Economy, Environment and Government: Study on the Path of Supply-Side Reform Forced by the Fog-Haze

Unbalanced development in term as industrial structure and the efficiency use of energy have aggravated environmental pollution to different degrees resulting in the increase of range, time and degree of fog-haze. This, in turn, forced the government to carry out supply-side reforms, to improve energy efficiency and optimize the industrial structure to weaken the environmental pollution. To tackle these problems, this work provides an index system for the issues related to fog-haze, uses a non-linear ST-SVAR model to reflect the effects of industrial structure and energy use efficiency on fog-haze. Results indicated that: First, current industrial structure and energy use efficiency have greater impact on the comprehensive equation of fog-haze risk than itself. With the passage of time, this influence is still gradually expanding. Second, the equations of industrial structure and energy use efficiency are strongly influenced by themselves, and other variables as the current period have less impact on them. Finally, the non-linear or asymmetric relationship is shown among industrial structure, energy use efficiency, and the fog-haze comprehensive risk equation.


Introduction
Rapid economic growth has escorted economic development and has also caused serious environmental pollution. The most prominent performance is fog-haze pollution [1]. Fog-haze not only negatively affects human life, but also restricts

Literature Review
Many scholars have studied the impact of industrial structure on fog-haze pollution. From the perspective of source control, through the expansion of a new energy-environment-economic model, integrating dynamic input-output models and multi-objective models, studying the impact of industrial structure adjustment on China's energy-saving and emission reduction targets, and screening three best solutions [23]. Liu [24]. The results shown that industrial restructuring and industrial transfer from eastern China to the west had led to significant differences in pollutant emissions in various cities. Ji conducted a quantitative analysis of the socioeconomic drivers of PM 2.5 through evaluation [25]. The results show that income, urbanization and service industries have a significant impact on PM 2.5 pollution.
Income has always had a positive effect on PM 2.5 , but with the increase of urbanization level or income level, the effect will gradually decrease; there is an inverted U-shaped relationship between urbanization and PM 2.5 .
Some scholars have also studied the effects of energy structure on fog-haze pollution. Guan discussed Beijing's regulation of energy structure adjustment measures from clean energy, coal pollution control for retailer, automobile exhaust and regional joint treatment, and then controls fog-haze [3]. Ma used the spatial Dubin model to analyze the relationship between energy structure, traffic patterns, and haze pollution. Measures to improve fog-haze pollution were proposed in the short-term and long-term, respectively [26]. Li employs the modified dynamic SBM model to analyze the energy efficiency and AQI efficiency of 31 cities in China from 2013 to 2016 [27]. The results show that for the performance of overall efficiency indicators, there are still 22 cities that need to significantly improve their overall efficiency. In order to better solve the problem of justice in the distribution of energy resources, Raphael proposed a new method, that is, these countries establish a sovereign wealth fund [28]. He proposed ensuring that justice is a key policy goal for energy taxation and aimed to contribute to the emerging literature of sovereign wealth funds.
Research on the supply-side structural reforms, Ram and Charles examined the impact of CO 2 emission reductions in Indonesia during the period 2003-2017 from a perspective of long-term integrated resource planning [29].
The results of the study indicate that both supply and demand have an impact on reducing carbon dioxide, sulfur dioxide and nitrogen oxide emissions. However, the impact of supply plays a leading role. Lin and Su examined the main issues of high energy consumption, high energy structure pollution and serious overcapacity of the industrial system in China's supply system, and proposed the main content of supply-side reform [30]. In order to better deal with the relationship between steady growth, structural adjustment and risk prevention, Liu proposed that it is necessary to break through the dilemma of industrial struc-Journal of Applied Mathematics and Physics ture adjustment through supply-side structural reforms to improve economic efficiency and international competitiveness [31].
Although the previous literature covers many aspects, it is still not comprehensive enough: 1) From the perspective of research methods, most studies have discussed the linear relationship between smog pollution and industrial structure and energy structure, while ignoring the possibility of nonlinear relationships between them. 2) From the perspective of the research objective, environmental issues are not only related to economic development, industrial structure and energy structure, but also to government policy decisions.
Therefore, this article cites nonlinear thoughts to study the dynamic relationship between environment, economy, and government. According to the research results, the path selection of the government's supply-side reform was finally provided. This biggest difference between this article and the above article is that it takes into account the contemporaneous effects between endogenous variables.
Considering that the reacts about the risk of fog-haze quickly to changes in energy use efficiency and industrial structure, the current fog-haze risk comprehensive equation will include the current industrial structure and energy use efficiency, and measure the ventilator impact of both fog-haze risks.

Theoretical Background
Environmental pollution is not only related to economic development also has the role of government policies. The economy-environment-government is a subsystem that influences each other. Starting from the industrial structure and energy use efficiency, this section describes how the industrial structure and energy use efficiency, the environment, and the supply-side reform interact with each other.
First, the industrial structure, energy use efficiency and the environmental pollution present a game relationship. The industrial structure dominated by heavy industry has made great contributions to economic development, but, it has brought about fog-haze pollution that cannot be ignored [32] [33]. Environmental deterioration will in turn inhibit economic development. If things go on like this, this kind of game relationship will sink into dead circulation. This relationship between them has also prompted us to think about what kind of industrial structure can contribute to economic development and bring about the least fog-haze pollution.
Second, the fog-haze pollution and the supply-side reform have shown an overwhelming situation. Fog-haze pollution not only causes traffic congestion [34], also poses a serious threat to the health of the public. While inhibiting economic development, it also wastes natural resources. Reducing fog-haze pollution, supply-side reform has become an indispensable measure [35]. The government must take timely measures to start from the supply side, optimize supply, restrain the deterioration of fog-haze pollution, and minimize environmental damage [36]. If, the government remains indifferent, fog-haze pollution Journal of Applied Mathematics and Physics will become uncontrollable.
Third, there is a new relationship among industrial structure, energy use efficiency and supply-side reform. To solve the fog-haze pollution from the supply side, the government's work must focus on the main cause of fog-haze pollution [30]. The industrial structure needs to be reformed, in order to reduce heavy industry, develop the tertiary industry, and stimulate the continuous development of new industrial chains with high energy efficiency to promote balanced and comprehensive development of the industrial structure [37]. Starting from industrial supply and energy supply, search for the industrial model with healthy economic growth and less environmental pollution [32].

Method and Model
To answer the above questions, in this section, we will introduce the indicator system and model. The connection between theory and empirical study can clearly explain the relationship between the economic-environmental-government subsystems, thus providing support for the path selection of structural reforms on the supply side.

The Comprehensive Index of Fog-Haze Risk
In order to comprehensively measure the level of fog-haze pollution in life, we selected three indicators of air pollutants, industrial pollutant emissions [38], and transportation pressure.
The pollutants in the air can fully measure the concentration of inhalable particles and the emissions of SO 2 and NO 2 . Industrial pollutants can fully measure the emissions of sulfur dioxide and nitrogen oxides in industrial activities.
Transport pressure can measure the emissions of other sulfur dioxide and nitrogen oxides in life. Finally, we select the indicator system as shown in Table 1.
Take Shaanxi province as an example, getting the metrics in the above table, based on the air quality bulletin published and Shaanxi Statistical Yearbook from Table 1. System of basic indicators of fog-haze risk.

The Industrial Structure and Energy Efficiency Index
This study measures the impact of economic development on the environment from the perspective of industrial structure and energy use efficiency. It considers the heavy industry and energy industry with a large proportion of environmental pollution, and selects corresponding measurement indicators.
In order to study the imbalanced industrial structure and energy use efficiency in economic development, the work selected two indicators of the proportion of heavy industrial output in total industrial output value and conversion efficiency of energy processing as shown in Table 2.

The Model
Existing studies are based on linear assumptions to examine the economic-environment relationship. Is the relationship between them linear? To answer this question, it is necessary to measure the relationship between them with non-linear effects.
The traditional VAR model is usually used to describe the dynamic changes between sequences, without considering the contemporaneous relationship between variables as [39]. For illustrate the contemporaneous effects of the industrial structure and energy use efficiency on the comprehensive index of fog-haze risk, by resetting the VAR model as [40]: x zgy eoe F ′ = t zgy denotes the proportion of heavy industrial output in total industrial output value to measures the importance of the industrial structure; t eoe denotes the conversion efficiency of energy processing to measure the importance of energy structure; t F indicates the comprehensive index of fog-haze risk. C is a 3 1 × dimensional cross-sectional vector, 10 20 B denotes that the comprehensive index of fog-haze risk is affected by the current value industrial structure and energy structure, while the industrial δ , 20 δ respectively indicate that the comprehensive index of fog-haze risk is affected by the current value of the industrial structure and energy efficiency.
In order to identify the nonlinear and asymmetric effects of industrial structure and energy use efficiency on the comprehensive index of fog-haze risk, based on the smooth migration vector autoregressive model, this work provides a nonlinear smooth migration structure vector autoregressive model to judge the dynamic adjustment mechanism between them.
To better reflect the nonlinear relationship between the comprehensive index of fog-haze risk, industrial structure, and energy structure, the ST-SVAR model was established based on the above model: The transfer function can be set as a logic function and an exponential function.
Logical transfer function can be expressed as: Index transfer function can be expressed as: The value of the transfer function is between 0 and 1, which reflects the smooth transition between the parameters in the model. After determining the transfer variables of each variable and performing the setting test of the transfer function, the form of the transfer function is finally determined.

Empirical Results
Based on the above-mentioned theoretical analysis and research methods, we obtain the data of Shaanxi Province from 2006 to 2017 to empirically analyze the impact of economic development on environmental pollution, and so as to provide path options for structural reforms on the supply side.

Data Acquisition
We Based on the principle that the percentage of accumulated contribution of variance exceeds 85% [41], we extract the principal components of the three factors. By rotating the factors we obtain a relatively satisfactory common factor.
As can be seen from Table 3 After determining the economic significance among the principal components as shown in Table 4, we can obtain the principal component model by combining the component score coefficient matrix as follows: The comprehensive index model of fog-haze risk is as follows: ( )

Determination of Lag Order
By comparing the AIC and SC information criteria, the model has a lag order of two, according to the following Table 5.
When we compare AIC and SC in both cases, the results show that the second-order lag test results are more persuasive Therefore, we determine that the lag order is 2 orders.

Linear Test
Because there are unrecognized redundant parameters in the model, Taylor's approximation was used to test the model linearly.

Selection Principle
Taking the fog-haze comprehensive index equation as an example, introduce the linear test of the ST-SVAR model and the selection principle of the transfer variable. Terasvirta and Anderson for the above-mentioned ST-SVAR model [42], a third-order Taylor approximation is performed at 0 λ = as follows: where, , 0,1, 2,3 i i ω = is the corresponding regression coefficient vector. The Taylor equation can be used to perform linear tests on the equations. The null hypothesis is set to: If, the result rejects the null assumption that the model is linear, then the significance probabilities of the statistics corresponding to different transitional variables can be further compared. The variable corresponding to the minimum probabilistic probability is the transitional variable of the model.

Linear Test Results
Unit root test of the above three sequences using the ADF test method, and the test results show that these sequences are all stable. After determining the lag order, we perform linear tests for each equation. The included explanatory variables were used as the transfer variables, and the test results are shown in Table 6 below.
For the fog-haze risk comprehensive index equation, when Journal of Applied Mathematics and Physics

Transfer Function
After determining the final transfer variable, select the transfer function according to the verification procedure given by Terasvitta and Anderson, and set the constraint test as follows: For the above constraint test, if the corresponding significance probability of the test 02 H is the smallest, the transfer function selects the form of the exponential function. Conversely, the transfer function is set as the form of the logistic function.
According to the transition variables determined in the linear test, set the transfer function for each equation and test them. The test results are shown in the following Table 7.
In the case of Hypothesis 1 and Hypothesis 2, the test value of the fog-haze risk comprehensive equation is greater than 0.04 (Hypothesis 3). That is, according to the principle of minimum value, the fog-haze risk comprehensive index equation rejects 03 H at the significance level of 10%, so the transfer function of the fog-haze risk comprehensive index equation is set as the logic function form.
According to the principle of taking the minimum value, the test value of the Hypothesis 3 is in accordance with the selection condition. It can determine the transfer function of the energy use efficiency equation also in the form of a logical function.
However, for the industrial structural equation, the minimum value appears in the case of Hypothesis 2, it rejects 02 H at the significance level of 10%, so set Journal of Applied Mathematics and Physics

Results
Aiming at the nonlinear ST-SVAR model, use a system estimation method that simultaneously estimates all parameters in the model equation.
Before estimating the parameters, we need to scale the transfer function in order to avoid the influence of the dimension of the transfer variable on the slope parameter γ .
The scaled logic transfer function is as follows: The scaled index transfer function is as follows: were,  Aiming at the energy use efficiency equations, energy use efficiency has greater impact on it. Fog-haze comprehensive risk and industrial structure equation also has an impact on it, but its role gradually diminishes over time.
Although some of the regression coefficients are not significant, that, do not affect the significant results of the overall regression coefficients. Therefore, the rationality of the establishment of non-linear ST-SVAR model was verified and three equations were obtained. According to Table 8, we can get the following result: This explains that the comprehensive index of fog-haze risk is affected by the current industrial structure and energy structure.

Conclusions and Policy Implications
This work cites a nonlinear ST-SVAR model that discusses the relationship be- 2) The non-linear or asymmetric relationship is shown among industrial structure, energy use efficiency, and the fog-haze comprehensive risk equation.
3) To solve the fog-haze pollution, the supply-side reform is imperative.
Based on the above discussion, the following are the suggestions for the path selection of the government's supply side reform: Fourth, Improve the Macro-control System: To deepen the reform of fiscal, taxation, financial, investment, and financing systems, the government will improve the budget decision and management system, increase investment in environmental protection. Aiming at companies with low environmental pollution, and imposing penalties for additional taxation on companies with serious environmental pollution, the government should provide support for tax deductions to stimulate healthy competition in different areas.