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Impacts of GDP, Fossil Fuel Energy Consumption, Energy Consumption Intensity, and Economic Structure on SO 2 Emissions: A Multi-Variate Panel Data Model Analysis on Selected Chinese Provinces

: Atmospheric pollution gradually become a focus of concern all over the world owing to its detrimental inﬂuence on human health as well as long range impact on global ecosystem. This paper investigated the relationship among SO 2 emissions, GDP, fossil fuel energy consumption, energy consumption intensity, and economic structure of ﬁve provinces in China with the highest SO 2 emissions spanning from 2002–2015 based on panel data model. Through comparatively analyzing the coefﬁcients in the established panel data model for Hebei, Henan, Inner Mongolia, Shandong, and Shanxi, we can obtain that: (1) fossil fuel energy consumption made the most devotion to SO 2 discharge compared with GDP, energy consumption intensity, and economic structure. And the more the fossil fuel energy consumption, the more the devotion made by it to SO 2 discharge. (2) GDP devoted less to SO 2 emissions than fossil fuel energy consumption, and the larger the scale of the economy, the greater the contribution made by it to SO 2 emissions. (3) The higher the proportion of the secondary industry added value accounted in GDP, the more the devotion made by the economic structure and energy consumption intensity to SO 2 emissions. Through analyzing the Granger causality examination results, it can be concluded that: (1) there existed a bi-directional causal relationship between fossil fuel energy consumption and SO 2 emissions among ﬁve selected provinces. (2) There existed uni-directional causal nexus running from GDP to SO 2 emissions, from energy consumption intensity to SO 2 emissions, and from economic structure to SO 2 emissions among ﬁve chosen provinces. Based on the empirical analysis, several policy implications were proposed to provide references for policy makers, which were (1) Giving full play to the guiding role of price signals, and improving the price policy for desulfurization. (2) Formulating a new comprehensive evaluation system to measure the regional development level considering economic development and environmental impacts. (3) Exploring renewable and sustainable energy sources to substitute for fossil fuel energy according to regional resources endowment. (4) Developing high value added and low pollution emissions industries and reducing the proportion of secondary industry.


Introduction
As the second largest economy in the world, China is facing serious environmental issues owing to rapid development of economy. From the start of 2003, an uncommon heavy fog and haze weather swept over central and eastern region in China, which drew widespread attention from public [1,2]. income and pollutants by taking energy related variables, urbanization, and technique progress variables into the function. Kohler [26] found that there existed a long term relationship between environmental quality, per capita energy use and foreign trade in South Africa. Shahbaz et al. [27] investigated the co-integration relationship and causal relationship among industrialization, electricity consumption, and CO 2 emissions of Bangladesh, and found that environmental Kuznets curve existed between industrial process and CO 2 discharge. Wang et al. [28] investigated the relationship among economic development, urbanization, and sulfur dioxide discharge employing panel data model based on semi-parametric panel fixed effects regression. The results showed that an inverted U-shape curve existed between economic development and sulfur dioxide discharge. Zhou et al. [7] verified an inverse N-shape relationship between sulfur discharge and economic development, and proved the positive influence of technical progress on sulfur discharge reduction.
Although environmental Kuznets curve analysis model is widely applied in researching the relationship between economic growth and contaminants, many scholars and policymakers put forward their critiques on the concept of environmental Kuznets curve hypothesis and methodology in previous researches. Firstly, the inverse U-shape environmental Kuznets curve is not applicable for all kinds of pollutants, and only small amount of empirical analysis support an inverse U-shape environmental Kuznets curve for some primary atmospheric contaminants [19]. Shafik and Bandyopadhyay [29] examined a panel of 149 countries during the period of , and found that among ten indicators of environmental quality, only two indicators fitted for an environmental Kuznets curve path. Secondly, a large amount of studies on environmental Kuznets curve hypothesis researched cross-sectional data and summarized an exclusive development path for different countries or provinces, which was criticized for the invalidity of cross-sectional technique [30,31].
Above all, considering about the shortcomings of existing literatures on researching the relationship between sulfur emissions and socio-economic driving forces, this paper established a multi-variate panel data model for five provinces with the largest sulfur dioxide (SO 2 ) emissions in China taking economic development, fossil fuel energy consumption, energy consuming intensity, and economic structure into consideration spanning the period of 2002-2015. The main contributions of this paper are as follows: (1) Combining economic development, energy consumption, technical advancement, and economic structure together to analyze the contribution of each variable to SO 2 emissions. To the best of our knowledge, this paper is the first study in the field of investigating the relationship between sulfur discharge and socio-economic forces to simultaneously explore the contribution of economic growth, energy consumption, technical progress, and economic structure to SO 2 emissions using panel data unit root test and panel co-integration theory. Additionally, granger causality test is also employed to investigate the causal relationship between these four data sequences and SO 2 emissions. (2) The contribution of economic development, energy consumption, technical advancement, and economic structure to SO 2 discharge can be quantitatively analyzed regarding to different provinces. Based on panel data model, cross-sectional technique can be fully exploited, thus, the contribution degree of four variables to SO 2 discharge can be quantitatively measured according to five different provinces, and the causal relationship direction among these four variables and SO 2 discharge can be obtained with respect to different provinces. Therefore, the policymakers of different provinces can formulate effective and practicable policies to reduce the discharge of SO 2 according to the empirical analysis results.
The rest parts of this paper are conducted as below. Section 2 majors on introducing the methodology used in this paper and the framework of this research. Section 3 provides the data sources and pre-analysis. The empirical analysis will be carried out in Section 4. Section 5 draws the conclusions and provides policy implications.

Test for Cross-Sectional Dependence
The establishment of panel data model should start with cross-sectional dependence test which is necessary for choosing appropriate methods for testing unit root. This paper used the Pesaran cross-sectional dependence test for cross-sectional dependence examination [32]. The original panel data model can be written as: where i = 1, 2, . . . , N and t = 1, 2, . . . , T, β it demonstrates a vector of parameters for K × 1 to be evaluated, x it indicates a K × 1 vector of explanatory variables, α i implies parameters which do not change with provinces and µ it is supposed to be independently and identically distributed. The null and the alternative hypotheses are represented as follow: where: The statistic of the Breusch-Pagan [33] LM test is provided as Equation (5): where theρ ij means the coefficients estimated by the residuals of the model. Pesaran [32] improved the LM test by proposing an alternative test regarding to the average value ofρ ij calculated by: Pesaran [32] certified the advancement of this test in small samples which is suitable for this study.

Test for Panel Unit Root
Panel unit root test methods employed in previous literatures can generally be classified into two categories. The first category takes cross-sectional independence into consideration, such as Hadrid [34], Choi [35], Levin, Lin and Chu (LLC test) [36], Im, Pesaran and Shin (IPS test) [37] and some others. The second category considers cross-sectional dependence, including the test methods proposed by Bai and Ng [38], Moon and Perron [39], Pesaran [40], Phillips and Sul [41], and Smith et al. [42]. The equation applied to test stationary is listed as Equation (7): where i = 1, 2, . . . , N is used to represent province; t = 1, 2, . . . , T demonstrates time point; X it indicates the explanatory variables containing fixed effects or individual time trend; ρ i means the coefficient for auto-regression; and ε it implies interference term of stable series. As Equation (7) may be an autocorrelation, higher order differential delay terms were explored by Levin et al. [36]: where p i represents the lags amount in the regression. Im et al. [37] verified a t-bar statistic as the average value of the individual ADF statistic as Equation (9): where t ρi means the individual t-statistic to test for the original hypothesis.

Test for Panel Co-Integration
If the variables sequences are confirmed to be stable in the same order, the process should step to panel co-integration test employing Pedroni's co-integration test method [43] of which the regression equation can be written as: where i implies various provinces, t indicates different time points, M represents the number of explanatory variables, β 1i , β 2i , β Mi demonstrates the coefficients of explanatory variables, α i means the intercept component, and e it is the evaluated residual on behalf of the divergence from the long-run. Pedroni co-integration examination method is made up of two groups. One is based on the within dimension approach including panel v-statistic, panel ρ-statistic, panel PP-statistic and panel ADF-statistic. The other one is based on the between dimension approach consisted of group ρ-statistic, group PP-statistic and group ADF statistic.

Test for Panel Data Model Form
The primary three forms for panel data model are random effects, fixed effects, and regression models for pooled. The Hausman examination method and the likelihood ratio (LR) examination method are usually employed to test for the proper form of panel data model [44].
The forms of panel data models contain the constant intercepts and coefficients model, the variable intercepts and invariable coefficients model, and the changed intercepts and coefficients model [45]. These three forms of panel data models are shown in Equations (11)- (13).
where i represents the provinces, t demonstrates the time point, α i means the intercept, β i indicates coefficient, and µ it implies the error component. In order to verify the panel data model form, F-test will be utilized to decide if the following two null hypotheses should be accepted or rejected by computing the residual sum of squares (RSS) of Equations (11)- (13).
Sustainability 2018, 10, 657 6 of 20 where F 1 is for the H 1 hypothesis of which the slopes are invariable and the intercepts are different, F 2 is for the H 2 hypothesis of which the slopes and the intercepts are all unchanged, S 1 , S 2 , and S 3 are the residual sum of squares of Equations (11)- (13). Additionally, N, T, and k indicate the amount of provinces, years, and explanatory variables.
Considering the significance level and T > k + 1, if F 2 is less than the critical value, then we accept H 2 and the panel data model form should be Equation (11), otherwise, it needs to examine hypothesis H 1 . If F 1 is more than the critical value, H 1 should be rejected and the panel data model form should be Equation (13), otherwise, it should be Equation (12).

Test for Causality
Engle and Granger [46] proposed that if two data sequences are co-integrated, then there exists Granger Causal relationship. At the aim of investigating causality among different data sequences, the Granger causality examination method [47] is utilized to testify if one data sequence has an impact on another. For Granger causality, if Y can be forecasted more precisely through using the data of both X and Y than using Y, then we can conclude that the variable X Granger causes Y. This method is employed in this paper to identify the causal relationships among different variables: Equation (18) shows the null hypothesis of the Granger causality examination method, which demonstrates "X does not Granger-cause Y". Equation (19) is used to test if Y Granger-causes X.

Theoretical Framework
The theoretical framework is demonstrated in Figure 1. The empirical analysis can be proceeded with following steps.
Step 1: Test for cross-sectional dependence The empirical analysis will start with cross-sectional dependence test to identify the methods used in unit root test and panel co-integration test.
Step 2: Test for panel unit root After confirming whether it is necessary to consider cross-sectional dependence, the stable of all variables need to be examined and the methods used in this stage depend on the results of step 1. Only if all variables are stable in the same order, the empirical analysis can proceed to co-integration test.
Step 3: Test for panel co-integration After verifying all variables are stationary at the same order, Pedroni co-integration examination method will be used for testing whether there exists long-term relationship among SO 2 emissions and all independent variables. If not, the panel data model cannot be established, otherwise, we need to determine the form of panel data model. Step 4: Test for panel data model form At this step, LR test and Hausman test are used to determine the fixed effect or random effect of panel data model. And then F-test will be employed to judge whether the panel data model is constant intercepts and coefficients model, the variable intercepts and invariable coefficients model, or the changed intercepts and coefficients model. After identifying the model form, the panel data model can be estimated.
Step 5: Test for Granger causal relationship At the aim of further and better understanding the relationship among SO 2 emissions, GDP, fossil fuel energy consumption, energy consumption intensity, and economic structure, Granger causality examination method will be used to explore the nexus among these variables. And the direction of causal nexus can provide policy making references for policy makers. Step 5: Test for Granger causal relationship At the aim of further and better understanding the relationship among SO2 emissions, GDP, fossil fuel energy consumption, energy consumption intensity, and economic structure, Granger causality examination method will be used to explore the nexus among these variables. And the direction of causal nexus can provide policy making references for policy makers.

Study Area and Data Sources
At the aim of exploring the contributions of various socio-economic factors to SO2 discharge and specifically carrying out policy recommendations to reduce SO2 emissions, this paper investigates the contributions of economic development, fossil fuel energy consumption, energy consuming intensity, and economic structure to SO2 discharge using the data of five provinces with the highest SO2 emissions in China during the period of 2002-2015. The provinces selected in this paper are Hebei,

Study Area and Data Sources
At the aim of exploring the contributions of various socio-economic factors to SO 2 discharge and specifically carrying out policy recommendations to reduce SO 2 emissions, this paper investigates the contributions of economic development, fossil fuel energy consumption, energy consuming intensity, and economic structure to SO 2 discharge using the data of five provinces with the highest SO 2 emissions in China during the period of 2002-2015. The provinces selected in this paper are Hebei, Henan, Inner Mongolia, Shandong, and Shanxi.
For economic development, gross domestic production (GDP) is selected to represent the growth of economy, and is converted to the constant price using 2002 as basic period. For fossil fuel energy consumption, it is made up of the use amount of coal, crude oil, and natural gas. The statistical unit is 10 4 ton for coal and crude oil consumption, and 10 8 cubic meter (m 3 ) for natural gas consumption. On the purpose of maintaining consistency of statistical requirements, the consumption units are converted to 10 4 ton coal equivalent (tce) employing the coefficients suggested in the China Energy Statistic Yearbook, which are 0.7143 tce/t, 1.4286 tce/t, and 1.33 tce/10 3 m 3 for the consuming of coal, crude oil, and natural gas [48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67]. For energy consuming intensity, it is represented by the value of energy consumption for the whole society divided by GDP (constant price taking 2002 as basic period). For economic structure, it is expressed by the value of the added value for secondary industry divided by GDP due to the high proportion of added value of secondary industry in GDP. All data of the variables mentioned above as well as the data of SO 2 emissions are collected from the official website of National Statistics Bureau of China. This dataset on SO 2 emissions in provincial level allowed this research from provincial aspects over a long period.
The representation forms of all independent variables and SO 2 discharge used in the panel data model are listed in Table 1. The data of SO 2 discharge, GDP, fossil fuel energy consumption, and energy consuming intensity are transformed into natural logarithmic form. The data of the economic structure is multiplied by 100 before being converted into the natural logarithmic form.

Pre-Analysis
Since the high level of SO 2 discharge can cause acid rain weather which seriously damage to human health, it is necessary to research on the contributions of significant socio-economic factors on SO 2 emissions to provide references for policy makers. Figure 2 displays the spatial distribution of SO 2 discharge in 32 primary provinces of China in the year of 2010 and 2015. As can be seen from the figure, the SO 2 emissions amount of Inner Mongolia, Shanxi, Hebei, Shandong, Henan, Sichuan, and Guizhou in 2010 were more than 1100 thousand tons, which contributed more than 40% of SO 2 emissions in China. During the period of 2011-2015, the SO 2 emissions amount of most provinces showed downward trend. However, the amount of SO 2 discharge in Hebei, Henan, Inner Mongolia, Shandong, and Shanxi still maintained more than 1100 thousand tons, among which Shandong ranked the first with 1525.67 thousand tons and Hebei ranked the fifth with 1108.37 thousand tons. Therefore, these five provinces are selected to be the objects of this study aiming at better understanding the relationships between SO 2 emissions and significant socio-economic indicators in these five provinces.        The development trend of energy consuming intensity from 2002 to 2015 for five provinces is illustrated in Figure 5. As depicted in the figure, the energy consuming intensity of Hebei, Henan, Inner Mongolia, Shandong and Shanxi all showed decrease tendency from 2002 to 2015. Based on the analysis of GDP and fossil fuel energy consumption, we can conclude that the energy use efficiency of Shanxi was the lowest, thus the energy consuming intensity of Shanxi should be the highest. It can also be obtained from the figure that the energy consuming intensity of Shanxi ranked the first which   The development trend of energy consuming intensity from 2002 to 2015 for five provinces is illustrated in Figure 5. As depicted in the figure, the energy consuming intensity of Hebei, Henan, Inner Mongolia, Shandong and Shanxi all showed decrease tendency from 2002 to 2015. Based on the analysis of GDP and fossil fuel energy consumption, we can conclude that the energy use efficiency of Shanxi was the lowest, thus the energy consuming intensity of Shanxi should be the highest. It can  The development trend of energy consuming intensity from 2002 to 2015 for five provinces is illustrated in Figure 5. As depicted in the figure, the energy consuming intensity of Hebei, Henan, Inner Mongolia, Shandong and Shanxi all showed decrease tendency from 2002 to 2015. Based on the analysis of GDP and fossil fuel energy consumption, we can conclude that the energy use efficiency of Shanxi was the lowest, thus the energy consuming intensity of Shanxi should be the highest. It can also be obtained from the figure that the energy consuming intensity of Shanxi ranked the first which is much higher than other four provinces.
The development tendency of economic structure for the selected five provinces from 2002 to 2015 is implied in Figure 6. As displayed in the figure, at the beginning of the analysis phase, the proportion of secondary industry added value accounted for GDP in Shandong was the highest with 50.46%, and then it reduced to 46

Results for Cross-Sectional Dependence Examination
The initial stage of empirical analysis should be cross-sectional dependence examination which decides the methods selected to test unit root. The results of Pesaran cross-sectional dependence examination method utilized in this study are shown in Table 2. As can be seen from p-value in Table  2, the null hypothesis can be rejected at 5% confidence level. Therefore, the methods employed to test

Results for Cross-Sectional Dependence Examination
The initial stage of empirical analysis should be cross-sectional dependence examination which decides the methods selected to test unit root. The results of Pesaran cross-sectional dependence

Results for Cross-Sectional Dependence Examination
The initial stage of empirical analysis should be cross-sectional dependence examination which decides the methods selected to test unit root. The results of Pesaran cross-sectional dependence examination method utilized in this study are shown in Table 2. As can be seen from p-value in Table 2, the null hypothesis can be rejected at 5% confidence level. Therefore, the methods employed to test data stationary and co-integration relationship should take cross-sectional dependence into consideration.

Results for Unit Root Examination
Since the following analysis should take cross-sectional dependence into consideration, at the stage of testing data series stationary, L.L&C, IPS, Augmented Dickey Fuller-Fisher (ADF-Fisher) and Phillips-Perron Fisher (PP-Fisher) examination approaches are selected. As shown in Table 3, regarding to the probability values in the brackets of different examination methods for variables, we can obtain that all variables are not stable in their level form. Then all variables need to be differenced. ∆lnSO 2 , ∆lnGDP, ∆lnFEC, ∆lnECI, and ∆lnES are utilized to represent the first difference form for SO 2 emissions, GDP, fossil fuel energy consumption, energy consumption intensity, and economic structure in natural logarithmic form. Since all of the probability values in brackets are less than the confidence level, SO 2 emissions and four independent variables are stable in the same order.

Results for Panel Co-Integration Examination
As all variables are first order differential stationary, the empirical analysis can step to panel co-integration test. Pedroni's co-integration examination approach is utilized in this stage and the statistic values of it are illustrated in Table 4. Since all values of probability are less than the confidence level, it can be verified that the long-term co-integration relationship is existed among SO 2 discharge, GDP, fossil fuel energy consumption, energy consumption intensity, and economic structure. Notes: a points to 1% level of confidence; b refers to 5% confidence level.

Model Form Determination
As there exists long-run co-integration relationship among all variables, the empirical analysis can proceed to model estimation. First, the effect of panel data model needs to be identified. Generally, LR test and Hausman test are used to determine whether the panel data model is fixed effect or random effect. The test results are shown in Table 5. With regard to LR examination approach, the probability value is less than the significance level, which implies that the panel data model should be fixed effect. As for Hausman examination results, based on the value of cross-section random and probability values of all independent variables, it is verified that the panel data model should be fixed effect.

Statistic
Prob. Then the panel data model form of the unchanged intercepts and coefficients model, the changed intercepts and unchanged coefficients model, and the changed intercepts and coefficients model need to be determined employing F-test. Firstly, we need to obtain three sum square residual values represented by S 1 , S 2 , and S 3 of Equations (11)-(13), respectively. Secondly, we can calculate the values of F 1 and F 2 statistics based on Equations (14) and (15). Thirdly, the panel data model form can be confirmed. If the value of F 2 is smaller than the critical value F 2,α ((N − 1)(K + 1),(NT − N(K + 1)), the panel data model is confirmed to be the model with constant intercepts and coefficients, if not, the hypothesis H 1 will be examined. If the value of F 1 is larger than the critical value F 1,α ((N − 1)K,(NT − N(K + 1)), the panel data model is verified to be the model with changed intercepts and coefficients, if not, it is judged to be the model with variable intercepts and invariable coefficients. In accordance with the results for F-test, both of the values of F 1 and F 2 statistics are more than the critical values at the given significance level. Therefore, the established panel data model should be the changed intercepts and coefficients model as Table 6 shown.

Estimation for Panel Data Model and Provincial Comparative Analysis
Based on the above examination process, a fixed effect panel data model with changed intercepts and coefficients consists of SO 2 discharge, GDP, fossil fuel energy consumption, energy consumption intensity, and economic structure can be established for Hebei, Henan, Inner Mongolia, Shandong, and Shanxi. The estimated coefficients of the panel data model and the test results for the effectiveness of the model are listed in Table 7. The value of R 2 is 0.9981 which means the fitting effect of the model is pretty good. The value of F-statistic is 129.0515, which is much more than the critical value representing the coefficients of the model are all significant. Therefore, it can be confirmed that the established panel data model is valid and significant. As all variables are converted to logarithmic form, the coefficients represent elasticities. Through analyzing the coefficients, we can conclude that: (1) Compared with GDP, energy consumption intensity, and economic structure, fossil fuel energy consumption makes the greatest contribution to SO 2 discharge. Since fossil fuel energy contain large amount of sulfur element, the combustion of fossil fuel energy will release a great deal of sulfide which is the main source of sulfur dioxide. Through comparing the different contributions of fossil fuel energy consumption to SO 2 emissions for different provinces, it can be summarized that the more the fossil fuel energy consumption, the greater the contribution made by it to SO 2 emissions. Since the fossil fuel energy consumption amount of Shandong ranked the first, the contribution of fossil fuel energy consumption to SO 2 emissions is the greatest, followed by Shanxi. The contribution of fossil fuel energy consumption to SO 2 emissions in Henan is the smallest.
(2) The contribution of GDP to SO 2 emissions is less than fossil fuel energy consumption, but more than energy consumption intensity and economic structure. Compared with the contribution of fossil fuel energy consumption to SO 2 emissions, GDP make much less contribution to SO 2 emissions. Through comparing the different devotions of GDP to SO 2 emissions among five selected provinces, it can be concluded that the larger the scale of the economy, the greater the devotion made by it to SO 2 emissions. Since the economy scale of Shandong ranked the first, the devotion of GDP to SO 2 emissions in Shandong is the greatest, followed by Henan. GDP in Shanxi contributes the smallest to SO 2 emissions. (3) The more the proportion of the secondary industry added value accounted in GDP, the greater the contribution of the economic structure and energy consumption intensity to SO 2 emissions. From the overall trend of the proportion for secondary industry added value accounted in GDP, the proportions of secondary industry added value accounted in GDP for Shandong and Inner Mongolia are higher than other three provinces, and the contributions of energy consumption intensity and economic structure to SO 2 emissions of Shandong and Inner Mongolia are higher than Hebei, Henan, and Shanxi.

Analysis for Granger Causality Relationship
The results of Granger causality test for the established panel data model are listed in Table 8. If the probability values in brackets are less than the specific significance level, there exists Granger causality relationship between these two variables. As illustrated in Table 8, we can summarize that: (1) For the selected five provinces, there exists a bi-directional causality relationship between fossil fuel energy consumption and SO 2 emissions. This indicates that the decrease of fossil fuel energy consumption can contribute to the reduction of SO 2 emissions, while the decrease of SO 2 emissions will have a negative impact on fossil fuel energy consumption. (2) There exist uni-directional causal relationships running from GDP to SO 2 emissions, from energy consumption intensity to SO 2 emissions, and from economic structure to SO 2 emissions. That means the increase of GDP will lead to the raise of SO 2 emissions, the decrease of energy consumption intensity will bring about the decline of SO 2 emissions, and the decrease of the proportion for the secondary industry added value accounted in GDP will result in the reduction of SO 2 emissions. But there is no feedback from SO 2 emissions to GDP, energy consumption intensity, and economic structure.

Conclusions and Policy Implications
This paper investigated the relationship among SO 2 emissions, GDP, fossil fuel energy consumption, energy consumption intensity, and economic structure of five provinces in China with the highest SO 2 emissions employing panel data model approach spanning from 2002 to 2015. Based on panel unit root examination to confirm all variables are stable in the same order, panel co-integration examination to verify all variables are co-integrated in the long-term, and model form determination tests, the panel data model can be estimated and the Granger causality examination can be conducted, which provided strong evidence on the complicated relationships among these variables. Through analyzing the established panel data model, the main conclusions are as follows: (1) Fossil fuel energy consumption makes the greatest contribution to SO 2 discharge compared with GDP, energy consumption intensity, and economic structure. And the more the fossil fuel energy consumption, the greater the devotion made by it to SO 2 emissions. (2) GDP makes less contribution to SO 2 emissions than fossil fuel energy consumption, and the larger the scale of the economy, the greater the contribution made by it to SO 2 emissions. (3) The higher the proportion of the secondary industry added value accounted in GDP, the more the devotion made by the economic structure and energy consumption intensity to SO 2 emissions.
Through investigating the causal relationship among SO 2 emissions, GDP, fossil fuel energy consumption, energy consumption intensity, and economic structure of Hebei, Henan, Inner Mongolia, Shandong, and Shanxi using Granger causality examination method, we can obtain that: (1) A bi-directional causal relationship exists between fossil fuel energy consumption and SO 2 emissions among five selected provinces. (2) Uni-directional causal nexus exist running from GDP to SO 2 emissions, from energy consumption intensity to SO 2 emissions, and from economic structure to SO 2 emissions among five chosen provinces.
The empirical analysis of this research can make policy makers better understand the complex relationships between SO 2 emissions, GDP, fossil fuel energy consumption, energy consumption intensity, and economic structure of Hebei, Henan, Inner Mongolia, Shandong, and Shanxi. In this way, the significant influencing factors of SO 2 discharge can be found, and effective and practicable policies can be formulated according to the contributions of different factors and causality relationships. Therefore, regarding to the above econometric analysis, the following recommendations are put forward: (1) Giving full play to the guiding role of price signals, and improving the price policy for desulfurization. Based on the empirical analysis, it can be found that the consumption of fossil fuel energy makes the greatest contribution to SO 2 emissions. However, in the long-run, energy consumption of various provinces in China will still depend on fossil fuel energy to a large extent. Therefore, in the process of using fossil fuel energy, desulfurization equipment is needed to reduce sulfur emissions. Considering about the increase of the cost for enterprises using desulfurization equipment, it is necessary to implement a certain price subsidy policy so that the increased cost due to the use of desulfurization technique can be covered, which can also encourage enterprises to develop desulfurization technology. (2) Formulating a new comprehensive evaluation indicator to measure the regional development level considering economic development and environmental impacts. Currently, GDP is deemed as the significant indicator to evaluate the regional development level, which neglects the environmental impact. Therefore, it is essential to establish a comprehensive evaluation system for measuring regional development level, which can not only consider the level of economic development, but also take the impact of pollutant emissions on the environment in the process of rapid economic development into consideration.
(3) Exploring renewable and sustainable energy sources to substitute for fossil fuel energy. Considering the high pollution and limited nature of fossil fuel energy, we should actively exploit other renewable energy to take over the use of fossil resources. Based on regional resources endowment, people in inland areas should energetically develop the use of wind energy and solar energy, such as Hebei, Henan, Inner Mongolia, and Shanxi, while people in coastal areas should positively explore the use of hydropower and tidal energy, such as Shandong. (4) Developing high value added and low pollution emissions industries and reducing the proportion of secondary industry. According to the empirical analysis, we can obtain that the higher the proportion of the secondary industry accounted in GDP, the more the devotion made by the economic structure and energy consumption intensity to SO 2 emissions. Therefore, policy makers should draft related policies for economic structure adjustment, which should aims at reducing the proportion of secondary industry and developing high value added and low pollution emissions industries. Additionally, policies related to improve energy using efficiency should be executed.
Based on the above analysis, although the results of this study are inspiring, we also have lots of work needed to be done in the future. The policy effect of reducing SO 2 emissions should be quantified and added to the panel data model to analyze the function of policies in decreasing SO 2 discharge. Additionally, it is also necessary to analyze the influence of renewable energy and its relevant policies on SO 2 emissions. Therefore, policymakers can grasp the policy effect, and propose more effective policies and measures.