The effect of environmental regulation on air quality: A study of new ambient air quality standards in China

Accepted Manuscript The effect of environmental regulation on air quality: A study of new ambient air quality standards in China Kunlun Wang, Hongchun Yin, Yiwen Chen PII:

S0959-6526(19)30072-1

DOI:

https://doi.org/10.1016/j.jclepro.2019.01.061

Reference:

JCLP 15448

To appear in:

Journal of Cleaner Production

Received Date: 2 October 2018 Revised Date:

13 December 2018

Accepted Date: 7 January 2019

Please cite this article as: Wang K, Yin H, Chen Y, The effect of environmental regulation on air quality: A study of new ambient air quality standards in China, Journal of Cleaner Production (2019), doi: https:// doi.org/10.1016/j.jclepro.2019.01.061. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Title : The effect of environmental regulation on air quality：A study of new Ambient Air Quality Standards in China

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Authors & Affiliations: Kunlun Wang, College of Management and Economics, Tianjin University, Tianjin 300072, China(Corresponding Author), E-mail:[email protected] Hongchun Yin, College of Management and Economics, Tianjin University, Tianjin 300072, China, E-mail: [email protected] Yiwen Chen, School of public affairs, Xiamen University, Xiamen 361005, China, E-mail: [email protected]

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The effect of environmental regulation on air quality: A study

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of new Ambient Air Quality Standards in China

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This paper has not been submitted elsewhere in identical or similar form, nor will it be

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during the first three months after its submission to the Publisher.

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Abstract: Fine particulate matter is becoming a primary component of air

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pollution in China, damaging public health and economic growth. Although

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environmental regulation is an important instrument to control air pollution,

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studies on the effect of environmental regulation are mixed. Do environmental

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regulations help improve the air quality in urban centers, and what mechanism

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explains their effects in China? In order to explore the causal relationship

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between environmental regulations and air quality, the effectiveness of the New

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Ambient Air Quality Standards—primarily the monitoring of PM2.5—was

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evaluated using the Difference-in-Differences (DID) method to weaken the

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endogeneity problems. Our results suggest that the New Standards reduce the

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concentration of PM2.5 and emissions of SO2 in pilot cities in neither the

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short-term nor the long-term. Our heterogeneous analyses show that the

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monitoring efforts, pollution control efforts, and ownership structures can affect

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outcomes of New Standards significantly, and that they have different influences

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ACCEPTED MANUSCRIPT on various categories of pollutants. Our paper implies that the Chinese

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government should make more efforts beyond setting up environmental

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regulations.

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Keywords: air pollution; environmental regulations; natural experiment; air

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quality standards; Difference-in-Differences (DID)

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Introduction

In recent years, environmental pollution has become a major concern in

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China, with far-reaching adverse effects on public health and economic

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development. The entire compliance rates of all prefecture-level cities’ air quality

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were 51.69% and 89% in 2005 1 and 2011 2, respectively. After updating the

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Ambient Air Quality Standards, only 73 of all 338 prefecture-level cities in China

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met the national ambient air quality standards in 2015 3 . The average

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1.The 2005 China Environmental Bulletin is available at:

http://www.mee.gov.cn/hjzl/zghjzkgb/lnzghjzkgb/201605/P020160526558688821300.pdf 2.The 2011 China Environmental Bulletin is available at:

http://www.mee.gov.cn/hjzl/zghjzkgb/lnzghjzkgb/201605/P020160526563389164206.pdf 3. The 2015 China Environmental Bulletin is available at: 2

ACCEPTED MANUSCRIPT concentration of fine particulate matter (PM2.5) is over 80ug/m3 in eastern China

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(Van Donkelaar et al., 2010). The Chinese population-weighted average PM2.5

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concentration was 52ug/m3 in 2015, which contributed to roughly 17% of all

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deaths in China (Rohde & Muller, 2015). Ebenstein et al. (2017) suggested that a

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person’s life expectancy in North China declined by 0.64 years after a PM10

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concentration increase of 10ug/m3. High concentrations of PM2.5 increase the

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frequency of stroke, heart disease, lung cancer, and mental health issues (Zhang

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et al., 2017). Other studies have shown that air pollution hurts economic activities

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badly. Xia et al. (2016) utilized a developed input-output model to estimate the

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monetary value of total losses caused by severe air pollution in China, the results

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of which suggested that air pollution in 30 provinces affects 72 million

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employees and cause 346.26 billion CNY in losses—approximately 1.1% of the

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national GDP—in 2007. From 2000 to 2010, the losses of health and productivity

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caused by air pollution amounted to 6.5% of the GDP in China (Crane & Mao,

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2015). Gao et al. (2015) estimated that the severe haze event in January 2013

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caused economic consequences of up to 253.8 million US dollars.

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http://211.81.63.2/cache/8/03/www.mee.gov.cn/6be585d7d4386618f922616dd8fcec87/P0201606 02333160471955.pdf 3

ACCEPTED MANUSCRIPT Grossman and Krueger (1991) found that the relationship between

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economic development and pollution is an inverted-U curve, which shows that

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pollution will decline as per capita income rises. A critical review of the

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Environmental Kuznets Curve (EKC) stated that its shape is not fixed (Dasgupta

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et al., 2002; Özokcu, & Özdemir, 2017). The shape of an EKC is influenced by

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the environmental regulation of the region. Some literature states that regulation

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is the dominant factor in explaining the decline in pollution. Shapiro and Walker

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(2015) used U.S. manufacturing data to examine the role of several factors in

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reducing the air pollution from 1990 to 2008. Their results showed that

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environmental regulation contributes more to the reduction of pollution emissions

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than do changes in trade or productivity.

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Fine particulate matter is attracting more and more public attention. The

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Chinese government revised the national ambient air quality standards, which set

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the concentration standards of air pollutants, and introduced PM2.5 into the

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monitoring system for the first time. Much of the literature focuses on

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command-and-control environmental regulations (CACs). Theoretically, the roles

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and effects of these environmental regulations are evident and can improve air

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quality significantly. However, their actual effects are ambiguous in both

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ACCEPTED MANUSCRIPT developed and developing countries. Greenstone (2004) tested the effectiveness

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of the Clean Air Act in reducing the concentration of sulfur dioxide (SO2

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hereafter), concluding that it played a minor role in the reduction of the

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concentration of SO2 from the 1970s to the 1990s in the U.S.A. Greenstone and

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Hanna (2014) found that Supreme Court Action Plans (SCAPs) improved the air

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quality in India. However, Blackman and Kildegaard (2010) found no evidence

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that stricter regulation increased the adoption of cleaner technology when

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studying plant-level data. The effects of driving restrictions on air pollution are

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also debatable (Davis, 2008; Gallego et al., 2013; Sun et al., 2014; Viard & Fu,

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2015; Lu, 2016; Zhong et al., 2017).

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Scholars have done further studies to analyze and summarize the factors

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influencing the outcomes of environmental regulation. Duflo et al. (2013)

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performed a randomized controlled trial of audits, with results suggesting that

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assuring the accuracy of audit reports could improve regulatory compliance and

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reduce emissions. In line with previous studies, this experiment emphasized the

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importance of monitoring. The enforcement of environmental regulations is an

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essential determinant of their effectiveness (Bao et al. 2013; Li & Weng, 2014;

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ACCEPTED MANUSCRIPT Carrillo et al. 2016). Shi et al. (2016) found that regulatory policies had a more

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significant effect in cities, which have more state-owned thermal power plants.

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However, Wang and Jin (2007) and Jiang et al. (2014) concluded that state-owned

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enterprises could reduce their payment of emissions charges due to their stronger

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bargaining power, leading to higher levels of pollution. The introduction of

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ecological and environmental performance into local bureaucrats’ performance

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evaluation systems can enhance the effectiveness of environmental regulations

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(Kahn et al., 2015; Tang et al., 2016; Tang et al., 2018; Chen et al., 2018). In

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summary, these factors may provide some explanation as to the mixed effects of

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environmental regulations.

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Another reason that the actual effects of policies are unclear is

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endogeneity, which increases the difficulty of measuring and analyzing the causal

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relationships between regulations and environmental quality improvements

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within observational studies. The selection of samples biases the estimate of the

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causal relationship, which makes it difficult to match the treated and control

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groups correctly. Further biases are caused by key omitted variables that correlate

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with both regulations and environmental quality. Also, cities with more severe

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pollution are more likely to formulate more stringent environmental regulations.

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ACCEPTED MANUSCRIPT This bilateral causality increases the difficulty of analyzing the effects of

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regulations. Finally, as it is challenging to determine the strictness of

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environmental regulations, measurement error causes more biases in estimation.

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Local policy experiments have been used when conducting reform in

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China since the 1980s. They are regarded as a factor behind the success of

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China’s rapid development since 1978 (Xu, 2011). The experience from pilot

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areas is beneficial to the adjustment or further implementation of a policy in a

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broader scope. According to the experimental method of econometrics, the local

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policy experiment—when regarded as a natural experiment—is an effective tool

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to weaken the threat of endogeneity in observational studies (Li et al., 2017). For

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example, Chen and Cheng (2017) used the difference-in-differences (DID)

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method to assess the effectiveness of Chinese Two Control Zones (TCZ) policy,

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which is regarded as a quasi-natural experiment, and found that stricter

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environmental regulation led to a lower level of pollution due to industrial

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activities in TCZ prefectures. Qiu and He (2017) applied the DID method to

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investigate the effectiveness of the Green Traffic Pilot Cities Program, with

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results suggesting that green traffic reduced air pollution significantly in pilot

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cities.

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ACCEPTED MANUSCRIPT The Chinese government implemented the New Standards as a pilot policy.

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Its implementation can be considered as a natural experiment, which allows us to

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observe the causal interactions of the regulations and air quality throughout a

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counterfactual framework. The regulations are called the New Ambient Air

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Quality Standards, enacted by The Ministry of Environmental Protection of

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China (MEPC) in 2012. With China’s rapid economic development, PM2.5 has

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become a major component of air pollution, especially haze. Therefore, the New

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Standards, as the first environmental regulation monitoring PM2.5, is meant to be

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explored and studied in depth. However, there is little current research focusing

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on the outcomes of the New Standards (Wang et al., 2015; Lu et al., 2017).

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As for our main conclusions, we found that the implementation of the New

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Standards had no positive effects on air quality in 2012, or in the longer term. We

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examined three dimensions (monitoring effect, pollution control efforts, and

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ownership structure) to explore their mechanisms.

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Our research has some advantages over previous studies. It is the first

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paper to perform an empirical ex-post evaluation of the New Standards. In order

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to examine the effect of the New Standards on PM2.5, we acquired the

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concentration of PM2.5 from the annual global PM2.5 grid data map. Few studies 8

ACCEPTED MANUSCRIPT use PM2.5 concentration data from almost all of China’s prefecture-level cities.

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In particular, we explored how CAC regulations work on various mechanisms as

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useful instruments to control air pollution, based on an effective identification

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strategy.

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This paper contributes to the discussion surrounding environmental

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regulation by capturing the causal relationship between air quality and

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environmental regulations. The policy pilot of the New Standards in China

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provides an attractive natural experiment that overcomes the endogeneity

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problems of observational studies. Moreover, we found that the New Standards

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imposed heterogeneous effects on two air pollutants, which means that the

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mismatch between objectives and outcomes needs to be further considered in the

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policy design.

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The remaining sections of our paper are organized as follows: Section 2

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introduces the history and background information of the Ambient Air Quality

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Standards. The two prefecture-city datasets, the methods, and the model setting

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are laid out in Section 3. The empirical analysis is shown in Section 4. Section 5

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presents the analysis of heterogeneous effects. Finally, Sections 6 and 7 present a

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complete discussion and conclusion. 9

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2.

Background

Since the mid-1980s, China’s government has implemented a variety of

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regulations to resolve environmental issues. SO2 is a primary pollutant, which has

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been monitored and controlled by the APPCL (the law about prevention and

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treatment of air pollution) and Two-Control-Zone policy (Acid Rain Control

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Areas and SO2 Pollution Control Areas) since 1998. However, these two policies

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did not change the trend of rising SO2 emissions in China. Faced with a serious

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situation, the Chinese central government took more action to control the

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emission of SO2 with the 10th Five-Year-Plan, which stated that the emissions

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should be reduced by around 10% compared to emission levels in 2000. Under

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the period of the 11th Five-Year Plan, the central government supervised local

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governments to improve their environmental performance by setting up the

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Mandatory Target Performance Evaluation System (Chen et al., 2018).

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Rapid economic development not only increased the emission of

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pollutants; it also changed the composition of air pollution in the past decades. In

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recent years, fine particulate matter has become a more serious problem (Wu et

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al., 2016). Chan and Yao (2008) showed that most regions had high 10

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Yangtze River Delta region. However, the official reports and regulations

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regarding air quality did not include fine particulate matter before 2012. The gap

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between the official reports and what the public sees every day regarding air

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quality was great, which jeopardized the credibility of the government.

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To rectify this, the MEPC revised and implemented the Ambient Air

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Quality Standards in 2012, which introduced fine particulate matter into the new

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air quality monitoring system for the first time. The air quality standards were

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first published in 1982 and notably revised in 1996 and 2000. The main

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differences between the new and old regulations are: (1) An adjustment of the air

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environmental function areas, (2) the imposition of Class

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zone

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standards on both

, (3) the addition of limitations on PM2.5 and the average

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and zone

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2. In the context of the Ambient Air Quality Standards (GB3095-1996), the Class I standards are

imposed on zone

, the Class

standards are imposed on zone

, etc. The annual average

concentration of SO2 is different in different classes. The Class I standards for SO2 are lower

than 20 µ g/m3 ; standards for Class II are higher than 20 µ g/m3, but no more than 60 µ g/m3; and

Class III standards range from 60 µ g/m3 to 100 µ g/m3.

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ACCEPTED MANUSCRIPT ozone concentration, and (4) stricter emissions limitations on other pollutants,

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such as PM10, NO2, and PB. Moreover, stricter regulations were introduced for

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monitoring data statistics and updating technical and analytical standards for

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some pollutants to make monitoring data more easily understood by the public.

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The New Standards were implemented step-by-step. In 2012, the policy

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was carried out in municipalities and provincial capitals in developed regions

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such as the Beijing-Tianjin-Hebei Metropolitan Region, the Pearl River Region,

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and the Yangtze River Delta Region. In total, there were 74 pilot cities in the first

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stage. In 2013, the policy was enacted in 113 key environmental protection cities;

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in 2015, it was implemented in all prefecture-level cities, and in 2016, it was

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implemented nationwide. Before 2012, the rate of failure to meet PM10, SO2, and

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NO2 standards was 93.55%, 41.94%, and 74.19% across 31 cities. After 2012, the

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rate of failure declined to 86.84%, 13.16%, and 71.05%, respectively, across 38

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cities. The policy has now been in effect for five years in the first pilot regions,

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but its effects have remained unexplored.

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Atmospheric SO2 emissions are a major contributor to PM2.5 in China

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(Schreifels et al., 2012). In several major Chinese cities, sulfates constitute 20–35%

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of the ambient PM2.5 (Li et al., 2009; Pathak et al., 2009; Tan et al., 2009). The 12

ACCEPTED MANUSCRIPT high levels and spreading trend of PM2.5 and O3 are the result of an expanding

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secondary particle production atmospheric chemical reaction process, rather than

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direct emissions (Jin et al., 2016). Furthermore, SO2 is a primary pollutant that

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can be controlled at the source, and which induces some of this secondary

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pollution. Because SO2 emission detection has a long history, the recorded data

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are detailed and easily accessible. With these reasons in mind, we selected the

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concentration of PM2.5 and SO2 emissions as our indicators of air quality.

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3.

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3.1 Data and descriptive statistics

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Materials and Methods

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For the first stage, the 74 pilot cities were selected as key environmental

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protection cities. All were located in densely populated metropolitan areas with

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high economic activity. In 2011, the industrial SO2 emissions of 113 key

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environmental protection cities reached 10.3 million tons, accounting for 46.4%

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of the total emissions in the country. In these cities, the MEPC established 700

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automatic air checkpoints and a complete environmental monitoring system,

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which enriches the data by providing historical information. To improve the

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quality of the environmental data, the department also conducts cross-validation

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ACCEPTED MANUSCRIPT through various approaches such as material balance calculations and formula

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calculations. To ensure the high quality of the data, the first dataset (Dataset 1)

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was based on the key environmental protection cities. After data matching, 104

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key cities were selected from all 113 cities in Dataset 1. Among them were 50

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pilot cities and 54 non-pilot cities, which were divided between the treated and

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the control groups. The air quality of the key environmental protection cities is

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shown in appendix A. (From Map I to Map

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in appendix)

Following the implementation of the New Standards, 113 key

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environmental protection cities became pilot cities in 2013. Therefore, in order to

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test the long-term effect of the New Standards, we established a second dataset

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(Dataset 2) which included all of the prefecture-level cities in China. We regarded

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the first-stage cities as the pilot cities and the other prefecture-level cities

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(excluding the 113 key environmental protection cities) as the non-pilot cities.

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After data matching, there were 73 pilot cities and 150 non-pilot cities in Dataset

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2.

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Our data mainly came from the China City Statistical Yearbook, China

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Environment Yearbook, CEInet Statistics Database, and China City Data in EPS

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Database from 2006 to 2014. The first dataset contained 728 sample data points 14

ACCEPTED MANUSCRIPT (104*7), and the second dataset contained 2007 data points (223*9). If missing

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values existed at the beginning or end of the time series (2006 or 2014), the

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extrapolated time-trend method was used; otherwise, linear interpolation was

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used to fill the missing values in the sequence.

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The MEPC did not monitor and record the concentration of PM2.5 in the

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annual Ecological Environment Bulletin before 2012. While implementing the

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New Air Quality Standards, the Chinese government introduced PM2.5 as a major

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pollutant into the air quality monitoring system for the first time. Some more

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recent studies have used limited PM2.5 data from specific cities to do some

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research on haze in China, while other studies have employed remote sensing

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technology to revert the PM2.5 concentration from Aerosol Optical Depth (AOD)

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released by NASA. In our study, we followed the above instructions to get the

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PM2.5 concentrations of Chinese sample cities from the Global Annual PM2.5

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Grids at the Socioeconomic Data and Application Center at Columbia University.

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We downloaded the annual PM2.5 grids from their homepage and used the

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administrative boundaries of Chinese cities to match the PM2.5 concentration in

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every city

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. After matching and calculating, we retrieved the PM2.5

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ACCEPTED MANUSCRIPT concentrations of 95 cities from Dataset 1 and 219 cities from Dataset 2. That is

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another advantage of this study; there are few papers on haze in China that

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include almost all prefecture-level cities with their PM2.5 concentration data.

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Furthermore, the factors influencing haze can be classified into three

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clusters, which determine the formation of PM2.5 in a given city. The first cluster

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is economic development, including the GDP, industrial structure and energy

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consumption, etc. The second cluster is social determinants, such as

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transportation, urbanization, heating and so on. The third cluster contains the

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natural conditions—temperature, humidity, and wind speed all affect the

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formation and spread of pollutants.

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In order to study the effects of the New Standards, we need to control

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other factors contributing to the air quality. We selected some control variables

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from among the economic, social, and natural factors (Ma et al., 2016; Cheng et

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al., 2017; Chong et al., 2017; Wu et al., 2018). The main source of air pollution is

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industrial emissions, and these emissions will increase as economic activities

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grow. Therefore, we controlled for the economic development and industrial

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ACCEPTED MANUSCRIPT structure, measured by economic growth rate and the ratio of secondary

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industries’ added values. Regarding social factors, transportation is one of the

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most critical drivers of air pollution. Public transportation can reduce the use of

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private cars, and good traffic conditions can decrease congestion in a city. We

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selected for the number of people riding public transportation and per capita road

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area to use in measuring the traffic conditions in each city. As far as natural

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conditions, green plants can purify the air and change the meteorological factors

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in a city, so we used the green plant's coverage rate in a city’s built-up area as a

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control variable. These five control variables were labeled as rindus2, index,

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trans, proad, and rgreen, respectively. In order to deal with potential

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heteroscedasticity and the dependence on the regression model setting, we took

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some continuous variable as their natural logarithm. Table 1 shows the

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descriptive statistics of variables.

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Table 1

We regarded the implementation of the New Standards as a natural

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experiment. Due to its application at different times to different cities, we could

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split these cities into two groups: the treated group and the control group. The

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treatment group contained the pilot cities, and the control group contained the 17

ACCEPTED MANUSCRIPT non-pilot

cities.

This

natural

experiment

allowed

us

to

adopt

the

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difference-in-differences (DID) method to explore the effects of the New

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Standards. The standard DID equation is outlined in equation (1). The logic

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behind the DID method is that the control group could present a suitable

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counterfactual outcome of the treated group, so by comparing the changes of the

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two groups over the same period, you can get a net policy effect. The overall

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methodolody of this paper is presented in Graph A.

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ACCEPTED MANUSCRIPT DDD Model

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Effect of the New Standards on air quality

Common Trend Assumption

Monitoring The Main Regression Results

The Main Regression Results

Pollution Control Ownership

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Capturing causal relationship between air quality and environmental regulation

Eliminating the endogeneity problems caused by the measurement errors, key omitted variables and bilateral causality

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Graph A. Logical framework

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Exploring the channels and mechanisms of environmental regulation

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In this paper, we added some control variables into equation (1) as covariates so that we could get the DID estimation specification in equation (2).

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Pollution = α + jiaohu2012 + β policy + β year2012 + ε (1) Pollution = α + jiaohu2012 + λ ! " + ω + ϕ + ε (2) 305

Where Pollution

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thousand people in city i at year t, jiaohu2012

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jiaohu2012 = policy ∗ year2012 . policy = 1 if a city carried out the new

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standards in 2012 and = 0 if a city was not a pilot city. year2012 =1 if

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year >=2012, otherwise = 0. ω are city-fixed effects, capturing time-invariant

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characteristics in different cities; ϕ are year-fixed effects, capturing yearly

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factors common to all cities. For example, the tendency toward reducing

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is the concentration of PM2.5 or SO2 emissions per ten

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is the interaction term, and

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industrial SO2 emissions. "

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and ε

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correlation, we clustered the standard errors at the city level. Most important was

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the coefficient of jiaohu2012 , which revealed the impacts of the New Standards.

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3.2 Common Trend Assumption

represents the covariates, like industrial structure,

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is the error term. To relieve potential heteroscedasticity and serial

According to the logic of the DID method, the key to finding a causal

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relationship is that the treated group would have the same trend as the control

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group over the same period if it did not carry out the New Standards. If the

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common trend assumption is violated, the DID method does not give a satisfying

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result. We compared the pre-existing time trend of outcome variables to confirm

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whether the assumption was valid or not.

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Figure 1 and Figure 2 show the trend in the average annual PM2.5

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concentration and the annual per capita SO2 emissions of the pilot cities and

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non-pilot cities from 2006 to 2012, respectively. As shown in these figures,

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before the treatment periods, the concentration of PM2.5 of two groups increased

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in 2007, and then the concentration of PM2.5 fell between 2007 and 2012. The

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two groups had the same time trend. The emissions of SO2 in both the treated

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group and the control group fell between 2006 and 2009 before increasing, which

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average PM2.5 concentration and annual average SO2 emission before 2012

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might suggest that they would have had the same post-trend if there were no

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policy in the treated group. However, the evidence from the two figures cannot

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provide a solid conclusion about the common trend. Therefore, we will explore

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the common trend assumption in depth within the robustness check section.

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Fig. 1. Trends in concentration of PM2.5

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Fig. 2. Trends in per capita sulfur dioxide emissions 336

337

4.

338

4.1 Main Results

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Empirical framework

Our main estimation results are presented in Table 2. The dependent

341

variable is lnpm in columns (1) and (2). In the last two columns, the dependent

342

variable is pso2. Column (1) represents the result from equation (2)—controlling

343

the pollution control efforts, the city-fixed effects, and year-fixed effects—but the

344

coefficient of the interaction term is not significant. Column (2) controls for other 22

ACCEPTED MANUSCRIPT control variables. Its estimation coefficient is robust when compared to column

346

(1), but it remains insignificant. The dependent variable is pso2 in columns (3)

347

and (4). In column (3), the coefficient of the interaction term is negative, but it is

348

not significantly different from zero. After controlling for additional variables,

349

the coefficient of the interaction term is still not significant, but it becomes

350

positive. In Table 2, we find that all four interaction terms are insignificant,

351

indicating that the cities where the New Standards had been implemented did not

352

improve in air quality.

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There are two possible reasons why the New Standards might not have

354

improved the air quality in pilot cities. First, there may be leading or lagging

355

effects of the New Standards, so we could not capture the effect in 2012. Second,

356

the average treatment effect of the New standards—represented by the coefficient

357

of the DID model—is zero. A useful policy requires appropriate conditions. The

358

New Standards did not meet their overall requirements, resulting in a mixed

359

effect in all pilot cities, though they were effective in some cities.

360

4.2 Robustness Checks

361

Lags, leads, and time trends: Since the MEPC began asking for suggestions from

362

many institutions and experts in 2010 and then took two years to publish the final

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23

ACCEPTED MANUSCRIPT edition of the Ambient Air Quality Standards, there may have been an expectation

364

effect; that is, the effects of the New Standards may have appeared before 2012.

365

Meanwhile, the New Standards may also have lagging effects on pilot cities. We

366

followed instructions from Laporte & Windmeijer (2005) and Cai et al. (2016) to

367

accommodate leading and lagging effects, estimating all leading and lagging

368

effects in one equation. The estimation equation is as follows: (

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)

'*(

+

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Pollution = α + ! δ' jiaohu' + λ ! " + ω + ϕ + ε (3) Where jiaohu' is the interaction term for 2006-2014, and the omitted year is

370

2006. We care the most about δ' , which represents the effects of the New

371

Standards every year from 2007 to 2014. The other variables are the same as in

372

equation (2). As the key environmental protection cities dataset, the Dataset 2 is

373

limited in 2012, we conducted this exercise by using an alternative dataset,

374

Dataset 2 (Data source: China Cities Statistics Yearbook).

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In Figure 4 we find that these coefficients have a clear upward trend, but

376

their 95% confidence interval includes zero before 2012. Right after the New

377

Standards were implemented in 2012, these estimates fall slightly, but their 95%

378

confidence interval also includes zero. In Figure 3 and Figure 4, the 95% 24

ACCEPTED MANUSCRIPT confidence interval of these estimated coefficients includes zero from 2007 to

380

2014, and the coefficients of lnpm (Figure 3) fluctuate more severely than those

381

of pso2 (Figure 4). These results demonstrate that the implementation of the New

382

Standards did not have any leading or lagging effects on air quality. The two

383

figures test the common trend assumption thoroughly, showing that the treated

384

group and the control group share the same trend during the pre-treatment period.

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Fig. 3. Leading and lagging effects of PM2.5

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Fig. 4. Leading and lagging effects of pso2 Placebo test: In this section, we will analyze the effect of omitted variables.

386

Specifically, let jiaohu ≡ policy ∗ year

387

E2jiaohu34 , ε34 6 ≠ 0 and E2jiaohu34 , ε034 6 = 0. In other words, /

388

the environmental regulations and outcome variables. Hence, our estimator δ98 is:

and ε = ./ + ε0 , such that is related to both

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: lim < = (" = ")> (" = ?) = + .(" = ")> (" = /) @AB < ≠ CD .(" = ")> (" = /) ≠ 0 (4)

389

In order to know whether our estimation equation is correct in this paper,

390

we performed the same placebo test as many other papers, randomly selecting a

391

pilot city from our sample cities as the virtual treated group (Chetty et al., 2009;

392

Cai et al., 2016; Li et al., 2016; Fu and Gu, 2017). We randomly selected 50 cities 26

ACCEPTED MANUSCRIPT from all 104 cities and then let their policyF = 1, and the other cities’ policyF =

394

0 . In addition, we set up a new interactive term as jiaohuF = policyF ∗

395

GHIJ2012 . Because of the random generation process, the coefficient of jiaohuF

396

will be zero when our equation has no omitted variables. We repeated the random

397

generation process 500 times for each dependent variable in our paper to increase

398

the power of the placebo test.

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We plotted the 500 coefficients and p-values of jiaohuF and presented

400

their kernel density distribution in Figure 5 and Figure 7 (Figure 5 for lnpm,

401

Figure 7 for pso2). In addition, our estimates from column 2 and column 4 of

402

Table 2 are around the center of both distributions, which means that the effects

403

of the New Standards do not differ significantly from zero. Furthermore, we

404

plotted the 500 coefficients and presented their fitted mean value line in Figure 6

405

and Figure 8 (Figure 6 for lnpm, Figure 8 for pso2). We find that the mean values

406

are -0.0006308 (S.E:0.000668, T-test: -0.9443) for lnpm as the dependent variable

407

and 0.0047076 (S.E:0.0038374, T-test: 1.2268) for lnpso2 as the dependent

408

variable. The 95% confidence intervals of two fitted mean value lines include

409

zero, which means that the mean value of the 500 random estimates is zero. These

410

results suggest that our DID equations are not severely biased by omitted

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variables. Meanwhile, our main results in Table 2 are robust.

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Fig. 5. The kernel density of 500 estimates of lnpm6

6.Notes: X axis represents the coefficients of false interaction term from 500 random regression, the left y axis shows the p-values of each false interaction term, and the right y axis indicates that kdensity coefficients from each random regression. The red curve is the kernel density distribution of all 500 estimates, whereas the blue dots are corresponding p-values. The red vertical line in the true coefficient of interaction term from column 2 in Table 2. 29

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Fig. 7. The kernel density of 500 estimates of lnpso27

Fig. 8. Plotting coefficients and the fitted mean-values of lnpso2

7.Notes: X axis represents the coefficients of false interaction term from 500 random regression, the left y axis shows the p-values of each false interaction term, and the right y axis indicates that kdensity coefficients from each random regression. The red curve is the kernel density distribution of all 500 estimates, whereas the blue dots are corresponding p-values. The red vertical line in the true coefficient of interaction term from column 4 in Table 2. 30

ACCEPTED MANUSCRIPT

412

5.

Heterogeneity in the regulation effects

Much of the literature shows that the effects of environmental regulations

414

are mixed because of tax evasion, the shadow economy, and limited monitoring

415

and enforcement efforts (Liu et al., 2013; Chen et al., 2018; Bento et al., 2018).

416

In this section, we explored how some of these characteristics affect the

417

effectiveness of the New Standards by testing the different effects of the New

418

Standards across cities with different characteristics. If the New Standards

419

improve air quality in cities with specific features, we will accept the explanation

420

that only the mixed average treatment effect is zero. Furthermore, we can know

421

which types of cities the New Standards affect most strongly through

422

heterogeneous analysis.

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We divided the whole sample into three subsamples based on number of

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monitoring points, as well as pollution control efforts, and ownership structure.

425

First, we tested whether monitoring air pollution more carefully could reduce air

426

pollution or not. Knowing the air quality can let Bureaucrats of Environmental

427

Protection (BEPs) strengthen the enforcement of environmental regulations or

428

implement more specific policies. Most current studies show that monitoring 31

ACCEPTED MANUSCRIPT pollution or compliance can reduce emissions (Duflo et al., 2013; Escobar &

430

Chavez, 2013). In our paper, we collected the numbers of air quality monitoring

431

points in each province from 2006 to 2012. To deal with the endogeneity problem,

432

we employed a difference-in-differences-in-differences (DDD) method to find out

433

how the numbers of monitoring points in a region affected the outcomes of the

434

New Standards. We constructed sanlnpoint = lnpoint ∗ policy ∗ year2012 ,

435

and we expanded model (2). The expanded model is as follows:

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Pollution = α + sanlnpoint + βjiaohu2012 + γlnpoint + λ ! " + ω + ϕ + ε （5）

We restricted our regression to the treated group and the control group,

437

respectively. The results are represented in Table 3 (the control group regression

438

results can be found in columns (5) and (6)). The coefficients of the triple

439

interaction term were most important. If its coefficients are not significantly

440

different from zero in the treated group, it shows that the monitoring efforts did

441

not affect the New Standards, which means that increasing the understanding of

442

the air pollution through the addition of monitoring stations did not improve air

443

quality in the context of the New Standards.

444

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As shown in Table 3, the triple interaction terms are significant in the 32

ACCEPTED MANUSCRIPT middle two columns, while in the others they are not significantly different from

446

zero. The results in the first two columns mean that adding monitoring points did

447

not reduce the concentration of PM2.5 in the treated group. In the middle two

448

columns, the triple interaction terms are both significantly negative at 0.01,

449

which means that cities with more monitoring stations had lower SO2 emissions

450

in the treated group compared to those cities with fewer monitoring stations

451

facing the same environmental regulations. Specifically, when the number of

452

monitoring points was increased by 1%, the SO2 emissions per ten thousand

453

people decreased by around 28%, which means SO2 emissions per ten thousand

454

people decreased by about 113 tons. The coefficients of the triple interaction

455

terms in the last two columns are not significant, which further shows that our

456

results are robust because the control group did not implement the New

457

Standards.

459

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Table 3

Next, we wondered if cities with differing levels of pollution control

460

efforts

exhibited

divergent

average

treatment

461

environmental regulations is not only related to the number of policies formulated.

462

The enforcement of environmental regulations is also a critical factor that affects 33

effects.

The

impact

of

ACCEPTED MANUSCRIPT their effectiveness (Shimizu, 2017; Shen & Lin, 2017). For our empirical strategy,

464

we used the completed industrial pollution treatment investment to measure the

465

pollution control efforts. Therefore, in order to test whether the effect of the New

466

Standards varies depending on pollution control efforts, we expanded equation (2)

467

and constructed sanlninvest = lninvest ∗ policy ∗ year2012 . The expanded

468

model is as follows:

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+ ϕ + ε (7)

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Pollution = α + sanlninvest + βjiaohu2012 + γlninvest + λ ! " + ω

In order to examine this assumption, we restricted our sample to the

470

treated group and the control group in Dataset 1. The coefficients of the triple

471

interaction terms were most important. When the coefficient in the treated group

472

sample is significant, and the coefficient in the control group sample is not

473

significant, we can verify that the assumption is valid. Otherwise, the New

474

Standards have no effects in any pilot cities. The treated group regression results

475

are shown in Table 4 (the control group regression results can be found in

476

columns (5) and (6)).

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477

From the results in Table 4, we know that the triple interaction terms are

478

not significantly different from zero, and these results are robust after adding the 34

ACCEPTED MANUSCRIPT control variables in the first two columns. The results show that investing in

480

industrial pollution treatment programs did not reduce the concentration of

481

PM2.5 in the treated group. In the middle two columns, the triple interaction

482

terms are both significantly negative at 0.05, which means that investing in

483

industrial pollution treatment programs did indeed reduce the SO2 emissions

484

when a city faced the same environmental regulations. When the amount invested

485

increases by 1%, the SO2 emissions per ten thousand people decrease by between

486

12.2% and 13.1%, which means that the SO2 emissions per ten thousand people

487

decrease by between 48.92 tons and 52.53 tons. In the last two columns, the triple

488

interaction terms are both not significant, which further strengthens our

489

conclusion.

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Table 4

There are differing impacts in cities where different proportions of the

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492

economy are state-owned. Some studies have examined the influence of different

493

economic ownership structures on the effects of environmental regulations (Wang

494

and Jin, 2007; Meyer and Pac, 2013). In this research, we constructed sansoe =

495

soe ∗ policy ∗ year2012 and established a model (8):

35

ACCEPTED MANUSCRIPT Pollution = α + sansoe + βjiaohu2012 + γsoe + λ ! " + ω

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+ ϕ + ε (8) We chose the share of employment in the state-owned sectors to measure

497

the share of the state-owned economy in one city. The regression results are

498

shown in Table 5 (the control group regression results can be found in columns (5)

499

and (6)).

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From the results in Table 5, we know that the triple interaction terms are

501

significantly different from zero at 0.05, and these results are strengthened after

502

adding the control variables in the first two columns. The results show that

503

compared to regions where a low proportion of the economy is state-owned, the

504

effects of the New Standards declined in areas where a higher proportion of the

505

economy was owned by the state. Specifically, when the share of the state-owned

506

economy increased by 1%, the concentration of PM2.5 in the treated group

507

increased by between 0.129% (0.056 µ g/m3) and 0.148% (0.064 µ g/m3). In the

508

middle two columns, the triple interaction terms are both significantly negative at

509

0.01, which means that compared to regions where a lower proportion of the

510

economy is state-owned, cities with a higher proportion of state-owned economy

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ACCEPTED MANUSCRIPT facing the same environmental regulations will have more SO2 emissions.

512

Specifically, when the share of the state-owned economy increased by 1%, the

513

SO2 emissions per ten thousand people increased by between 0.966% and

514

0.988%, which means SO2 emissions per ten thousand people increased by

515

between 3.87 tons and 3.96 tons.

517

6.

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Discussion

In conclusion, our paper was based on a natural experiment approach and

519

employed the DID method to make an empirical ex-post evaluation of the New

520

Standards. Our results show that the concentrations of PM2.5 and the SO2

521

emissions in pilot cities were not significantly lower than that in non-pilot cities

522

in 2012. In other words, the average treatment effect of the New Standards was

523

zero in pilot cities. Moreover, we verify that the regulation had no long-term

524

effects. Our heterogeneity analysis of the New Standards found that adding

525

monitoring stations, increasing investment in industrial pollution treatments, and

526

decreasing the proportion of the economy owned by the state could improve air

527

quality in pilot cities. In addition, the effects of those factors vary depending on

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ACCEPTED MANUSCRIPT 528

the types of pollutants. Our research aims to increase the discussion about the effects of CAC

530

environmental regulations, taking the New Standards as an example. Unlike many

531

previous studies which criticized its costs, its incentives to firms, and so on, our

532

paper focused on discovering under what conditions CAC regulations are

533

practical tools to control air pollution. Our results show that the SO2 emissions

534

per ten thousand people decreased by around 28% as the number of air quality

535

monitoring points increased by 1%. In line with Duflo et al. (2013) and Escobar

536

and Chavez (2013), our results show that inspections can strengthen the impact of

537

environmental regulations by spurring regulatory compliance.

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Some studies suggest that Chinese laws and regulations have limited

539

effects and meager implementation rates (Allen et al., 2005; Dam, 2006). In

540

particular, some papers show that China’s environmental laws and regulations

541

have been incompletely implemented in the actual operation process (Wang et al.,

542

2003). Many studies indicate that pollution control efforts are one of the most

543

indispensable aspects of environmental regulation enforcement (Shimizu, 2017;

544

Shen & Lin, 2017). Similar to the above studies, we documented that an increase

545

of 1% in the completed industrial pollution treatment investment leads to a

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ACCEPTED MANUSCRIPT 546

decrease in SO2 emissions per ten thousand people of 13.1%. Why do ownership structures affect the performance of environmental

548

regulations in a region? Ownership can alter air pollution at least through two

549

channels: (1) Since state-owned enterprises have more social responsibilities and

550

the government has more influence over state-owned enterprises, emission

551

reduction is stronger in regions where the state-owned portion of the economy is

552

higher (Shi et al., 2016). Moreover, (2) the relationship between state-owned

553

enterprises and local governments is closer, leading to state-owned enterprises

554

having more bargaining power with BEPs, which decreases the effects of

555

environmental regulations (Wang & Jin, 2007; Jiang et al., 2014). Our results

556

verify that the second explanation suits the New standards. They show that when

557

the share of state-owned employment increases by 1%, the concentration of

558

PM2.5 and SO2 emissions per ten thousand people in the treated group increases

559

by 0.064 µ g/m3 and 3.96 tons, respectively. In other words, because these large

560

state-owned enterprises are essential sources of local employment, they are

561

inspected more flexibly (Wang & Wheeler, 2005).

562

7.

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Conclusions and Recommendations 39

ACCEPTED MANUSCRIPT China is increasingly concerned about environmental issues, and

564

environmental regulations are the most common instruments to control air

565

pollution. Although China has set up a series of policies to protect the

566

environment, the effectiveness of environmental regulations is still unclear,

567

especially CAC regulations. Thus far, previous studies have mainly criticized

568

CAC regulations about their costs and incentive problems, but the effect of CAC

569

regulations as instruments needs to be further explored, particularly their

570

heterogeneity effects. This paper fills these gaps by employing a natural

571

experiment to examine the causal relationship between air quality and the New

572

Standard to weaken endogeneity problems and discovering under what conditions

573

the New Standards can improve air quality in pilot cities.

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Given these results, we can provide the following policy recommendations.

575

Our findings show that increasing monitoring effects and investing more money

576

into pollution treatment can reinforce the impacts of environmental regulations.

577

Also, ownership structures can alter the effects of environmental regulations by

578

altering the bargaining capacity of the local government. Therefore, creating

579

independent BEPs should strengthen the effects of environmental regulations. For

580

instance, China’s central government is promoting vertical reforms of local BEPs,

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40

ACCEPTED MANUSCRIPT and we should pay attention to the uniformity of the authority and responsibility

582

of these institutions. We found that the effects of the New Standards on SO2

583

emissions and the concentration of PM2.5 are not the same. Our study suggests

584

that there are some cases where the outcomes and policy objectives may be

585

mismatched. Therefore, in the process of formulating environmental regulations,

586

policy-makers should consider the nature of different air pollutants, such as their

587

sources and different chemical generation processes.

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There are some limitations to our research. Firstly, we acquired PM2.5

589

concentration data from the global annual PM2.5 grid map released by Columbia

590

University’s SEDAC, which has been widely used in recent research. The

591

SEDAC PM2.5 concentration data comes from the AOD, and is detected by

592

multiple satellite instruments. However, the AOD has trouble distinguishing

593

water vapor from fine particles, and it was unavailable in all cities and at all

594

times. Though the two measures have a strong positive relationship, the

595

ground-based instruments are more accurate at a given point over an extended

596

period. With more PM2.5 monitoring stations being set up, we can more easily

597

get enough information to properly reflect changes in air quality.

598

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Secondly, our research object is air quality in the pilot area. Our treated 41

ACCEPTED MANUSCRIPT group and control group are key environmental protection cities, so it is not a

600

random sample of all prefecture-level cities. Our natural experiment method has

601

robust internal validity, but the results in this paper may not reflect the net effect

602

of the New Standards nationwide. This problem will be addressed in our future

603

research.

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Thirdly, some factors weaken the regulatory pressure of environmental

605

regulations, including weak regulatory institution, incomplete enforcement of

606

regulations, a shortage of pollution treatment facilities, the economic structure

607

and economic ownership, and the difficulties of inspecting regulated objects. In

608

this paper, we confirm that monitoring effects, investment in pollution abatement,

609

and changes in economic ownership could change the effects of the New

610

Standards across different cities. In the future, the scope of this study could be

611

extended to examine how other factors affect the outcomes of environmental

612

regulations—for example, the target-setting programs and environmental

613

performance of local governments.

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ACCEPTED MANUSCRIPT Notes

615

1. The 2005 China Environmental Bulletin is available at:

616

http://www.mee.gov.cn/hjzl/zghjzkgb/lnzghjzkgb/201605/P020160526558688821

617

300.pdf

618

2. The 2011 China Environmental Bulletin is available at:

619

http://www.mee.gov.cn/hjzl/zghjzkgb/lnzghjzkgb/201605/P020160526563389164

620

206.pdf

621

3. The 2015 China Environmental Bulletin is available at: http://

622

www.mee.gov.cn/6be585d7d4386618f922616dd8fcec87/P0201606023331604719

623

55.pdf

624

4. In the context of the Ambient Air Quality Standards (GB3095-1996), the Class

625

I standards are imposed on zone

626

etc. The annual average concentration of SO2 is different in different classes. The

627

Class I standards for SO2 are lower than 20 µ g/m3, standards for Class II are

628

higher than 20 µ g/m3 but no more than 60 µ g/m3, and Class III standards range

629

from 60 µ g/m3 to 100 µ g/m3.

630

5. The SEDAC homepage is

, the Class

standards are imposed on zone

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43

,

ACCEPTED MANUSCRIPT http://sedac.ciesin.columbia.edu/data/set/sdei-global-annual-gwr-pm2-5-modis-m

632

isr-seawifs-aod/data-download.

633

6.Notes: X axis represents the coefficients of false interaction term from 500

634

random regression, the left y axis shows the p-values of each false interaction

635

term, and the right y axis indicates that kdensity coefficients from each random

636

regression. The red curve is the kernel density distribution of all 500 estimates,

637

whereas the blue dots are corresponding p-values. The red vertical line in the true

638

coefficient of interaction term from column 2 in Table 2.

639

7.Notes: X axis represents the coefficients of false interaction term from 500

640

random regression, the left y axis shows the p-values of each false interaction

641

term, and the right y axis indicates that kdensity coefficients from each random

642

regression. The red curve is the kernel density distribution of all 500 estimates,

643

whereas the blue dots are corresponding p-values. The red vertical line in the true

644

coefficient of interaction term from column 4 in Table 2.

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845

Pollution, and Health: Evidence from Driving Restrictions in Beijing.

846

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847

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RI PT

844

SC

848

Figure legends

850

Fig. 1. Trends in concentration of PM2.5

851

Fig. 2. Trends in pso2

852

Fig. 3. Leading and lagging effects of PM2.5

853

Fig. 4. Leading and lagging effects of pso2

854

Fig. 5. The kernel density of 500 estimates of lnpm

855

Fig. 6. Plotting coefficients and the fitted mean-values of lnpm

856

Fig. 7. The kernel density of 500 estimates of lnpso2

857

Fig. 8. Plotting coefficients and the fitted mean-values of lnpso2.

AC C

EP

TE D

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849

858 859 860 56

ACCEPTED MANUSCRIPT 861 862

864

RI PT

Tables

Vars

OBS

Mean

S.D

Min

pm25

665

39.60

16.67

8.11

lnpm

665

3.57

0.49

2.09

pso2

728

625.37

lnpso2

728

6.06

policy

728

0.48

year2012

728

0.14

jiaohu2012

728

0.07

project

712

19.38

lnproject

684

tletter

728

lntletter

728

soer

715

RV

index EV

SV

82.85

µ g/m3

4.42

--

0.57

5030.78

Ton per ten thousand persons

0.98

-0.56

8.50

--

0.50

0

1

--

0.35

0

1

--

0.25

0

1

--

24.58

0

205

item

1.12

0

5.32

--

18696.47

22334.84

50

115392

items

9.15

1.35

3.91

11.66

--

48.92

17.91

7.66

94.13

%

728

13.25

3.33

-1.20

29.02

%

728

52.27

11.99

17.71

90.97

%

AC C

rindus2

Units

2.43

EP

KV

Max

571.13

TE D

DV

SC

Table 1 Descriptive Statistics

M AN U

863

invest

728

55.02

54.51

1.38

361.26

100 million yuan

lninvest

728

3.60

0.94

0.32

5.89

--

trans

728

45071.05

67918.05

90

516517

ten thousand persons

lntrans

728

9.96

1.28

4.50

13.15

--

proad

728

11.79

6.04

0.31

64

m2

lnproad

728

2.35

0.48

-1.17

4.16

--

rgreen

728

39.32

6.75

5.55

70.30

%

865

Note: DV denotes the dependent variables, including PM2.5 and so2 emissions per capita. KV denotes the key variables,

866

including policy, year2012, and the interaction term jiaohu2012. RV denotes regional factors influencing the effect of

867

the New Standards, like enforcement, public participation, and economic ownership; what’s more, EV and SV are

57

ACCEPTED MANUSCRIPT control variables, which represent some variables in economic development and other variables in social factors. To

869

weaken the heteroscedasticity during model formation, we took a continuous variable to its natural logarithm.

AC C

EP

TE D

M AN U

SC

RI PT

868

58

ACCEPTED MANUSCRIPT 870

Table 2 Main Results

(1)

(2)

(3)

(4)

lnpm

lnpm

lnpso2

lnpso2

-0.0152

-0.0151

-0.0712

-0.0732

(0.0145)

(0.0139)

(0.0869)

(0.0861)

0.00589

0.00592

-0.0292

-0.0280

(0.00481)

(0.00487)

(0.0201)

(0.0201)

policy*year2012

lnproject

index

RI PT

VARIABLES

-0.000554

0.00610

rindus2

0.00181* (0.000947) -0.00123

M AN U

rgreen

(0.00485)

SC

(0.00185)

(0.000753)

lntrans

-0.00145 (0.00507) 0.00423 (0.00383)

0.000844 (0.0219)

lnproad

-0.000361 (0.0109)

3.617***

City-Fixed Effect Year-Fixed Effect

R-squared

6.169***

(0.224)

(0.0663)

(0.309)

Y

Y

Y

Y

Y

Y

Y

Y

624

624

684

684

0.410

0.414

0.082

0.089

95

95

104

104

AC C

Number of ID

6.338***

(0.0158)

EP

Observations

3.567***

TE D

Constant

871

Notes: Robust Standard Errors, clustered at the city level. ***significant at p < 0.01, **significant at p < 0.05,

872

*significant at p < 0.1

873 874 875

59

ACCEPTED MANUSCRIPT 876

Table 3 Regulation effects of different numbers of monitoring points

(1)

(2)

(3)

(4)

(5)

(6)

lnpm

lnpm

lnpso2

lnpso2

lnpm

lnpso2

0.0103

0.00784

-0.287***

-0.286***

0.00626

-0.116

(0.0140)

(0.0147)

(0.0886)

(0.0871)

(0.0196)

(0.104)

-0.0157

-0.00942

0.0820

0.0826

-0.0492*

-0.210

(0.0158)

(0.0175)

(0.0677)

(0.0704)

(0.0238)

(0.140)

0.00770

0.00461

-0.0366

-0.0357

0.000626

0.0106

(0.00587)

(0.00553)

(0.0286)

(0.0278)

(0.00549)

(0.0373)

-0.000245

0.00124

0.00669

(0.0107)

(0.00244)

(0.00732)

0.00204

0.00349**

-0.00991

lnpoint

lnproject

index

-0.00246 (0.00342) -0.000105

M AN U

rindus2

SC

lnpoint*policy*year2012

RI PT

VARIABLES

rgreen

lntrans

(0.0113)

(0.00146)

(0.00797)

-0.000818

0.000496

-0.000991

0.000758

(0.00132)

(0.00468)

(0.00137)

(0.00483)

0.0325*

0.000664

(0.0189)

(0.0253)

-0.00362

-0.0198*

TE D

lnproad

(0.00142)

(0.0421)

Constant

(0.0102)

3.812***

3.527***

5.597***

5.478***

3.669***

7.912***

(0.0785)

(0.273)

(0.330)

(0.735)

(0.258)

(0.979)

Y

Y

Y

Y

Y

Y

Year-Fixed Effect

Y

Y

Y

Y

Y

Y

Observations

242

242

277

277

278

294

R-squared

0.539

0.547

0.168

0.168

0.421

0.088

Number of ID

44

44

50

50

51

54

AC C

EP

City-Fixed Effect

877

Notes: Robust Standard Errors, clustered at the Province level. *** significant at p < 0.01, **significant at p < 0.05,

878

*significant at p < 0.1

879

60

ACCEPTED MANUSCRIPT 880 881

Table 4 Regulation effects on pollution control efforts

(1)

(2)

(3)

(4)

(5)

(6)

lnpm

lnpm

lnpso2

lnpso2

lnpm

lnpso2

0.00562

0.00414

-0.131**

-0.122**

-0.000182

0.103

(0.0178)

(0.0180)

(0.0633)

(0.0562)

(0.0161)

(0.0958)

-0.00244

-0.00337

0.0792

0.0816

-0.0350

-0.00400

(0.0209)

(0.0211)

(0.0640)

(0.0661)

(0.0248)

(0.0775)

0.0123*

0.0112*

-0.0381

-0.0344

0.000520

-0.0147

(0.00613)

(0.00587)

(0.0287)

(0.0285)

(0.00409)

(0.0300)

0.00390

0.000346

0.00757

lnyanshouzhilitouzi

lnproject

-0.00276

M AN U

index

rindus2

rgreen

(0.00844)

(0.00199)

(0.00630)

0.000680

0.00517

0.00267**

-0.00839

(0.00123)

(0.00788)

(0.00113)

(0.00648)

-0.000956

0.00317

-0.00136

0.00510

(0.000802)

(0.00408)

(0.00106)

(0.00587)

0.00388

3.697***

-0.0108

(0.0336)

(0.0214)

-0.00498

-0.00222

(0.0276)

(0.0117)

3.718***

5.817***

5.377***

3.636***

6.789***

(0.0771)

(0.371)

(0.252)

(0.470)

(0.198)

(0.466)

Y

Y

Y

Y

Y

Y

Year-Fixed Effect

Y

Y

Y

Y

Y

Y

Observations

292

292

333

333

332

351

R-squared

0.479

0.483

0.176

0.181

0.398

0.067

Number of ID

44

44

50

50

51

54

EP

Constant

(0.00389)

TE D

lntrans

lnproad

SC

lnyanshouzhilitouzi*year2012*policy

RI PT

VARIABLES

AC C

City-Fixed Effect

882

Notes: Robust Standard Errors, clustered at the Province level. ***significant at p < 0.01, **significant at p < 0.05,

883

*significant at p < 0.1

884 61

ACCEPTED MANUSCRIPT 885

Table 5 Regulation effects of ownership

(2)

(3)

(4)

(5)

(6)

VARIABLES

lnpm

lnpm

lnpso2

lnpso2

lnpm

lnpso2

soer*policy*year2012

0.00129**

0.00148**

0.00988***

0.00966***

-0.000918

-0.00566

(0.000627)

(0.000695)

(0.00366)

(0.00355)

(0.000840)

(0.00416)

0.000596

0.000562

-0.00590

-0.00597

(0.000611)

(0.000645)

(0.00437)

(0.00432)

0.0154**

0.0135*

-0.0318

-0.0276

(0.00732)

(0.00724)

(0.0301)

(0.0306)

soer

lnproject

-0.00346 (0.00355)

0.00147

(0.000673)

(0.00312)

8.74e-05

-0.0190

(0.00692)

(0.0263)

0.00140

0.000220

0.00571

(0.0101)

(0.00219)

(0.00568)

0.000236

0.00652

0.00259*

-0.0106

M AN U

rindus2

8.18e-05

SC

index

RI PT

(1)

(0.00134)

(0.00820)

(0.00151)

(0.00687)

-0.000659

0.00327

-0.00124

0.00468

(0.000766)

(0.00447)

(0.00125)

(0.00597)

rgreen

lntrans

-0.00711

(0.0327)

(0.0225)

-0.0166

-0.000804

TE D

lnproad

0.0115

(0.0262)

Constant

3.654***

3.643***

6.307***

5.843***

3.492***

6.888***

(0.0408)

(0.375)

(0.216)

(0.421)

(0.221)

(0.433)

Y

Y

Y

Y

Y

Y

EP

City-Fixed Effect

(0.0129)

Y

Y

Y

Y

Y

Y

Observations

288

288

327

327

325

344

AC C

Year-Fixed Effect

R-squared

0.470

0.475

0.187

0.194

0.380

0.070

Number of ID

44

44

50

50

50

53

886

Notes: Robust Standard Errors, clustered at the city level. ***significant at p < 0.01, **significant at p < 0.05,

887

*significant at p < 0.1

888

62

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

Highlights: 1. The average treatment effects in treated group of New Ambient Air Quality Standards on SO2 emissions and concentration of PM2.5 are zero. 2. Monitoring efforts and pollution investments can significantly reduce sulfur dioxide emissions in pilot areas, but will not affect the pilot areas 3. The effect of environmental regulation will be weakened in areas with a high proportion of state-owned economy, both for sulfur dioxide emissions and fine particulate matter concentrations. 4. A possible reason for an ineffective environmental regulation is that the effect of the environmental regulation is heterogeneous because of different characteristics.