# How Technological Innovation Influences Environmental Pollution: Evidence from China

Authors: Duan Xin; Zhang Zhi Sheng; Sun Jiahui
Journal: Complexity
 (2022)

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## Abstract

Technological innovation has an important impact on environmental pollution. In this paper, first, we analyze the influence mechanism of technological innovation on environmental pollution and then design the index system of technological innovation. Then, we use the entropy method to calculate the technological innovation level of different regions in China based on provincial panel data from 2004 to 2016. Finally, the panel vector autoregression model (PVAR) is adopted, and taking the discharge of sewage, solid waste, and exhaust gas as the research objects, the impact of technological innovation on them is empirically analyzed. The results show that China’s technological innovation level is steadily improving, but there are significant differences in the impact of technological innovation on wastewater, waste gas, and solid waste. Specifically, technological innovation can contribute to an increase in wastewater and solid waste emissions. However, the impact of this technological innovation on them is not equal. Secondly, the impact of technological innovation on exhaust emissions is to inhibit exhaust emissions in the short term and promote exhaust emissions in the long term. Finally, there are clear differences between them in terms of the specific impact of changes in wastewater, solid waste, and exhaust emissions. Changes in wastewater discharges and solid waste generation are largely derived from their own effects, while the role of technological innovation is supportive and insignificant. The change in exhaust emissions is initially influenced by itself, but in the long run, the influence of technological innovation gradually increases and eventually exceeds its own influence. Based on these research results, this paper puts forward corresponding policy suggestions to speed up environmental pollution control.


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## Body

## 1. Introduction

A rapid development of the economy is important to increase employment opportunities, improve people’s living standards, and promote social stability. However, during any developmental process, environmental pollution is prone to occur, which has attracted the attention of governments and academics worldwide to avoid a deterioration of the environment. As the largest developing country, China has experienced rapid economic growth since the reform and opening-up policies in 1978. By 2019, China’s gross domestic product (GDP) had reached 14.5 trillion dollars. China’s rapid economic development, however, came at the cost of massive resource input and ecological destruction [1]. According to statistics, the annual economic loss in China due to environmental pollution is approximately 2% to 3% of the GDP [2]. In order to alleviate the environmental pollution caused by economic development, China has made great efforts to develop science and technology in the hope of promoting the transformation and upgrading of economic development through the development of science and technology and ultimately alleviating environmental pollution. Therefore, how the development of technological innovation affects the emission of environmental pollutants has become a topic of general concern.The Porter hypothesis represents the most prominent research on the relationship between technological innovation and environmental pollution. It states that appropriate environmental regulation can encourage innovative activities and, in turn, improve the productivity of enterprises to offset the costs caused by environmental protection [3]. Since then, much research has involved empirical tests of the Porter hypothesis, which proposes environmental regulation as a way to enhance the innovation ability of enterprises [4–6]. Other studies support the compliance cost theory, which suggests that environmental regulation increases the manufacturer’s production costs, weakening technological advances [7, 8]. Generally, these studies focus on analyzing the impact of environmental regulation on technological innovation, especially those focusing on carbon dioxide emissions. Most of these studies are performed in both dynamic and static aspects [9]. Among these studies, Fan et al. and Kumar and Managi found that technological innovation in developed countries can reduce carbon dioxide emissions. In developing countries, these emissions have increased [10, 11]. Mao et al. analyzed the impact of technological innovation on water pollution intensity and found that technological innovation has different effects on pollution reduction under different levels of water pollution intensity [12]. Wang and Luo analyzed the impact of technological innovation on environmental pollution from the perspective of foreign direct investment (FDI) quantity and quality. The study found that when the FDI level was low, the scientific and technological innovation ability aggravated the degree of environmental pollution, while when the FDI level crossed a higher threshold, the scientific and technological innovation ability improved the environmental quality [13]. In addition, many scholars have paid attention to the dynamic relationship between technological innovation and pollutant emissions [14]. Since environmental pollution often involves multiple media (i.e., air, water, and solid waste), few studies have analyzed the actual impact of technological innovation on different types of environmental pollution. Therefore, we first analyze the influencing mechanisms of technological innovation on environmental pollution, and then, based on the provincial panel data of China, utilize the panel vector autoregressive model (PVAR) model to analyze its impact on wastewater discharge, exhaust emission, and solid waste discharge. This study provides the following contributions: on the one hand, it enriches the available research on the impact of technology on pollution control; on the other hand, as the largest developing country, China’s experience will provide abundant lessons for other developing countries.This paper is structured as follows: Section2 analyzes the theory behind the influencing mechanisms of technological innovation on environmental pollution. Section 3 illustrates the methods and data sources, followed by the research results in Section 4. Section 5 presents the conclusions, including policy implications.


## 2. Influence Mechanism of Technological Innovation on Environment Pollution

From Figure1, we can clearly find how technological innovation affects the emission of environmental pollution. Next, we explain Figure 1 in detail. Based on existing research, technological innovation affects environmental pollution through the following three paths: changing energy consumption, industrial structure, and technology application to environmental governance. Firstly, the industrial structure is not only an important carrier of economic activities but also a factor with an important impact on the ecological environment. It has the dual function of being a resource converter and an environmental regulator [15]. In modern society, compared with primary and tertiary industries, the secondary industry has the greatest impact on environmental pollution. In China, for example, energy consumption is mainly concentrated in the industrial sector, which also produces the most waste gas emissions, wastewater, and residues in the economic sector [16]. Studies have found that technological innovation has an important impact on upgrading the industrial structure and changes in structure in a unified and strict legal market environment, decreasing the intensity of pollutant emissions [17–19] (see Figure 1). Secondly, technological innovation can affect energy consumption efficiency [20]. At present, China’s energy consumption is still dominated by coal and oil, which are significant sources of environmental pollution and are often wasted during consumption [21]. Finally, technological innovation often means that the corresponding scientific and technological achievements can be used to control environmental pollution [22]. For example, Cinderby and Forrester applied a geographic information system (GIS) to control air pollution and found that GIS not only promotes local governance to be more responsible for improving air quality but also strengthens the interactive relationship among local governments, environmental scientists, and the public [23]. Furthermore, the application of technology can facilitate the reporting of environmental damage behavior of enterprises by the public on a real-time basis, thereby strengthening the capacity of government regulation in environmental pollution. Therefore, the application of technological innovations to the field of environmental governance can change the ability to control environmental pollution and ultimately have a profound impact on the sustainability of the environment [24].Figure 1
The influence mechanism of technological innovation (TECINN) on environmental pollution.


## 3. Model Settings and Data Description

### 3.1. Design of a Comprehensive Index System of Technological Innovation

The direct manifestation of the level of technological innovation is its effectiveness, which is determined by the environment, inputs, and outputs. Therefore, based on existing research and considering the availability of data [25–27], we selected 15 indicators (the proportion of the economically active population with a junior college degree or above, GDP per capita, the proportion of funding for science and technology in financial funding, the Internet penetration rate, and so on) [28, 29], based on environmental technological innovation (TECINN), investment in TECINN, output of TECINN, and achievements of TECINN to construct an index system for technological innovation (see Table 1).Table 1
Comprehensive index system of technological innovation.
The total systemFirst-level indexThe secondary indexTechnological innovation systemThe environment of technological innovationThe proportion of the economically active population with a junior college degree or above (%), GDP per capita (%), the proportion of funding for science and technology in financial funding (%), the Internet penetration rate (%)Investment in technological innovationFull-time equivalent of R&D personnel per 10,000 people, proportion of R&D expenditure to GDP (%), proportion of enterprises with R&D institutions (%), internal expenditure of R&D, expenditure of large and medium-sized high-tech enterprises (ten thousand yuan)Output of technological innovationNumber of scientific and technological papers (papers), number of patent authorization (items), technology market turnover (100 million yuan)Achievements of technological innovationSales income of new products (10,000 yuan), export of high-tech products accounting for delivery value (100 million yuan), energy consumption per unit GDP (tons of standard coal/10,000 yuan), labor productivity (10,000 yuan/person)


### 3.2. Model of Comprehensive Level of Technological Innovation

Firstly, the raw data is standardized to eliminate the impact of different units and dimensions of the index, as follows:(1)Positive indexes:yij=maxxij−xijmaxxij−minxij,(2)Negative indexes:yij=xij−minxijmaxxij−minxij,where the xij represents the sample value, maxxijandminxij represents the maximum and minimum values of the sample data, respectively.Secondly, the index weight is calculated. There are two different methods for the calculation of index weight, a subjective and objective weighting method. In this paper, the entropy method as the objective weighting method is adopted to calculate the weight of each index, as follows:(3)pij=yij∑i=1nyij,ej=−1lnn∑i=1npijlnpij,(4)wj=1−ei∑i=1n1−ei.Finally, the comprehensive level of technological innovation (UT) is calculated by the following formula:(5)UT=∑j=1mwijyij,∑j=1mwij=1.


### 3.3. Panel-Data Vector Autoregressive Model

The single-dimensional vector autoregression model (VAR), established by Sims in 1980, was used to describe the impact of variables on a specific variable [30]. However, the VAR model did not support panel data; therefore, Holtz-Eakin, Newey, and Rosen extended it to a panel data structure [31]. Compared with the VAR model, the panel-data vector autoregressive model (PVAR) can lower the requirements for data volume and form and effectively control the estimation bias caused by spatial and individual heterogeneity [32]. To investigate the relationship between technological innovation and environmental pollution, this paper constructs a panel vector autoregressive model (PVAR) for empirical tests, as follows:(6)Yit=γ0+αi+βt+∑j=1pγjΥi,t−j+εit,i=1,2,3,…,N,t=1,2,3,…,T,where the subindexes i refer to the province, t refers to time. Yit is the endogenous variable that changed over time, and region, γ0 and γj indicate the estimated coefficients of the constant term and lagged endogenous variable, respectively; p is the lag period, αi is a vector of individual effects, which indicates the otherness of the cross-sectional.βt is a time effect vector used to explain the temporal characteristics of variables. εit is the random disturbance item.


### 3.4. Variable Selection and Data Sources

We used the entropy method to measure the level of technological innovation in different regions of China, and the discharge status of environmental pollutants is selected from the three indicators of solid waste generation, sulfur dioxide discharge, and wastewater discharge. This study uses a yearly panel dataset of 30 provinces and cities in mainland China, except Tibet, from 2004 to 2016. The data were sourced from China Science and Technology Statistical Yearbook (2005–2017), China Statistical Yearbook (2005–2017), and China Environmental Statistics Yearbook (2005–2017).


## 3.1. Design of a Comprehensive Index System of Technological Innovation

The direct manifestation of the level of technological innovation is its effectiveness, which is determined by the environment, inputs, and outputs. Therefore, based on existing research and considering the availability of data [25–27], we selected 15 indicators (the proportion of the economically active population with a junior college degree or above, GDP per capita, the proportion of funding for science and technology in financial funding, the Internet penetration rate, and so on) [28, 29], based on environmental technological innovation (TECINN), investment in TECINN, output of TECINN, and achievements of TECINN to construct an index system for technological innovation (see Table 1).Table 1
Comprehensive index system of technological innovation.
The total systemFirst-level indexThe secondary indexTechnological innovation systemThe environment of technological innovationThe proportion of the economically active population with a junior college degree or above (%), GDP per capita (%), the proportion of funding for science and technology in financial funding (%), the Internet penetration rate (%)Investment in technological innovationFull-time equivalent of R&D personnel per 10,000 people, proportion of R&D expenditure to GDP (%), proportion of enterprises with R&D institutions (%), internal expenditure of R&D, expenditure of large and medium-sized high-tech enterprises (ten thousand yuan)Output of technological innovationNumber of scientific and technological papers (papers), number of patent authorization (items), technology market turnover (100 million yuan)Achievements of technological innovationSales income of new products (10,000 yuan), export of high-tech products accounting for delivery value (100 million yuan), energy consumption per unit GDP (tons of standard coal/10,000 yuan), labor productivity (10,000 yuan/person)


## 3.2. Model of Comprehensive Level of Technological Innovation

Firstly, the raw data is standardized to eliminate the impact of different units and dimensions of the index, as follows:(1)Positive indexes:yij=maxxij−xijmaxxij−minxij,(2)Negative indexes:yij=xij−minxijmaxxij−minxij,where the xij represents the sample value, maxxijandminxij represents the maximum and minimum values of the sample data, respectively.Secondly, the index weight is calculated. There are two different methods for the calculation of index weight, a subjective and objective weighting method. In this paper, the entropy method as the objective weighting method is adopted to calculate the weight of each index, as follows:(3)pij=yij∑i=1nyij,ej=−1lnn∑i=1npijlnpij,(4)wj=1−ei∑i=1n1−ei.Finally, the comprehensive level of technological innovation (UT) is calculated by the following formula:(5)UT=∑j=1mwijyij,∑j=1mwij=1.


## 3.3. Panel-Data Vector Autoregressive Model

The single-dimensional vector autoregression model (VAR), established by Sims in 1980, was used to describe the impact of variables on a specific variable [30]. However, the VAR model did not support panel data; therefore, Holtz-Eakin, Newey, and Rosen extended it to a panel data structure [31]. Compared with the VAR model, the panel-data vector autoregressive model (PVAR) can lower the requirements for data volume and form and effectively control the estimation bias caused by spatial and individual heterogeneity [32]. To investigate the relationship between technological innovation and environmental pollution, this paper constructs a panel vector autoregressive model (PVAR) for empirical tests, as follows:(6)Yit=γ0+αi+βt+∑j=1pγjΥi,t−j+εit,i=1,2,3,…,N,t=1,2,3,…,T,where the subindexes i refer to the province, t refers to time. Yit is the endogenous variable that changed over time, and region, γ0 and γj indicate the estimated coefficients of the constant term and lagged endogenous variable, respectively; p is the lag period, αi is a vector of individual effects, which indicates the otherness of the cross-sectional.βt is a time effect vector used to explain the temporal characteristics of variables. εit is the random disturbance item.


## 3.4. Variable Selection and Data Sources

We used the entropy method to measure the level of technological innovation in different regions of China, and the discharge status of environmental pollutants is selected from the three indicators of solid waste generation, sulfur dioxide discharge, and wastewater discharge. This study uses a yearly panel dataset of 30 provinces and cities in mainland China, except Tibet, from 2004 to 2016. The data were sourced from China Science and Technology Statistical Yearbook (2005–2017), China Statistical Yearbook (2005–2017), and China Environmental Statistics Yearbook (2005–2017).


## 4. Results

### 4.1. Comprehensive Level of Technological Innovation

According to formula (1)–(5), the comprehensive level of technological innovation in China from 2004 to 2016 can be calculated. Table 2 shows the specific comprehensive level. The mean of the comprehensive level of technological innovation in different regions shows the overall trend of technological innovation in China from 2004 to 2016 (Figure 2).Table 2
Comprehensive level of technological innovation in 30 provinces.
AreaYear2004200520062007200820092010201120122013201420152016Beijing0.1010.1570.2400.3390.3750.4230.5330.5810.6600.6510.7450.8320.891Tianjin0.1340.1830.2290.2750.2400.2840.3990.4470.6220.6530.7620.8890.924Hebei0.1270.1690.1830.2220.2580.3740.4710.4630.6150.6710.6980.7650.943Shanxi0.0530.0960.1680.2200.2890.3670.3670.4290.5560.8030.7490.7730.818Inner Mongolia0.1510.2480.2410.1800.2470.2910.3840.4210.6030.6140.5700.6420.667Liaoning0.3180.2820.3110.3010.3240.4190.5540.5320.5950.5990.6260.6760.847Jilin0.2900.2290.2590.2420.2270.3600.3550.3630.4410.5880.6170.6980.797Heilongjiang0.1590.2490.2050.2640.2710.3740.4880.4770.6130.6030.6190.7880.765Shanghai0.0350.1640.3170.3240.3350.5160.5850.5730.6210.6310.7130.7780.887Jiangsu0.0600.1000.1330.1900.2220.3530.5140.5660.7330.7990.8280.9010.970Zhejiang0.0550.1550.2700.3040.3290.3450.4600.4960.5960.6630.6970.7650.874Anhui0.0490.0700.1010.1350.1520.2300.3780.4280.5340.7040.7740.8420.918Fujian0.1580.1950.1560.2030.3250.3800.4950.4860.6140.6830.6460.7330.760Jiangxi0.0910.1380.2160.2790.1960.3020.3710.3520.4180.6060.6430.7210.840Shandong0.0740.1170.1660.2350.3140.3500.5220.5430.6200.7340.7460.7800.837Henan0.0650.1490.1710.2360.2280.2770.4030.4640.5650.7080.7310.7730.871Hubei0.0770.1080.1380.1960.2250.2980.4370.4460.5430.6580.7260.7830.871Hunan0.0870.0920.1350.2130.2540.2770.3830.4040.5360.6920.7420.8150.867Guangdong0.0680.0750.1550.2070.2750.3380.4760.4690.5610.6820.6680.8420.959Guangxi0.1540.2090.2210.2050.2340.2870.4340.4490.5280.6660.6700.6080.721Hainan0.1230.1270.2140.2290.3910.3860.4770.5440.6000.6990.6240.7520.857Chongqing0.1090.1470.1870.2100.2600.2440.3900.4040.4500.6050.6780.7020.884Sichuan0.1770.1980.2230.2610.2180.2940.3260.3450.5850.5590.6530.7600.750Guizhou0.1250.1330.2230.1710.2170.3480.3910.4130.4240.5050.5810.6770.749Yunnan0.2320.1880.1780.1920.1970.2170.3690.3250.5130.6040.5750.6860.736Shaanxi0.2140.1430.1620.1980.2070.2890.3590.3830.5130.5590.6120.6690.771Gansu0.1350.1840.2310.2400.2870.3880.3940.4700.5500.6480.7200.7870.776Qinghai0.1830.1810.1940.2320.2830.4510.3550.5440.5870.5360.5820.6370.755Ningxia0.0820.1070.1460.2230.2080.3140.4010.4920.5930.5220.5800.7180.812Xinjiang0.2250.1260.1350.2570.3040.3000.5220.5370.5430.5610.5910.6750.682Mean0.1300.1570.1970.2330.2630.3360.4330.4620.5640.6400.6720.7490.827Figure 2
China’s comprehensive level of technological innovation from 2004 to 2016.It can be seen from Table2 and Figure 2 that the level of China’s technological innovation has increased since 2004. In 2004, the average comprehensive level of technological innovation was just 0.13, but after decades of development, this level has reached 0.827 in 2016, which shows that China’s efforts to promote technological progress by increasing the investment and training of personnel have been effective. However, we found significant regional differences in the level of Chinese technological innovation. In 2016, a lower level of technological innovation of 0.6–0.8 is observed in Inner Mongolia, Guangxi, Guizhou, Yunnan, Gansu, and Qinghai, the economic level of these areas is also relatively backward. By contrast, Beijing, Tianjin, Shanghai, Jiangsu, and Guangdong, which are relatively developed regions in China, had a higher level of 0.8. Based on the results in different regions of China, we analyzed the specific impact of technological innovation progress on the emission of environmental pollutants.


### 4.2. Impact of Technological Innovation on the Emission of Environmental Pollutants

#### 4.2.1. Panel Unit Root Test

Before using the PVAR model to analyze the impact of technological innovation on the emission of wastewater, solid waste, and exhaust gas, unit root testing must be used to test the stability of various variables. For the unit root test of panel data, the Levin, Lin & Chu (LLC test), Im, Pesaran and Shin W-stat (IPS test), AdF-Fisher Chi-Square (ADF test), and PPFisher Chi-Square (PP test) were adopted [33]. The test results are shown in Table 3. According to the results, it can be found that the original data of technological innovation, wastewater, solid waste, and exhaust gas become a stationary sequence after a first-order difference, which shows that they can be tested for cointegration.Table 3
Results of unit root test.
VariableLLC-testIPS-testADF-testPP-testResultWastewater−14.9361∗∗∗−7.25428∗∗∗−5.32203∗∗∗−5.21888∗∗∗SmoothΔwastewater−18.4038∗∗∗−10.9611∗∗∗−9.05958∗∗∗−11.1499∗∗∗SmoothSolid waste−0.498844.371374.100155.35996Not smoothΔsolid waste−13.304∗∗∗−6.18938∗∗∗−6.37182∗∗∗−8.14711∗∗∗SmoothExhaust gas−13.246∗∗∗−8.20254∗∗∗−6.71452∗∗∗−9.14431∗∗∗SmoothΔexhaust gas−12.472∗∗∗−8.0203∗∗∗−7.2352∗∗∗−11.1865∗∗∗SmoothTECINN−4.8807∗∗∗0.9227756.136354.2141Not smoothΔTECINN−17.4002∗∗∗−9.52198∗∗∗186.539∗∗∗−12.9367∗∗∗SmoothNote.∗∗∗, ∗∗, and ∗ shows significance at the 1% level, 5% level, and 10% level, respectively.△ represents the 1st order difference.


#### 4.2.2. Cointegration Test

According to the cointegration test method proposed by Baltagi and Kao and Pedroni [34, 35], this study cointegrated the data of technological innovation at comprehensive level, wastewater discharge, solid waste, and exhaust emissions, and the results are shown in Table 4. According to the results of the cointegration test, there is a cointegration relationship between the comprehensive level of technological innovation and the data on environmental pollution emission (Table 5).Table 4
Results of the optimal lag order test.
VariableLagAICBICHQICTECINN-wastewater119.51∗20.25∗19.80∗219.61220.4519.95319.9120.8720.29419.8820.9820.32TECINN-solid waste115.04∗15.78∗15.34∗215.2416.0815.58315.7716.7316.16416.1717.2716.61TECINN-exhaust gas18.479.208.7628.819.669.15137.478.437.8646.30∗7.41∗6.75∗Table 5
Results of cointegration root test.
VariableTest statisticsTest resultConclusionTECINN-wastewaterPanel v-Statistic2.61∗∗∗Cointegration relationship existsPanel rho-Statistic−0.19Panel PP-Statistic−5.99∗∗∗Panel ADF-Statistic−0.66Group rho-Statistic2.43Group PP-Statistic−5.92∗∗∗Group ADF-Statistic−1.21TECINN-solid wastePanel v-Statistic5.01∗∗∗Cointegration relationship existsPanel rho-Statistic−0.14Panel PP-Statistic−0.60Panel ADF-Statistic−3.30∗∗∗Group rho-Statistic2.06Group PP-Statistic−0.82Group ADF-Statistic−1.50TECINN -exhaust gasPanel v-Statistic5.53∗∗∗Cointegration relationship existsPanel rho-Statistic−5.03∗∗∗Panel PP-Statistic−10.32∗∗∗Panel ADF-Statistic−8.59∗∗∗Group rho-Statistic−1.71∗∗Group PP-Statistic−10.91∗∗∗Group ADF-Statistic−9.59∗∗∗Note.∗∗∗ means p<0.01; ∗∗ means p<0.05; and ∗ means p<0.1.


#### 4.2.3. PVAR Model Regression Analysis

The optimal lag order of the model needs to be determined to test the PVAR model [36]. The existing literature is mainly selected by Akaike’s information criterion (AIC), Bayesian information criterion (BIC), Hannan and Quinn information criterion (HQIC) to judge the optimal lag order [37, 38]. The test results are shown in Table 4. According to the order with the most passes of all inspection values as the criterion for determining the optimal lag order, we can find that in technological innovation-wastewater and technological innovation-solid waste, the optimal lag period is 1, while the lag period of technological innovation and exhaust gas is 4.


#### 4.2.4. Impulse Response Analysis

Impulse response analysis can help analyze how technological innovation affects pollutant emissions. This study sets the number of responses to 10 periods and Monte Carlo simulations to 1000 times to analyze the impulse impact of technological innovation on wastewater discharge, solid waste generation, and exhaust gas emissions. The results are shown in Figure3. In Figure 3, the abscissa represents the lag period, the ordinate represents the degree of impulse response, the two curves at the top and bottom represent the upper and lower bounds of the 95% confidence interval, respectively, and the intermediate curve stands for the value of the impulse response.Figure 3
Impulse response results of technological innovation to environmental pollution: (a) impulse response of wastewater to TECINN, (b) impulse response of waste solid to TECINN, and (c) impulse response of exhaust gas to TECINN.
(a)(b)(c)Figure3(a) shows the response of wastewater discharge to technological innovation. Figure 3(a) shows that after receiving a standard deviation impulse from technological innovation, the wastewater discharge remains with a positive response in the 0th period until reaching a peak in the5th lag phase, eventually, technological innovation has a positive effect that leads to an increase in wastewater discharge. Figure 3(b) indicates that technological innovation has an initial positive impulse on solid waste discharge, which gradually weakens over time. Figure 3(c) shows a negative response of the exhaust gas, but over time, however, in the long term, technological innovation promotes the increase in exhaust emissions. These findings are consistent with the study by Kumar and Managi, who found that technological innovation in developing countries promotes rather than curbs CO2 emissions [11].


#### 4.2.5. Variance Decomposition

Impulse response analysis cannot be used to evaluate the importance of different impulses to specific variables [33]. Therefore, a variance decomposition is utilized to analyze the contribution of various structural impulses to the fluctuation of environmental pollution. The results are shown in Table 6.Table 6
Variance decomposition.
Variable periodWastewaterSolid wasteExhaust gasWastewaterTECINNSolid wasteTECINNExhaust gasTECINN11.0000.0001.0000.0001.0000.00020.9980.0021.0000.0000.8710.12930.9960.0040.9990.0010.6860.31440.9920.0080.9990.0010.5440.45650.9890.0110.9980.0020.4740.52660.9850.0150.9980.0020.4310.56970.9820.0180.9970.0030.3980.60280.9790.0210.9970.0030.3580.64290.9770.0230.9970.0030.3410.659100.9750.0250.9960.0040.3390.661110.9730.0270.9960.0040.3370.663120.9720.0280.9960.0040.3370.663Table6 reflects that in the 1st period, 100% of the explanatory power of the change in wastewater comes from itself, but as time passes, the impact of wastewater discharge on itself gradually decreases, and the impact of technological innovation on wastewater discharge gradually increases. By the 12th period, 97.2% of the change in wastewater discharge can be explained by itself, and the contribution of technological innovation to it reaches 2.8%. Based on the above structure of variance decomposition, it can be concluded that the change of China’s wastewater discharge is mainly affected by itself, and the influence of technological innovation on wastewater discharge is not significant. In the discharge of solid waste, the explanatory power of the change in solid waste is 100% in the 1st period. With the passage of time, the impact of solid waste emissions on its own shows a gradual downward trend, and the impact of technological innovation on it shows a gradually enhancing trend. By the 12th period, 99.6% of the change in solid waste emissions can be explained by itself, and technological innovation contributes 0.4% to solid waste. Similar to the conclusion on wastewater discharge, the change in China’s solid waste discharge is mainly influenced by itself, while technological innovation has little impact on it. In the case of the emission change of exhaust gas, the explanatory power of the change in exhaust gas is 100% in the 1st period. However, with the passage of time, the impact of exhaust gas emissions on itself shows a gradual decrease, while the impact of technological innovation increases. However, it is worth noting that by the 12th period, 33.7% of the variation in exhaust gas can be explained by itself, and 66.3% of this change was attributable to technological innovation. According to the results, China’s exhaust gas emissions are initially affected by itself, but in the long term, they are mainly influenced by technological innovation.


## 4.1. Comprehensive Level of Technological Innovation

According to formula (1)–(5), the comprehensive level of technological innovation in China from 2004 to 2016 can be calculated. Table 2 shows the specific comprehensive level. The mean of the comprehensive level of technological innovation in different regions shows the overall trend of technological innovation in China from 2004 to 2016 (Figure 2).Table 2
Comprehensive level of technological innovation in 30 provinces.
AreaYear2004200520062007200820092010201120122013201420152016Beijing0.1010.1570.2400.3390.3750.4230.5330.5810.6600.6510.7450.8320.891Tianjin0.1340.1830.2290.2750.2400.2840.3990.4470.6220.6530.7620.8890.924Hebei0.1270.1690.1830.2220.2580.3740.4710.4630.6150.6710.6980.7650.943Shanxi0.0530.0960.1680.2200.2890.3670.3670.4290.5560.8030.7490.7730.818Inner Mongolia0.1510.2480.2410.1800.2470.2910.3840.4210.6030.6140.5700.6420.667Liaoning0.3180.2820.3110.3010.3240.4190.5540.5320.5950.5990.6260.6760.847Jilin0.2900.2290.2590.2420.2270.3600.3550.3630.4410.5880.6170.6980.797Heilongjiang0.1590.2490.2050.2640.2710.3740.4880.4770.6130.6030.6190.7880.765Shanghai0.0350.1640.3170.3240.3350.5160.5850.5730.6210.6310.7130.7780.887Jiangsu0.0600.1000.1330.1900.2220.3530.5140.5660.7330.7990.8280.9010.970Zhejiang0.0550.1550.2700.3040.3290.3450.4600.4960.5960.6630.6970.7650.874Anhui0.0490.0700.1010.1350.1520.2300.3780.4280.5340.7040.7740.8420.918Fujian0.1580.1950.1560.2030.3250.3800.4950.4860.6140.6830.6460.7330.760Jiangxi0.0910.1380.2160.2790.1960.3020.3710.3520.4180.6060.6430.7210.840Shandong0.0740.1170.1660.2350.3140.3500.5220.5430.6200.7340.7460.7800.837Henan0.0650.1490.1710.2360.2280.2770.4030.4640.5650.7080.7310.7730.871Hubei0.0770.1080.1380.1960.2250.2980.4370.4460.5430.6580.7260.7830.871Hunan0.0870.0920.1350.2130.2540.2770.3830.4040.5360.6920.7420.8150.867Guangdong0.0680.0750.1550.2070.2750.3380.4760.4690.5610.6820.6680.8420.959Guangxi0.1540.2090.2210.2050.2340.2870.4340.4490.5280.6660.6700.6080.721Hainan0.1230.1270.2140.2290.3910.3860.4770.5440.6000.6990.6240.7520.857Chongqing0.1090.1470.1870.2100.2600.2440.3900.4040.4500.6050.6780.7020.884Sichuan0.1770.1980.2230.2610.2180.2940.3260.3450.5850.5590.6530.7600.750Guizhou0.1250.1330.2230.1710.2170.3480.3910.4130.4240.5050.5810.6770.749Yunnan0.2320.1880.1780.1920.1970.2170.3690.3250.5130.6040.5750.6860.736Shaanxi0.2140.1430.1620.1980.2070.2890.3590.3830.5130.5590.6120.6690.771Gansu0.1350.1840.2310.2400.2870.3880.3940.4700.5500.6480.7200.7870.776Qinghai0.1830.1810.1940.2320.2830.4510.3550.5440.5870.5360.5820.6370.755Ningxia0.0820.1070.1460.2230.2080.3140.4010.4920.5930.5220.5800.7180.812Xinjiang0.2250.1260.1350.2570.3040.3000.5220.5370.5430.5610.5910.6750.682Mean0.1300.1570.1970.2330.2630.3360.4330.4620.5640.6400.6720.7490.827Figure 2
China’s comprehensive level of technological innovation from 2004 to 2016.It can be seen from Table2 and Figure 2 that the level of China’s technological innovation has increased since 2004. In 2004, the average comprehensive level of technological innovation was just 0.13, but after decades of development, this level has reached 0.827 in 2016, which shows that China’s efforts to promote technological progress by increasing the investment and training of personnel have been effective. However, we found significant regional differences in the level of Chinese technological innovation. In 2016, a lower level of technological innovation of 0.6–0.8 is observed in Inner Mongolia, Guangxi, Guizhou, Yunnan, Gansu, and Qinghai, the economic level of these areas is also relatively backward. By contrast, Beijing, Tianjin, Shanghai, Jiangsu, and Guangdong, which are relatively developed regions in China, had a higher level of 0.8. Based on the results in different regions of China, we analyzed the specific impact of technological innovation progress on the emission of environmental pollutants.


## 4.2. Impact of Technological Innovation on the Emission of Environmental Pollutants

### 4.2.1. Panel Unit Root Test

Before using the PVAR model to analyze the impact of technological innovation on the emission of wastewater, solid waste, and exhaust gas, unit root testing must be used to test the stability of various variables. For the unit root test of panel data, the Levin, Lin & Chu (LLC test), Im, Pesaran and Shin W-stat (IPS test), AdF-Fisher Chi-Square (ADF test), and PPFisher Chi-Square (PP test) were adopted [33]. The test results are shown in Table 3. According to the results, it can be found that the original data of technological innovation, wastewater, solid waste, and exhaust gas become a stationary sequence after a first-order difference, which shows that they can be tested for cointegration.Table 3
Results of unit root test.
VariableLLC-testIPS-testADF-testPP-testResultWastewater−14.9361∗∗∗−7.25428∗∗∗−5.32203∗∗∗−5.21888∗∗∗SmoothΔwastewater−18.4038∗∗∗−10.9611∗∗∗−9.05958∗∗∗−11.1499∗∗∗SmoothSolid waste−0.498844.371374.100155.35996Not smoothΔsolid waste−13.304∗∗∗−6.18938∗∗∗−6.37182∗∗∗−8.14711∗∗∗SmoothExhaust gas−13.246∗∗∗−8.20254∗∗∗−6.71452∗∗∗−9.14431∗∗∗SmoothΔexhaust gas−12.472∗∗∗−8.0203∗∗∗−7.2352∗∗∗−11.1865∗∗∗SmoothTECINN−4.8807∗∗∗0.9227756.136354.2141Not smoothΔTECINN−17.4002∗∗∗−9.52198∗∗∗186.539∗∗∗−12.9367∗∗∗SmoothNote.∗∗∗, ∗∗, and ∗ shows significance at the 1% level, 5% level, and 10% level, respectively.△ represents the 1st order difference.


### 4.2.2. Cointegration Test

According to the cointegration test method proposed by Baltagi and Kao and Pedroni [34, 35], this study cointegrated the data of technological innovation at comprehensive level, wastewater discharge, solid waste, and exhaust emissions, and the results are shown in Table 4. According to the results of the cointegration test, there is a cointegration relationship between the comprehensive level of technological innovation and the data on environmental pollution emission (Table 5).Table 4
Results of the optimal lag order test.
VariableLagAICBICHQICTECINN-wastewater119.51∗20.25∗19.80∗219.61220.4519.95319.9120.8720.29419.8820.9820.32TECINN-solid waste115.04∗15.78∗15.34∗215.2416.0815.58315.7716.7316.16416.1717.2716.61TECINN-exhaust gas18.479.208.7628.819.669.15137.478.437.8646.30∗7.41∗6.75∗Table 5
Results of cointegration root test.
VariableTest statisticsTest resultConclusionTECINN-wastewaterPanel v-Statistic2.61∗∗∗Cointegration relationship existsPanel rho-Statistic−0.19Panel PP-Statistic−5.99∗∗∗Panel ADF-Statistic−0.66Group rho-Statistic2.43Group PP-Statistic−5.92∗∗∗Group ADF-Statistic−1.21TECINN-solid wastePanel v-Statistic5.01∗∗∗Cointegration relationship existsPanel rho-Statistic−0.14Panel PP-Statistic−0.60Panel ADF-Statistic−3.30∗∗∗Group rho-Statistic2.06Group PP-Statistic−0.82Group ADF-Statistic−1.50TECINN -exhaust gasPanel v-Statistic5.53∗∗∗Cointegration relationship existsPanel rho-Statistic−5.03∗∗∗Panel PP-Statistic−10.32∗∗∗Panel ADF-Statistic−8.59∗∗∗Group rho-Statistic−1.71∗∗Group PP-Statistic−10.91∗∗∗Group ADF-Statistic−9.59∗∗∗Note.∗∗∗ means p<0.01; ∗∗ means p<0.05; and ∗ means p<0.1.


### 4.2.3. PVAR Model Regression Analysis

The optimal lag order of the model needs to be determined to test the PVAR model [36]. The existing literature is mainly selected by Akaike’s information criterion (AIC), Bayesian information criterion (BIC), Hannan and Quinn information criterion (HQIC) to judge the optimal lag order [37, 38]. The test results are shown in Table 4. According to the order with the most passes of all inspection values as the criterion for determining the optimal lag order, we can find that in technological innovation-wastewater and technological innovation-solid waste, the optimal lag period is 1, while the lag period of technological innovation and exhaust gas is 4.


### 4.2.4. Impulse Response Analysis

Impulse response analysis can help analyze how technological innovation affects pollutant emissions. This study sets the number of responses to 10 periods and Monte Carlo simulations to 1000 times to analyze the impulse impact of technological innovation on wastewater discharge, solid waste generation, and exhaust gas emissions. The results are shown in Figure3. In Figure 3, the abscissa represents the lag period, the ordinate represents the degree of impulse response, the two curves at the top and bottom represent the upper and lower bounds of the 95% confidence interval, respectively, and the intermediate curve stands for the value of the impulse response.Figure 3
Impulse response results of technological innovation to environmental pollution: (a) impulse response of wastewater to TECINN, (b) impulse response of waste solid to TECINN, and (c) impulse response of exhaust gas to TECINN.
(a)(b)(c)Figure3(a) shows the response of wastewater discharge to technological innovation. Figure 3(a) shows that after receiving a standard deviation impulse from technological innovation, the wastewater discharge remains with a positive response in the 0th period until reaching a peak in the5th lag phase, eventually, technological innovation has a positive effect that leads to an increase in wastewater discharge. Figure 3(b) indicates that technological innovation has an initial positive impulse on solid waste discharge, which gradually weakens over time. Figure 3(c) shows a negative response of the exhaust gas, but over time, however, in the long term, technological innovation promotes the increase in exhaust emissions. These findings are consistent with the study by Kumar and Managi, who found that technological innovation in developing countries promotes rather than curbs CO2 emissions [11].


### 4.2.5. Variance Decomposition

Impulse response analysis cannot be used to evaluate the importance of different impulses to specific variables [33]. Therefore, a variance decomposition is utilized to analyze the contribution of various structural impulses to the fluctuation of environmental pollution. The results are shown in Table 6.Table 6
Variance decomposition.
Variable periodWastewaterSolid wasteExhaust gasWastewaterTECINNSolid wasteTECINNExhaust gasTECINN11.0000.0001.0000.0001.0000.00020.9980.0021.0000.0000.8710.12930.9960.0040.9990.0010.6860.31440.9920.0080.9990.0010.5440.45650.9890.0110.9980.0020.4740.52660.9850.0150.9980.0020.4310.56970.9820.0180.9970.0030.3980.60280.9790.0210.9970.0030.3580.64290.9770.0230.9970.0030.3410.659100.9750.0250.9960.0040.3390.661110.9730.0270.9960.0040.3370.663120.9720.0280.9960.0040.3370.663Table6 reflects that in the 1st period, 100% of the explanatory power of the change in wastewater comes from itself, but as time passes, the impact of wastewater discharge on itself gradually decreases, and the impact of technological innovation on wastewater discharge gradually increases. By the 12th period, 97.2% of the change in wastewater discharge can be explained by itself, and the contribution of technological innovation to it reaches 2.8%. Based on the above structure of variance decomposition, it can be concluded that the change of China’s wastewater discharge is mainly affected by itself, and the influence of technological innovation on wastewater discharge is not significant. In the discharge of solid waste, the explanatory power of the change in solid waste is 100% in the 1st period. With the passage of time, the impact of solid waste emissions on its own shows a gradual downward trend, and the impact of technological innovation on it shows a gradually enhancing trend. By the 12th period, 99.6% of the change in solid waste emissions can be explained by itself, and technological innovation contributes 0.4% to solid waste. Similar to the conclusion on wastewater discharge, the change in China’s solid waste discharge is mainly influenced by itself, while technological innovation has little impact on it. In the case of the emission change of exhaust gas, the explanatory power of the change in exhaust gas is 100% in the 1st period. However, with the passage of time, the impact of exhaust gas emissions on itself shows a gradual decrease, while the impact of technological innovation increases. However, it is worth noting that by the 12th period, 33.7% of the variation in exhaust gas can be explained by itself, and 66.3% of this change was attributable to technological innovation. According to the results, China’s exhaust gas emissions are initially affected by itself, but in the long term, they are mainly influenced by technological innovation.


## 4.2.1. Panel Unit Root Test

Before using the PVAR model to analyze the impact of technological innovation on the emission of wastewater, solid waste, and exhaust gas, unit root testing must be used to test the stability of various variables. For the unit root test of panel data, the Levin, Lin & Chu (LLC test), Im, Pesaran and Shin W-stat (IPS test), AdF-Fisher Chi-Square (ADF test), and PPFisher Chi-Square (PP test) were adopted [33]. The test results are shown in Table 3. According to the results, it can be found that the original data of technological innovation, wastewater, solid waste, and exhaust gas become a stationary sequence after a first-order difference, which shows that they can be tested for cointegration.Table 3
Results of unit root test.
VariableLLC-testIPS-testADF-testPP-testResultWastewater−14.9361∗∗∗−7.25428∗∗∗−5.32203∗∗∗−5.21888∗∗∗SmoothΔwastewater−18.4038∗∗∗−10.9611∗∗∗−9.05958∗∗∗−11.1499∗∗∗SmoothSolid waste−0.498844.371374.100155.35996Not smoothΔsolid waste−13.304∗∗∗−6.18938∗∗∗−6.37182∗∗∗−8.14711∗∗∗SmoothExhaust gas−13.246∗∗∗−8.20254∗∗∗−6.71452∗∗∗−9.14431∗∗∗SmoothΔexhaust gas−12.472∗∗∗−8.0203∗∗∗−7.2352∗∗∗−11.1865∗∗∗SmoothTECINN−4.8807∗∗∗0.9227756.136354.2141Not smoothΔTECINN−17.4002∗∗∗−9.52198∗∗∗186.539∗∗∗−12.9367∗∗∗SmoothNote.∗∗∗, ∗∗, and ∗ shows significance at the 1% level, 5% level, and 10% level, respectively.△ represents the 1st order difference.


## 4.2.2. Cointegration Test

According to the cointegration test method proposed by Baltagi and Kao and Pedroni [34, 35], this study cointegrated the data of technological innovation at comprehensive level, wastewater discharge, solid waste, and exhaust emissions, and the results are shown in Table 4. According to the results of the cointegration test, there is a cointegration relationship between the comprehensive level of technological innovation and the data on environmental pollution emission (Table 5).Table 4
Results of the optimal lag order test.
VariableLagAICBICHQICTECINN-wastewater119.51∗20.25∗19.80∗219.61220.4519.95319.9120.8720.29419.8820.9820.32TECINN-solid waste115.04∗15.78∗15.34∗215.2416.0815.58315.7716.7316.16416.1717.2716.61TECINN-exhaust gas18.479.208.7628.819.669.15137.478.437.8646.30∗7.41∗6.75∗Table 5
Results of cointegration root test.
VariableTest statisticsTest resultConclusionTECINN-wastewaterPanel v-Statistic2.61∗∗∗Cointegration relationship existsPanel rho-Statistic−0.19Panel PP-Statistic−5.99∗∗∗Panel ADF-Statistic−0.66Group rho-Statistic2.43Group PP-Statistic−5.92∗∗∗Group ADF-Statistic−1.21TECINN-solid wastePanel v-Statistic5.01∗∗∗Cointegration relationship existsPanel rho-Statistic−0.14Panel PP-Statistic−0.60Panel ADF-Statistic−3.30∗∗∗Group rho-Statistic2.06Group PP-Statistic−0.82Group ADF-Statistic−1.50TECINN -exhaust gasPanel v-Statistic5.53∗∗∗Cointegration relationship existsPanel rho-Statistic−5.03∗∗∗Panel PP-Statistic−10.32∗∗∗Panel ADF-Statistic−8.59∗∗∗Group rho-Statistic−1.71∗∗Group PP-Statistic−10.91∗∗∗Group ADF-Statistic−9.59∗∗∗Note.∗∗∗ means p<0.01; ∗∗ means p<0.05; and ∗ means p<0.1.


## 4.2.3. PVAR Model Regression Analysis

The optimal lag order of the model needs to be determined to test the PVAR model [36]. The existing literature is mainly selected by Akaike’s information criterion (AIC), Bayesian information criterion (BIC), Hannan and Quinn information criterion (HQIC) to judge the optimal lag order [37, 38]. The test results are shown in Table 4. According to the order with the most passes of all inspection values as the criterion for determining the optimal lag order, we can find that in technological innovation-wastewater and technological innovation-solid waste, the optimal lag period is 1, while the lag period of technological innovation and exhaust gas is 4.


## 4.2.4. Impulse Response Analysis

Impulse response analysis can help analyze how technological innovation affects pollutant emissions. This study sets the number of responses to 10 periods and Monte Carlo simulations to 1000 times to analyze the impulse impact of technological innovation on wastewater discharge, solid waste generation, and exhaust gas emissions. The results are shown in Figure3. In Figure 3, the abscissa represents the lag period, the ordinate represents the degree of impulse response, the two curves at the top and bottom represent the upper and lower bounds of the 95% confidence interval, respectively, and the intermediate curve stands for the value of the impulse response.Figure 3
Impulse response results of technological innovation to environmental pollution: (a) impulse response of wastewater to TECINN, (b) impulse response of waste solid to TECINN, and (c) impulse response of exhaust gas to TECINN.
(a)(b)(c)Figure3(a) shows the response of wastewater discharge to technological innovation. Figure 3(a) shows that after receiving a standard deviation impulse from technological innovation, the wastewater discharge remains with a positive response in the 0th period until reaching a peak in the5th lag phase, eventually, technological innovation has a positive effect that leads to an increase in wastewater discharge. Figure 3(b) indicates that technological innovation has an initial positive impulse on solid waste discharge, which gradually weakens over time. Figure 3(c) shows a negative response of the exhaust gas, but over time, however, in the long term, technological innovation promotes the increase in exhaust emissions. These findings are consistent with the study by Kumar and Managi, who found that technological innovation in developing countries promotes rather than curbs CO2 emissions [11].


## 4.2.5. Variance Decomposition

Impulse response analysis cannot be used to evaluate the importance of different impulses to specific variables [33]. Therefore, a variance decomposition is utilized to analyze the contribution of various structural impulses to the fluctuation of environmental pollution. The results are shown in Table 6.Table 6
Variance decomposition.
Variable periodWastewaterSolid wasteExhaust gasWastewaterTECINNSolid wasteTECINNExhaust gasTECINN11.0000.0001.0000.0001.0000.00020.9980.0021.0000.0000.8710.12930.9960.0040.9990.0010.6860.31440.9920.0080.9990.0010.5440.45650.9890.0110.9980.0020.4740.52660.9850.0150.9980.0020.4310.56970.9820.0180.9970.0030.3980.60280.9790.0210.9970.0030.3580.64290.9770.0230.9970.0030.3410.659100.9750.0250.9960.0040.3390.661110.9730.0270.9960.0040.3370.663120.9720.0280.9960.0040.3370.663Table6 reflects that in the 1st period, 100% of the explanatory power of the change in wastewater comes from itself, but as time passes, the impact of wastewater discharge on itself gradually decreases, and the impact of technological innovation on wastewater discharge gradually increases. By the 12th period, 97.2% of the change in wastewater discharge can be explained by itself, and the contribution of technological innovation to it reaches 2.8%. Based on the above structure of variance decomposition, it can be concluded that the change of China’s wastewater discharge is mainly affected by itself, and the influence of technological innovation on wastewater discharge is not significant. In the discharge of solid waste, the explanatory power of the change in solid waste is 100% in the 1st period. With the passage of time, the impact of solid waste emissions on its own shows a gradual downward trend, and the impact of technological innovation on it shows a gradually enhancing trend. By the 12th period, 99.6% of the change in solid waste emissions can be explained by itself, and technological innovation contributes 0.4% to solid waste. Similar to the conclusion on wastewater discharge, the change in China’s solid waste discharge is mainly influenced by itself, while technological innovation has little impact on it. In the case of the emission change of exhaust gas, the explanatory power of the change in exhaust gas is 100% in the 1st period. However, with the passage of time, the impact of exhaust gas emissions on itself shows a gradual decrease, while the impact of technological innovation increases. However, it is worth noting that by the 12th period, 33.7% of the variation in exhaust gas can be explained by itself, and 66.3% of this change was attributable to technological innovation. According to the results, China’s exhaust gas emissions are initially affected by itself, but in the long term, they are mainly influenced by technological innovation.


## 5. Conclusion

### 5.1. Conclusion and Policy Implications

Based on provincial panel data of China from 2004 to 2016, this study analyzes the impact mechanisms of technological innovation on environmental pollution and then empirically analyzes how China’s technological innovation affects the discharge of wastewater, solid waste, and waste gas. This is the biggest difference between this study and the existing literature, which failed to analyze different pollutant emissions.In conclusion, the level of technological innovation in China has been rising steadily. However, technology plays a promoting rather than inhibiting role in the discharge of wastewater, solid waste, and waste gas emissions, which is consistent with existing research. For example, Yu and Du found that China’s independent technological innovation activities would accelerate CO2 emissions, especially when the speed of economic growth slows down [9]. Kumar and Managi indicated that developing countries tend to promote CO2 emissions while technologically advancing [11]. In addition, Li et al. have found that technological progress in central and western China has increased the discharge of water pollutants [39]. The fundamental reason why technological innovation has promoted the emission of pollutants is that, in the development process, China focused on improving economic effectiveness while ignoring environmental benefits. As a result the environmental costs in China were low, or in some cases, nonexistent. Meanwhile, a lack of environmental awareness has caused pollution. We can expect that with the slowdown of economic growth, technological innovation will further promote the emission of pollutants [9]. We also found that the changes in China’s wastewater discharge and solid waste production are mainly due to their own influence. However, variations in exhaust emissions, in the short term, are mainly influenced by themselves. In the long term, the influence of technological innovation on exhaust emissions gradually amplify and eventually surpass the influence of its own.We propose that the government should further improve the system and policies for environmental pollution control, which are needed to establish strict discharge standards and protection laws. In addition, China must invest in the development of environmentally friendly technology, even when facing an economic recession. In this case, to achieve the coordinated development of the economy and the environment, it is essential to decrease the negative impact of pollution emissions and promote sustainable economic development.


### 5.2. Research Limitation and Prospect

In the context of increasingly serious global environmental pollution and the continuous development of technology, this study takes China as an example to empirically analyze the impact of technological innovation on pollutant emissions. A limitation of this study is that we adopted the entropy method and the PAVR model. Future research could use other approaches to analyze the complex relationship between technological innovation and environmental pollutant emissions. In addition, due to the significantly uneven development of economic levels, technological innovation, and energy consumption among different regions in China, it is necessary to further broaden the research objects and data sources in the future to enrich and improve the conclusions of this paper.


## 5.1. Conclusion and Policy Implications

Based on provincial panel data of China from 2004 to 2016, this study analyzes the impact mechanisms of technological innovation on environmental pollution and then empirically analyzes how China’s technological innovation affects the discharge of wastewater, solid waste, and waste gas. This is the biggest difference between this study and the existing literature, which failed to analyze different pollutant emissions.In conclusion, the level of technological innovation in China has been rising steadily. However, technology plays a promoting rather than inhibiting role in the discharge of wastewater, solid waste, and waste gas emissions, which is consistent with existing research. For example, Yu and Du found that China’s independent technological innovation activities would accelerate CO2 emissions, especially when the speed of economic growth slows down [9]. Kumar and Managi indicated that developing countries tend to promote CO2 emissions while technologically advancing [11]. In addition, Li et al. have found that technological progress in central and western China has increased the discharge of water pollutants [39]. The fundamental reason why technological innovation has promoted the emission of pollutants is that, in the development process, China focused on improving economic effectiveness while ignoring environmental benefits. As a result the environmental costs in China were low, or in some cases, nonexistent. Meanwhile, a lack of environmental awareness has caused pollution. We can expect that with the slowdown of economic growth, technological innovation will further promote the emission of pollutants [9]. We also found that the changes in China’s wastewater discharge and solid waste production are mainly due to their own influence. However, variations in exhaust emissions, in the short term, are mainly influenced by themselves. In the long term, the influence of technological innovation on exhaust emissions gradually amplify and eventually surpass the influence of its own.We propose that the government should further improve the system and policies for environmental pollution control, which are needed to establish strict discharge standards and protection laws. In addition, China must invest in the development of environmentally friendly technology, even when facing an economic recession. In this case, to achieve the coordinated development of the economy and the environment, it is essential to decrease the negative impact of pollution emissions and promote sustainable economic development.


## 5.2. Research Limitation and Prospect

In the context of increasingly serious global environmental pollution and the continuous development of technology, this study takes China as an example to empirically analyze the impact of technological innovation on pollutant emissions. A limitation of this study is that we adopted the entropy method and the PAVR model. Future research could use other approaches to analyze the complex relationship between technological innovation and environmental pollutant emissions. In addition, due to the significantly uneven development of economic levels, technological innovation, and energy consumption among different regions in China, it is necessary to further broaden the research objects and data sources in the future to enrich and improve the conclusions of this paper.



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