5 concentration changes from the perspective of short-term impacts and long-term contributions. As the traditional pillar industry of economic growth in Liaoning Province, the contributions of the secondary industry to regional pollution is predictable. According to the above data and results, we also found that the dependence of economic growth on the secondary industry in Liaoning Province was weakened, and the EKC curve also showed that economic growth did not always lead to the increase in PM2.5 concentrations. In 2014, the number of days of severe pollution (150–250 μg/m3) in Shenyang reached 22 days; in 2018, the number of days of severe pollution was only 2 days. Although there is still a big gap between China's pollution level and the world standard, the improvement of atmospheric environment is obvious. This informs us that the transformation of economic structure is effective for the managemen<sup>t</sup> of atmospheric pollution. However, improving energy efficiency and developing and utilizing clean energy is the key direction of taking into account both economic growth and environmental protection [49,50].

Among the socioeconomic variables, the urbanization process only showed the long-term impact on PM2.5 concentration changes, and the contribution was weak. In other words, the urban expansion and population growth had little direct effects on the changes of PM2.5 concentration, but indirectly affected the changes through economic growth and the industrialization process [19]. The causality diagram (Figure 5) showed that PM2.5 changes, industrialization and economic growth also affected the urbanization process in both the short and long term. As the level of urbanization in each period is closely related to the pollution exposure [51], the goal of "new-type urbanization" is not only to

emphasize the rapid urbanization, but also to meet the health needs of residents [52]. Therefore, the study on relationships between regional environment and socioeconomic factors is necessary for the phased managemen<sup>t</sup> of regional pollution, and more variables may be added according to the data availability and research objectives.
