The Impact of Government and Public Dual-Subject Environmental Concerns on Urban Haze Pollution: An Empirical Research on 279 Cities in China

Abstract

Based on 279 cities in China from 2011 to 2019 as research samples, this study used a fixed-effect regression model to investigate the influence of government and public dual-subject environmental concerns on urban haze pollution. The results show that: (1) Government and public environmental concerns have a significant negative impact on urban haze pollution. The results are still valid after a series of robustness tests and controlling for endogenous problems. (2) Further research found that dual-subject environmental concerns have a stronger negative effect on urban haze pollution in areas where there is a low level of Internet development and in western regions. (3) Through the test of the intermediary mechanism, it can be seen that the environmental concerns of the government and the public can reduce haze pollution by reducing policy uncertainty and capital element misallocation. In general, the policy effect is greater than the capital allocation effect. The research conclusions of this study will help to deepen the interpretation of the role of the government and the public in environmental governance and also provide a reference for cities to further promote haze governance.

1. Introduction

In the process of industrialization, many countries have encountered serious haze pollution and governance problems. The Industrial Revolution began in the UK, which was also the first country to have environmental problems. China’s economy has expanded rapidly since the reform and opening up, but the extensive economic development model has seriously damaged the ecological environment. Many cities in China have suffered from haze in recent years. According to the 2021 China Eco-Environment status Bulletin (see more details at: https://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/ (accessed on 27 May 2022)), the average PM2.5 concentration in 339 cities in China was 30 μg/m³, which severely exceeded the average annual limit of 10 ug/m³ set by the World Health Organization [1]. Although, compared with 2011, China has made some progress in control, the haze problem is still serious (see Figure 1). Many studies have shown that severe haze pollution not only harms residents’ health, induces asthma, cancer, and other diseases, and increases mortality [2,3,4,5,6], but it also adversely affects economic growth [7,8,9,10,11]. Therefore, tackling haze pollution has become a key component of sustainable development.

In the process of environmental governance, the government and the public play an important role [12]. To control haze, the Chinese government has made many efforts; 12 provinces, including Beijing, Tianjin, Hebei, Shanghai, and Anhui, have proposed quantitative targets, such as reducing PM2.5 concentration and reducing pollutant emissions, in the air pollution prevention and control content of the government work report. In 2016, the newly revised Air Pollution Prevention and Control Law was officially implemented.

As the victims of environmental pollution incidents, the public’s participation in environmental governance can help reduce the loss of environmental resources caused by market and government failures. Pargal (1996) found that when government regulation “fails”, the informal regulation of public participation can play an effective role in monitoring polluters to undertake environmental improvement behaviors [13]. The “Measures for Public Participation in Environmental Protection” promulgated by China in 2015 and the “Measures for Public Participation in Environmental Impact Assessment” were officially implemented on 1 January 2019, both clarifying that the public can use e-mail and other traditional channels to express their environmental demands. This formed a multi-governance structure. Therefore, the core issue of this paper is how dual-subject environmental concern impacts haze pollution. What is the difference between the impact of government and public dual-subject concern on urban haze pollution under different Internet development levels and geographical locations in cities? What is the main intermediate mechanism? Which one is more effective? The solution to the above problems is conducive to correctly understanding the relationship between dual-subject environmental concerns and haze pollution. It is important for the city to implement proper pollution prevention and control.

The main contributions of this study lie in the following three aspects: First, this paper incorporated government environmental concerns, public environmental concerns, and urban haze pollution into the same framework. Compared with existing research, this study will help to deepen the interpretation of the role of the government and the public in environmental governance. Second, we found the mediating role of environmental policy uncertainty and capital factor mismatch and compared the mediation effect of the government and the public in terms of policy and resource allocation. Third, in addition to dividing the sample into traditional eastern cities, central cities, and western cities, this paper also discusses the impact of government and public environmental concerns on haze pollution at different Internet development levels based on the number of broadband-access households.

The rest of the paper is structured as follows. Section 2 includes a literature review and research hypotheses. In Section 3, the methods and data used in this study are briefly explained. Section 4 presents the empirical results. Section 5 provides a discussion of the results. Section 6 provides conclusions and future research orientations.

2. Literature Review and Research Hypotheses

2.1. Literature Review

Environmental Concern and Urban Haze Pollution

The concept of environmental concern emerged in the 1970s, and the most widely accepted definition is provided by Dunlap [14]. He defined environmental concern as the degree to which people are aware of environmental problems and support their solution or the degree to which people are willing to make personal efforts to solve them.

Most existing research on government environmental concerns focuses on specific government measures. First, environmental regulation as an essential tool to alleviate environmental problems has been widely studied and applied in various countries and regions [15]. Second, increasing expenditure on environmental protection is also an important measure of the government. For example, Huang, J.-T. (2018) took 30 provinces in China from 2008 to 2013 as a research sample, using the spatial Durbin model, and found that government spending on environmental protection can effectively reduce sulfur dioxide emissions [16]. In addition, the government also performs other measures, such as strengthening the environmental label certification of enterprises and supporting the development of new energy enterprises [17,18,19]. In summary, many scholars have confirmed the important role of the government in environmental governance.

However, the relationship between public environmental concern and environmental pollution is inconclusive. The first view is that public environmental concerns can help reduce haze pollution. Wu Wenqi et al. (2021) used a dynamic spatial econometric model to find that the public opinion of new media networks has a deterrent and supervisory effect on corporate pollution and has a significant positive effect on reducing haze pollution [20]. Long, F et al. (2022) use a fixed-effects model to find that public environmental issues can mitigate differences in urban and rural pollution intensity by influencing the government’s regulatory behavior [21]. The second view is that public environmental concerns cannot impact environmental pollution. Chao, H et al. (2016) used the number of letters and visits to express public appeals. The study found that public requests will not lead to an increase in environmental regulation investment, so environmental pollution cannot be reduced [22]. A possible reason for this was that the traditional petition methods do not play a useful role in supervision.

At present, many scholars have carried out a large amount of research on the relationship between government environmental concern, public environmental concern, and environmental pollution. However, the literature incorporating government environmental concern and public environmental concern into the same research framework is limited, and few scholars analyze the mediating role of environmental policy and capital factor allocation in their relationship. Therefore, this paper incorporates the dual-subject environmental concerns of the government and the public, environmental policy uncertainty, capital element mismatch, and haze pollution into a unified framework and discusses the relationship between them.

2.2. Research Hypotheses

2.2.1. Government Environmental Concern and Urban Haze Pollution

First, the government’s environmental concern is where its responsibilities lie. For the first time, China’s Sixth Five-Year Plan has incorporated environmental protection, with a separate chapter specifying its goals, tasks, key tasks, and implementation measures. Second, China incorporates environmental protection into the performance appraisal of officials, creating competition among local governments [23]. Environmental issues have increasingly become the focus of government attention. When the problem of haze pollution occurs, the government has always played an important role in resource allocation and regulatory policies for environmental governance [24].

In terms of environmental policy, China’s current Air Pollution Prevention and Control Law was enacted in 1987 and was revised twice in 1995 and 2000. In 2013, the Chinese government formulated the Air Pollution Prevention Action Plan. The plan proposed to speed up the formulation (revision) of emission standards for key industries, as well as standards for vehicle fuel consumption, oil products, and heating measurement standards. In 2014, China revised the Environmental Protection Law, which is known as the most stringent environmental regulation in history. In 2017, the Chinese government included Resolutely Fighting the Blue Sky Defense War in the Government Work Report. In addition, the government will further strengthen the construction of the joint prevention and control standard system for air pollution, etc. The coverage and operability of environmental policies continue to increase, and policy blanks continue to decrease. The existing environmental policies have been continuously improved, and the uncertainty of environmental policies has been reduced. Existing research shows that environmental policy uncertainty is not conducive to pollution control and may also exacerbate pollution. For example, Kalamova M. et al. (2012) examined the impact of policy uncertainty on environmental patenting activity in 23 OECD countries from 1986 to 2007. They found that a 10% increase in policy uncertainty resulted in a 1.2–2.8% decrease in environmental patenting activity [25]. Atsu, F. et al. (2021) found that economic policy uncertainty increases carbon monoxide emissions both in the short and long term [26]. In addition, increased economic policy uncertainty can lead to increased carbon emissions [27]. From this, it can be seen that the more attention the government pays to the environment, the less uncertainty there will be in environmental policy and the more effective haze pollution control will be.

Regarding the allocation of capital factors, the Chinese government has adjusted the direction of capital investment to solve environmental problems. On the one hand, given the vital role of technological innovation in reducing haze pollution, the Chinese government has accelerated its investment in technological innovation. At the same time, the government encourages enterprises to speed up the upgrading of production technology. This encourages the technological innovation of enterprises by reducing taxes, granting subsidies, and lowering the threshold for technical innovation loans [28]. The government increases the construction of technical talent systems and talent training by expanding the scale of professional talent enrollment. In addition, the government has set industry thresholds for foreign capital, especially the threshold of environmental protection indicators, to restrict the inflow of foreign capital to high-pollution and high-emission enterprises and realize the clean investment and application of foreign capital at the source [29]. On the other hand, by lowering the loan financing threshold for advanced industrial ports, the government encourages enterprises to implement the value chain climbing strategy and enterprise technological innovation to achieve industrial upgrading [30,31,32]. In addition, considering the importance of clean energy in reducing pollution emissions, the Chinese government encourages enterprises to implement energy conversion and upgrades to replace traditional energy with clean energy, reduce the burning of fossil energy, and thus reduce the emission of coarse solids [33,34,35]. Therefore, the government will optimize the allocation of capital elements to reduce haze pollution by adjusting the flow of factors to technological innovation, talent training, low-energy-consumption industries, and clean energy.

In sum, government environmental concerns can reduce haze pollution by reducing environmental policy uncertainty and capital factor mismatch. Therefore, this study proposes research Hypothesis 1:

Hypothesis 1.

Following increased government environmental concern, the urban haze pollution will be lower.

Hypothesis 1a.

Government environmental concerns can impact haze pollution by reducing environmental policy uncertainty.

Hypothesis 1b.

Government environmental concerns can impact haze pollution by reducing capital factor misallocation.

2.2.2. Public Environmental Concern and Urban Haze Pollution

Public concerns about the environment are diverse and complex, including environmental perception, environmental attitudes, and environmental behaviors, etc. It can be seen that environmental perception is the premise for the generation of public environmental concern. Haze weather is different from other weather and has a strong environmental awareness [24]. On the one hand, the public can clearly detect the odor in the air or judge the severity of haze based on air visibility. On the other hand, the Ministry of Environmental Protection of China has implemented a third-party platform for air quality monitoring in major cities and implemented environmental information data disclosure. The public can access air-quality-related data across the country anytime and anywhere through mobile Internet clients [24].

According to Maslow’s demand theory, with the development of the social economy and the improvement of public income level and education level, people increasingly pay attention to their living environment, especially the demand for a better ecological environment. To achieve greater happiness and satisfaction, the public expresses environmental concerns in various ways, such as “vote with hands”, “vote with feet”, and “vote with money”. Dong K. et al. (2017) used the contingent valuation method (CVM) and interval regression model to test the value of public willingness to pay (WTP) for three haze mitigation options in a Beijing urban area. Survey results showed that more than 80% of respondents are willing to pay for haze reduction in exchange for cleaner air [36]. As an important force in environmental governance, the public also plays an important role in environmental policy and resource allocation.

In terms of policy, the public notifies the government of behaviors that damage the environment through petitions, complaints, Internet platforms, etc., which helps the government to reduce information asymmetry and formulate policies more accurately. Especially with the rapid development of Internet technology and mobile client technology, it is easier for the public to participate in smog management [37]. The public can search and track environmental issues closely related to individuals through an Internet platform and express personal opinions, implement personal supervision rights, and provide policy recommendations on environmental issues. The government can analyze environmental topics of public concern through the management and control of media information and through big data analysis methods [38]. The government can not only obtain effective information and reduce the information asymmetry between the government and enterprises but also reduce the cost of government supervision [39,40,41]. Therefore, the government can help to optimize policies and measures for environmental governance by aggregating and verifying the information and suggestions fed back by the public. At the same time, this improves the objective, pertinence, and precision of environmental governance policies, reduces the disorder and implementation costs of environmental policies, and thus achieves the goal of reducing haze pollution [42,43].

In terms of capital factor allocation, the increase in public environmental concerns will increase the demand for green products and encourage enterprises to invest in environmental governance. Especially in the competitive environment of the survival of the fittest, to gain more market share, companies will start to manufacture environmentally friendly products, develop low-carbon technologies, etc., to improve environmental performance [44] and establish a green image of the company to gain competitiveness. Enterprises with high energy consumption, high pollution, and high emissions will be eliminated from the market due to environmental pollution. Guo, M. et al. (2020) found that the higher the public environmental concern, the lower the return on the stock of polluting companies [45]. Therefore, the public environmental concern will allow capital to flow into green and environmental protection projects, and reducing investment in polluting enterprises will help to optimize the industrial structure and achieve the goal of reducing haze pollution.

In the process of public participation in environmental protection, the information asymmetry between the government and the public can be effectively reduced, so that the government can implement policies more accurately and effectively to achieve the goal of reducing urban haze. At the same time, public environmental concern can also achieve the goal of reducing haze pollution by reducing the mismatch of capital factors. Therefore, this study proposes research Hypothesis 2:

Hypothesis 2.

Following increased public environmental concern, the urban haze pollution will be lower.

Hypothesis 2a.

Public environmental concern impacts haze pollution by reducing environmental policy uncertainty.

Hypothesis 2b.

Public environmental concern impacts haze pollution by reducing capital factor misallocation.

The theoretical mechanism diagram of the government and public’s dual-subject environmental concerns regarding urban haze pollution is shown in Figure 2.

3. Methodology and Data

3.1. Model Construction

3.1.1. Direct Effect Model

Drawing on the research of Long, F et al. (2022) [21], based on theoretical mechanisms and research hypotheses, to study the effects of government and public dual-subject environmental concerns on haze pollution in China, the following panel data model is constructed:

$$Haz{e}_{it}=\alpha +{\beta }_{1}E{C}_{it}+{\beta }_2{X}_{it}+{\gamma }_i+{\delta }_t+{\epsilon }_{it}$$

(1)

where $\alpha$ represents a constant term; i represents the city; t represents the year; Haze_it indicates haze pollution; EC_it indicates the government environmental concern or public environmental concern; X_it is the control variables, including per capita energy consumption (PCEC), industrial structure (IS), emission of industrial flue dust (Dust), greening level (Green), traffic condition (Traffic), level of industrial development (ID), education level (Edu), and sewage treatment rate (STR). ${\delta }_t$ is a fixed effect of time, and ${\gamma }_i$ is a fixed effect of individuals. To examine the impact of government and public dual-subject environmental concerns on urban haze pollution, we must pay attention to the coefficient sign and significance of ${\beta }_1$ in Formula (1).

3.1.2. Intermediary Effect Model

According to the main results, both government environmental concern and public environmental concern can effectively reduce haze pollution. Reducing environmental policy uncertainty and capital factor mismatches may be the main intermediary mechanisms. Therefore, according to Formula (1), this paper constructs Formulas (2) and (3) to form a mediation model. Then, this study uses the mediation model to test the mediation mechanism.

$$M_{it}=\alpha +{\beta }_{1}E{C}_{it}+{\beta }_2{X}_{it}+{\gamma }_i+{\delta }_t+{\epsilon }_{it}$$

(2)

$$Haz{e}_{it}=\alpha +{\beta }_{1}E{C}_{it}+{\beta }_2{X}_{it}+{\beta }_3{M}_{it}+{\gamma }_i+{\delta }_t+{\epsilon }_{it}$$

(3)

Here, $\alpha$ represents a constant term; i represents the city; t represents the year; Haze_it indicates haze pollution; EC_it indicates the government environmental concern or public environmental concern; $M_{it}$ indicates the mediation mechanism, including environmental policy uncertainty or capital factor mismatches; X_it is the control variables, including per capita energy consumption (PCEC), industrial structure (IS), emission of industrial flue dust (Dust), greening level (Green), traffic condition (Traffic), level of industrial development (ID), education level (Edu), and sewage treatment rate (STR). ${\delta }_t$ is a fixed effect of time, and ${\gamma }_i$ is a fixed effect of individuals. In order to test the mediating effect of environmental policy uncertainty and capital factor mismatch, we must pay attention to the significance of ${\beta }_1$ in Formula (2) and the significance of ${\beta }_3$ in Formula (3).

3.2. Sample Selection and Data Processing

This study took 279 cities in China from 2011 to 2019 as the research sample. The reasons for this are as follows: (1) Cities with missing data for three consecutive years were excluded. (2) A small amount of missing data was filled by interpolation. (3) Since the year of Baidu index data that can be obtained in this paper starts in 2011, in order to maintain consistency in the dates of the data, this paper started from 2011. (4) As the data of some Chinese prefecture-level cities involved in the article were only updated to 2019, the end of the study period was set to 2019.

3.3. Variable Selection and Data Source

3.3.1. Dependent Variable

Haze pollution (PM2.5). This paper used the NASA raster data released by the Atmospheric Analysis Group of Dalhousie University in Canada, referred to the research of van Donkelaar Aaron et al. (2019) [46], and used ArcGIS software to further analyze the regional data set in China, as represented by the PM2.5 concentration values of local-level cities.

3.3.2. Independent Variable

Government environmental concern (GEC). The government generally releases signals through the Government Work Report. Chen et al. (2016) used the ratio of the total number of words in five terms, namely “environment”, “environmental protection”, “pollution”, “emission reduction”, and “energy consumption”, to the total number of words in the full text of the government work report as a proxy variable for government environmental governance [47]. Pan X et al. (2022) recorded the frequencies of words related to government environmental governance, such as “environmental protection”, “environmental governance”, and “PM2.5” and calculated the ratio of the total frequency of the above words to the total word frequency of the full text of the government work report, indicating government environmental concern [48]. According to existing research, this paper extracted “PM2.5”, “ecology”, “emission reduction”, “green”, “sulfur dioxide”, “chemical oxygen demand”, “pollution”, “energy consumption”, “environmental protection”, “air”, “PM10”, “pollution”, “carbon dioxide”, and “low carbon” word frequencies from the local government work report. Specifically, one was added to the total number of word frequencies of the above keywords, and the logarithm was considered to represent the government environmental concern. The higher the value, the higher the government concern about the environment.

Public environmental concern (PEC). This study drew on the work of Long, F et al. (2022) [21] and Li W. et al. (2021) [49] in using the Baidu search index to characterize public environmental concerns. Specifically, the Baidu index webpage was searched for the subject words of “environmental pollution”, “sulfur dioxide”, “haze pollution”, and “sewage”; one was added to the total number of searches, and the logarithm was considered to characterize public environmental concerns. The higher the value, the higher the public concern about the environment.

3.3.3. Intermediary Variable

Environmental Policy Uncertainty (EPU). Baker et al. (2016) developed a new index of economic policy uncertainty (EPU) based on newspaper coverage frequency [50]. Brandon et al. (2016) pointed out that new and different planning approaches and policy development can help reduce uncertainty in climate change policy [51]. In order to combine the actual situation in China, this study used the number of revisions and releases of environmental protection standards in the “Twelfth Five-Year Plan for National Environmental Protection Standards” and the “Thirteenth Five-Year Plan for National Environmental Protection Standards” to measure environmental policy uncertainty. The larger the value, the smaller the environmental policy uncertainty.

Capital misallocation (CM). This study drew on the method of Chen Y et al. (2011) to measure the capital misallocation [52]. The higher the value, the higher the return on capital, so the lower the capital misallocation.

3.3.4. Control Variable

On the basis of the studies of Ma, R. et al. (2019) [53], Zhou Q et al. (2019) [54], Jia, R et al. (2021) [55], and Yuan, H et al. (2021) [56], we introduce the following control variables: (1) per capita energy consumption (PCEC), which is measured using the ratio of the total energy consumption of each city to the total number of permanent residents and take the logarithm; (2) industrial structure (IS), which is measured by the proportion of secondary industry output in GDP; (3) emission of industrial flue dust (Dust), which is measured using the logarithm of industrial flue dust in the current year; (4) greening level (Green), which is measured using the greenery area of the per capita park; (5) traffic condition (traffic), which is measured using the actual number of buses operating in the municipal district at the end of the year; (6) level of industrial development (ID), which is measured using the number of industrial enterprises above the designated size; (7) education level (Edu), which is measured using the number of college students per 10,000 people; (8) sewage treatment rate (STR). The descriptive statistics and definitions of all variables utilized in this study are reported in

Table 1. Statistical description of variables.

	Variable	Definition	Obs	Mean	Std. Dev.	Min	Max
Dependent variable	Haze	Urban haze pollution concentration	2415	6.180	6.260	0.220	71.000
Independent variable	GEC	Government environmental concern	2415	4.110	1.100	0.000	7.340
	PEC	Public environmental concern	2415	3.430	0.490	0.000	4.710
Intermediary variable	EPU	Environmental policy uncertainty	2415	7.079	0.315	6.800	7.440
	CM	Capital misallocation	2415	0.700	0.240	0.010	2.880
Control variable	PCEC	Per capita energy consumption	2415	0.510	3.750	0.010	166.630
	IS	Industrial structure	2415	0.450	0.140	0.040	0.990
	Dust	Emission of industrial flue dust	2415	9.720	1.160	4.030	15.460
	Green	Greening level	2415	39.940	10.100	0.360	376.580
	Traffic	Traffic condition	2415	1614.920	3101.620	29.000	35,809.000
	ID	Level of industrial development	2415	1272.260	1505.400	20.000	11,042.000
	Edu	Education level	2415	9.770	17.010	0.020	120.000
	STR	Sewage treatment rate	2415	90.000	10.540	24.000	100.000

.

3.4. Data Source

The data sources for this study are China Urban Statistical Yearbook (2012~2020), public data from Dalhousie University in Canada (2011~2019), and the government work report (2011~2019).

4. Empirical Results

4.1. Estimation Result of the Direct Effect

The panel data need to be tested by Hausman before deciding whether to select a fixed-effect model or a random effect model [15]. In this paper, the Hausman test was carried out on the government environmental concern and urban haze pollution and the public environmental concern and urban haze pollution, which all obtained Prob > chi2 = 0.0000. Therefore, the fixed-effects model was applicable in this paper.

To estimate the results accurately, this paper used the fixed-effects model without and with control variables in turn. The impact of the government environmental concerns and the public environmental concerns on urban haze pollution, respectively, was estimated.

The test of the relationship between the government environmental concern and urban haze pollution is presented in columns (1) and (3) of

Table 2. Benchmark regression.

	Haze	Haze	Haze	Haze
VARIABLES	(1)	(2)	(3)	(4)
GEC	−0.901 ***		−0.460 ***
	(0.068)		(0.062)
PEC		−0.645 ***		−0.339 ***
		(0.052)		(0.052)
PCEC			0.002	−0.005
			(0.007)	(0.007)
IS			3.780 ***	4.429 ***
			(0.470)	(0.481)
Dust			0.599 ***	0.622 ***
			(0.039)	(0.038)
Green			−0.002	−0.002
			(0.003)	(0.003)
Traffic			−0.0001 **	−0.0001 *
			(0.000)	(0.000)
ID			−0.0004 ***	−0.0004 ***
			(0.000)	(0.0001)
Edu			−0.063 ***	−0.051 ***
			(0.012)	(0.012)
STR			−0.018 ***	−0.015 ***
			(0.003)	(0.003)
Constant	9.270 ***	8.833 ***	3.309 ***	2.018 ***
	(0.235)	(0.213)	(0.638)	(0.601)
F statistic	201.130 ***	199.240 ***	220.310 ***	219.080 ***
Observations	2415	2415	2415	2415
R-squared	0.076	0.068	0.297	0.294
Number of id	279	279	279	279

Notes: *, **, and *** represent the significance level at 10%, 5%, and 1%, respectively, and standard errors are shown in parentheses.

. It can be seen from columns (1) and (3) that, regardless of whether control variables were added or not, there was a negative correlation between government environmental concerns and urban haze pollution, which was significant at the 1% level. When the control variable was added, the adjusted R² increased from 0.076 to 0.297, which improved the explanatory power of the model. Hypothesis H₁ of this study was basically confirmed. The results show that with the increase in the government’s environmental concern, the level of environmental management will be improved, and haze pollution will be reduced.

The test of the relationship between the public environmental concern and urban haze pollution is presented in columns (2) and (4) of Table 2. It can be observed from columns (2) and (4) that regardless of whether control variables were added or not, there is a negative correlation between the public environmental concern and urban haze pollution, which was significant at the 1% level. When the control variable was added, the adjusted R² increased from 0.068 to 0.294, which improved the explanatory power of the model. The research results show that the higher the public’s environmental concerns, the lower the urban haze. Research Hypothesis H2 of this paper was basically confirmed.

4.2. Robustness Tests

4.2.1. Replacing Measures of Explanatory Variables

To further verify the reliability of the estimated results, this paper used the word frequency method for “PM2.5” and “haze pollution” to measure government environmental concerns and public environmental concerns, respectively. In order to avoid the interference of zero values, one was added to obtain the logarithm, and the model was entered. The results are shown in columns (1) and (2) of

Table 3. Robustness test.

	(1)	(2)	(3)	(4)
	Replacing measures of explanatory variables		Explanatory variables lagging by one period
VARIABLES	Haze	Haze	Haze	Haze
GEC	−0.219 ***
	(0.069)
PEC		−0.116 ***
		(0.022)
L.GEC			−0.266 ***
			(0.068)
L.PEC				−0.508 ***
				(0.053)
PCEC	0.0004	−0.001	0.002	−0.006
	(0.007)	(0.007)	(0.008)	(0.008)
IS	3.804 ***	4.357 ***	4.632 ***	4.478 ***
	(0.475)	(0.484)	(0.538)	(0.527)
Dust	0.630 ***	0.636 ***	0.573 ***	0.558 ***
	(0.039)	(0.038)	(0.043)	(0.042)
Green	−0.003	−0.002	−0.007	−0.007
	(0.00305)	(0.00304)	(0.00479)	(0.00470)
Traffic	−0.0001 ***	−0.00008 **	−0.0001 ***	−0.00007 *
	(0.000)	(0.000)	(0.000)	(0.000)
ID	−0.0005 ***	−0.0004 ***	−0.0003 **	−0.0001
	(0.000)	(0.000)	(0.000)	(0.000)
Edu	−0.063 ***	−0.053 ***	−0.037 ***	−0.016
	(0.012)	(0.012)	(0.014)	(0.014)
STR	−0.021 ***	−0.015 ***	−0.019 ***	−0.012 ***
	(0.003)	(0.003)	(0.004)	(0.00393)
Constant	1.736 ***	0.979	2.404 ***	3.564 **
	(0.604)	(0.613)	(0.766)	(1.620)
Observations	2415	2415	2102	2102
F statistic	214.910 ***	217.490 ***	199.700 ***	207.490 ***
R-squared	0.283	0.289	0.286	0.312
Number of id	279	279	278	278

Notes: *, **, and *** represent the significance level at 10%, 5%, and 1%, respectively, and standard errors are shown in parentheses.

.

It can be seen from column (1) that the regression coefficient between government environmental concerns and urban haze pollution was −0.219, p (<0.01). It can be seen from column (2) that the regression coefficient between public environmental concerns and urban haze pollution was −0.116 p (<0.01). This result is consistent with the main results, demonstrating the robustness of the main results.

4.2.2. Explanatory Variables Lagged by One Period

There may be a time lag effect between the government and the public starting to pay attention to haze and affecting haze pollution. Therefore, this paper used the core explanatory variables government environmental concern and public environmental concern lagging by one period to introduce into the regression model. The results are shown in columns (3) and (4) of Table 3. The regression results were still significantly negative at the 1% level. This is consistent with the main results, which further supports the robustness of the core conclusions of the article.

4.2.3. Endogenous Test

This study may pose a reverse causality problem and lead to the emergence of endogeneity problems, which will affect the consistency of the estimated results. Therefore, this paper attempted to solve this problem by adopting the instrumental variable method. In this paper, the haze pollution variable with one lag period was used as the instrumental variable of this paper, and the dynamic system GMM was used for testing. Using the dynamic GMM model, we aimed to determine whether consistent estimates require first-order autocorrelation in the residual sequence term of the regression model, with no sign of second-order autocorrelation [42]. The regression results are shown in

Table 4. Endogenous test.

	(1)	(2)
VARIABLES	Haze	Haze
GEC	−0.875 ***
	(0.157)
PEC		−0.270 ***
		(0.080)
PCEC	0.003 ***	−0.003 *
	(0.001)	(0.002)
IS	3.931 ***	4.718 ***
	(1.305)	(1.415)
Dust	0.811 ***	0.955 ***
	(0.119)	(0.126)
Green	−0.011	−0.013
	(0.014)	(0.015)
Traffic	−0.0001 *	−0.0001
	(0.000)	(0.000)
ID	0.0002	0.0002
	(0.000)	(0.000)
Edu	−0.029	−0.025
	(0.029)	(0.031)
STR	−0.011	−0.011
	(0.009)	(0.010)
Observations	1824	1824
Number of id	278	278
AR (1) p	−3.260 [0.001]	−3.420 [0.001]
AR (2) p	−0.500 [0.614]	−0.150 [0.882]
Hansen p	270.780 [0.492]	274.890 [0.423]

Notes: * and *** represent the significance level at 10% and 1%, respectively, and standard errors are shown in parentheses.

.

First, the coefficient of the government environmental concern was found to be −0.875 (p < 0.01), which proves the robustness of the main regression results. The coefficient of the public environmental concern was found to be −0.270 (p < 0.01), which proves that the main regression results are still robust after controlling for endogeneity issues.

Second, according to the empirical results from AR (1) and AR (2) of Table 4, we conclude that there is a first-order autocorrelation in the residual sequence term of the regression model, with no sign of second-order autocorrelation. The Hansen p value indicates that it is impossible to reject the validity of instrumental variables, indicating that the estimation results of the GMM are effective and reliable. The results once again validate the robustness of the main results.

4.3. Heterogeneity Test

4.3.1. Internet Development Level

As general information technology, the Internet has penetrated all aspects, levels, and fields of society and has had a huge impact on economic and social development [57]. As an important support for today’s information transmission, the Internet provides technical and platform support for public environmental concerns and government environmental concerns. As a result, cities with better Internet development will pay more attention to environmental concerns. Therefore, based on the number of Internet access users, this paper divided the sample into areas with more Internet access users and areas with low numbers of Internet access users, representing areas with high Internet access and areas with low Internet access, respectively. The regression results are shown in

Table 5. Heterogeneity effect analysis—Internet development level.

	H	L	H	L
VARIABLES	Haze	Haze	Haze	Haze
GEC	−0.339 ***	−0.439 ***
	(0.086)	(0.099)
PEC			−0.211 **	−0.253 ***
			(0.091)	(0.075)
PCEC	−0.592 **	0.002	−0.590 **	−0.004
	(0.296)	(0.008)	(0.298)	(0.008)
IS	5.648 ***	2.287 ***	5.645 ***	3.133 ***
	(0.755)	(0.751)	(0.768)	(0.764)
Dust	0.491 ***	0.575 ***	0.531 ***	0.612 ***
	(0.060)	(0.065)	(0.059)	(0.064)
Green	0.004	−0.016 **	0.004	−0.016 **
	(0.003)	(0.006)	(0.003)	(0.006)
Traffic	−0.000006	−0.000003	−0.000004	−0.000003
	(0.000)	(0.000)	(0.000)	(0.000)
ID	−0.0003 ***	−0.0003	−0.0003 **	−0.000
	(0.000)	(0.000)	(0.000)	(0.000)
Edu	−0.044 ***	−0.125 *	−0.039 ***	−0.096
	(0.012)	(0.065)	(0.012)	(0.067)
STR	−0.038 ***	−0.006	−0.035 ***	−0.003
	(0.008)	(0.004)	(0.008)	(0.004)
Constant	4.485 ***	3.278 ***	3.487 ***	1.592 *
	(1.157)	(0.950)	(1.124)	(0.874)
F statistic	64.790 ***	204.090 ***	63.510 ***	202.100 ***
Observations	1197	1198	1197	1198
R-squared	0.353	0.160	0.346	0.152
Number of id	224	216	224	216

Notes: *, **, and *** represent the significance level at 10%, 5%, and 1%, respectively, and standard errors are shown in parentheses.

.

In areas with better Internet development, the regression coefficients of government environmental concerns and public environmental concerns regarding haze pollution were −0.339 (p < 0.01) and −0.211 (p < 0.05), respectively. In areas with limited Internet development, the regression coefficients of government environmental concerns and public environmental concerns regarding haze pollution were −0.439 (p < 0.01) and −0.253 (p < 0.01), respectively.

The regression results showed that, in areas with a high level of Internet development, the significance or coefficient between government environmental concerns and public environmental concerns regarding haze pollution was lower than that in areas with a low level of Internet development.

4.3.2. Regional Heterogeneity

China is a large country with a vast territory, and cities in different locations have great differences in resource endowment, factor rationing, and policy implementation [30]. Regression analysis based on an overall city sample may mask regional differences. Therefore, this paper divided the sample into eastern cities, central cities, and western cities, in order to investigate the differential impact of government environmental concern and public environmental concern regarding haze pollution. The results are shown in

Table 6. Heterogeneity effect analysis—regional heterogeneity.

	Government Environmental Concern			Public Environmental Concern
	East	Central	West	East	Central	West
VARIABLES	Haze	Haze	Haze	Haze	Haze	Haze
GEC	−0.230 ***	−0.359 ***	−0.596 ***
	(0.070)	(0.090)	(0.158)
PEC				−0.236 ***	−0.243 ***	−0.262 **
				(0.066)	(0.084)	(0.117)
PCEC	−1.062 ***	−0.307	0.00369	−0.910 ***	−0.362	−0.003
	(0.226)	(0.400)	(0.0105)	(0.232)	(0.402)	(0.011)
IS	3.701 ***	4.284 ***	3.914 ***	4.009 ***	4.787 ***	5.063 ***
	(0.492)	(0.709)	(1.290)	(0.506)	(0.735)	(1.287)
Dust	0.436 ***	0.516 ***	0.745 ***	0.468 ***	0.539 ***	0.760 ***
	(0.050)	(0.050)	(0.102)	(0.050)	(0.050)	(0.103)
Green	0.004 *	−0.012	−0.012	0.005 **	−0.013 *	−0.014
	(0.002)	(0.008)	(0.010)	(0.002)	(0.008)	(0.010)
Traffic	−0.0001 **	−0.0004 ***	0.00001	−0.00001	−0.0004 ***	0.0000004
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
ID	0.000001	−0.001 ***	−0.003 ***	0.000006	−0.001 ***	−0.003 ***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.001)
Edu	−0.019	−0.021	−0.070 **	−0.010	−0.015	−0.060 *
	(0.014)	(0.018)	(0.033)	(0.014)	(0.018)	(0.034)
STR	−0.014 ***	−0.013 ***	−0.014 **	−0.012 **	−0.012 ***	−0.011
	(0.004)	(0.004)	(0.007)	(0.005)	(0.004)	(0.008)
Constant	1.337 *	4.201 ***	5.169 ***	0.504	3.291 ***	3.189 **
	(0.783)	(0.917)	(1.613)	(0.748)	(0.882)	(1.501)
F statistic	84.350 ***	79.400 ***	279.200 ***	81.030 ***	71.830 ***	274.490 ***
Observations	876	857	682	876	857	682
R-squared	0.401	0.429	0.262	0.402	0.423	0.251
Number of id	100	98	81	100	98	81

Notes: *, **, and *** represent the significance level at 10%, 5%, and 1%, respectively, and standard errors are shown in parentheses.

.

The coefficients between government environmental concerns and haze pollution were −0.230 (p < 0.01), −0.359 (p < 0.01), and −0.596 (p < 0.01) in eastern, central, and western cities, respectively. The coefficients between public environmental concerns and haze pollution were −0.236 (p < 0.01), −0.243 (p < 0.01), and −0.262 (p < 0.05) in eastern, central, and western cities, respectively.

The regression results showed that, in the eastern city, the coefficient between government environmental concerns and public environmental concerns regarding haze pollution was lower than that.

4.4. Estimation Result of the Intermediary Effect

4.4.1. Analysis on the Mechanism of Government Environmental Concerns Affecting Urban Haze Pollution

The theoretical and empirical analyses indicated that the rapid increase in the pressure from the government’s environmental concern resulted in a significantly lower probability of environmental policy uncertainty and capital misallocation. The regression results are shown in

Table 7. The intermediary mechanism of government environmental concern.

	(1)	(2)	(3)	(4)
VARIABLES	EPU	Haze	CA	Haze
GEC	0.211 ***	−0.132 **	0.0271 ***	−0.453 ***
	(0.012)	(0.063)	(0.009)	(0.062)
PCEC	0.003 *	0.007	−0.001	0.002
	(0.001)	(0.007)	(0.001)	(0.007)
IS	−1.022 ***	2.194 ***	−0.434 ***	3.669 ***
	(0.092)	(0.461)	(0.067)	(0.475)
Dust	−0.190 ***	0.304 ***	−0.024 ***	0.593 ***
	(0.008)	(0.042)	(0.006)	(0.039)
Green	0.0002	−0.002	−0.003 ***	−0.003
	(0.001)	(0.003)	(0.0004)	(0.003)
Traffic	0.000005 ***	−0.000002	0.0000001	−0.0001 **
	(0.000)	(0.000)	(0.000)	(0.000)
ID	0.000009 ***	−0.00030 ***	−0.0001 ***	−0.0005 ***
	(0.000)	(0.000)	(0.000)	(0.000)
Edu	0.016 ***	−0.039 ***	−0.0005	−0.063 ***
	(0.002)	(0.012)	(0.002)	(0.012)
STR	0.009 ***	−0.004	0.006 ***	−0.017 ***
	(0.001)	(0.003)	(0.0004)	(0.003)
CA				−0.256 *
				(0.152)
EPU		−1.552 ***
		(0.106)
Constant	7.488 ***	14.930 ***	0.450 ***	3.424 ***
	(0.124)	(1.001)	(0.0912)	(0.642)
F statistic	4.99 ***	240.59 ***	33.11 ***	218.53 ***
Observations	2415	2415	2415	2415
R-squared	0.579	0.362	0.176	0.298
Number of id	279	279	279	279

Notes: *, **, and *** represent the significance level at 10%, 5%, and 1%, respectively, and standard errors are shown in parentheses.

.

Column (1) of Table 7 verifies the relationship between government environmental concerns and environmental policy uncertainty, and the regression coefficient was 0.211 (p < 0.01). The regression result means that the higher the government’s environmental concern, the lower the uncertainty of environmental policy. When the government pays more attention to environmental issues, it will pay attention to the formulation of environmental policies, optimize the deficiencies of existing policies, or promulgate new regulations to supplement existing policies. Compared with column (3) of Table 2, the regression coefficient of government environmental concern and haze pollution in column (2) of Table 7 changed from −0.460 to −0.132 (p < 0.05). At the same time, the regression coefficient of environmental policy uncertainty and haze pollution was −1.552 (p < 0.01), and the mediating effect has passed the significance test. This shows that the government’s environmental concerns can reduce haze pollution by reducing environmental policy uncertainty. The regression results verified the study Hypothesis H1a.

Column (3) of Table 7 verifies the relationship between government environmental concerns and capital factor mismatch, and the regression coefficient was 0.0271 (p < 0.01). The regression results mean that the higher the government’s attention to the environment, the smaller the mismatch of capital factors. When the government pays more attention to environmental issues, it will optimize the allocation of capital elements, so that funds are effectively invested and earmarked for specific purposes, to improve the return on capital and reduce the occurrence of insufficient or excessive capital investment. For example, the government will provide government subsidies for the new energy industry to support its development [19,28], while heavily polluting enterprises will levy environmental protection taxes to restrict their development to achieve the purpose of environmental protection and urban haze pollution reduction. Compared with column (3) of Table 2, the regression coefficient of government environmental concern and haze pollution in column (4) of Table 7 changed from −0.460 to −0.453. At the same time, the regression coefficient between capital factor mismatch and haze pollution was −0.256 (p < 0.1), and the mediating effect has passed the significance test. This shows that the government’s environmental concern can achieve the goal of reducing haze pollution by reducing the mismatch of capital elements. The regression results verified the research Hypothesis H1b.

4.4.2. Analysis on the Mechanism of Public Environmental Concerns Affecting Urban Haze Pollution

The theoretical and empirical analyses indicated that the rapid increase in the pressure from the public’s environmental concern resulted in a significantly lower probability of environmental policy uncertainty and capital misallocation. The regression results are shown in

Table 8. The intermediary mechanism of public environmental concern.

	(1)	(2)	(3)	(4)
VARIABLES	EPU	Haze	CA	Haze
PEC	0.151 ***	−0.103 **	0.0220 ***	−0.333 ***
	(0.010)	(0.052)	(0.007)	(0.052)
PCEC	0.006 ***	0.004	−0.001	−0.005
	(0.001)	(0.007)	(0.001)	(0.007)
IS	−1.311 ***	2.374 ***	−0.476 ***	4.302 ***
	(0.095)	(0.477)	(0.069)	(0.486)
Dust	−0.201 ***	0.307 ***	−0.025 ***	0.615 ***
	(0.008)	(0.042)	(0.005)	(0.039)
Green	−0.000001	−0.002	−0.003 ***	−0.002
	(0.001)	(0.003)	(0.000)	(0.003)
Traffic	0.000004 ***	−0.000001	−0.0000001	−0.00001 *
	(0.000)	(0.000)	(0.000)	(0.000)
ID	0.000005 ***	−0.0003 ***	−0.0001 ***	−0.0004 ***
	(0.000)	(0.000)	(0.000)	(0.000)
Edu	0.010 ***	−0.035 ***	−0.001	−0.051 ***
	(0.002)	(0.012)	(0.002)	(0.012)
STR	0.008 ***	−0.003	0.005 ***	−0.013 ***
	(0.001)	(0.003)	(0.001)	(0.003)
CA				−0.267 *
				(0.152)
EPU		−1.568 ***
		(0.104)
Constant	8.085 ***	14.690 ***	0.524 ***	2.158 ***
	(0.119)	(1.019)	(0.086)	(0.606)
F statistic	4.820 ***	240.780 ***	32.660 ***	217.300 ***
Observations	2415	2415	2415	2415
R−squared	0.563	0.361	0.176	0.295
Number of id	279	279	279	279

Notes: *, **, and *** represent the significance level at 10%, 5%, and 1%, respectively, and standard errors are shown in parentheses.

.

Column (1) of Table 8 verifies the regression results of public environmental concerns and environmental policy uncertainty, with a regression coefficient of 0.151 (p < 0.01). This shows that the public’s environmental concern can significantly reduce the uncertainty of environmental policies. The public can reduce the government’s information asymmetry by publishing relevant information, complaints, etc., and the government can help to improve the existing environmental policies after obtaining accurate information [21], thereby reducing the uncertainty of environmental policy. Column (2) verifies the mediating role of environmental policy uncertainty between public environmental concerns and haze pollution. Compared with column (4) of Table 2, the regression coefficient of public environmental concern and haze pollution in column (2) of Table 8 changed from −0.339 to −0.103 (p < 0.05). At the same time, the regression coefficient of environmental policy uncertainty and haze pollution was −1.568 (p < 0.01), and the mediating effect passed the significance test. Public environmental concerns can reduce the uncertainty of environmental policies, prevent companies from exploiting policy loopholes, and achieve the purpose of precise and effective haze control. The regression results verified research Hypothesis H2a.

Column (3) of Table 8 verifies the regression results of the mismatch between public environmental concerns and capital factors, and the regression coefficient was 0.0220 (p < 0.01). This shows that public environmental concern can significantly reduce the mismatch of capital factors in cities. On the one hand, the public can purchase environmentally friendly products, so that enterprises can devote themselves to the production of environmentally friendly products, increase research and development, and improve environmental performance [44]. On the other hand, the above tendency to invest in energy-saving and environmental protection enterprises has caused funds to flow to environmental protection enterprises and projects, which will help reduce the occurrence of haze pollution [45]. Column (4) verifies the mediating role of capital factor mismatch between public environmental concerns and haze pollution. Compared with column (4) of Table 2, the regression coefficient of public environmental concerns and haze pollution in column (4) of Table 8 changed from −0.339 to −0.333 (p < 0.01). At the same time, the regression coefficient of capital factor mismatch and haze pollution was −0.267 (p < 0.1), and the mediating effect has passed the significance test. Public environmental concerns can reduce haze pollution by reducing the mismatch of capital factors. The regression results verified research Hypothesis H2b.

In sum, both government environmental concern and public environmental concern can affect haze pollution by reducing policy uncertainty and capital factor mismatch. However, which one has a greater effect on policy uncertainty and capital factor mismatch? In order to answer this question, the article further conducts research.

According to the regression coefficient between government environmental concern and haze pollution, the mediating effect of environmental policy uncertainty was 0.712, and the mediating effect of capital factor mismatch is 0.015. Between public environmental concern and haze pollution, the mediating effect of environmental policy uncertainty was 0.698, and the mediating effect of capital factor mismatch was 0.017. In terms of policy, the role of the government is greater than that of the public. In resource allocation, the role of the public is greater than that of the government. In general, the mediating role of environmental policy is greater than the allocation of capital factors.

5. Discussion

5.1. Analysis of the Direct Effect

First, there was a significant negative correlation between government environmental concerns and urban haze pollution during the study period. On the one hand, the government will actively take measures to control haze pollution due to its responsibilities and assessment needs [23]. On the other hand, the government has an authoritative influence on environmental governance.

Second, public environmental concerns can significantly reduce urban haze. On the one hand, haze pollution is highly visible and harmful, and it easily attracts public attention. On the other hand, the development of Internet technology has also become an important basis for public participation in environmental governance, enabling the public to quickly respond to behaviors that damage the environment [20]. Therefore, the government should actively carry out environmental education and training activities and establish environmentally friendly schools and communities to enhance the public’s awareness of the environment and green consumption, as well as to cultivate environmental protection professionals.

Third, in places with a low level of Internet development, the dual-subject environmental concerns of the government and the public have a more significant impact on urban haze pollution. This shows that the development of the Internet has provided technical support for the government and the public to pay attention to the environment and environmental supervision, but the main cause of environmental concern is environmental pollution itself. In places with a high number of households with Internet access, Internet development can promote the industrial green total factor ratio through industrial structure upgrading and technological innovation [57], which will help reduce haze pollution. Therefore, the development of the Internet has weakened the role of environmental concerns.

Fourth, in the western region, the dual-subject environmental concerns of the government and the public have a stronger impact on haze pollution. On the one hand, the eastern region has a high level of development and environmental regulation, and at the same time, local governments can speed up urban economic transformation through technological innovation [58], which can effectively solve environmental problems. Therefore, the impact of environmental concerns on haze pollution has been weakened to a certain extent. On the other hand, the resource curse effect mainly occurs in inland cities in the mid-west [59]. In addition, compared with the eastern region, the central and western regions have weaker environmental regulations, and polluting enterprises will choose the central region or the western region as the destination of migration. If this situation continues, the central and western regions will likely become a “sanctuary for pollution” for companies [60]. The transfer of polluting enterprises has caused serious environmental problems in the central and western regions and is more likely to cause environmental concerns. Therefore, the suppression levels of the two environmental concerns on haze pollution show the phenomenon of western region > central region > eastern region. The government should implement a strict enterprise access system for less-developed regions, raise the entry threshold for highly polluting, high-emission, and high-energy-consumption enterprises, eliminate the existence of “pollution shelters”, and encourage enterprises to adopt technology, transformation, and other positive ways to achieve the decoupling of the economy and environmental pollution.

5.2. Analysis of the Intermediary Effect

First, dual-subject environmental concerns can affect urban haze pollution by reducing environmental policy uncertainty. The government’s role is more influential than the public role. The main reason for this is that the government is the main body in formulating policies and has absolute authority and credibility in formulating policies. The public reduces the asymmetry of information by means of petitions, complaints, etc., and also contributes to the formulation of environmental policies, but it mainly plays an auxiliary role. The revision of environmental policy can help to control air pollution [61]. The government should broaden and optimize the channels for the public to give feedback on environmental information, strengthen the effective screening of public feedback information with the help of Internet technology, conduct on-the-spot verification, timely response, and accurate monitoring, punish violations as required, and quickly revise relevant regulations or introduce new corresponding regulations to ensure the comprehensiveness and accuracy of regulations [62].

Second, dual-subject environmental concerns can affect urban haze pollution by reducing environmental resource allocation. The role of the public is greater than that of the government. A possible reason for this is that the main body of enterprises is profit-oriented. In order to gain a larger market share, they will cater to the public’s green consumption preferences and actively carry out green and environmental protection behaviors to enhance their corporate image. Additionally, the way the public votes regarding money makes it easier for money to be allocated to environmental protection projects [24,45].

6. Conclusions and Future Research Orientations

6.1. Research Result

This paper took 279 cities in China from 2011 to 2019 as the research sample, using a fixed-effect regression model to investigate the effect of government environmental concern and public environmental concern on urban haze pollution. Overall, the main conclusions of this study are drawn as follows:

(1) Government environmental concern and public environmental concern have a significant negative impact on urban haze pollution. The results were still valid after a series of robustness tests and controlling for endogenous problems.

(2) Further research found that dual-subject environmental concerns have a stronger negative effect on urban haze pollution in areas where there is a low level of Internet development and in western regions.

(3) Through the test of the intermediary mechanism, it was observed that between government environmental concern and haze pollution, the mediating effect of environmental policy uncertainty was 0.712, and the mediating effect of capital factor mismatch was 0.015. Between public environmental concern and haze pollution, the mediating effect of environmental policy uncertainty was 0.698, and the mediating effect of capital factor mismatch was 0.017. Therefore, in terms of policy, the role of the government is greater than that of the public. In resource allocation, the role of the public is greater than that of the government. In general, the mediating role of environmental policy is greater than the allocation of capital factors. Therefore, strengthening the effective interaction between the government and the public will help China reach the standards set by the World Health Organization as soon as possible.

6.2. Future Research Orientations

Although this paper studies the direct and indirect effects of government and public environmental concerns on haze pollution, it still has the following shortcomings: (1) The use of word frequency to represent environmental concerns has certain limitations, mainly because government and public environmental concerns are expressed in many different forms. For example, the public expresses environmental demands not only through online searches but also through petitions, lawsuits, etc. (2) The measurement of environmental policy uncertainty does not take into account the policy uncertainty caused by changes in officials, etc. (3) The paper ignores the spatial effect of environmental concerns on haze pollution by using a fixed-effect model.

Based on existing research, we will consider enriching measurement methods for environmental concerns, environmental policy uncertainty, and the spatial effect of environmental concerns on environmental pollution in future research to improve the comprehensiveness and reliability of the estimated results.