1. Introduction
Chinese sustainable development policy defined that all provinces should implement the coherent policy that considers the dynamic changes of the country. At the same time, to reach these goals the local authorities should provide active policy in environmental regulations, green investment, green education, etc. [
1]. Moreover, the implemented policies should not limit the country’s economic growth [
2]. With the development practice, environmental protection and economic growth are greatly related, which provokes the controversial discussion on the role of each in the country’s development among world experts. The scholars [
3,
4,
5,
6,
7] confirmed that economic growth attracts additional financial resources to the country and generates added value for society. From the other side, the priory studies empirically justified that economic growth without effective regulations provokes environmental degradation. However, past studies [
8,
9] have shown that economic growth with effective environmental policy could bring the economic benefits in long-term.
It should be noted that the Henan provinces are the leader of coal production in China. Moreover, the Henan provinces occupied the fifth place on petroleum and natural gas production [
10]. Consequently, it has caused the overpollution of the provinces and the declining well-being of the people. Thus, the government should implement relevant and effective mechanisms to reduce nature degradation without restricting economic growth. Moreover, it necessary to consider the interconnection between the changes of the ecological and economic indicators of the country’s development. In this case, the paper contributes to the development of a scientific approach (based on vector autoregression model) to detect the intertwined dynamics of the indicators that revealed the economic growth and environmental quality for the Henan provinces of China.
Paper has the following structure: literature review—analysis of the theoretical framework of the relationship between economic growth and environmental pollution as core indicators of environmental quality; materials and methods—explaining the methods and instruments to check the interconnection between economic growth and environmental pollution; results—explaining the core findings of the investigation; discussion and conclusions—comparison analysis of the obtained data with the previous investigation; policy implications, study limitations and direction for future investigations.
2. Literature Review
The world scientific community and experts have already aggreged that economic development should not increase the eco-destructive impact on the environment. Thus, Dasgupta and Heal [
11] suggested that economic and ecological development could coexist progress together. Economic development did not necessarily mean that the economy and the environment could be in a mutually coordinated relationship at the cost of large energy consumption. At the same time, Hettige and Mani [
12] confirmed that environmental pollution could be reduced by implementing strict management mechanisms to improve the ecological environment. The study [
13] confirmed the strong relationship between factors of economic growth, public health, and environmental quality for central EU countries for 2005–2018 years. Based on EKC theory, the papers [
14,
15] proved a similar conclusion for China on linking economic growth and environmental pollution. Hence, the study [
14] applied a regression model to check the hypothesis of the investigation. In addition, Bildirici and Gökmenoğlu [
16] analyzed the link between economic development, environmental pollution, and renewable energy. Applying the MS-VAR and MS-Granger Causality, they confirmed the bidirectional causality between CO2 and economic growth for G7 countries for 1961–2013.
The paper [
17] analyzed the ethical aspects of the relationship between economic growth, CO
2 emissions, and energy consumption among 106 countries for 1971–2011. Using the panel vector autoregression and impulse response function analyses, the study [
17] proved the bidirectional causality between energy consumption and economic growth. At the same time, they did not confirm that renewable energy consumption was conducive to economic growth. The paper [
18] showed the long-term link between environmental and economic development. Moreover, they analyzed the role of energy consumption from the transport sector (as the biggest energy consumer in Asia countries) in environmental and economic development. Considering the findings [
18] concluded that increasing the energy consumption of the transport sector and GDP growth by 1% provoked the degradation of the environmental quality by 0.57% i 0.46, respectively.
Furthermore, they highlighted that extending green innovation in the transport sector allowed increasing economic growth without environmental degradation. The study [
19] confirmed that spreading energy innovations allowed declining greenhouse gas emissions and boosted economic growth in 17 OECD countries. It should be noted that the study [
19] also based investigations on EKC theory. The past study [
20] applies the dynamic ARDL model to prove the linking among environmental degradation and economic growth for Pakistan. Their findings confirmed the EKC curve for the case. However, the results [
21] for the USA case have the controversial conclusions for decomposed and undecomposed. Thus, the composed model did not confirm the EKC theory for the USA case but the composed model did confirm the EKC theory for USA.
Le and Sarkodie [
22] analyzed the developing economies to check the hypothesis on the relationship between energy consumption (renewable and traditional energy) and environmental and economic development. Considering the findings, [
22] outlined the necessity to implement policy on extending green energy and restructuring the economy. Moreover, it allowed reducing CO
2 emissions.
The research [
23] analyzed the panel data of Artic region for 1990–2017 years. They analyzed the following indicators to check the relationship between economic growth and environmental degradation: financial development, natural resources, globalization, consumption of non-renewable and renewable energy, greenhouse gas emissions, GDP per capita. Based on the empirical findings, the scholars concluded the dynamic interconnections between consumption of non-renewable and renewable energy, greenhouse gas emissions, and economic growth.
The paper [
24] confirmed the link between waste, economic growth, and greenhouse gas emission in the Danish case. Thus, the findings of Machine Learning allowed concluding that economic growth provoked the increase in waste that polluted the environment. Namlis and Komilis [
25] proved a similar conclusion on the significant correlation between municipal waste and economic growth in the case of EU countries. However, the paper [
26] applied the Waste Kuznets curve to check the hypothesis on the relationship between waste and socioeconomic indicators of the Australian state of New South Wales. The empirical findings confirmed that regions with high socioeconomic indicators had a more effective waste management system, which minimizes the negative impact on the environment. In China’s example, scholars [
27] confirmed that increasing SO
2 emissions led to declining indicators of economic growth. The findings of [
28] justified the inverted U-shape EKC between economic growth and SO
2 emissions. At the same time, the studies [
28,
29] concluded that innovations and trade openness provoked the increasing SO
2 emissions. However, urbanization reduced SO
2 emissions in emerging Asian economies in the long-run perspective. Thus, the unidirectional relation causality was from urbanization to SO
2 emissions and from SO
2 emissions to economic growth in the short run.
Considering the research results of experts and scholars, the relationship between the economy and the environment in Henan Province was studied using the VAR model.
4. Results
The first step in modeling the time series is to test for stationarity, which is adopted from the past studies [
45]. The test for stationarity is a commonly used ADF test, with the test results in
Table 1.
The conclusions drawn from the above table show that, for between 90% and 99% of confidence, the ADF value of each variable is greater than its critical value. It confirms that the time-series data are non-stationary, and the resulting test results cannot reject the null hypothesis of the unit root. The new series obtained after the first-order difference of the above variables all have ADF values less than their cutoff at 90% confidence and p < 0.05. Hence, the time-series data have been stationary after the first-order difference.
In judging the best lag order, the principle selected here is the AIC, SC minimum criterion. The best lag period results of the VAR model are shown in
Table 2.
According to
Table 2, the VAR model has the smallest AIC, SC value at the first-order lag, and thus the optimal lag period is 3.
Since the time-series data are non-stationary in most cases, if the non-stationary time-series data are single-integrated of the same order, then there may be a specific equilibrium relationship between the data. The Granger causality test was conducted to examine causality among the variables in
Table 3.
From the Granger causality test (
Table 3),
p > 0.05 accepts the null hypothesis. It indicates that there is no Granger causal relationship between the two variables. The value of
p < 0.05 rejected the null hypothesis that Granger is causal between the two variables [
36,
37,
38,
39]. The findings confirmed that GDP growth causes exhaust gas production and that SO
2 will also influence wastewater.
According to the cointegration theory [
40,
41], the three endogenous variables are first-order single consolidation variables, so there may be a consolidation relationship. The following co-consolidation analysis considered industrial wastewater discharge, industrial waste gas discharge, and SO
2 discharge. The results are shown in
Table 4.
The results from the maximum feature root test in
Table 4 show that at 95% confidence, the null hypothesis is not rejected. Hence, there is indeed a long-term equilibrium relationship between human resident GDP and industrial wastewater, industrial non-completeness, and SO
2. The co-integral equation is:
The above co-integer equation treats it logarithmically for all the variables, and then the coefficients in this co-consolidation equation are expressed as elasticity. When the production of industrial solid waste gas and SO2 volume increased by 1% each, GDP per capita increased by 0.2199% and 0.3467%, respectively. Declining the emission of industrial waste by 1% led to decreasing GDP per capita by 0.5152%.
For the pulse response analysis, it is beneficial to explore further the relationship between GDP per capita and related factors. Take the pulse response first step to ensure the model’s stability. The AR root icon could be tested with the following results (
Table 5).
The VAR model established in this paper is 2, the number of variables is 4, and the number of characteristic roots equals 8. The results in
Table 5 show that the inversion of the above 8 roots is less than 1, and 8 points fall in the unit circle, proving that the VAR model is stable. If at least one feature root is equal to 1 or greater than 1, some points are on or outside the unit circle, which shows that the VAR model is unstable. Therefore, the VAR model established above could pass the stability test, and the established model could be considered stable. That is, the next pulse response analysis could be performed.
The following impulse response analysis included the three established environmental pollution indicators and economic growth index VAR model. The results are shown in
Figure 4.
The horizontal axis represents the lag of the impact period (years), and the vertical axis represents the response to the impact. The model information impact lag period is set to 10 years. The dashed line indicates the standard deviation band, and the meaning of the impulse response may occur.
As can be seen from
Figure 4a, after a positive impact of SO
2, GDP per capita began to increase during phase 2, declined during phase 2 to 3, from phase 3 experienced a continuous decline later in phase 4, and achieved stability after phase 6 (response value of 0.005).
Figure 4b reveals that after a positive impact of industrial wastewater discharge, the GDP per capita began to increase in the second phase and has grown slowly since the third phase (response value of 0.05).
Figure 4c demonstrate that after a negative impact on industrial wastewater discharge, the GDP per capita dropped during periods 1 and 2 and grew slowly after phase 2 and remained in a steady state (response value of −0.03).
Given a positive impact on GDP per capita, the response map of each environmental pollution index can be obtained (
Figure 5).
There was a positive increase in industrial waste gas emissions during phase 1, reaching the maximum in phase 3 (response value 0.051), while phase 3 began to decline slowly and stabilized after phase 7 (response value 0.026) (
Figure 5a). Industrial emissions grew negative during phase 1, rising to positive at phase 4 (0.002), the maximum (response 0.05) and a slow decline after phase 68, and finally stabilizing (response 0.001) (
Figure 5b). SO
2 production decreased from phase 1 to 2; phase 2 was at the lowest point (response value-0.15) and rose after phase 2, while it experienced a straight end period at a positive and stable state after phase 5 (response value 0.05) (
Figure 5c).
The GDP per capita immediately responded to the impact of a standard deviation. Moreover, the response is positive and has been in a slowly rising trend. Later, it demonstrated that China’s economy is growing. The GDP per capita responds to the decline immediately after a standard deviation impact of SO
2, and we can see the slow declining trend from
Figure 1. It indicates that the relationship between the two is weak. GDP per capita also positively impacts wastewater and waste gas emissions, which have been rising, further showing a positive correlation.
The variance decomposition is able to decompose the variance of each variable in the model to each perturbation term (
Table 6). It provides the relative impact of the various variables within each perturbation term VAR model to further judge the relationship between economic growth and environmental pollution.
As can be seen from the variance decomposition of the GDP per capita, in the first phase, the GDP per capita is all due to its own perturbation term. The other influencing factors have basically no influence. Over time, GDP per capita is less and less affected by itself and significantly enhanced by wastewater, exhaust gas, and SO2.
5. Discussion
The results of this analysis empirically justify that economic growth provokes natural degradation. Thus, the Granger causality test allows concluding that an increase in GDP leads to the development of exhaust gas production. The obtained findings enrich the investigation undertaken by past studies for Chinese provinces [
46,
47,
48]. Moreover, as in the past studies [
49,
50,
51,
52,
53,
54,
55] the results confirm that SO
2 has an statistically significant effect on wastewater. The researchers [
49,
50] proved that the intensity of the SO
2 impact relates to the frequency of the GDP changes. The findings showed that 1% growth of SO
2 and industrial solid waste provoke the GDP per capita growth by 0.22% and 0.35%, respectively. At the same time, reducing industrial waste by 1% leads to falling GDP per capita by 0.52%.
Such tendencies could be explained by inefficient environmental system protection at the industrial companies in the energy sector [
49,
55,
56,
57,
58,
59]. Thus, they ignore environmental regulations and do not modernize with respect to ecofriendly technologies and green innovation. Moreover, the environmental regulation should be modernized and guarantee the transparency and accountability of ecological protection activities of the companies.
Moreover, the researchers [
54,
55,
56,
57,
58,
59] divided the two types of ecological regulations into command-and-control regulations and market-based incentive environmental regulations. Thus, the command-and-control regulations provide the strict control and obligatory consideration of indicated ecological norms and standards. They could cause the modernization of ecological equipment and limit the development eco-destructive projects. However, they will not be conducive to the development of green innovation in the country. The study [
54] proves that implementation of new environmental protection legislations in China provoked the slowdown of industrial development. The market-based incentive environmental regulations focused voluntary implementation and incorporating of green factors in the business process. They allow attracting additional green investment to the companies due to the developing of a responsible and green image. Moreover, in the long-term, it leads to reducing the negative impact on the environment without the restriction of company’s productivity. Moreover, market-based incentive environmental regulations are the basis of the green development of EU countries, which are the leader in green growth [
54,
55,
56,
57,
58,
59,
60,
61,
62].
6. Conclusions
The study applied the long-term equilibrium equation between the economic growth and environmental pollution level and the impulse response analysis to check the dynamic action mechanism between economic growth and environmental pollution. The investigation is based on analyzing GDP per capita, industrial wastewater emission, and industrial exhaust emission in Henan Province from 1994 to 2020. The results show that economic growth exacerbates the pollution of the environment (especially the air environment), which is related to the development model of equal emphasis on the service industry and heavy industry in Henan Province. More enterprises ignore the protection of the environment to seek economic growth. Economic growth and SO2 emissions have a positive interaction, strongly driving growth, and economic growth reduces SO2 emissions to some extent.
Thus, considering the results of the analysis of the dynamic relationship between economic growth and environmental pollution in Henan Province, the following suggestions to promote sustainable development could be outlined.
It is necessary to strengthen environmental supervision and accelerate the adjustment of the energy structure and the utilization of new energy. Furthermore, the government should accelerate the research and application of emerging environmental protection technologies. It is necessary to encourage the development of environmental protection industries dominated by the comprehensive utilization of waste.
The development of Henan provinces mostly depends on the industrial sector. At the same time, industrial sector is one of the core generators of natural pollution. The obtained results confirmed the interconnection between GDP per capita (which is mostly generated by the industrial sector) and environmental degradation. In this case, the restructuring of the industrial sector could be conducive to the declining of natural pollution and promoting sustainable development. Scholars [
2,
63] have justified that the restructuring of the industrial sector requires the relevant innovative technologies, which appeared to be due to Industry 4.0. Furthermore, the innovative technologies make it possible to reduce the environmental costs [
64,
65]. Moreover, the effective way of restructuring the industrial sector should be based on the best practices. Thus, the authorities should share information about the best practices and develop the industrial network. In addition, it is necessary to develop light industry and promote clean production in the industry, including heavy industry.
Despite the actual findings, the investigation has several limitations. Thus, in further investigations, the object of investigation should be extended. Moreover, economic growth and green development depend on the vast range of indicators that could boost or limit the countries and regions’ development. In this case, in further investigations, it is necessary to consider the quality of governance, the globalization process, urbanization, etc. Furthermore, future investigations must consider the linear and non-linear causal relationships between the mentioned indicators.