Import Ban and Clean Air:
Estimating the Effect of China’s Waste Import Ban on
the Ozone Pollution
Jinsong Li
Kenji Takeuchi
February , 2021
Discussion Paper No.2104

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANImport Ban and Clean Air:
Estimating the Eﬀect of China’s Waste Import Ban on
the Ozone Pollution
Jinsong Li∗, Kenji Takeuchi†
Graduate School of Economics, Kobe University
Rokko, Kobe 657-8501, Japan

Abstract
This study investigates the eﬀects of the plastic waste ban on local air quality in
China. Using city-level daily ozone concentrations, we examine whether the pollution
levels diﬀer between coastal and inland cities in China after the import ban. Obtained
results show that the daily ozone concentration lowered by 2.2% in coastal cities after
the import ban. Additional analyses suggest that the eﬀect is heterogeneous: thereduction is larger in later period, larger in cities with dirty baseline pollution, andsmaller in cities with higher rural population density. These results are suggestive of
the impact of import ban as an indirect policy instrument.

∗†

E-mail: shimakaze2199@gmail.com
E-mail: takeuchi@econ.kobe-u.ac.jp1

1

Introduction
Air pollution is detrimental to human health and causes substantial loss, particularly

in a fast-growing economy like China. In 2017, the Global Burden of Disease estimatedthat in China, approximately 852,000 deaths were attributable to PM 2.5 exposures and an
additional 178,000 chronic respiratory disease-related deaths to ozone (Health Eﬀect Institute, 2019). Air pollution can cause huge economic losses, which were equivalent to 9.92%
of China’s annual GDP in 2013 (World Bank and Institute for Health Metrics and Evaluation, 2016). Recent studies suggest that the eﬀects of air pollution are diverse, including
cognitive performance decline (Zhang, 2018), labor productivity loss (He et al., 2019), andsleeplessness (Heyes et al., 2019).
The Chinese government has taken several actions to reduce the severity of air pollution.
As a result, the annual average concentrations of PM 2.5, PM 10, and NO2 in China gradually
decreased from 2014 to 2018. In contrast, the concentration of surface ozone has increased.
The surface ozone has become one of the major air pollutants in the cities under Stage I
monitoring, based on the amended ambient air quality standard. As Figure 1 shows, whilemore cities under Stage I monitoring met the standard for many pollutants from 2016 to2017, fewer cities met the standard for ozone. Despite China seeing a steady decline in PM
2.5 exposures, it had the most population-weighted seasonal concentration of ozone among
the world’s 11 most populous countries (Health Eﬀect Institute, 2019).

[Figure 1]

Plastic recycling generates volatile organic compounds (VOCs), which react with NOx
in the presence of sunlight to form ground-level ozone. China has been the world’s largest
importer of plastic waste. It processes huge amounts of hard-to-recycle plastics from othercountries, accounting for approximately 56% of the global market (Brooks et al., 2018).
However, in July 2017, the Chinese government introduced a policy that imposed a ban,2

eﬀective from January 1, 2018, on the import of several kinds of waste, including plastic
waste. As a result of this ban, the volume of plastic waste imported to China has greatly
reduced. According to the China Recycling Industry Report 2019, it has reduced from 5.83
million tons in 2017 to 0.05 million tons in 2018, by almost 99% (Figure 2). Meanwhile, thesupply fell short of demand in the plastic waste market. The market price of plastic waste
increased by 6% to 16%, depending on the type of plastic materials.

[Figure 2]

This study investigates the eﬀects of the plastic waste ban on air quality in China byfocusing on surface-level ozone. After the import ban, the imports of plastic waste decreased
and the market price increased. It supposedly lead to a slowdown of the manufacturing
process that releases VOCs, and thus, the VOC emissions also reduced. The reduction of
VOCs further reduced the ozone concentration, as VOCs and NOx are the main precursorsof ozone formation. Therefore, we can expect that the air quality improved in the areas
around the plastic recycling facilities.
This study contributes to the literature on the economics of air pollution in developing
countries in two ways. First, many studies on the eﬀect of air pollution control focus onpolicies that directly aﬀect emission sources, such as shutdown and installation of control
equipment (Cao et al., 2009), driving restrictions (Viard and Fu, 2015), and subsidies towinter heating (Almond et al., 2009). Nevertheless, in the case of plastic recycling, thenumber of pollution sources is large, and it is often diﬃcult to enforce the regulation. Importban can be regarded as an indirect policy instrument that reduce the amount of plastic
waste used by small-scale recyclers. This study exploits the unique event and investigates
the impact of a substantial drop in the input for the polluting activity. Second, althoughseveral studies have evaluated the global impact of plastic waste ban (Brooks et al., 2018; Quet al., 2019; Huang et al., 2020), they did not focus on the domestic environmental impact.

3

One of the aims of the policy is to alleviate local severe pollution; hence, it is necessary tounderstand whether and how the ban could improve the local environment.
The remainder of this paper is structured as follows. Section 2 brieﬂy describes the policy
background and explains air protection control in China related to the ozone concentration.
Section 3 reviews previous studies. Section 4 describes our empirical strategy, the model, andthe dataset. Section 5 presents the empirical results, robustness checks, and heterogeneouseﬀects. Section 6 concludes our paper.

2

Background

2.1

VOCs Emission During Plastic Recycling

A large volume of plastics is produced and recycled in China. In 2014, the production
amount of all kinds of plastics in China was 73.9 million tons, and the corresponding consumption amount was 93.3 million tons. In the same year, the amount of waste plastic
recycled was 28.3 million tons, which accounted for approximately 30% of the total consumption amount (Cao et al., 2019).
Plastic recycling includes material recycling, chemical recycling, and energy recovery(Al-Salem et al., 2009; Lazarevic et al., 2010). While there are many material recycling
plants in China, melting temperatures in the material recycling method range from 200°C
to 300°C, which may produce harmful components, such as VOCs. Pollutants are often
emitted into the atmosphere without suﬃcient treatment. For example, Huang et al. (2013)
report that most of the plastic recycling activities in one of the largest plastic recycling centre
in China, Xingtan (Guangdong Province) are carried out in open air with no measures totreat the emitted harmful gases. In addition, many devices in small workshops recycling theimported plastic wastes were not qualiﬁed which could release harmful exhausts to the air(China Economic Daily, Blocking foreign garbage completely, 2015/5/28 ). He et al. (2015)
analyzed the emissions of diﬀerent kinds of plastic solid waste and found that the melting4extrusion of seven diﬀerent thermoplastics could produce a substantial volume of VOCs. The
total VOC concentrations emitted during recycling were highest in ABS, and were lowest inpolycarbonates.

2.2

Controlling the VOC Emissions

In January and February 2013, severe haze covered many provinces in China. This led
to a widespread environmental concern that pushed the Chinese government to tighten the
air pollution regulations (Jin et al., 2016). During 2013 and 2014, China rolled out a new,nationwide air pollution program in three waves across cities to provide real-time monitoringof six air pollutants, including PM2.5, PM10, O3 , CO, NO2 , and SO2 (Greenstone et al.,2020). Since then, the Chinese government has implemented various policies to alleviate
severe air pollution.
The control measures for ozone have largely been untouched in China, however, until the
13th Five-Year Plan for the Prevention and Control of VOCs Pollution was introduced in2017 (Health Eﬀects Institute, 2019). Through this policy, the government emphasized theimportance of reducing VOCs as the main precursors of ozone. The ozone concentrationsin the cities under Stage I monitoring based on the new air quality standard increased by
10.8% from 2013 to 2016. The government then set a target for the local governments of
the VOC heavy-polluted areas in 16 provinces to reduce the VOC emissions by 10% before
2020.

2.3

The Ban on Import of Plastic Waste

The Chinese government had been controlling the import of waste for a long time andeventually concluded it with a ban. The government has frequently revised the Law of thePeople’s Republic of China on Prevention and Control of Environmental Pollution by Solid
Waste in recent years. Since its introduction in 1996, the law has been revised four times in
2005, 2013, 2015, and 2016 (Sun, 2019). Furthermore, the government has been strengthen5ing border control. In 2013, the Green Fence campaign was carried out to enhance bordercontrols. Sun (2019) employed DID methods and found that the Green Fence campaign had
a signiﬁcant eﬀect in reducing the waste imported into China. The government then revised
the campaign to the so-called Blue-Sky Campaign and implemented stricter regulations every
year from 2017 onward.
The ban on the import of plastic waste was announced in July 2017. The Chinese
government set three targets in this policy called the Implementing Plan for Banning Foreign
Trash Imports and Promoting Reform in the Solid Waste Import Management System. The
ﬁrst target was to reduce the import of solid waste to reduce the environmental damages
gradually. The second was to regulate the recycling industry by inspecting illegal behaviors,
revising related laws, monitoring smuggling, and promoting job changes. The last one wasto increase the recycling rate of domestic waste, improve the recycling technology, and build
new and cleaner recycling plants.
As part of the policy, the Ministry of Ecology and Environment of China carried out acampaign in July 2017 to manage and control the recycling industry. The ministry examined
the acts of 1,768 large recycling companies and found that 60% of them were operatingillegally. The illicit acts included using invalid ﬁlters, making fake monitoring data, trading
the permit of importing solid waste to other unqualiﬁed ﬁrms, and building new plants
without permission (PRC Central China, 2017).
It should be noted that a company needs to obtain a permit from the authorities to importsolid waste. The permits is obtained only by companies with qualiﬁed facilities that meet thestandards set by the government. However, as mentioned before, some of these companies
sell their permits to other ﬁrms and small workshops that do not meet the standards or evenwith no ﬁlter installed. The permits illegally sold by qualiﬁed companies help those ﬁrms
and workshops with no permits to obtain materials and process them without enough carefor the environment.

6

3

Method and Data

3.1

Methodology

This paper employs a DID method to identify the treatment eﬀects and mitigate possible
endogeneity problems (Meyer, 1995). City-level daily 8-hour average ozone concentration(Ozone) is used as a dependent variable to estimate the eﬀect of the plastic waste ban. The
baseline model is formulated as follows:

Ozoneit = β1 Af tert + β2 T reati + β3 DIDit + Xit + ξt + ϵit ,(1)

where Ozoneit denotes the ozone level in city i in day t, and After is the dummy variable
that equals to 1 for the period after the import ban (January 1, 2018), and 0 otherwise.
Treat is a dummy variable for the cities in the treatment group. DID is the interactionof Treat and After that captures the treatment eﬀect. We expect that the coeﬃcient of
the interaction term DID will be negative if the ban reduces the ozone concentrations. X
denotes the control variables including the daily average NO2 concentrations and the daily
maximum temperatures from 2017 to 2018. This is because the formation of ozone is related
to VOCs, NOx, and temperature. We use the daily highest temperature to control the eﬀectof the temperature as for the highest ozone concentration of a day should appear around thehighest temperature. ξ is the month ﬁxed eﬀects, and ϵ is the error term.
The treatment group contains all the cities in three coastal provinces: Guangdong, Shandong, and Zhejiang, as for most of the plastic recycling ﬁrms are located in these provinces
and Hebei province (China Recycling Industry Report, 2012 & 2016). Because the plastic islight and bulky, it is not eﬃcient to transfer the imported plastic waste to inland provinces.
Although we do not have exact data regarding the location of plastic recycling facilities,
anecdotal evidence suggests that these ﬁrms tend to concentrate in coastal areas. Hebei
province is excluded from our analysis because the province is located in the Jing-Jin-Ji area

7

(including Beijing, Tianjin and Hebei). Because the environmental policies in Jing-Jin-ji
area are often diﬀerent from other areas, it is diﬃcult to control the eﬀect of other policies
that could potentially cause estimation bias.
The control group contains all the cities in three inland provinces: Sichuan, Hubei, andHunan. All provinces in the treatment and control groups are listed in the VOC heavypolluted areas mentioned in the VOCs reduction policy to control the eﬀect of the countrywide VOCs reduction policy of the 13th 5-Year-Plan. Figure 3 shows the study area where
the dark grey area indicates the treatment group and the light grey area is the control group.
To conﬁrm the validity of the selection of treatment and control groups, we use the Alibaba online market (https://www.1688.com/) and implemented a keyword search by “Recycled Plastics” in Chinese. As for the online market group the sellers by province, we foundthere were 1,989 recycled plastic materials sellers in Guangdong, 2,036 sellers in Zhejiang,
1,109 sellers in Shandong and 578 sellers in Hebei. In contrast, there were 103 sellers in
Sichuan, 105 in Hubei and 54 in Hunan. The ﬁndings are supportive of our selection of thetreatment groups and control groups.
[Figure 3]

3.2

Data

This study uses city-level daily data. The data on ozone and NO2 in cities of theseprovinces are collected from the China Air Quality Online Monitoring and Analysis Platform.1 This website collects real-time data on pollutants from the Ministry of Ecology andEnvironment of China and automatically calculates daily average data. The daily maximum
temperature data were obtained from Tianqihoubao,2 which collects historical weather data
covering 34 provinces and provides data at 2,290 counties. The daily data for 47 cities in thetreatment group and 43 cities in the control group were collected from 2017 to 2018. Due todata loss between December 1, 2017, and December 12, 2017, we removed the data for the12

http://www.aqistudy.cn
http://www.tianqihoubao.com

8corresponding period of 2018.
As Table 1 shows, there are 63,540 observations, and the average concentration of ozone
is 94.17µg/m3 . The average concentration in the treatment group was higher than that in
the control group, while the concentration of NO2 is almost the same.
[Table 1]
The acceptable concentration of ozone set by the WHO is 100 µg/m3 . We can see from
Figure 4 that China suﬀers from severe ozone pollution. It is also obvious that compared with2017, the average daily concentration of ozone in 2018 has reduced. However, it is diﬃcult tounderstand the trend in both groups from the graph of the daily average concentration fromthe ﬁgure. Therefore, we draw the graph of the monthly average concentration in Figure
5. This reveals that the monthly concentration of ozone exhibits an M-shaped annual trend
in both groups. The ﬁgure also shows that the trend in both groups was parallel in mostmonths of 2017.
[Figure 4][Figure 5]

4

Results and Disscution

4.1

Main Results: DID Estimation

The estimation results are reported in Table 3. Columns 1 and 3 contain month ﬁxed
eﬀects, while columns 2 and 4 do not. The result shows a negative relationship between
the import ban and ozone concentration. The coeﬃcient of the treatment indicator DID
is negative and statistically signiﬁcant at the 1% level in all the columns of Table 2. The
estimated eﬀect in column 2 corresponds to a decrease of 2.2µg/m3 in the ozone concentrationin the coastal provinces, which is about 2.2% of the daily average ozone concentration in thetreatment group. The result supports the hypothesis that the import ban from 2018 reduced
the ozone concentrations in the coastal cities.
9

[Table 2]
We also ﬁnd that both the estimated coeﬃcient for NO2 and the maximum temperature
are positive and signiﬁcant. Because VOCs, NOx, and temperatures are the major determinants of ozone chemistry (Jin and Holloway, 2015), the result is reasonable. However, therelationship between ozone and VOCs is non-linear and complex. To better understand themechanism, we explore this point further in the next subsection.

4.2

Robustness Check: Ozone-NOx-VOC Sensitivity

This subsection investigates the robustness of our main ﬁndings. We exploited the ozoneNOx-VOC sensitivity to redeﬁne the treatment and control groups to examine the sensitivityof the results. The main result in the previous subsection is based on the list of the VOC
heavy-polluted areas at province level published by the government. Considering studies
that identify the exact ozone-NOx-VOC sensitivity over China, we redeﬁne the control andtreatment groups based on the ozone-NOx-VOC sensitivity as an alternative criterion tochoose the study area.
Analysis in this subsection uses the ozone photochemical regime plotted by Jin and
Holloway (2015) to reselect the control and treatment groups. We exclude cities in the NOxlimited regime; therefore, cities in both groups were chosen from the VOC-limited regime
and transitional regime. This is because, in the NOx-limited regime, the ozone concentration
decreases with the reduction of NOx and increases with the reduction of VOCs, which could
lead to a biased result. In the VOC-limited and transitional regimes, the ozone concentration
decreases with the reduction of VOCs. Deﬁned in this way, we can expect that the control
and treatment groups would react similarly to the change in VOCs.
By redeﬁnition, we have 28 cities in the treatment group from Guangdong, Shandong, andZhejiang provinces. The control group contains 20 cities from Sichuan, Hubei, Hunan, andAnhui provinces. We expect a stronger treatment eﬀect in this regression because the ozone
in both groups is more sensitive to the reduction in VOCs. There are 33,888 observations,10and the average ozone concentration is 101.5µg/m3 , as shown in Appendix: Table 1.
[Table 3]
The estimation results based on the reselected groups are reported in Appendix: Table
2. The coeﬃcient of the treatment indicator DID is negative and statistically signiﬁcant at
1% level in all the columns. The estimated eﬀect in column 1 corresponds to a decrease of
approximately 2.44 µg/m3 or 2.4 % of the daily average ozone concentrations in the citiesof the treatment group. Although the percentage is almost the same as the main model, thecoeﬃcient of DID is larger than the main result, as expected.
[Table 4]

4.3

Heterogeneous Eﬀects

In this subsection, we explore the heterogeneity in the treatment eﬀect to better understand the impact of the import ban. We ﬁrst investigate if the eﬀect of the ban varies astime evolves. Then, we explore the diﬀerence in eﬀects among cities with higher or lower
baseline ozone concentrations. Further, we examine the diﬀerence between the cities among
diﬀerent levels of rural populations to examine the heterogeneous impact among them.
First, the eﬀect of the ban may not appear immediately because many companies aresupposed to keep stock of recyclable materials to maintain a stable supply. Even if theimport of plastic waste is banned, many recycling ﬁrms can keep processing plastic waste
using stocks for some periods. Therefore, the eﬀect of the ban may change with time. We
establish the following model to examine the time eﬀect:
Ozoneit =β1 af terQ1t + β2 af terQ2t + β3 af terQ3t + β4 af terQ4t + β5 T reati ,+ β6 T reati ∗ af terQ1t + β7 T reati ∗ af terQ2t + β8 T reati ∗ af terQ3t ,(2)+ β9 T reati ∗ af terQ4t + Xit + ξt + ϵit ,where Ozoneit denotes the ozone level in city i in day t and Treat is a dummy variable for the11

cities in the treatment group. afterQ1, afterQ2, afterQ3, and afterQ4 are dummy variables
for each quarterly period in 2018. X denotes the control variables including the daily average
NO2 concentrations. The maximum temperature is omitted because of collinearity. ξ is themonth ﬁxed eﬀects, and ϵ is the error term, which is the same as in the main model. Further,
the dataset is the same as that used in the baseline model.
[Table 5]
The results in Table 3 show that the treatment eﬀect increases from the ﬁrst quarter tothe third quarter. We can infer from the result that the recycling ﬁrms used the stocks tomaintain their production, and many of them used up the stocks before the third quarter.
The results indicate that the treatment eﬀect becomes positive in the fourth quarter. It
suggest that the result was temporal, because some of the recycling ﬁrms might ﬁnd newsources of plastic waste and worked harder to cover the loss by the import ban.
Second, the eﬀect is likely to be diﬀerent among cities with diﬀerent characteristics, forexample, the ozone concentrations before the import ban. We employ the diﬀerence-indiﬀerence-in-diﬀerences (DDD) method to examine the diﬀerent eﬀects between cities withhigher concentrations at the baseline with those with lower concentrations. In other words,
we combine three types of variations: the time variation (i.e., before and after the ban on
importing plastic waste), the city variation (i.e., coastal cities and inland cities), and thepollution variation (i.e., cities with higher ozone concentrations in 2017 versus cities withlower concentrations). We deﬁne cities with concentrations higher than the third quartile as
dirty cities. The estimated model is formulated as follows:
Ozoneit =β1 Af tert + β2 T reati + β3 Dirtyi + β4 DIDit + β5 Af tert ∗ Dirtyi ,(3)+ β6 T reati ∗ Dirtyi + β7 DDDit + Xit + ξt + ϵit ,where Dirty describes the city whose ozone concentration in 2017 is higher than the third
quartile. DID is the interaction of Treat and After. DDD is the interaction of Treat, After,and Dirty that we are interested in.
12[Table 6]
The results are reported in Table 4. The coeﬃcient of the treatment indicator DDD isnegative and statistically signiﬁcant at the 1% level in all the columns. The result implies
that the eﬀect of the ban in cities with higher baseline ozone concentrations is larger than inother cities. A possible explanation for this is that more plastic recycling ﬁrms are locatedin dirty cities.
Lastly, the rural population density could inﬂuence the import ban. As mentioned above,
many farmers in coastal provinces are involved in the plastic recycling activity. Because theprice of plastic waste is very low, one could even start the business on their own farm in
the rural area. Many farmers in China are recycling plastic waste in their farms. Thus, we
assume that the number of small recycling workshops will be larger in cities with a larger
rural population. According to People’s Daily (Ease the burden of the environment andimprove the quality of the eco- nomic growth, 2018/05/05), these workshops recycle plastics
with low technology and without controlling pollutant emissions. Therefore, we examine
whether the eﬀect of the ban in areas with higher rural populations is stronger than thatin other areas. We employ a DDD method to investigate this question, and the model isformulated as follows:
Ozoneit =β1 Af tert + β2 T reati + β3 Rurali + β4 DIDit + β5 Af tert ∗ Rurali ,(4)+ β6 T reati ∗ Rurali + β7 DDDit + Xit + ξt + ϵit ,where Rural is a dummy variable that takes value, 1 if the rural population density in a city
in 2017 is higher than the third quartile of the populations in all the cities in 2017 (0.28
thousand people per km3 ), and 0, otherwise. DID is the interaction of Treat andAfter. DDD
is the interaction of Treat, After, and Rural.
The data of the rural population were obtained from the oﬃcial websites of the statistical
department of each province. The city-level rural population density is calculated by dividing
the rural population in the city by the area of the city. As there are two cities lacking rural
13

population data, the dataset includes 88 cities rather than 90 cities in the baseline model.
[Table 7]
The estimation results are reported in Table 5. The coeﬃcient of the treatment indicator
DDD is positive and statistically signiﬁcant at the 1% level in all the columns. The results
indicate that after the ban, the ozone concentration was higher in cities with higher rural
population densities in the treatment area. This implies that the treatment eﬀect is weakerin cities with small workshops. We think this is because the enforcement in rural involves
high monitoring costs. In other words, the ban is not very eﬀective in terms of reducing
activities of the small recycling workshops, which could cause great damage to the local
environment.

5

Conclusions
This study investigated the eﬀects of the ban on the import of plastic waste on air

quality using China’s city-level daily ozone concentration data. First, the results of ourbaseline DID model suggest that the ban might help reduce the ozone concentrations incoastal cities where many recycling ﬁrms are located. The estimated coeﬃcients suggestthat the ban contributes to a decrease in the ozone concentration in the treatment groupby about 2.2µg/m3 . We also check the robustness by redeﬁning the treatment and control
groups based on the ozone-NOx-VOC sensitivity. Second, we conduct analyses to explore
the heterogeneous eﬀects. We ﬁnd that the eﬀect of the ban gets stronger from the ﬁrst
quarter to the third quarter. Moreover, the result of the DDD methods indicates that theeﬀect of the ban in dirty cities could be larger. In addition, the ozone concentrations incities with higher rural population density are less aﬀected by the ban. Overall, the results
suggest that the ban did help to improve the air quality albeit slightly.
Because we are not able to capture the exact location and the situation of the plastic
recycling ﬁrms and workshops, it is challenging to deﬁne the treatment and control groups14

at the exact city level or even monitoring-point level. Moreover, as the ban was implemented
recently, the availability of other outcome variables is limited. Despite these limitations, ourapproach allows us to examine the impact of import ban in a simple framework.
In the long-run, the import ban will lead to an increase in recycling of domestic plastic
waste. Several big cities in China, such as Shanghai, have already started waste sorting from2019. It will increase the domestic supply of recyclables, reviving the domestic plastic waste
recycling industry. To maintain the local air quality, the plastic recycling industry need totransform its process with adoption of new technologies.

References
Al-Salem, S.M., Lettieri, P., Baeyens, J., 2009. Recycling and recovery routes of plastic solid
waste (PSW): A review. Waste Management, 29(10), 2625-2643.
Almond, D., Chen, Y., Greenstone, M., Li, H., 2009. Winter heating or clean air? Unintended
impacts of China’s Huai river policy. American Economic Review, 99 (2), 184-90.
Brooks, A.L., Wang, S., Jambeck, J.R., 2018. The Chinese import ban and its impact on
global plastic waste trade. Science Advances, 4 (6), eaat0131.
Cao, J., Garbaccio, R., Ho, M. S., 2009. China’s 11th ﬁve-year plan and the environment:
reducing SO2 emissions. Review of Environmental Economics and Policy, 3 (2), 231-250.
Cao, Z., Chen, Q., Li, X.,Zhang, Y., Ren, M., Sun, L., Wang, M., Liu, X., Yu, G., 2019. The
non-negligible environmental risk of recycling halogenated ﬂame retardants associated withplastic regeneration in China.Science of the Total Environment, 646 (2019), 1090-1096.
China
Economic
Daily,
2018.
Blocking
foreign
garbage
http://paper.ce.cn/jjrb/html/2018-05/28/content 363586.htm (in Chinese).

completely.

Greenstone, M., He, G., Li, S., Zou, E., 2020. China’s War on Pollution: Evidence from theFirst Five Years. Review of Environmental Economics and Policy, forthcoming.
He, J., Liu, H., Salvo, A., 2019. Severe air pollution and labor productivity: Evidence fromindustrial towns in China. American Economic Journal: Applied Economics, 11 (1), 173-201.
Health Eﬀects Institute, 2019. State of Global Air 2019. Special Report. Boston, MA: HealthEﬀects Institute.https://www.stateofglobalair.org/sites/default/ﬁles/soga 2019 report.pdf.

15

He, Z., Li, G., Chen, J., Huang, Y., An, T., Zhang, C., 2015. Pollution characteristics andhealth risk assessment of volatile organic compounds emitted from diﬀerent plastic solid
waste recycling workshops. Environment International,77, 85 ‒ 94.
Heyes, A., Zhu, M., 2019. Air pollution as a cause of sleeplessness: Social media evidence
from a panel of Chinese cities. Journal of Environmental Economics and Management, 98,
102247.
Huang, Q., Chen, G., Wang, Y., Chen, S., Xu, L., Wang, R., 2020. Modelling the global
impact of China s ban on plastic waste imports. Resources, Conservation & Recycling, 154,
104607.
Huang, D., Zhou, S., Hong, W., Feng, W., Tao, L., 2013. Pollution characteristics of volatileorganic compounds, polycyclic aromatic hydrocarbons and phthalate esters emitted fromplastic wastes recycling granulation plants in Xingtan Town, South China. Atmospheric
Environment, 71,327-334.
Jin, X., Holloway, T., 2015. Spatial and temporal variability of ozone sensitivity over China
observed from the Ozone Monitoring Instrument. Journal of Geophysical Research: Atmospheres, 120, 7229-7246.
Jin, Y., Andersson, H., Zhang, S., 2016. Air pollution control policies in China: A retrospective and prospects. International Journal of Environmental Research and Public Health, 13,1219.
Lazarevic, D., Aoustin, E., Buclet, N., Brandt, N., 2010. Plastic waste management in thecontext of a European recycling society: Comparing results and uncertainties in a life cycle
perspective. Resource Conservation & Recycling., 55(2), 246 ‒ 259.
Meyer, B., 1995. Natural and quasi-experiments in Economics. Journal of Business & Economic Statistics, 13 (2), 151-161.
Ministry of Commerce of the People s Republic of China, 2019. China Recycling Industry Report. http://ltfzs.mofcom.gov.cn/article/ztzzn/an/201910/20191002906058.shtml (inChinese).
Ministry of Ecology and Environment of China, 2018. Report on the State of the Ecology andEnvironment in China 2018. https://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/ (in Chinese).
People s Daily, 2018. Ease the burden of the environment and improve the quality of the economic growth. http://www.gov.cn/xinwen/2018-05/05/content 5288201.htm (in Chinese).
PRC Central Government, 2017. Implementing Plan for Banning Foreign Trash Imports and Promoting Reform in the Solid Waste Import Management System.
http://www.gov.cn/zhengce/content/2017-07/27/content 5213738.htm (in Chinese).
PRC Central Government, 2017. The results for the campaign to ﬁght against illegal
behaviors of the ﬁrms recycling imported solid wastes. http://www.gov.cn/xinwen/201707/29/content 5214447.htm (in Chinese).
16PRC Central Government, 2018. The results of the Fifth campaign to ﬁght against thesmuggling. http://www.gov.cn/xinwen/2018-12/20/content 5350416.htm (in Chinese).
Qu, S., Guo, Y., Ma, Z., Chen, W., Liu, J., 2019. Implications of China s foreign waste banon the global circular economy. Resources, Conservation & Recycling, 144, 252 ‒ 255.
Sun, M., 2019. The eﬀect of border controls on waste imports: Evidence from China’s Green
Fence campaign. China Economic Review, 54, 457 ‒ 472.
Tsai, C., Chen, M., Chang, K., Chang, F., Mao, I., 2009. The pollution characteristics ofodor, volatile organochlorinated compounds and polycyclic aromatic hydrocarbons emitted
from plastic waste recycling plants. Chemosphere, 74, 1104 ‒ 1110.
Viard, V.B., Fu, S., 2015. The eﬀect of Beijing s driving restrictions on pollution andeconomic activity. Journal of Public Economics, 125, 98-115.
WHO, 2016. Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur
dioxide. http://whqlibdoc.who.int/hq/2006/WHO SDE PHE OEH 06.02 eng.pdf.
World Bank and Institute for Health Metrics and Evaluation, 2016. The Cost of Air Pollution:
Strengthening the Economic Case for Action. Washington, DC: World Bank.
Zhang, X., Chen, X., Zhang, X., 2018. The impact of exposure to air pollution on cognitive
performance. Proceedings of the National Academy of Sciences, 115 (37), 9193-9197.

17

Tables

Variable

Table 1: Descriptive statistics
Obs
Mean Std. Dev.

Min

Max

All

Ozone(µg/m3 )NO2(µg/m3 )Max Temp(°C)
Treat(dummy)
After(dummy)
DID(dummy)

63,54063,54063,54063,54063,54063,540

94.17
30.91
23.06
0.522
0.500
0.261

44.84
17.00
9.222
0.500
0.500
0.439

1
2
-20
0
0
0

586
176
41
1
1
1

33182
33182
33182
33182
33182
33182

99.16
30.96
23.53
1
0.5
0.5

47.42
17.12
9.4
0
0.5
0.5

1
2
-20
1
0
0

586
168
41
1
1
1

30358
30358
30358
30358
30358
30358

88.72
30.86
22.54
0
0.5
0

41.14
16.80
9
0
0.5
0

1
2
-5
0
0
0

300
176
41
0
1
0

Treatment Group

Ozone(µg/m3 )NO2(µg/m3 )Max Temp(°C)
Treat(dummy)
After(dummy)
DID(dummy)
Control Group

Ozone(µg/m3 )NO2(µg/m3 )Max Temp(°C)
Treat(dummy)
After(dummy)
DID(dummy)

18

Table 2: The eﬀects of the ban on the ozone concentrations
Ozone
(1)
(2)
(3)
(4)
After
Treat
DID
NO2
Max Temp
Month FE
Constant-2.567*** -2.784*** -3.130*** -3.125***(0.361)(0.372)(0.398)(0.472)9.081*** 8.896*** 12.16*** 12.17***(0.432)(0.443)(0.463)(0.518)-2.210*** -2.168*** -3.442*** -3.447***(0.579)(0.597)(0.620)(0.702)0.351*** 0.296***(0.00995) (0.00965)
2.447*** 2.616***(0.0280)(0.0169)
Y
N
Y
N
16.77***(0.681)22.00***(0.603)56.48***(0.467)90.28***(0.345)Observations
63,54063,54063,540R-squared
0.322
0.279
0.228
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

63,5400.017

Table 3: Descriptive statistics: ozone-NOx-VOC sensitivity
Variable
Obs
Mean Std. Dev. Min Max
All

Ozone(µg/m3 )NO2(µg/m3 )Max Temp(°C)
Treat(dummy)
After(dummy)
DID(dummy)

33,88833,88833,88833,88833,88833,888

101.5
37.24
22.15
0.583
0.500
0.292

19

49.78
20.65
9.662
0.493
0.500
0.455

1
2
-19
0
0
0

324
269
41
1
1
1

Table 4: The eﬀects of the ban: ozone-NOx-VOCs sensitivity
Ozone
(1)
(2)
(3)
(4)
After
Treat
DID
NO2
Max Temp
Month FE
Constant-5.249*** -5.342*** -4.254*** -4.350***(0.611)(0.640)(0.658)(0.791)
5.565*** 5.488*** 7.746*** 7.746***(0.628)(0.659)(0.675)(0.796)-2.437*** -2.400*** -3.220*** -3.196***(0.855)(0.891)(0.909)(1.077)-0.141*** -0.158***(0.0155)(0.0137)2.701*** 2.698***(0.0419)(0.0240)YN
Y
N
42.60***(1.042)47.85***(0.965)57.63***(0.657)100.1***(0.581)

Observations
33,88833,88833,888R-squared
0.359
0.306
0.286
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

33,8880.008

20

Table 5: The eﬀects of the ban for diﬀerent periods
VARIABLES
(1)
(2)
(3)
(4)
afterQ1

0.440-20.82***(0.590)(0.588)-3.175*** 16.71***(0.778)(0.678)-1.673** 13.67***(0.754)(0.655)-3.400*** -25.10***(0.754)(0.746)12.02*** 12.16***(0.458)(0.518)-1.251-1.584*(0.785)(0.877)-3.175*** -3.872***(1.024)(1.068)-12.13*** -12.71***(0.955)(1.004)4.674*** 5.622***(0.939)(1.083)0.373***
0.00163(0.0104)(0.0107)YN-0.706-20.82***(0.582)(0.587)-4.434*** 16.70***(0.793)(0.675)-2.524*** 13.65***(0.764)(0.646)-5.080*** -25.09***(0.767)(0.745)12.16*** 12.16***(0.463)(0.518)-1.585**-1.585*(0.780)(0.878)-3.875*** -3.875***(1.046)(1.068)-12.71*** -12.71***(0.972)(1.005)5.626*** 5.626***(0.939)(1.082)

YN

39.13***(0.689)90.23***(0.477)54.78***(0.514)90.29***(0.345)Observations
63,54063,540R-squared
0.248
0.088
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

63,5400.232

63,5400.088

afterQ2
afterQ3
afterQ4
Treat
Treat*afterQ1
Treat*afterQ2
Treat*afterQ3
Treat*afterQ4NO2
Month FE
Constant

21

Ozone
After
Treat
Dirty
DID
Treat*Dirty
After*Dirty
DDD
NO2
Max Temp
Month FE
Constant

Table 6: The eﬀect on dirty cities
(1)
(2)
(3)

(4)

-5.184*** -5.016*** -5.117*** -5.150***(0.365)(0.378)(0.411)(0.487)-12.35*** -9.844***
1.139*
1.138*(0.616)(0.620)(0.670)(0.654)21.45*** 19.68*** 12.40*** 12.35***(0.571)(0.564)(0.568)(0.641)2.296*** 2.194***
1.207
1.229
(0.794)(0.822)(0.897)(0.893)17.18*** 15.49*** 8.788*** 8.824***(0.782)(0.803)(0.850)(0.861)
9.948*** 10.50*** 10.91*** 10.88***(0.651)(0.670)(0.724)(0.882)-13.57*** -14.08*** -14.91*** -14.91***(1.100)(1.137)(1.223)(1.338)0.193*** 0.226***(0.00991) (0.00950)
3.368*** 2.825***(0.0329)(0.0168)Y
N
Y
N
13.00***(0.682)

18.69***(0.588)55.89***(0.486)89.71***(0.343)Observations
63,54063,54063,540R-squared
0.389
0.342
0.253
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

63,5400.042

22

Table 7: The eﬀect on cities with higher rural population
Ozone
(1)
(2)
(3)
(4)

Treat
After
Rural
DID
Treat*Rural
After*Rural
DDD
NO2
Max Temp
Month FE
Constant

10.20*** 10.09*** 13.34*** 13.34***(0.497)(0.511)(0.534)(0.593)-2.313*** -2.540*** -2.624*** -2.687***(0.416)(0.427)(0.457)(0.532)9.109*** 9.162*** 8.635*** 8.638***(0.628)(0.644)(0.694)(0.829)-3.612*** -3.506*** -5.166*** -5.125***(0.669)(0.689)(0.712)(0.801)-4.525*** -4.843*** -4.520*** -4.523***(1.027)(1.054)(1.101)(1.234)-0.999-0.920-1.748*-1.647(0.842)(0.868)(0.941)(1.135)4.067*** 3.918*** 5.208*** 5.064***(1.366)(1.408)(1.474)(1.670)0.356*** 0.303***(0.0100) (0.00972)
2.440*** 2.596***(0.0282)(0.0171)
Y
N
Y
N
14.42***(0.709)19.81***(0.634)54.55***(0.503)88.03***(0.389)Observations
62,12862,12862,128R-squared
0.323
0.279
0.228
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

62,1280.021

23

Figures

Figure 1: Cities Meeting the Stage I Monitoring Standard
Source: Report on the State of the Ecology and Environment in China 2018

Figure 2: Import of Plastic Waste of ChinaSource: China Recycling Industry Report 201924

Figure 3: The Control Group (Light grey) and Treatment Group (Dark grey)25

Figure 4: Daily Average Ozone Concentrations

26

Figure 5: Monthly Average Ozone Concentrations

27