Next Article in Journal
Food Producers in The Peri-Urban Area of Mexico City. A Study on the Linkages between Social Capital and Food Sustainability
Next Article in Special Issue
How Efficient China’s Tiered Pricing Is for Household Electricity: Evidence from Survey Data
Previous Article in Journal
The Importance of Receiving Training in Computerized Technology for Nurses to Maintain Sustainability in the Health System
Previous Article in Special Issue
Has the Inter-Regional Power Transmission Promoted Economic Development? A Quantitative Assessment in China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 14
Issue 23
10.3390/su142315959
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Research on the Green Effect of Environmental Policies—From the Perspective of Policy Mix

by
Zixiao Liu
1,
Zengming Wu
2,* and
Mengnan Zhu
1
1
School of Economics, Xiamen University, Xiamen 361005, China
2
School of Management, Xiamen University, Xiamen 361005, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(23), 15959; https://doi.org/10.3390/su142315959
Submission received: 2 November 2022 / Revised: 28 November 2022 / Accepted: 28 November 2022 / Published: 30 November 2022
(This article belongs to the Special Issue Environmental Impact Assessment and Green Energy Economy)
Browse Figures
Versions Notes

Abstract

:
Environmental protection policy serves as an effective means for the government to curb environmental pollution and promote high-quality economic development. The government must weigh the effects of different policy mixes. From the perspective of policy combination, this paper discusses the green effect of environmental protection policy theoretically and empirically. First and foremost, this paper sorts out the reforming time of environmental protection taxes and the situation of the low-carbon pilot city, and puts forward two hypotheses. Furthermore, by referring to the environmental protection tax, the policy for the low-carbon pilot city, and the urban air quality indicator from 2014 to 2020, this paper explores the green effect of the environmental protection policy and further validates the consolidation effect of the policy mix on the green effect. The study reveals a significant decrease in the air pollution level in regions with higher standards for levying an environmental protection tax. The conclusion remains robust via parallel trend testing and substitution of the subject variables. Furthermore, an analysis of the policy mix of an environmental protection tax indicates that the policy mix of an environmental protection tax and low-carbon city produces a significant green effect, which not only curbs air pollution but also reduces greenhouse gas emissions. An in-depth analysis shows that an environmental protection tax has the best green effect in the first and second areas of a low-carbon pilot market. The synergies of low-carbon pilot effects are higher in areas with low and middle tax rates.

1. Introduction

The governance effect of environmental policies has become a pressing issue of concern recently, and dealing with environmental issues is an inevitable problem in the course of development in all countries. As the contradictions between economic growth and environmental pollution are frequently discussed, environmental pollution problems have caused great concerns for environmental governance. The central government has decided to change its GDP-based unidimensional view of performance and has taken environmental pollution improvement as a key performance indicator for local government [1]. This aims to make use of an optimized promotion appraisal system and “pro-green” incentives to engage local officials to emphasize environmental investment while developing the economy, thus promoting the comprehensive establishment of a green and low-carbon society and sustainable economic development [2]. Particulate air pollutants, such as PM2.5, CO, and SO2, mainly come from industrial production, transportation, and coal combustion [3].
There have been debates among scholars about the governance effects and economic impacts of environmental policies [4]. Some take the view that formulating environmental regulatory policies helps achieve environment-protection goals, reduce pollution and carbon emissions, and accelerate the energy transition in developing countries [5,6,7]. Environmental regulatory policies focus on improving the quality and quantity of green investment, increasing investment in renewable resources, and speeding up technological iteration mainly through increasing the scale of government investment and strengthening the external supervision of the capital market [8,9,10], whereby enterprises have to engage in green technology innovation and increase the effectiveness of resource utilization, which will ultimately lead to less pollution and fewer emissions [11,12]. In this process, the government plays an important role. On the one hand, with a restrictive environmental policy, incumbent firms bear a heavy burden of environmental costs, and they are forced to readjust and improve their production and business practices, ultimately achieving a reduction in pollution emissions as well as an improvement in environmental quality [13,14]. On the other hand, incentive-based policies such as emission reduction subsidies can be implemented to create a difference in the comparative advantage of technological innovation among different enterprises and directly promote technological progress and investment in environmental protection equipment [15,16].
Others openly oppose this, arguing that environmental taxes from environmental regulations lead to higher pressure on corporate production costs, fewer innovation inputs, and more pollutant emissions [17]. Based on the data collected from the U.S. manufacturing sector, Brunnermeier and Cohen [18] pointed out that there are no innovation incentives from environmental policies. Leeuwen and Mohnen [19] pointed out that environmental policies even inhibit the efficiency of technological innovation and reduce the productivity of manufacturers. Hu et al. [20] attributed China’s failure to motivate the corporate sector to reduce carbon emissions to the generally low environmental tax rate. Government intervention has also not always played a positive role in attuning the relationship between economic development and environmental quality. In some literature, government intervention was reported to undermine the market mechanism and result in environmental degradation [21]. Local governments, constrained by the fiscal decentralization system, lowered the environmental standards in succession in order to attract more foreign investment, and the constraining force of environmental policies was further reduced [22]. Meanwhile, due to the lack of government regulation, stricter environmental regulation will instead force low-tech polluting firms to engage in hidden economic activities that will pollute, which exacerbates their impact on the surrounding environment [23]. In addition, firm heterogeneity can also lead to the failure of government intervention in environmental protection [24].
In recent years, facing the increased greenhouse effect, countries around the world have proposed carbon reduction schemes to reduce direct emissions from fossil fuels and vigorously develop clean energy. One of the major challenges that all of humanity needs to face in the 21st century is global climate change caused by the emission of carbon dioxide and other greenhouse gases. Under the framework of the Paris Agreement, national governments have reached a clear consensus on climate change. It is a fundamental step in the global response to climate change that each country should take measures to reduce greenhouse gas emissions for alleviating climate change, and should achieve carbon neutrality by mid-century. Achieving peak carbon dioxide emissions and carbon neutrality within 30 years is not only a solemn promise that China has made for global climate change but also a strategy for China to accelerate its economic restructuring and continuously improve its economic competitiveness to embrace the zero-carbon economy era. As early as 2010, China launched three batches of low-carbon pilot cities, taking the lead in attempting to improve the efficiency of urban carbon emissions and exploring a win–win solution to the problem of balancing carbon emission reduction and economic development.
From the above, it can be seen that despite many studies on environmental policies, there are few studies on the policy mix of environmental protection, especially on air pollution control and carbon emission reduction. As important embodiments of environmental governance and double-carbon (carbon peaking and carbon neutrality) goals, they are of great significance to create a green and low-carbon economic growth model for high-quality economic development. However, further study should be conducted on whether the two policies interact with each other to jointly reduce pollutants and carbon dioxide emissions and promote the low-pollution and low-carbon corporate transformation, which in turn contributes to green, low-carbon, and high-quality development of the whole region.
For this reason, this paper constructs a multiple-difference model, which is used to analyze the influence of a combination of environmental protection policies on local air pollution and carbon dioxide emissions, and evaluate the green effect of the policy mix. Specifically, this paper adopts the data on urban air pollution and CO2 emission from 2014–2020 and selects the set of environmental protection tax policies and low-carbon city pilot programs to conduct an empirical test. The results show that a combination of environmental protection policies produces a significant green effect. To be specific, the air quality in the relevant areas was improved significantly after the environmental protection tax was levied. Under the superimposed effect of the low-carbon pilot program, improvements in air quality and greenhouse gas emission reductions became more apparent. An in-depth analysis shows that the environmental protection tax has the best green effect in the first and second areas of the low-carbon pilot market. The synergies of low-carbon pilot effects are higher in areas with low and middle tax rates.
Compared with previous studies, this paper mainly discusses the green effect of environmental protection policies from the perspective of policy combination which is also an important innovation point of this article. The existing literature only focuses on the impact of a certain policy, such as air pollution control [7,9,10,20] or is only concerned about low-carbon pilot projects [25,26,27], but does not examine the impact of different policies. This paper conducts an in-depth analysis of the impact of the combination of environmental protection tax collection and low-carbon city pilots. The empirical test of the difference in the effect of the policy combination is mainly carried out through the DDD (difference-in-difference-in-differences) model and group regression. The DDD model is used to test the overall effect of the policy combination. We further analyze the effect of policy combination by performing group regression on different batches of low-carbon pilot cities. In addition, the effects of low-carbon pilot projects under different environmental protection taxes (high, medium, and low) are analyzed.
This paper makes the following possible contributions: First, existing research only focuses on the impact of a certain policy, such as pollution control or low-carbon pilot, lacking analysis of the impact of different policies. by combining an environmental protection tax policy and a low-carbon pilot program, and starting from two important governmental goals, environmental pollution control and greenhouse gas emission reduction, this paper comprehensively reflects the comprehensive governance effect of the policy mix, while making up for the lack of research about policy rationality. Second, this paper carries out multiple-difference methods (difference-in-difference and difference-in-difference-in-difference methods) for strategy identification, which guarantees the accuracy and scientific validity of causality identification and avoids the endogeneity caused by reverse causality as much as possible. This helps strengthen the credibility of the conclusions. Third, the empirical results of the further analysis show that a combination of environmental protection tax policies and low-carbon pilot programs helps to reduce air pollution and CO2 emissions; in addition, the longer the implementation of the low-carbon pilot policy, the better the synergistic effect will be, and the lower the environmental protection tax rate, the better the synergistic effect will be, which provides effective experience for the government sector to formulate more reliable environmental policies.
The paper is divided into seven parts: Part II discusses the institutional background and research hypotheses, data descriptions and estimation strategies are given in Part III, Part IV reports the regression results, Part V makes a further discussion, Part VI is the heterogeneity test, and Part VII draws conclusions.

2. Institutional Background and Research Hypotheses

An environmental tax can green the entire tax system in a broadly speaking sense, and in a narrow sense, regulate the pollutant-generating process from extraction to production, emission, and consumption as a practical application of the Pigovian tax theory. As an important initiative of ecological civilization, China’s Environmental Protection Tax Law, implemented in 2018 and based on the sewage charging system, is the national environmental economic policy with the longest history and the most complete system, which realizes a smooth transition between charging and taxation. It is a pollution tax targeting the emission chain and is based on pollutant emissions. It follows the principle of “polluters as payees” and internalizes the external costs of environmental pollution. Therefore, the “environmental protection tax” mentioned in this study is a “pollution tax” in a narrow sense, while the “environmental protection tax policy” is a policy system that specifies formulation, payment, collection, and management of an environmental protection tax according to the Environmental Protection Tax Law.
Table 1 is a summary of important reform points of environmental protection tax and sewage charges in China: (1) in 1982, sewage charges were formally introduced, but only levied and used by the environmental authority upon excessive emissions. They were credited into the departmental budget for exclusive use, such as subsidizing key pollutant discharging units to control pollution sources and to take comprehensive environmental pollution control measures; (2) in 1988, the subsidy was adjusted as loanable; (3) in 1993, sewage that did not exceed the discharge standard was also charged, but only excessive emissions of air pollutants were charged; (4) in 2003, the principle of instance pollution charge was applied to all pollutant emissions, and the contents of the top three pollutants were selected for charging the tax upon exhaust gas and sewage; (5) in 2014, the charge standard was raised, and the charges for parts of key pollutants in exhaust gas and sewage were doubled. The minimum charge was 1.2 yuan and 1.4 yuan per pollution equivalent in exhaust gas and sewage, respectively; (6) in 2018, the Environmental Protection Tax Law was formally implemented, under which the pollution charges were incorporated into the fiscal budget and not earmarked for its specified purpose only. In addition, the polluter-as-payee principle was legalized to increase the cost of tax avoidance [28]. The sewage charge standard was deemed as the minimum tax rate that could be adjustable within the upper limit. China took the first lead in implementing regionally differentiated environmental tax rates [29].
In particular, the 2018 Environmental Protection Tax Law is an important policy on environmental protection in recent years. On 25 December 2016, the Environmental Protection Tax Law, which was formally approved, marks the shift from “charge to tax” and is a milestone in the process of improving the green taxation system and ecological civil construction in China. After the implementation of the law, some provinces and municipalities raised their tax rate for taxable pollutants, indicating an increase in the environmental protection tax rate; others have offset the pollutant charge system under the principle of “unchanged tax burden “. This is attributed to the formation of a natural causality identification strategy to well solve the endogenous problem.
The introduction of an environmental protection tax improves the efficiency of tax governance and the actual tax burden of enterprises, thus achieving the policy goal of emission reduction and air pollution control. First, according to basic theories related to environmental economics, the internalization of external emission discharges through environmental protection taxes, etc., will lead to a reduction in the marginal cost savings for pollution discharging by the manufacturers, and the optimal pollution discharging level of manufacturers will be lower than the level before the tax was levied. Therefore, an increase in the burden of the environmental protection tax can force manufacturers to reduce emissions and achieve the goal of environmental pollution control [30]. Secondly, the improved legal hierarchy strengthens the legal rigidity of the environmental protection tax, while the reform of the tax operators and the taxation model helps to reduce the pollution information asymmetry between the organization in charge and the polluters, improving the efficiency of tax collection and governance. In addition, positive incentives such as preferential policies for taxpayers and negative constraints such as penalties increase the taxpayers’ initiative in paying the environmental protection tax [31], thus curbing air pollutant emissions from the origin; finally, in the regions where the standard for tax collection is raised, the legal environmental protection tax rate is directly increased, in addition to the above changes. Therefore, compared with the regions where the policy of tax offsetting is implemented, these regions witness reduced pollution discharges from enterprises, thus strengthening the air pollution control.
Hypothesis 1 (H1).
The introduction of an environmental protection tax has a significant positive effect on air pollution and air quality. The air quality in the region implementing higher tax collection standards is improved more obviously than that in the region with tax offsetting.
In fact, before the official proposal of the “double carbon” (carbon peaking and carbon neutrality) goals, China had already incorporated a response to climate change into its national economic and social medium- and long-term development plans, and carried out pilot programs in three batches of national low-carbon cities in 2010, 2012, and 2017, respectively, which laid a solid policy for early realization of the “double carbon” goals. The pilot program of low-carbon cities, as a preliminary application of the “double carbon” policy and based on the Diffusion of Innovations principle, under which a phased approach was taken to realize the goals, was also the test run of the policy designed to address the disharmony between ecological environmental goals and economic development under governmental support. A total of eight cities in five provinces participated in the first pilot program in 2010, while a total of 81 cities in 6 provinces were included in the list of low-carbon pilot cities in the second (2012) and third (2017) pilot programs, as of 2021.
Although the effect of a low-carbon pilot program in reducing emissions at the macro level has been sufficiently verified [25,26], the specific effect of the program in promoting the development of high-polluting enterprises at the micro level is still in dispute [27,32]. The transformation of business operations to green and low-carbon, brought about by the upgrading of industrial structure, is the only way to achieve the dual-carbon goals [33]. There are many policies that affect corporate energy conservation, emission reduction, and technological progress, such as government regulation, macro risk, and digital finance [34]. However, previous studies mostly focused on the impact of a certain policy, and seldom analyzed different policy combinations. Specifically, few studies have been conducted on a cross-sectoral combination of environmental protection policies, especially on air pollution control and carbon emission reduction. As two important embodiments of environmental governance and the double-carbon goal, the environmental protection tax and the low-carbon pilot city program serve as important means in their respective areas. However, further study should be conducted on whether the two policies interact with each other to jointly reduce pollutants and carbon dioxide emissions and promote green, low-carbon, and high-quality development of the whole region.
Hypothesis 2 (H2).
The combination of the environmental protection tax and the low-carbon pilot program creates a significant green effect.

3. Data Specification and Estimation Strategy

3.1. Data Specification

This paper uses the data of prefecture-level cities across China collected from 2014 to 2020, which mainly includes: (1) Air quality data: The annual air quality data of prefecture-level cities mainly consist of AQI (air pollution index), PM2.5, PM10, SO2, CO, NO2, and O3 data, collected from the real-time national urban air quality monitoring platform of the China National Environmental Monitoring Centre. The annual concentration averages are obtained by processing daily data; (2) Greenhouse gas data: CO2 data was mainly sourced from the global CO2 emission data released on the website of the Center for Global Environmental Research. The Chinese raster data were first extracted, and then (1km × 1km) aggregated into the total CO2 emission panel data (in tons) for each city in China. (3) Data on the change of collection standards in each region after the introduction of environmental protection tax are collected manually from relevant provincial policy documents; (4) Data on low-carbon pilot cities were manually collected mainly from governmental documents; (5) Data on other variables of cities source from the China City Statistical Yearbook, among which nominal variables were adjusted using the urban consumer price index to actual variables based on 2014 values.

3.2. Benchmark Model Regression

This paper examines the impact of a local environmental policy mix on air pollution control and greenhouse gas emissions based on the above-mentioned data. Considering the potential impact of local government policies, the level of urban economic development, financial status, and environmental governance on air pollution and greenhouse gas governance, the following basic econometric model is set in this paper.
environment_qualityct = α0 + β0Treat*Postct+ γ0CVct + δcj+ δt+ εct
environment_qualityct = α0 + β0Treatc*Postt*Low_carbonc+ γ0CVct + δcj+ δt+ εct
where c indicates the city, t indicates the time, environment_qualityct indicates the air pollution and greenhouse gas in c city. Explanation variables include Postt, Treatc and Low_carbonc. Postt: Environmental protection tax implementation time virtual variable (post). Before 2018, Postt is 0; after 2018, Postt is 1. Treatc: Treatment of taxable pollutants for taxable standards. The areas where taxable pollutants have taxable pollutants are the experimental group area, Treatc is 1; the area where the tax standard of taxable pollutants is unchanged is 0. Low-carbon city feature variable Low_carbonc: If the area is a low-carbon pilot city, the value is 1; otherwise, the value is 0. The reason for using this indicator instead of the discharge indicators of the panel data industry is to avoid industry emission indicators that change over time that may cause endogenous problems in models. δc is the fixed effect of the city; δt is the time-fixed effect; CVct are control variable vectors; εct is a stochastic error term.

3.3. Variable Descriptions

3.3.1. Explained Variables

The explained variables include air quality data and greenhouse gas data. The air quality data are selected from the annual air quality data of prefecture-level cities, mainly including AQI (Air Pollution Index), PM2.5, PM10, SO2, CO, NO2, O3; as for the greenhouse gas data, the CO2 emissions of prefecture-level cities are selected.

3.3.2. Core Explanatory Variables

The core explanatory variables are local environmental protection policies, which mainly include an environmental protection tax and a low-carbon pilot city program. As the environmental protection tax was introduced in 2018, dummy variables for introduction time and taxation standard are established respectively. The dummy variable is set to 0 before 1 January 2018, and 1 afterward; the dummy variable is set to 1 for areas where the taxation standard is raised, and 0 for areas where the taxation standard remains unchanged. According to the relevant policy documents of the Environmental Protection Tax Law, areas with high tax rates (Beijing, Shanghai, Tianjin, and Hebei) and areas with medium tax rates (Henan, Shandong, Jiangsu, Guangxi, Guangdong, Hubei, Chongqing, Sichuan, Shanxi, Hunan, Guizhou, Hainan, Zhejiang, Inner Mongolia, and Yunnan) are assigned a value of 1, and the rest of the regions (Liaoning, Jilin, Jiangxi, Gansu, Qinghai, Anhui, Shaanxi, Xinjiang, Ningxia, Heilongjiang, Fujian, and Tibet) that maintain the minimum quota are assigned a value of 0.
There were three batches of pilot cities in 2010, 2012, and 2017, respectively. The dummy variable is set to 1 for areas selected as low-carbon pilot cities, and 0 for other areas. The choice of low-carbon cities varies from pilot cities to provinces. In 2010, the first batch of 8 cities served as pilot areas, in 2012, the second batch, and in 2017, the third batch. As of 2021, a total of 81 cities have been included in the low-carbon city pilot list. These cities are assigned a value of 1, and the rest of the unselected cities are assigned a value of 0. The details are as follows:
The first batch: Tianjin, Chongqing, Shenzhen, Xiamen, Hangzhou, Nanchang, Guiyang, Baoding.
The second batch: Beijing, Shanghai, Shijiazhuang, Qinhuangdao, Jincheng, Hulunbuir, Jilin, Greater Khingan Mountains, Suzhou, Huaian, Zhenjiang, Ningbo, Wenzhou, Chizhou, Nanping, Jingdezhen City, Ganzhou City, Qingdao City, Jiyuan City, Wuhan City, Guangzhou City, Guilin City, Guangyuan City, Zunyi City, Kunming City, Yan’an City, Jinchang City, Urumqi City.
The third batch: Wuhai City, Shenyang City, Dalian City, Chaoyang City, Xunke County, Nanjing City, Changzhou City, Jiaxing City, Jinhua City, Quzhou City, Hefei City, Huaibei City, Huangshan City, Lu’an City, Xuanzhou City City, Sanming City, Gongqing City, Ji’an City, Fuzhou City, Jinan City, Yantai City, Weifang City, Changyang Tujia Autonomous County, Changsha City, Zhuzhou City, Xiangtan City, Chenzhou City, Zhongshan City, Liuzhou City, Sanya City, Qiongzhong Li and Miao Autonomous County, Chengdu City, Yuxi City, Pu’er City, Lhasa City, Ankang City, Lanzhou City, Dunhuang City, Xining City, Yinchuan City, Wuzhong City, Changji City, Yining City, Hotan City, Ala Seoul.

3.3.3. Control Variables

In order to further improve the preciseness and reliability of this paper, control variables were added to the multiple-difference model to reduce the interference in the explanatory variables. Then, they were grouped into environmental pressure, environmental governance capacity, and degree of environmental protection reform.
Environmental pressure: (1) the share of the second industry (second_industry), denoted as the share of the second industry in GDP. As industrial emission is the major source of air pollution, cities with a higher share of secondary industry tend to face greater environmental pressure; (2) the year-end population (lnpeople), denoted as the logarithm of the year-end population. The population size is a challenge to the city’s capacity for environmental carrying.
Environmental governance capacity: (1) GDP per capita (lnpgdp), which is denoted as the logarithm of GDP per capita and depends to some extent on the level of local economic development; (2) the centralized processing rate of sewage treatment plants (wat); (3) the harmless processing rate of household waste (tra); (4) the greening rate of built-up areas (gre).
Degree of environmental reform: (1) total gas supply (lngg), which is denoted as the logarithmic value. The higher the number, the greater the reform effort; (2) fiscal pressure (deficit), denoted as the ratio of fiscal deficit (fiscal expenditure—fiscal revenue) to regional GDP. The larger the value is, the greater the fiscal pressure will be, and the less the investment in environmental protection will become.

4. Empirical Results

4.1. Basic Regression Results

According to the estimation Equation (1), this paper, based on controlling urban fixed effects and yearly fixed effects, and by means of a difference-in-difference model, estimates the impact of an environmental protection tax policy on air pollution quality. The regression results are reported in Table 2, where Column (1) reveals the regression of the Air Pollution Index (AQI), and the estimated coefficient of the core explanatory variable (treat*post) is significantly negative at the significance level of 1%, which, on a preliminary basis, indicates that the levy of environmental protection tax significantly reduces pollution in the region. Urban-level air pollution indicators, namely PM2.5, SO2, and CO, are gradually introduced into Columns (2), (3), and (4). The estimated results show that the estimated coefficient of the core explanatory variables is significantly negative at the significance level of 1%. Specifically, by regressing the explanatory variables PM2.5, SO2, and CO, respectively, the estimated coefficients of the explanatory variables are significantly negative at the significance level of 1%. In other words, the regions with a levied environmental protection tax and raised tax standards have witnessed a significant reduction in PM2.5, SO2, and CO emissions, which indicates that the environmental protection tax restrains the pollution behavior of local enterprises to a large extent, hence a green effect.

4.2. Robustness Checks

4.2.1. Parallel Trend Test

The above analysis, on a preliminary basis, indicates that the levy of environmental protection tax alleviates air pollution. In an effort to further ensure the reliability of the research conclusions, this paper tests the robustness of the basic conclusions by means of parallel trend tests and substitution variables. In Figure 1, the first row corresponds to AQI and PM2.5 from left to right; the second row corresponds to SO2 and CO, respectively, from left to right.
Referring to 2018 as the base period, this study examines the situation three years before, as well as two years after, the implementation of the policy. The data from 2017 is excluded due to the problem of multicollinearity. The parallel trend test figure discloses that the implementation of the environmental protection policy has significantly reduced air pollution in such regions, especially in the second year after the implementation when the air pollution was effectively curbed. This further indicates that with the levy of the environmental protection tax, as well as the improved awareness and practical effort of the government, enterprises, and other relevant subjects, the actual effect of the environmental protection tax is gradually improved.

4.2.2. Substitute Variables

The Action Plan of Air Pollution Prevention and Control shows that more days of air pollution in a city indicate greater pressure for environmental control in this city. On that account, this paper adopts the number of air pollution days as the substitution variable for air pollution control. Specifically, the number of days in a year when the urban AQI exceeds 100 (lnAQI_num), as well as three substitution variables PM10, NO2, and O3, are used for the test. The estimation results, as reported in Columns (1)–(4) of Table 3, show that except for the coefficient of Column (4), which is insignificant, the coefficients of other columns are significantly negative. Whether AQI (lnAQI_num) or substitution indicator data is used to measure air pollution, the regression results show that the levy of the environmental protection tax has significantly reduced air pollution.

5. Further Discussion

Next, in terms of the research focus of this paper, namely the effect of the environmental policy mix on air pollution and greenhouse gas control, this study conducted a test by means of the difference-in-difference-in-differences (DDD) methods. This section, by respectively adopting the policy mix of environmental protection tax and low-carbon city, tested two main indicators that reflect the green effect. The empirical result in Table 4 reveals that the estimated coefficient of the policy mix corresponding to CO2 is significantly negative at the significance level of 5%, which suggests that the policy mix significantly reduces CO2 emissions. The estimated coefficient of the policy mix corresponding to AQI is significantly negative at the significance level of 5%, suggesting that the policy mix significantly improves air quality. From the perspective of economic principles, policy mixes of environmental protection at different dimensions can interact with each other to produce explicit market signals and more comprehensive policy coverage, and change enterprises’ medium-term and long-term expectations and business behavior, which ultimately drives forward the green, low-carbon, and high-quality development of the region.
In order to further analyze the effect of the policy combination, the effect of collecting environmental protection taxes on different low-carbon pilot cities (three batches of pilot cities) was used. In addition, the effects of low-carbon pilot projects under different environmental protection taxes (high, medium, and low) were analyzed. First, regression was performed according to different tax rates (high, middle, and low). It can be seen from Table 5 that the corresponding results of the three groups are all significantly negative, but the absolute values of the coefficients of the first batch and the second batch of pilot cities are larger, that is, the effect of environmental protection tax on pollution control is better. This also shows that the cities that carry out low-carbon pilot projects earlier have a better effect of their policy combination. Secondly, they are grouped according to different batches of low-carbon pilot projects. Table 6 shows that the coefficient of high tax rate areas is significantly positive, the coefficient is significantly negative in areas with low and medium tax rates, and the lower the tax rate, the better the effect of its policy combination. This shows that the collection of environmental protection tax should be gradual and the tax rate should be reasonable, and that an excessively high tax rate will have negative effects.

6. Heterogeneity Test

China is a large country, and there are great differences in natural endowments and marketization levels among provinces, especially in the eastern, central, and western regions. The eastern coastal areas developed rapidly, while the development of the central and western regions lagged behind. In addition, the financial development level of each province is an important difference, and finance is an important variable supporting the development of the real economy.
We refer to the literature of Zhao et al. (2021) [35], which groups according to the region and level of financial development. Among them, the degree of regional marketization is directly related to the level of economic development, so group regression was performed according to the east, middle, and west. The level of financial development is also an important factor affecting policies. The level of financial development is expressed by dividing the total amount of deposits and loans in a region by the GDP of the region. According to the median, regions were divided into regions with high financial development level and regions with low financial development level, before performing group regression.
According to the regression results grouped by financial development degree in Table 7, in areas with a higher financial development level, the effect of the policy combination is more effective, while in areas with a lower financial development level, the effect of the policy combination is poor, and the improvement in air quality is not significant. According to the regression results grouped by different regions in Table 8, the results of different explained variables are different. In the western region, the policy combination can significantly improve air quality. For CO2, the coefficients of both eastern and western regions are significantly negative, indicating that there is a significant green effect of the policy mix on GHG emission reduction, and the effect of the western region is greater than that of the central region and the eastern region.

7. Conclusions

As China is considerably accelerating its pace of industrialization and urbanization, endless environmental pollution issues manifest themselves in various forms, and climate warming is also posing a challenge to the entire world [36]. As a nation with a full sense of responsibility, China is determined to win the tough battle against pollution and embrace low-carbon development. It serves as an effective approach for the government to control environmental pollution in light of laws and policies, and also a feasible way to maintain high-quality economic development [37].
From the perspective of environmental economics, this paper, by means of theoretical hypothesis and empirical analysis, attempts to shed light on evidence of the influence of an environmental policy mix on environmental pollution. In terms of theoretical hypothesis, this paper sorts out the reform time of environmental protection tax and the situation of the low-carbon pilot city and puts forward two hypotheses. In terms of empirical analysis, this paper explores the green effect of the environmental protection policy and further confirms the consolidation effect of a policy mix on its green effect by referring to the environmental protection tax, the policy for the low-carbon pilot city, and the urban air quality indicator from 2014 to 2020. The study reveals a significant decrease in the air pollution level in regions with higher standards for levying environmental protection tax. The conclusion remains robust via parallel trend testing and substitution of the subject variables. Furthermore, this paper goes into deeper analysis by addressing the low-carbon pilot policy, and probes into the policy mix of environmental protection taxes, indicating that the policy mix of an environmental protection tax and a low-carbon city produces a significant green effect, which not only curbs air pollution but also reduces greenhouse gas emissions. An in-depth analysis shows that environmental protection tax has the best green effect in the first and second areas of the low-carbon pilot market. The synergies of low-carbon pilot effects are higher in areas with low and middle tax rates.
The findings of this paper help to deepen our understanding of the impact of environmental protection policy on pollution control, based on inspirations drawn from scientifically formulating environmental regulations and policies. Firstly, efforts can be made to optimize the regional industrial structure via air pollution control. The environmental protection tax increases the emission reduction cost of “high-pollution industries”, thereby promoting regional industrial transformation and upgrading, and furthermore reducing the release of pollutants. Secondly, the problem of green development shall be solved by relying on the “visible hand”. By incorporating environmental performance, local governments, motivated by environmental assessment and incentives, exert influences on enterprises’ environmental investment through strict environmental regulations, whereby the purpose of improving regional air and environmental quality can be achieved. Finally, the government should study and formulate more effective environmental policies. One solution is to make breakthroughs from different key aspects in the form of policy mixing, so as to achieve the purpose of green and low-carbon transformation. This requires the formulation of environmental control measures and intensity to be closely in line with the conditions of the local environment and industrial development, and unified management to be conducted from various aspects.

Author Contributions

Conceptualization, Z.W. and Z.L.; methodology, Z.W.; software, Z.W. and Z.L.; validation, Z.W. and Z.L.; formal analysis, Z.W., M.Z. and Z.L.; writing-original draft preparation, Z.W., M.Z. writing-review and editing, Z.W., M.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The main data come from the statistical yearbook published by the government and the real-time national urban air quality monitoring platform of the China National Environmental Monitoring Centre.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Chen, Z.; Kahn, M.E.; Liu, Y.; Wang, Z. The Consequences of Spatially Differentiated Water Pollution Regulation in China. J. Environ. Econ. Manag. 2018, 88, 468–485. [Google Scholar] [CrossRef]
  2. Zheng, S.; Kahn, M.E.; Sun, W.; Luo, D. Incentivizing China’s Urban Mayors to Mitigate Pollution Externalities: The Role of the Central Government and Public Environmentalism. Reg. Sci. Urban Econ. 2014, 47, 61–71. [Google Scholar] [CrossRef]
  3. Chang, S.; Lee, C. Ozone Variations through Vehicle Emissions Reductions Based on Air Quality Monitoring Data in Taipei City, Taiwan, from 1994 to 2003. Atmos. Environ. 2006, 40, 3513–3526. [Google Scholar] [CrossRef]
  4. Han, F.; Li, J. Assessing Impacts and Determinants of China’s Environmental Protection Tax On Improving Air Quality at Provincial Level Based on Bayesian Statistics. J. Environ. Manag. 2020, 271, 111017. [Google Scholar] [CrossRef] [PubMed]
  5. Bosquet, B. Environmental tax reform: Does it work? A survey of the empirical evidence. Ecol. Econ. 2000, 34, 19–32. [Google Scholar] [CrossRef]
  6. Wang, R.; Wijen, F.; Heugens, P. Government’s green grip: Multifaceted state influence on corporate environmental actions in China. Strat. Manag. J. 2018, 39, 403–428. [Google Scholar] [CrossRef]
  7. Karmaker, S.C.; Hosan, S.; Chapman, A.J.; Saha, B.B. The role of environmental taxes on technological innovation. Energy 2021, 232, 121052. [Google Scholar] [CrossRef]
  8. Wesseh, P.K.; Lin, B. Environmental policy and ‘double dividend’ in a transitional economy. Energy Policy 2019, 134, 110947. [Google Scholar] [CrossRef]
  9. Lambertini, L.; Pignataro, G.; Tampieri, A. The effects of environmental quality misperception on investments and regulation. Int. J. Prod. Econ. 2020, 225, 107579. [Google Scholar] [CrossRef]
  10. Mbanyele, W.; Wang, F. Environmental regulation and technological innovation: Evidence from China. Environ. Sci. Pollut. Res. Int. 2022, 29, 12890–12910. [Google Scholar] [CrossRef]
  11. Calel, R.; Dechezlepretre, A. Environmental Policy and Directed Technological Change: Evidence from the European Carbon Market. Rev. Econ. Stat. 2016, 98, 173–191. [Google Scholar] [CrossRef] [Green Version]
  12. Erik, H.; Patrick, M. Environmental Policy, Innovation, and Productivity Growth: Controlling the Effects of Regulation and Endogeneity. Environ. Resour. Econ. 2019, 73, 1315–1355. [Google Scholar]
  13. Jorgenson, D.J.; Wilcoxen, P.J. Environmental Regulation and U.S Economic Growth. Rand J. Econ. 1990, 21, 314–340. [Google Scholar] [CrossRef]
  14. Pang, A.; Shaw, D. Optimal emission tax with pre-existing distortions. Environ. Econ. Policy Stud. 2011, 13, 79–88. [Google Scholar] [CrossRef]
  15. Acemoglu, D.; Aghion, P.; Bursztyn, L.; Hemous, D. The environment and directed technical change. Am. Econ. Rev. 2012, 102, 131–166. [Google Scholar] [CrossRef] [Green Version]
  16. Aghion, P.; Akcigit, U.; Cagé, J.; Kerr, W.R. Taxation, Corruption, and Growth. Eur. Econ. Rev. 2016, 86, 24–51. [Google Scholar] [CrossRef] [Green Version]
  17. Krass, D.; Nedorezov, T.; Ovchinnikov, A. Environmental taxes and the choice of green technology. Prod. Oper. Manag. 2013, 22, 1035–1055. [Google Scholar] [CrossRef]
  18. Brunnermeier, S.B.; Cohen, M.A. Determinants of Environmental Innovation in US Manufacturing Industries. J. Environ. Econ. Manag. 2003, 45, 278–293. [Google Scholar] [CrossRef]
  19. Leeuwen, G.; Mohnen, P. Revisiting the Porter hypothesis: An empirical analysis of green innovation for The Netherlands. Econ. Innov. New Technol. 2017, 26, 63–77. [Google Scholar] [CrossRef] [Green Version]
  20. Hu, B.; Dong, H.; Jiang, P.; Zhu, J. Analysis of the Applicable Rate of Environmental Tax through Different Tax Rate Scenarios in China. Sustainability 2020, 12, 4233. [Google Scholar] [CrossRef]
  21. Fischer, C.; Springborn, M. Emissions targets and the real business cycle: Intensity targets versus caps or taxes. J. Environ. Econ. Manag. 2011, 62, 352–366. [Google Scholar] [CrossRef]
  22. Antony, M.; Ollivier, H. Beliefs, Politics, and Environmental Policy. Rev. Environ. Econ. Policy 2016, 10, 183–367. [Google Scholar]
  23. Blackman, A.; Lyon, T.; Sisto, N. Voluntary Environmental Agreements when Regulatory Capacity is Weak. Comp. Econ. Stud. 2006, 48, 682–702. [Google Scholar] [CrossRef]
  24. Sugeta, H.; Matsumoto, S. Green Tax Reform in an Oligopolistic Industry. Environ. Resour. Econ. 2005, 31, 253–274. [Google Scholar] [CrossRef]
  25. Wolff, H. Keep Your Clunker in the Suburb: Low-emission Zones and Adoption of Green Vehicles. Econ. J. 2014, 124, 481–512. [Google Scholar] [CrossRef] [Green Version]
  26. Markus, G. The effect of low emission zones on air pollution and infant health. J. Environ. Econ. Manag. 2017, 83, 121–144. [Google Scholar]
  27. Wang, Y.; Sun, X.; Guo, X. Environmental regulation and green productivity growth: Empirical evidence on the Porter Hypothesis from OECD industrial sectors. Energy Policy 2019, 132, 611–619. [Google Scholar] [CrossRef]
  28. Li, G.; Masui, T. Assessing the Impacts of China’s Environmental Tax Using a Dynamic Computable General Equilibrium Model. J. Clean. Prod. 2019, 208, 316–324. [Google Scholar] [CrossRef]
  29. Zhang, S.; Mendelsohn, R.; Cai, W.; Cai, B.; Wang, C. Incorporating Health Impacts into A Differentiated Pollution Tax Rate System, A Case Study in the Beijing-Tianjin-Hebei Region in China. J. Environ. Manag. 2019, 250, 109527. [Google Scholar] [CrossRef]
  30. Helfand, G.E.; Berck, P.; Maull, T. Handbook of Environmental Economics: The Theory of Pollution Policy; Elsevier: Amsterdam, The Netherlands, 2003; Volume 1, pp. 249–303. [Google Scholar]
  31. Slemrod, J. Tax compliance and enforcement. J. Econ. Lit. 2019, 57, 904–954. [Google Scholar] [CrossRef]
  32. Lori, D.; Nolan, H.N.; Robert, N.S. The effects of environmental regulation on technology diffusion: The case of chlorine manufacturing. Am. Econ. Rev. 2003, 93, 431–435. [Google Scholar]
  33. Zhao, J.; Jiang, Q.; Dong, X.; Dong, K.; Jiang, H. How does industrial structure adjustment reduce CO2 emissions? Spatial and mediation effects analysis for China. Energy Econ. 2022, 105, 105704. [Google Scholar] [CrossRef]
  34. Huang, H.; Mbanyele, W.; Fan, S.; Zhao, X. Digital financial inclusion and energy-environment performance: What can learn from China. Structural Change and Economic Dynamics. Struct. Change Econ. Dyn. 2022, 63, 342–366. [Google Scholar] [CrossRef]
  35. Zhao, H.; Yang, Y.; Li, N.; Liu, D.; Li, H. How Does Digital Finance Affect Carbon Emissions? Evidence from an Emerging Market. Sustainability 2021, 13, 12303. [Google Scholar] [CrossRef]
  36. Jia, Z.; Wen, S.; Liu, Y. China’s urban-rural inequality caused by carbon neutrality: A perspective from carbon footprint and decomposed social welfare. Energy Econ. 2022, 113, 106193. [Google Scholar] [CrossRef]
  37. Jia, Z. What kind of enterprises and residents bear more responsibilities in carbon trading? A step-by-step analysis based on the CGE model. Environ. Impact Assess. Rev. 2023, 98, 106950. [Google Scholar] [CrossRef]
Sustainability 14 15959 g001a 550Sustainability 14 15959 g001b 550
Figure 1. Parallel trend of difference-in-difference.
Figure 1. Parallel trend of difference-in-difference.
Sustainability 14 15959 g001aSustainability 14 15959 g001b
Table
Table 1. Reform Nodes of the Chinese Environmental Protection Tax.
Table 1. Reform Nodes of the Chinese Environmental Protection Tax.
YearCharge StandardUse of Funds
1982Implemented over standard chargeEarmarked for its specified purpose only, and used as the environmental protection subsidy
1988Kept original rate unchangedEarmarked for its specified purpose only, and used as the loanable fund for pollution abatement
1993Sewage that did not exceed the discharge standard was also chargedImplemented over standard charge
2003Enterprises were charged for discharge of all pollutants, and the charges for over-standard sewage discharge were doubledEarmarked for its specified purpose only, and used as a grant or subsidized loan
2014Raised the charges for part of key pollutantsKept original standard unchanged
2018Shifted charge to tax, capped the tax rate and canceled the practice of double taxIncorporated into the fiscal budget and not earmarked for its specified purpose only
Table
Table 2. Results of Basic Regression.
Table 2. Results of Basic Regression.
(1)(2)(3)(4)
AQIPM2.5SO2CO
Treat*Post−2.660 **−2.486 ***−3.854 ***−0.039 **
(0.922)(0.817)(1.089)(0.018)
second_industry0.122 *0.0761−0.234 ***−0.003 **
(0.069)(0.060)(0.077)(0.001)
lnpeople−4.084 ***−3.384 ***−0.1620.003
(1.489)(1.229)(1.367)(0.0302)
lnpgdp2.0911.9275.370**0.053
(1.802)(1.669)(2.192)(0.040)
wat−0.074 **−0.0400.097 ***0.002 ***
(0.035)(0.027)(0.033)(0.001)
tra0.0350.009−0.0360.000
(0.038)(0.031)(0.037)(0.001)
gre0.142 ***0.125 ***0.125 **0.002 *
(0.054)0.044(0.051)(0.001)
lngg−0.079−0.0340.8020.002
(0.461)0.470(0.654)(0.009)
deficit−4.384−3.581−0.380−0.110
(4.317)(3.511)(3.176)(0.134)
City FEYYYY
Year FEYYYY
N. Obs1389138913891389
Adj R-sq0.9100.9110.7800.825
Note: * represents p-value < 10%, ** represents p-value < 5%, and *** represent p-value < 1%, respectively. Standard errors of clustering at the city-industry level in parentheses.
Table
Table 3. Results of Substitute variables.
Table 3. Results of Substitute variables.
(1)(2)(3)(4)
AQI_numPM10NO2O3
Treat*Post−0.019 **−4.281 ***−1.883 ***2.172
(0.010)(1.137)(0.4810)(1.498)
second_industry0.0021 ***0.070−0.0600.170 *
(0.001)(0.098)(0.041)(0.099)
lnpeople−0.052 ***−4.955 **−1.471−1.602
(0.016)(2.139)(1.051)(2.083)
lnpgdp0.0184.499 *4.643 ***6.374 **
(0.022)(2.486)(1.312)(2.815)
wat−0.002 ***−0.0500.017−0.166 **
(0.001)(0.051)(0.033)(0.067)
tra0.001 *0.0670.050 **0.057
(0.000)(0.048)(0.024)(0.050)
gre0.002 **0.1653 **−0.091 *−0.005
(0.000)(0.065)(0.047)(0.085)
lngg−0.0040.984−0.443−1.323 **
(0.005)(0.685)(0.298)(0.599)
deficit0.020−8.3649.057 ***−10.647 **
(0.072)(6.114)(3.288)(4.411)
City FEYYYY
Year FEYYYY
N. Obs1371137113711371
Adj R-sq0.9350.8990.6910.789
Note: * represents p-value < 10%, ** represents p-value < 5%, and *** represent p-value < 1%, respectively. Standard errors of clustering at the city-industry level in parentheses.
Table
Table 4. Results of DDD.
Table 4. Results of DDD.
AQICO2
(1)(2)
Treat*Post*Low_carbon−2.700 **−0.0003 **
(0.961)(0.0001)
Contral VariablesYY
City FEYY
Year FEYY
N. Obs13891389
Adj R-sq0.6920.646
Note: ** represents p-value < 5%. Standard errors of clustering at the city-industry level in parentheses.
Table
Table 5. Results of different low-carbon pilot cities.
Table 5. Results of different low-carbon pilot cities.
First BatchSecond BatchThird Batch
AQI
(1)(2)(3)
Treat*Post−7.277 **−7.936 **−4.510 **
(2.882)(2.934)(1.835)
Contral VariablesYYY
City FEYYY
Year FEYYY
N. Obs37136176
Adj R-sq0.8780.6690.653
Note: ** represents p-value < 5%. Standard errors of clustering at the city-industry level in parentheses.
Table
Table 6. Results of different environmental protection taxes.
Table 6. Results of different environmental protection taxes.
High TaxMedium TaxLow Tax
CO2
(1)(2)(3)
Low_carbon2.449 ***−0.062 ***−0.516 ***
(0.010)(0.003)(0.049)
Contral VariablesYYY
City FEYYY
Year FEYYY
N. Obs62649326
Note: *** represent p-value < 1%. Standard errors of clustering at the city-industry level in parentheses.
Table
Table 7. Results of different finance development.
Table 7. Results of different finance development.
HighLowHighLow
AQICO2
(1)(2)(3)(4)
Treat*Post*Low_carbon−4.259 **1.293−0.0004 **−0.0004 *
(1.336)(1.722)(0.0002)(0.0002)
Contral VariablesYYYY
City FEYYYY
Year FEYYYY
N. Obs609728576677
Adj R-sq0.9060.9160.8780.845
Note: * represents p-value < 10%, ** represents p-value < 5%, respectively. Standard errors of clustering at the city-industry level in parentheses.
Table
Table 8. Results of different regions.
Table 8. Results of different regions.
EasternRegionCentral RegionWesternRegionEasternRegionCentral RegionWesternRegion
AQICO2
(1)(2)(3)(4)(5)(6)
Treat*Post*Low_carbon−1.6712.178−6.575 **−0.0003 *−0.0004 *−0.0005 *
(1.465)(1.730)(2.661)(0.0002)(0.0002)(0.0003)
Contral VariablesYYYYYY
City FEYYYYYY
Year FEYYYYYY
N. Obs508474407421386328
Adj R-sq0.9320.8900.8910.8450.8410.846
Note: * represents p-value < 10%, ** represents p-value < 5%, respectively. Standard errors of clustering at the city-industry level in parentheses.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Liu, Z.; Wu, Z.; Zhu, M. Research on the Green Effect of Environmental Policies—From the Perspective of Policy Mix. Sustainability 2022, 14, 15959. https://doi.org/10.3390/su142315959

AMA Style

Liu Z, Wu Z, Zhu M. Research on the Green Effect of Environmental Policies—From the Perspective of Policy Mix. Sustainability. 2022; 14(23):15959. https://doi.org/10.3390/su142315959

Chicago/Turabian Style

Liu, Zixiao, Zengming Wu, and Mengnan Zhu. 2022. "Research on the Green Effect of Environmental Policies—From the Perspective of Policy Mix" Sustainability 14, no. 23: 15959. https://doi.org/10.3390/su142315959

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop