**1. Introduction**

As the largest developing country in the world, China is facing great pressure from the international community to reduce emissions [1]. Coal, as a high-carbon energy source, has been burned in China for thousands of years. The burning of coal for heating has led to a significant increase in the TSP (Total Suspended Particle) level in northern China [2], and it is also the main cause of frequent respiratory diseases and skin diseases for urban residents in recent years [3]. Consequently, to improve air quality and show a pragmatic image to the international community, China has formally put forward the "dual-carbon" strategic goal of a "carbon peak in 2030 and carbon neutrality in 2060" [4–7]. Moving from coal to gas and electricity is the main way to reduce coal use and improve air quality in winter [8]. In this way, the effectiveness of the Coal to Gas and Electricity policy, promoted by the state through financial subsidies, especially to Beijing, Tianjin, and Hebei as important cities in northern China, has become the focus of society.

**Citation:** Zhang, J.; Wang, W.; Gao, L.; Deng, Z.; Tian, Y. Can the Coal-to-Gas/Electricity Policy Improve Air Quality in the Beijing–Tianjin–Hebei Region?—Empirical Analysis Based on the PSM-DID. *Atmosphere* **2022**, *13*, 879. https://doi.org/10.3390/ atmos13060879

Academic Editors: Duanyang Liu, Kai Qin and Honglei Wang

Received: 28 April 2022 Accepted: 27 May 2022 Published: 28 May 2022

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The prevention and control of air pollution have become China's top priority [9], and its governance policies and effects have triggered academic discussions. Research shows burning coal accelerates air deterioration [10,11]. In particular, the emission of air pollutants caused by coal-fired heating in winter is more harmful to air quality and human health than industrial sources with the same emissions [12,13]. Against the background of carbon peak and carbon neutralization, it is urgent to change the practice of coal-fired central heating. Therefore, the state promotes the Coal to Gas and Electricity policy through financial subsidies. At present, academic research on this issue is rare [14]. Li et al. studied the changes in air quality, energy efficiency, and residents' energy consumption before and after the implementation of the Coal to Gas and Electricity policy, based on the panel data of 41 cities in China from 2003 to 2015 [15]. Shi et al. studied the green coordinated development effect of the policy of coal to gas and electricity in Beijing, Tianjin, and Hebei [16]. Liu et al. analyzed the typical problems in the construction of the coal to gas project in Beijing and put forward relevant policy suggestions [17]. Yu et al. evaluated the green net benefit of the coal to gas project in Beijing, Tianjin, and Hebei, and considered that the coal to gas policy could improve green comprehensive efficiency by 0.3%–0.4% [18]. However, some scholars believe that the policies of coal to gas and electricity and "clean coal substitution" increase residents' heating costs [19]. Scholars mainly discuss air pollution control, green development, and the cost subsidies of the Coal to Gas and Electricity policy and rarely use more micro and refined data to analyze the air pollution control effect of the policy. Further research shows that the frequency of severe pollution weather in northern China coincides with the concentration of coal-fired heating in winter [20,21]. China's unique coal-fired heating measures in the north in winter have led to more prominent air pollution problems in northern China than in the south [22]. The air pollution problem of the Beijing–Tianjin–Hebei urban agglomeration in the northern region is particularly serious [23]. Early research found that although Beijing, Tianjin, Hebei, and the surrounding areas account for only 7.2% of the national land area, they consume 33% of the national coal, and the pollutant emission intensity per unit area is about four times the national average [24]. However, the research conclusions on the relationship between the air control effect and pollutant emissions in Beijing, Tianjin, and Hebei are not completely consistent. Some scholars found that environmental regulations can significantly reduce PM2.5 and SO2 concentrations [25]. Based on ecological environmental monitoring and meteorological observation data, Zhu Yuan-yuan et al. analyzed the characteristics of ozone concentrations in the major cities in the Beijing–Tianjin–Hebei region from 2016 to 2020 and found that ozone concentrations in the region increased by 11.6%, showed an overall trend of fluctuation during 2016 to 2019, and then decreased in 2020 [26]. In addition, few studies can clearly point out the clear relationship between the air quality index and specific pollutants in Beijing, Tianjin, and Hebei. Therefore, it is of great significance to use weekly micro panel data to test the effectiveness of the Coal to Gas and Electricity policy in the Beijing–Tianjin–Hebei region and to explore the specific impact of the policy on specific pollutants.

From the urban perspective, a regional joint prevention and control strategy must be adopted for air pollution control to avoid the "leakage effect" and "free-riding behavior" of air pollution control [27]. However, because of the limitations of research data and research methods, it is difficult to determine the dynamic and sustainable effects of air pollution control policies in urban agglomerations [28]. In addition, only a few articles use the policy evaluation method in econometrics [29], and the conclusions are inconsistent. Duwencui et al., using the Single DID method, found that the coordinated control of haze in Beijing, Tianjin, and Hebei had not improved the air quality in this area [30]. Wayi et al. found that the joint action of "2 + 26" cities in Beijing, Tianjin, Hebei and surrounding areas under the guidance of the action plan for comprehensive treatment of air pollution in autumn and winter helped improve the air quality in the region [31]. The Coal to Gas and Electricity policy in Beijing, Tianjin, Hebei, and surrounding areas has an obvious process from pilot to promotion, and the time and intensity of joint prevention and control

in different cities are not completely consistent [29]. Therefore, the estimation results using a single DID model are likely to include errors, resulting in unsolvable endogeneity problems [32]. Using the DID method, the most important premise is that the processing group and control group must meet the common trend assumption, that is, if there were no Coal to Gas and Electricity policy, there would be no systematic difference in the change in the trend of air quality between Beijing, Tianjin and Hebei and other regions over time. But in reality, this assumption of the DID method may not be satisfied. However, the PSM-DID method proposed and developed by Heckman et al. could effectively solve this problem and make the DID method meet the common trend hypothesis [33,34]. In addition, the time-varying DID model can capture the dynamic changes of the policy to measure the effect of the joint prevention and control policy of coal to gas and electricity in Beijing, Tianjin, and Hebei more accurately. Therefore, it is not only necessary to expand the sample number of the Beijing Tianjin Hebei urban agglomeration to improve the integrity of the impact of the Coal to Gas and Electricity policy on air quality, but it is also necessary to adopt the PSM-DID model and the time-varying DID model to evaluate the effectiveness and dynamics of policy implementation.

The purpose of this study is to test the impact of the Coal to Gas and Electricity policy on air quality in Beijing, Tianjin, and Hebei and find possible measures for improvement. Therefore, based on the weekly air quality and meteorological data of nineteen cities in Beijing, Tianjin, Hebei, and surrounding areas from 2015 to 2020, we used the PSM-DID method to evaluate the effectiveness and dynamics of the Coal to Gas and Electricity policy on air quality improvement. This article provides some important insights: First, this study adopts the method of propensity score matching (PSM) for the first time, and "wind speed" and "temperature" were finally obtained as characteristic variables from 6042 original atmospheric data of nine experimental groups and eleven control groups, which overcomes the endogenous problem between explanatory variables. Second, taking the implementation of coal to gas and electricity as a quasi-experiment, differences-indifferences (DID) was used to identify the impact of the policy on air quality and PM2.5, PM10, SO2, and CO. The results show that the Coal to Gas and Electricity policy has indeed improved the air quality in Beijing, Tianjin, and Hebei during the implementation period. The policy had a great impact on SO2 and PM10, while the effects on PM2.5 and CO were relatively weak. Third, the time-varying DID model was used to identify the dynamic sustainability effect of the Coal to Gas and Electricity policy. It proves that the policy has a strong impact in the initial stage. However, at the end of the implementation or near the end, the effect is greatly reduced, and it is far less obvious than at the beginning of the policy. These results held true after several robustness tests. The above conclusions based on the evaluation of the Coal to Gas and Electricity policy in Beijing, Tianjin, and Hebei may provide relevant references and lessons for air governance in other countries.

The rest of the study presents the data and methodology (Section 2), empirical results (Section 3), and robust tests (Section 4). Finally, we draw three conclusions (Section 5).

#### **2. Data and Methodology**

#### *2.1. Data and Variables*

The air quality index (AQI) and the concentration of individual pollutants are the air quality indicators that are most widely consulted by the public and highly valued by the national ecological and environmental department. Among them, the AQI is a dimensionless comprehensive index, which is obtained by standardizing the concentration index of each single pollutant, allowing it to be a comprehensive reflection of the daily air quality of the city. The value range is 0–500, and according to the size of the AQI, we divide urban air quality into six levels. The larger the value, the more serious the air pollution [35–37]. In addition, the assessment method of the action plan for air pollution prevention and control clearly points out that the annual average concentration decline ratios of PM2.5 and PM10 are used as the assessment index [38]. As one of the important indicators to measure whether SO2 and CO are polluted in the atmosphere. Therefore, this

paper selects the daily data of the AQI, PM2.5, PM10, SO2, and CO in Beijing, Tianjin, Hebei and surrounding areas from 2015 to 2020. According to the weighted average calculation, the weekly data were obtained for regression analysis. Relevant data comes from China's air quality online monitoring and analysis platform (https://www.AQIstudy.cn, accessed on 15 February 2022).

Research shows that weather conditions can have a significant impact on air quality [39,40]. Therefore, to ensure the accuracy of the study, the control variables selected in this paper include daily average temperature, daily average humidity, and wind level. The weekly data is calculated according to the weighted average of daily average data. Relevant data comes from China's air quality online monitoring and analysis platform (https://www.AQIstudy.cn, accessed on 15 February 2022).

## *2.2. Methodology*

The Propensity Score Matching (PSM) and Difference-in-Difference model (DID) method are used to evaluate the effect of policy implementation. The PSM is particularly suitable for studies using non-random data. Computing the average processing effect of the treatment group samples through the common support hypothesis test and balance hypothesis test can obtain basic unbiased estimates, thus obtaining a natural experiment under the condition of using non-random data. The influence of selective bias and confounding factors in the performance evaluation process can be excluded as far as possible by the PSM method, ensuring that the final estimated performance results are an unbiased "net effect". The PSM can solve the problem of sample selection bias, but cannot avoid the endogenous problem caused by variable omission; The DID can solve the endogenous problem through double difference, but cannot solve the problem of sample selection deviation well. Based on this, this paper combines PSM and DID for robustness estimation. At present, there are few articles using the policy evaluation method in econometrics [29]; moreover, even if a single DID model is used, the estimation results are likely to be biased, resulting in endogenous problems that cannot be reasonably solved [32]. Using the DID method, the most important premise is that the processing group and control group must meet the common trend assumption; that is, if there is no Coal to Gas and Electricity policy, there is no systematic difference in the changing trend of air quality between the Beijing– Tianjin–Hebei region and other regions over time. But in reality, this assumption of the DID method may not be satisfied. However, the PSM-DID method that has been proposed and developed can effectively solve this problem [33,34]. In addition, the time-varying DID model can capture the dynamic changes of the policy to measure the effect of the joint prevention and control policy of coal to gas and electricity in Beijing, Tianjin, and Hebei more accurately. Therefore, this study first takes *group* as the grouping variable, speed and temp among the control variables as the characteristic variables, and AQI as the output variable for PSM matching, which solves the problems of deviation and endogeneity between variables. Second, to avoid the problem of multicollinearity, we selected eight cities in the Tianjin Hebei region as the experimental group by using the atmospheric data from 2015 to 2020, and chose another eleven cities with similar geographical locations, similar air pollution, or similar population and economic levels as the control group, using the DID model to identify the effectiveness of the Coal to Gas and Electricity policy in the Beijing–Tianjin–Hebei region. Finally, the time-varying DID model is used to evaluate the effectiveness and dynamics of policy implementation.

### 2.2.1. Model Construction

This paper first uses the DID model to evaluate the effect of the Coal to Gas and Electricity policy on air pollution control in the Beijing–Tianjin–Hebei region and surrounding areas. The model is as follows:

$$Y\_{ct} = \beta\_0 + \beta\_1 Group\_c \times Policy\_t + \delta X\_{ct} + \varepsilon\_{ct} \tag{1}$$

where *Yct* represents the air quality index and single pollutant concentration of city C on date T, and *Groupc* indicates whether city C is in the experimental group or the control group. If it is the experimental group, the value is 1; otherwise, the value is 0. The dummy variable *Policyt* indicates whether the policy is executed. Policy implementation is 0 before implementation and 1 after implementation. The variable *Policyt* indicates the change of the air quality of the experimental group after the policy implementation, and the coefficient *β*<sup>1</sup> can be used to measure the effect of the air pollution prevention and control policy. The control variable *Xct* indicates other factors affecting air quality, including weather conditions (daily average temperature-*temp*, average daily humidity-*humidity*, and wind level-*speed*). The random perturbation term is represented by *ct*.

Although there were reports of "the Coal to Gas and Electricity policy" from 2003 to 2016, the frequency was relatively low. On 5 December 2017, the National Development and Reform Commission and other departments jointly issued the Plan for Clean Heating in Winter in Northern China (2017–2021), proposing for the first time to build a complete clean heating industry system in northern China within 3 to 5 years. According to the evolution time of the policy of coal to gas and electricity and considering the applicability of the model, we defined the period before the implementation of the policy as January 2015– 31 December 2017 and the period after the implementation of the policy as January 2018– 31 December 2020. The policy involved in this study is mainly concerned with heating. The value is 0 before the policy is implemented and 1 after the policy is implemented. We used weekly data, so the specific implementation date was 1 in the second week of 2018, and 0 in the previous week.

Based on similar geographical location, air pollution, level of economic development, and permanent population, the following choices were made. The experimental group included eight cities in the Beijing–Tianjin–Hebei area: Beijing, Tianjin, Shijiazhuang, Tangshan, Langfang, Baoding, Qinhuangdao, and Zhangjiakou. The control group contained eleven cities: Taiyuan, Yangquan, Changzhi, Jinan, Jining, Hohhot, Baotou, Zhengzhou, Jincheng, Datong, Kaifeng. When the time interaction between a certain city in the experimental group and the policy implementation was equal to 1, it meant that the city was incorporated into the Coal to Gas and Electricity policy at that time point. Before that time point, the interaction term was 0. The geographical location map is shown below (Figure 1).

**Figure 1.** The geographical location map.

#### 2.2.2. Descriptive Statistics

The sample size shown in the following table is the sample size after PSM treatment, and the original sample size is 6042, and the sample size after PSM treatment is 4063, which will be mentioned in the subsequent PSM analysis. From the value range of variables, the value range of each variable is within a reasonable range, and the outlier is not obvious. See Table 1.


**Table 1.** Description of variables and data.
