Trade Uncertainty and Firm Pollution: The Role ofEmission Cap
Haichao Fan† Guangyuan Guo‡ Yu Liu? Huanhuan Wang∗
December 4, 2020

Abstract
How does a reduction in trade policy uncertainty affect firms’ pollution behavior?
Guided by a simple model, we show that the answer to this question depends onwhether an emission cap exists. We find that the reduced uncertainty increases firm
output by comparable magnitudes across the regions, but they reduce firm SO2 emission intensity and firm total SO2 emissions only in regions with emission caps. The
decline in SO2 emissions is caused by reduced use of fossil fuel and more abatement
equipment. We also find that the reduced uncertainty improves firms’ productivity
when emission caps exist.
Keywords: Trade policy uncertainty, environmental regulation, emission cap, firm
pollution, firm productivity
JEL codes: F18, F64, Q56, Q58∗ We benefit from comments and suggestions from seminar participants at Fudan University.

We acknowledge financial support from the “Ten Thousand Talents Program (Young Talents)” of China, YouthInnovative Team on Humanities and Social Sciences of Fudan University and the self-supporting project ofInstitute of World Economy at Fudan University. † Fan: Institute of World Economy, School of Economics,
Fudan University, Shanghai, China (e-mail: fan haichao@fudan.edu.cn). Research fellow at Shanghai Institute of International Finance and Economics, Shanghai, China. ‡ Guo: School of Economics, Fudan
University, Shanghai, China (e-mail: glidis@163.com). ? Liu: Department of Public Economics, School of
Economics, Fudan University, Shanghai, China (e-mail: yu liu@fudan.edu.cn). ∗ Wang: East China Normal
University, Shanghai, China. (e-mail: ahwanghuanhuan@163.com).

1

1

Introduction

Globalization in recent decades has drastically transformed the economic and environmental landscapes of the world. Faced with a dilemma about economic prosperity andclean environment, developing countries may intentionally choose weaker environmental regulations to gain more from globalization, which may also become a source of comparative advantage (Levinson and Taylor, 2008; Hanna, 2010; Broner et al., 2012; Aichele
and Felbermayr, 2015). This institutional inefficacy has all too often been credited as thereason for the deteriorating environment in developing countries (Banerjee et al., 2008;
Alpert et al., 2012; Greenstone and Hanna, 2014). Is it possible that economic growth isobtained without sacrificing the environment? In particular, when a stringent environmental regulation is adopted, will a country fail to capture the gains from trade opportunities? We address these two questions in this study.
In October 2000, the U.S. government granted permanent normal trade relations (PNTR)
to China, which became effective upon China’s accession to the WTO at the end of 2001.
Prior to the conferral of PNTR, although Chinese exports to the US had been subject to
low tariffs, these tariffs were reviewed by the U.S. congress annually, which adds uncertainty to the tariffs faced by Chinese exporters. The conferral of PNTR ended trade policy
uncertainty (Pierce and Schott, 2016). Recent studies find that the end of trade uncertainty
had substantial effects on exports from China to the U.S. as well as internal migration in
China (Pierce and Schott, 2016; Handley and Limao, 2017; Facchini et al., 2019).
We use the reduction in trade policy uncertainty as an exogenous positive shock to
Chinese firms’ export opportunities. How will the export shock impact Chinese firms’
pollution behavior? On the one hand, the reduction in uncertainty incentivizes exports
and production, which increases pollution (a scale effect). On the other hand, the larger
production scale and higher profits may encourage firms to adopt better and possibly
cleaner machines and technology to improve energy efficiency, which reduces pollution
intensity, measured as pollution per unit of output (a technology effect). This technology
effect that reduces pollution is also documented in the literature (Levinson and Taylor,2008; Broner et al., 2012; Cherniwchan, 2017). What the literature has not yet examined
are the differential effects of trade on firm pollution with different extents of emission
control, modeled in our setting as emission caps faced by firms.
To more thoroughly analyze the effects of trade policy uncertainty reduction on firm
pollution, under the condition that firms face emission control, we develop a multicountry model with heterogeneous firms, where firms’ production entails environmental
emissions. In our model, firms make export decisions to maximize profits and face gov-

1

ernment emission control policies as a production constraint. Our model predicts thatwhen firms face an emission cap, reductions in trade policy uncertainty induce firms to
produce more output but emit less pollution. Reductions in uncertainty also induce firms
to use more labor to substitute for fossil fuel and invest more in abatement equipment. In
contrast, when firms face no emission control, although uncertainty reduction increases
firm production, it no longer reduces firm pollution.
We test the model predictions using data from three sources. The first dataset is theAnnual Survey of Industrial Firms (ASIF), which contains information on firm production, such as output, employment, capital, and intermediate inputs. Second, we use datafrom the Annual Environmental Survey of Polluting Firms (AESPF), which covers major pollutants at the plant level. It also includes information on firm abatement equipment investment. We focus on variables related to sulfur dioxide (SO2) emissions because
the emission cap in China mainly imposes constraints on firm SO2 emissions. The third
dataset is transaction-level import and export data from the China General Administration of Customs (CGAC).
Following Erten and Leight (2019) and Facchini et al. (2019), we compute trade policy uncertainty at the prefecture level. As the conferral of PNTR eliminates export policy
uncertainty from China to the U.S., firms in prefectures with higher levels of average uncertainty prior to the conferral would experience a greater decline in prefecture average
uncertainty. We use a difference-in-differences (DID) estimator that compares firm output
and SO2 emissions in high- and low-uncertainty prefectures before and after the conferral. We find that the reduction in uncertainty significantly increased firm production but
reduced SO2 emissions per unit of output (SO2 intensity). We conduct event studies onfirm output and SO2 intensity to justify our identification assumption for our DID estimator. The parallel trends show that the effect on output and SO2 intensity starts in 2001.
Firms in prefectures with high and low trade policy uncertainty would have followed thesame trends in production and SO2 emissions in the absence of PNTR conferral.
We then explore the heterogeneous effects across regions with different degree of environmental regulations. In 1998, two-control zones (TCZs) were established by the Chinese
government as special regions with more stringent SO2 emission regulations. We present
the geographic locations of the TCZs in Figure 1. The SO2 emission cap is more strictly
enforced in TCZs than in non-TCZs. The spatial variation in SO2 emissions control allows
us to examine the heterogeneous effects of uncertainty reductions in firm production andSO2 emissions in TCZs and non-TCZs. Consistent with our prediction, we find that the
reductions in trade policy uncertainty increase firm output in both TCZs and non-TCZs
by comparable magnitudes, but they reduce total firm emissions and emission intensity
2

only in TCZs.
What explains the effects of reduced uncertainty on emission intensity for firms in
TCZs? We find that the reduction in uncertainty decreases coal and fuel use, increases
manual labor, and increases investment in abatement equipment for firms in TCZs. In
contrast, we do not find comparable effects for firms in non-TCZs. In addition, we findthat firms in TCZs improve total factor productivity more as a result of the reduced uncertainty, consistent with the Porter hypothesis which states that a tougher environmental
standard can make firms more efficient and innovative (Porter and Van der Linde, 1995).1
Our study is related to two strands of literature. First, this paper is closely related tothe emerging literature that uses firm- or plant-level data to examine the impact of tradeon the environment. The current literature primarily focuses on developed countries–inparticular the U.S.–to document that exporters pollute less than non-exporters (Holladay, 2016; Cui et al., 2016; Forslid et al., 2018). Cherniwchan (2017) further analyzes theimpact of trade liberalization on a firm’s total emissions and emission intensity in the context of NAFTA. In a developing country context, Barrows and Ollivier (2016) use Indianfirm-level data to study the effects of an export demand shock on total emissions, output, and emission intensity. Gutierrez and Teshima (2018) use Mexican plant-level andsatellite imagery data to examine the impact of import competition generated by output
tariff reductions on plants’ environmental outcomes. Our paper complements these earlier studies by focusing on the impact of an export demand shock induced by reduced
trade policy uncertainty on Chinese firms’ total emissions, output and emission intensity.
Moreover, we examine the differences in these effects across regions with different degrees of environmental regulations on pollution emissions. To summarize, our paper isin line with the literature but also differs from it by emphasizing the role of the emission
control.
Second, our paper is also related to studies on trade policy uncertainty. Pierce andSchott (2016) study the effect of a reduction in U.S. trade policy uncertainty on U.S. manufacturing firms after China’s accession to the WTO. Handley and Limao (2017) use adynamic general equilibrium model to argue that a reduction in trade policy uncertainty
after China’s accession to the WTO significantly contributed to the country’s export boom
to the United States. Crowley et al. (2018), Garred (2018), and Imbruno (2019) provide em1 Our findings highlight the joint effects of export opportunity and domestic environmental regulation.

When firms face an opportunity to export, and existing domestic environmental regulations constrain pollution, firms may adjust production and improve efficiency to increase production scale and export. Thisis consistent with the Porter hypothesis (Porter and Van der Linde, 1995). The positive effects of more
stringent environmental regulations on firm productivity are also documented by Acemoglu et al. (2016),
Aghion et al. (2016), Gutierrez and Teshima (2018), and Aghion et al. (2020).

3

pirical evidence on how trade policy uncertainty affects exports and imports. Erten andLeight (2019) and Facchini et al. (2019) examine the impacts of the reduction in trade policy uncertainty associated with China’s WTO accession on structural transformation in
China and China’s “Great Migration”, respectively. The focus of our paper is the environmental consequences of trade policy uncertainty, which differs from the aforementioned
studies.
The remainder of the paper proceeds as follows. Section 2 introduces environmental
regulations and TCZs in China. Section 3 presents a simple model. Section 4 discusses ourempirical strategy and describes the data and measures. Section 5 reports the empiricalresults. Section 6 concludes the paper.

2

Institutional Background

The traditional method to control industrial pollution in China requires firms’ pollution
discharge to be below a given concentration value, which specifies the maximum level ofemission intensity (i.e., emissions per unit of output) for each pollutant. Failure to comply
with the requirement may trigger fines and penalties. This method, however, ignores thetotal amount of pollution of a firm, which leaves a loophole that results in high levels of
overall pollution at the firm and regional levels. To close this loophole, China adopted
an emission cap method in 1996 as a part of the 9th Five-year Plan. This method sets anoverall emission target for all major pollutants and has since been the main method forpollution control in China.2
Emission caps are common practice across countries. For example, the U.S. created the“Acid Rain Program” and Japan initiated the “Water-basin COD Emission Target Control
Program” to combat chemical emissions; there is also of course the worldwide carbon
emission reduction agreement under the Kyoto Protocol.3 In contrast to the common
cap-and-trade mechanism, the method in China sets an overall cap but has no tradingmechanism, primarily because China has not yet established institutions to support market transactions of emission permits.
In practice, the central government first sets a national cap, or emission target and thenallocates targets to each province, and each province then divides the targets among pre2 The 9th Five-year Plan defined 12 pollutants as “critical pollutants” and requires the total emissions ofeach pollutant in 2000 to be lower than those in 1995. The 10th Five-year Plan further mandated that theemissions of 6 major pollutants be reduced by 10% in 2005 relative to 2000 (see the “National Environmental
Bureau, Decomposition Plan on Emission Control of Critical Pollutants during the 10th Five-year Plan,
2001”).
3 See the United Nations Framework Convention on Climate Change.

4fectures in its jurisdiction for implementation. There are 333 prefectures in total, which
form a government hierarchy in between provinces and counties.4 The target assignments at the prefecture level take into account the population, economic size, industrial
structure, past emissions, and environmental quality of each prefecture.5 Each prefecturethen further assigns emission targets to firms; the detailed assignment rules are not madepublic by the government. Guaranteeing emission reductions demands not only careful scrutiny and government approval at the factory or establishment level, especially inhigh-pollution industries, but also requirements by the prefecture government that firms
adopt pollution abatement facilities such as desulfurization of coal or gas combustion
or to remove sources with high emission intensity and outdated production equipment.
The government regularly sends inspectors to monitor and record pollution emissions toenforce its regulation.
In effect, this emission cap method is applied nationwide and implemented throughthe government administrative hierarchy. The amounts of pollution emissions are recordedat the firm level before they are summed up at jurisdictional levels. Statistics are reported
through government hierarchies in a bottom-up manner and affirmed by the central government. The central government uses these statistics to evaluate officials’ competency
and accountability. This process takes place annually.
Among all pollutants that are regulated by an emission cap in China, SO2 is granted
the top priority. In January 1998, the central government enacted a “Two-control Zone”
policy to identify priority regions to reduce SO2 emissions to prevent acid rain. Prefectures with annual average precipitation pH values exceeding nationally mandated thresholds are designated as Acid Rain Control Zones: prefectures with annual average SO2
emission levels exceeding nationally mandated thresholds are designated as SO2 Control
Zones. Following these standards, a total of 175 prefectures were designated as “Twocontrol Zones” (TCZs) by the central government in 1998. More stringent environmental
regulations on emission caps are thus adopted in these zones. For example, new coalmines producing coal with sulfur content higher than 3% are prohibited, and any such
existing coal mines are to be shut down. No new thermal power plants combusting coal
are to be built near large prefectures. Firms in high-emission-intensity industries including the petrochemical, metallurgical, architecture material, and nonferrous metal industries are obliged to adopt pollution reduction equipment.6 These requirements are more
4 The 333 prefecture-level divisions include 7 prefectures, 293 prefecture-level cities, 30 autonomous

prefectures, and 3 leagues. We call all of these types of divisions prefectures for simplicity.
5 See the “National Environmental Bureau, Implementing Program on Emission Control of Critical Pollutants during the 9th Five-year Plan, 1997.”
6 See the “Approval of The Central Government on SO2 Control Zones and Acid Control Zones Plan,

5stringent in TCZs than those in non-TCZs.
Figure 2 plots the annual aggregate GDP and SO2 emissions in TCZs and non-TCZs
between 1999 and 2006. The GDP data are obtained from the CEIC database, and the SO2
emission data are collected from the China Statistical Yearbook on Environment, published by the Ministry of Ecology and Environment of China. We treat 1999 as the benchmark year (value=100). The figure shows that although GDP had been rising rapidly inboth types of region, the SO2 emissions in TCZs remained relatively stagnant between
1999 and 2006, which reveals the relatively more stringent SO2 emission control in TCZs.
The difference in the strength of environmental regulation between TCZs and non-TCZs
helps us to identify the differential impacts of reduced trade policy uncertainty on firm
behavior with and without an emission cap.

3

Model

In this section, we develop a multi-country trade model with heterogeneous firms wherefirms’ production entails environmental emissions.

3.1

Preferences

A representative consumer in country j has a constant elasticity of substitution (CES)
utility function given by:
"
Uj =

N Z

∑ ω ∈Ω

i =1

qij (ω ) σ −1
σ

# σ

σ −1

dω


+ Ψ Ej ,(1)

ij

where N represents the total number of countries and i is the source country of the product, ω indexes the product variety in set Ωij available in country j, qij denotes the quantity
of variety ω, σ > 1 is the elasticity of substitution between varieties, and Ψ Ej < 0 represents the negative impact of pollution emission Ej on the individual’s utility.7 Consumer
optimization yields the following the demand function for variety ω in country j: − σ σ −1
qij (ω ) = pij (ω )Pj Ij ,

(2)

1998.”

7 We assume that Ψ E0
j < 0 and Ψ E j < 0. That is to say, the impact of pollution emission on
individual’s utility is negative and this negative impact increases in pollution emission Ej .

6

where pij (ω ) is the price of variety ω faced by consumers in country j. The aggregate
h
i 1
R
1− σ
1− σ
N
price index in country j is defined by Pj = ∑i=1 ω ∈Ω pij (ω )dωand Ij is theijtotal spending in country j.

3.2

Firm behavior

Suppose the firm is in the market of monopolistic competition. Each firm in country i
produces only one kind of heterogeneous product and its productivity is ϕ. Like Melitz(2003), firms in country i need to pay f ie units of labor in order to acquire a blueprint θ
and then draw a productivity from a Pareto distribution, i.e., Gi ( ϕ) = 1 − bϕi , whereθ is the Pareto location parameter and bi represents the productivity of country i. Firms
in country i need to pay a fixed export cost, amounting to f ij units of domestic laborwhen exporting to country j. In addition, firms in country i also need to pay a tariff of tij
when exporting to country j. To characterize trade policy uncertainty faced by Chinese
firms exporting to the U.S. prior to PNTR conferral, we assume that the tariff can be set
either at a high non-PNTR value at t1ij with probability ηij or at a low PNTR value at t2ijwith probability 1 − ηij . The average tariff is therefore tij = ηij t1ij + 1 − ηij t2ij , where0 ≤ ηij ≤ 1.
Consider country i to be China. When j = U.S., ηij is above 0 prior to conferral, and it
is equal to 0 after conferral. When j 6= U.S., we assume that ηij remains zero throughout
the study period. In what follows, we analyze how the tariff change induced by thechange in the probability ηij affects the firm’s production and emission behavior.
Following Shapiro and Walker (2018), we assume that each firm produces two outputs: an industrial good and emissions. In order to reduce emissions, a fraction of θ ratio
of production inputs is used to reduce pollution and the other (1 − θ ) ratio of production
inputs is used to produce products. The fraction of labor θ spent in reducing pollution isan endogenous choice that ultimately varies with a firm’s productivity. The firm’s production function is given by the following equation:γ

1 − γiqij ( ϕ) = (1 − θ ) ϕlij i Mij,(3)

where lij is the labor demand, Mij is the demand for final material inputs by firms (itsproduction function is the same as utility function). Firms product emissions with thefollowing technology:γ1− γeij ( ϕ) = (1 − θ )1/α ϕlij i Mij i ,(4)
The firms need to pay the costs including the emission tax, pollution fee and so on. We
7assume that the pollutant cost per unit of emission is te,i .
We proceed by using Equation (3) to solve for (1 − θ ), and, in turn, be used to substitute for (1 − θ ) in Equation (4). This gives us an integrated expression for the joint
production of goods and emission, which exploits the fact that although pollution is an
output, it can equivalently also be treated as an input. Hence, we have:γ
1− γ 1− α αqij = ϕlij i Mij i
eij .

(5)

In equation (5), production use labor as well as emissions. The parameter α denotes
how intensive the industry is in the use of labor versus the use of emissions.

3.3

Firm’s behavior

In the first stage, given the wage wi , the pollutant cost per unit of emission te,i and theexport decision Iij , firms solve the following problem:"pij ( ϕ)qij ( ϕ)− wi lij ( ϕ) − Pi Mij − te,i eij ( ϕ)max ∑ Iij ( ϕ)τijj
 − σ σ −1 D
where qij ( ϕ) = pij ( ϕ)Pj X j#(6)(7)

1 − γiγqij ( ϕ) = (1 − θij ) ϕlij i Mij(8)

1 − γieij ( ϕ) = (1 − θij )1/α ϕlij i Mijγ(9)p ( ϕ)

where X jD is the demand by both consumers and firms, ijτ denotes the value obtainedij
by firms for one unit of export to country j and τij = 1 + tij . The previous problem is
equivalent to
max ∑ Iij ( ϕ) θij ,lijj1 − θij

γ
1− γ
ϕlij i Mij iτij σ −1



σ

σ −1

1

Pj σ Ijσ − wi lij ( ϕ) − Pi Mij − te,i 1 − θij

1/α

γ

(10)

8

1 − γi 

ϕlij i MijSolving this optimization problem by choosing θij , lij and Mij yields,
σ−1pij ( ϕ)qij ( ϕ) = τij te,i eij ( ϕ)
σ


σ−1
pij ( ϕ)qij ( ϕ) = τij wi lij ( ϕ) + γi te,i eij ( ϕ)
γi
σ


σ−1
pij ( ϕ)qij ( ϕ) = τij Pi Mij + (1 − γi ) te,i eij ( ϕ)( 1 − γi )
σ

(11)

α

(12)
(13)

The previous equations imply the optimal price equal to
γ 1− γ 1− α α
τ
w
P
te,i
ij
i i
σ
pij ( ϕ) = 1− ασ − 1 1− α αγϕ α ( 1 − α ) γi i ( 1 − γi ) 1 − γi(14)Firms decide on whether or not to sell in country j. The exporting productivity cutoff
ϕij∗ is determined by comparing their profit and the fixed exporting cost according to thefollowing equation:∗∗
1 pij ( ϕij )qij ( ϕij )= f ij wi
(15)
σ
τij
Hence, the productivity cutoff ϕij∗ amounts toϕij∗ =

where Θij =

3.4

σ
σ −1

στij f ij wi1(1−α)(σ−1)(16)Θij Pjσ−1 X jD
!1− σ



γ 1− γ 1− α αte,iτij wi Pi
α α ( ( 1 − α ) γi i ( 1 − γi ) 1 − γ i )
γ

!

.

1− α

Equilibrium

eij , is defined as:
The average productivity of exporting firm from country i to country j, ϕeij = 
ϕ



Z ∞
ϕij∗

ϕij

(1−α)(σ−1)

g ( ϕ)  dϕ1 − G ϕij∗

1(1−α)(σ−1)

=θθ − (1 − α ) ( σ − 1)1(1−α)(σ−1)

ϕij∗

(17)
eij is completely determined by the productivity
Since the average productivity level ϕcutoff ϕij∗ , the average profit and revenue levels from country i to country j are also tied9

to the cutoff level ϕij∗ :(1 − α) (σ − 1) rij ϕeij
(1 − α ) ( σ − 1)
eij =
πij ϕ
=
θ
σ
θ − (1 − α ) ( σ − 1) 
rij ϕij∗(18)σ

In the equilibrium with free entry, the expected value for potential entrants should beequal to the fixed entry cost f ie wi , i.e.,
Ni f ie wi = ∑ Ni
jwhere Yi denotes the total output:

b
ϕij∗

!θ


(1 − α ) ( σ − 1)
eij =
πij ϕ
Yi
θσ

(19)

Yi = ∑ j Xij(20)

where Xij denoting the aggregate trade flow from country i to country j, equals toθθ 1−(1−α)(σ−1) Θ (1−α)(σ−1) τ −1
Ni θσbθ τij f ij wi
ij
ij
Xij =
X jD
θ

 1−
θ
(1−α)(σ−1)
1− α σ −1
Θi(0 j )( )∑i0 Ni0 θσbθ τi0 j f i0 j wi0

where X jD is the sum of the consumers’ demand Ij and the firms’ demand
Consumer’s expenditure Ij satisfies:
Ij = w j L j + te,j Ej + ∑ tij Xij(21)(1−γj )(1−α)(σ−1)
σ

Yj .

(22)

i

The equation (12) implies that the total emission satisfies:
te,i Ei =σ−1σ−1α ∑ j Xij =
αYi
σ
σ

(23)

Trade balance condition, which means that the total expenditure amounts to sell revenue
plus tariff revenue, implies:wi L i =((1 − γi ) (1 − α) + α) (σ − 1)
1−
Yi
σ

10

(24)

Aggregate price could be rewritten as
Pj = ∑ Ni
i

b
ϕij∗

!θ Z

∞
ϕij∗

 1
pij ( ϕ)



= ∑
i

1− σ

g ( ϕ)  dϕ1 − G ϕij∗

Ni bθ θστij f ij wi

θ − (1 − α ) ( σ − 1)

Θij X jD!−θ(1−α)(σ−1)

1− σ

(25)

στij f ij wi
X jD

− 1−θ α(26)

Using a method inspired by Dekle, Eaton, and Kortum (2007; 2008), we denote the0post adjustment value of any variable x as x 0 and the change in its value as xb = xx , a hat
denotes the ratio between the counterfactual and factual value. The emission control system, together with Equations (19), (20), (22), (23), (24) and (26), construct the equilibrium
system with variables (wi , Ei , te,i , Pi Yi , Ii , Ni ). These four equations imply:bi =Y∑ λij Mij

(27)

te,j Ejt X
wj Lj
bj + ∑ ij ij Mij
bte,j E
bjb
w
Lj +
Ij
Ij
Ij
i

(28)

j

b
Ij =

bi = w
bi
bi N
Y

(29)

bi = bte,i E
bi
Y

(30)

bi = w
bi
Y


(31)


γ(1−α) b(1−γ)(1−α)bαbi τbij w
bi
Pbj = ∑ ξ ij N
Pi
te,i

bi
τbij w
bD
X

− θ

1− α

i

!1−

θ
(1−α)(σ−1)

− (1−θ α)


(32)

j

 θ

θ
1−
γ(1−α) b(1−γ)(1−α)bα − 1−α −1bi (τbij w
bi ) (1−α)(σ−1) τbij w
bi
Pi
te,i
τbij
N
Ij
bj ,
bD bD
b (1−γj )(1−αD)(σ−1)Yj Y
where Mij =
θ

1−

− θ X j , X j = X D I j +σX jγ(1−α) b(1−γ)(1−α)bα
j
1− α
1
−
α
σ
−
1
(
)(
)
b
bi0
b0
τbi0 j w
Pi0te,i0∑i0 Ni0 ξ i0 j τbi0 j w
i


Xij
τij Xij(1−γi )(1−α)(σ−1)
Dλij = ∑ X , ξ ij = ∑ τ X , Xi = ∑ Xij + ∑ tvi Xvi , Ii = 1 −∑ Xij +σj ij
i ij ijv
jj∑ tvi Xvi , Yi = ∑ Xij , wi Li =
v

j



1−

((1−γi )(1−α)+α)(σ−1)σ

∑ Xij , te,i Ei =jα ( σ −1)
∑ Xij .
σjFol-lowing Hsieh and Ossa (2016), we treat the mean change in all countries’ wage index as
the numeraire. Then, we can use the previous several equations to solve for the changes
in variables at general equilibrium in response to a reduction in trade uncertainty, given
bilateral trade data and values of the key parameters.
We assume that only China has adopted emission target control theme. As for countryi except from China, pollution cost bte,i in that country is unrelated with trade activities
11since they wouldn’t adopt emission target control theme. According to Equation (30), webi for i 6= China. For China, we set E
bi + ρκ, where 0 ≤ ρ ≤ 1
bi = Ybi = (1 − ρ) Y
have Eand 0 < κ ≤ 1. When ρ = 0, there is no emission controls in China. A higher value of ρ
corresponds to more stringent emission control. The value of κ reflects the emission cap.
For example, κ = 0.975 means the emission cap is the 97.5 percentage of its initial value.

3.5

Data, Parameters and Results

This section describes how we estimate the parameters in the quantitative analysis, thensimulates the estimated effects of a reduction in trade uncertainty.
The elasticity of substitution σ, the pollution elasticity α and the trade elasticity θ areestimated pursuant to steps suggested by Shapiro and Walker (2018). To be specific, wetake an overall value 0.011 for pollution elasticity α as Shapiro and Walker (2018), implying that firms pay 1.1 percent of their total production costs on pollution taxes. In accordance with Broda and Weinstein (2006) who estimate the product-specific elasticities
of substitution for 73 counties based on a nested constant-elasticity-substitution utility
function, we take the elasticity of substitution σ = 5.8 According to our theory, firms’
domestic sales follow a Pareto distribution with a shape parameter θ − (1 − α) (σ − 1).
Given the estimates of σ and α, we could then back out the trade elasticity θ by regressing the logarithm of the rank of firm domestic sales on the logarithm of firm domestic
sales, using Chinese firm-level manufacturing survey data from the National Bureau of
Statistics of China (NBSC). The estimated value of θ equals to 4.41.
In order to solve for the equilibrium relative changes, we also need the complete matrix consisting of the trade flows Xij and the tariff τij . We first obtain the trade flows at
year 2001 from World Input-Output Database (WIOD), covering 43 countries, and therest of the world. Then, we obtain their bilateral applied tariff data at year 2001 from the
WTO-TRAINS database. For U.S., its tariff imposed on imports from China is a functionof non-MFN applied tariff and MFN applied tariff before China’s accession to WTO. After
China’s accession to the WTO, it becomes the MFN applied tariff.
The first three panels of Figure 3 describes the impact of a reduction in policy uncertainty on output, emission and emission intensity, respectively. The last panel of Figure 3
reflects the impact of a reduction in policy uncertainty on 1 − θ.9 As shown in Figure 3, re8 We aggregate their estimates for China and U.S. by taking means, which equal to 6.19 and 4.17 respec-tively. Hence, we take σ = 5, a value in-between.
9 Figure 3 corresponds to the case of κ = 0.975. Figures A.1 and A.2 in the Appendix correspond to κ = 1
and κ = 0.95. We find similar patterns pertaining to the impact of diminished trade policy uncertainty
among figures with different values of κ.

12duction in trade policy uncertainty (i.e., higher probability of setting non-PNTR, η) raises
output and pollution emission. When there exists the emission control (i.e., ρ 6= 0), theemission intensity decrease. As to output, diminished trade policy uncertainty increases
output by 6%. More stringent environmental regulation only slightly weaken this output
effect if the probability of setting non-PNTR before China’s accession to WTO equals toone. After comparing two extreme situation when ρ = 0 and ρ = 1, we only find 0.13%
less output impact from decreased trade policy uncertainty when incorporating the influence from strict environmental regulation. If the emission cap is lower than its initialbenchmark value (i.e., κ < 1), the emission would turn from increase to decrease after areduction in policy uncertainty. Trade policy uncertainty would not affect the emission
intensity without emission control. It always equals to one. The emission cap reduces theemission intensity due to higher emission costs caused by more stringent environmental
regulation (see Figure 4). Meanwhile, the last panel shows the substitutive relation of inputs between firms’ production and emission control. Without emission control (ρ = 0),
more inputs would be used in production which is indicated by the increase of 1 − θ. Withstringent emission control (ρ = 1), more inputs are used in emission reduction which isindicated by the decrease of 1 − θ.

4

Empirical Specification, Data, and Measures

In this section, we specify our econometric models and describe the data and measuresthat we use for estimation.

4.1

Empirical specification

In this section, we present our empirical strategy to test our model predictions. Proposition 1 suggests that when an overall emission cap exists, reducing trade policy uncertainty
increases firm production but reduces firm SO2 emission intensity. We test this prediction
by estimating the following regression:yit = βUncertainty p × Postt + γX pt + ∑ θt Xi × δt + δt + γi + eit ,

(33)

t

where yit denotes firm i’s outcome in year t. Uncertainty p measures prefecture p’s export policy uncertainty prior to WTO accession. Following Facchini et al. (2019), we use
export-weighted uncertainty at the prefecture level to construct this variable. Postt is adummy variable indicating whether year t is post WTO accession. We control for the in13

teractions between year dummies and firm initial output and SO2 emissions, Xi , to allow
firms with different initial outputs and emissions to have different time paths. To alleviate biases caused by macroeconomic changes at the prefecture level, we also include thevector of controls X pt , which includes contemporaneous prefecture-level GDP per capita
and population density. δt and γi are year and firm fixed effects, respectively. Controllingfor year fixed effects ensures that our estimates will not capture effects due to other concurrent macroeconomic changes or common time trends, and controlling for firm fixed
effects eliminates any omitted variable bias caused by time-invariant firm characteristics.
eit is an error term capturing all unobserved factors that influence yit .
The main parameter of interest in equation (33) is β, which estimates how the change
in a firm’s outcome before and after WTO accession differs across firms in prefectures ofdifferent levels of pre-WTO export uncertainty. Our model predicts that, relative to firms
located in prefectures with low uncertainty, firms in prefectures with high uncertainty
prior to WTO accession increase output more. The overall emission control sets a constraint on total SO2 emissions and therefore SO2 emission intensity, i.e., per-unit output
SO2 emissions, is expected to decline. The effects on firm SO2 emissions can be ambiguous.10 To test these hypotheses, we use the logarithm of a firm’s output, SO2 emissions,or emission intensity of SO2 , as the dependent variable in equation (33). We expect β tobe positive, zero (or negative), and negative, respectively, in these regressions.
To further test whether emission control is indeed the key driver of our results, we
conduct a triple-difference estimation strategy to see if the policy effects are different forfirms located in TCZs from those in non-TCZs. Firms in TCZs face more stringent SO2
emission control. The regression takes the following form:
yit = β 0 TCZi × Uncertainty p × Postt + β 1 Uncertainty p × Postt + β 1 TCZi × Postt(34)+ γXct + ∑ θt Xi × δt + δt + γi + eit ,twhere TCZi is a dummy variable that indicates whether firm i is located in a two-control
zone during our study period. We are interested in the estimated coefficient, β 0 , whichshows the heterogeneous policy effects by firm location. Because firms located in TCZs
face more stringent emission control, we expect β 0 to be negative for firm SO2 emissions
and SO2 emission intensity. For firm output, the expected sign of β 0 is ambiguous. On the
one hand, there is a cost effect. Firms located in a TCZ face an emission cap that imposes
10 The firm SO emission cap may decline for two reasons.
2First, overall jurisdictional emissions are
required to decline, especially for the TCZs. Second, if we allow firms to enter and exit, reduction in policy
uncertainty increases firms’ profits and induces more firm entry, which entails a larger number of firms. As
a result, to keep overall emissions fixed, per-firm emission allowance should decline.

14

extra costs on firm production, and therefore, when trade policy uncertainty decreases,
output increases should be smaller than those of firms located in non-TCZs. On the other
hand, there is an efficiency effect. The emission cap may impel firms to adjust production
inputs and improve efficiency (Porter hypothesis), which increases firm output. The signof β 0 is determined by the relative importance of the two effects.
In addition, to study the mechanisms of these effects, we test whether firms use lessfossil fuel but more alternative inputs to reduce emissions. We also examine the effects
of reduced uncertainty on pollution abatement equipment. Finally, a reduction in uncertainty may also impel firms to improve production efficiency. Thus, we study the effects
on firm productivity. All these exercises follow the specifications in equations (33) and
(34).

4.2

Data source

To investigate the impact of trade uncertainty, we use the following three highly disaggregated firm-level datasets: (1) an annual firm-level manufacturing survey data from the
National Bureau of Statistics of China (NBSC); (2) firm-level pollution emission data fromthe Annual Environmental Survey of Polluting Firms (AESPF); and (3) firm-product-level
trade data from China’s General Administration of Customs (CGAC).
First, we use the firm-level data from the Annual Survey of Industrial Firms (ASIF),
which has been widely used in the literature on the Chinese economy (Brandt et al.,2009;Fan et al., 2015; Fan et al., 2018; Gan et al., 2016). This dataset contains rich firmlevel information from 1998 to 2007, including basic firm information (e.g., firm name,address, age, ownership structure, employment, capital stock, gross output, and value
added) and information on three major accounting statements (balance sheet, profit andloss account, and cash flow statement). We follow Fan et al. (2015) to clean the dataset.11
The second dataset on firms’ pollution comes from the Annual Environmental Surveyof Polluting Firms (AESPF) of China, which contains information on firms’ environmental
performance, including emissions of main pollutants (industrial effluent, waste air, COD,
NH3 , NOx , SO2 , smoke and dust, solid waste, noise, etc. ), pollution abatement equipment, and energy consumption (usage of freshwater, recycled water, coal, fuel, clean gas,etc. ), among others.12 Due to a lack of consistent firm identifiers across different data
11 We use the following standards to clean our dataset: (i) the total assets must be higher than the liquid

assets; (ii) the total assets must be greater than the total fixed assets; (iii) the total assets must be greaterthan the net value of the fixed assets; (iv) a firm must have a unique identification number; and (v) the firm
establishment time must be valid.
12 Similar to the Annual Survey of Industrial Firms (ASIF), which provides the basis for the macroeconomic indicators, the AESPF contains the source data for calculating macro-level environmental indicators,

15

sets, we match the aforementioned data by firm name.
The third dataset is transaction-level import and export data from the China General
Administration of Customs (CGAC). This dataset includes information on each importand export transaction of Chinese firms from 2000, including basic information about thefirms (e.g., company name, telephone, zip code, contact person), firm type (e.g., state
owned, domestic private, foreign invested, and joint ventures), and trade regime (e.g.,“Ordinary”, “Processing and Assembling” and “Processing with Imported Materials”).
The first 4 digits of firm identification in customs data represent the prefecture identifiers,
and thus we can use the customs data to calculate the import value by Chinese prefecturefrom the rest of world and the export value from each Chinese prefecture to the rest ofworld (in particular the U.S.) at the HS 6-digit level.

4.3

Measuresp Trade policy uncertainty
We compute the trade policy uncertainty faced by Chinese exporters to the U.S. using
the normal-trade-relations (NTR) gap measure developed by Feenstra et al. (2002). Thismeasure is built to calculate the NTR tariffs applied by the U.S. on WTO members andthe threatened tariffs (non-NTR tariffs) that would have been implemented had the mostfavored nation (MFN) status not been renewed to China by the U.S. Congress (Facchini
et al., 2019). Following the literature, we use the NTR gap in 1999 at the HS6 level prior
to the WTO accession that is widely used in recent studies (e.g., Handley and Limao,
2017, Erten and Leight, 2019, and Pierce and Schott, 2016). We then construct an exportweighted average of the NTR gap for each Chinese prefecture:
ExportUS HS6,p
NTRgap HS6 ,ExportUS HS6,p
HS6 ∑ HS6

Uncertainty p = ∑(35)

where NTRgap HS6 is the spread between the non-NTR tariff and NTR tariff at the Harmonized System (HS) 6-digit level in 1999. ExportUS HS6,p is the export value from Chinese
prefecture p to the U.S. at the HS 6-digit level in 2000, which can be computed using datafrom the customs data. In the robustness check section, we use labor employment and exports to all countries as alternative weights to demonstrate the consistency of our results
regardless of the weight choice. We also use the simple average NTR gap of all exports
from prefecture p as an additional robustness check.
Firm productivitypsuch as the statistics reported in the China Statistical Yearbook on Environment.

16

To estimate the impact of trade uncertainty on firm productivity, we compute various
measures of firm total factor productivity (TFP). Our primary TFP measure is revenue
TFP using the method of Olley-Pakes (hereafter O-P) (Olley and Pakes, 1996). We run
regressions of firm value-added against firm capital, which is computed using the perpetual inventory method, and firm employment.13 Following Brandt et al. (2009), we
deflate firms’ capital and value added, using the input price deflators and output pricedeflators, respectively. As in Amiti and Konings (2007), we control for a firm’s tradestatus in the TFP estimation by including two trade-status dummy variables—an export
dummy (equal to 1 for exports and 0 otherwise) and an import dummy (equal to 1 for imports and 0 otherwise). We also control for a post-WTO dummy, which equals 1 if a year
is after 2001 and 0 otherwise) in the O-P estimation because WTO accession represents a
positive demand shock for China’s exports. In addition to the O-P method, we also use
the Ackerberg-Caves-Frazer augmented O-P methods (Ackerberg et al., 2015) to estimate
TFP.
Other covariates
To isolate the impact of trade policy uncertainty, we also control for other concurrent
policy changes as robustness checks. According to Erten and Leight (2019) and Facchini
et al. (2019), major domestic reforms during China’s accession to the WTO included reductions in import tariffs, the elimination of import licensing requirements, the elimination of textile and clothing import quotas and reduced restrictions on FDI. We use dataon China’s import tariffs from the WITS database, data on export licensing requirementsfrom Bai et al. (2017), data export quota from Brambilla et al. (2010) and Handley and
Limao (2017), and data on the nature of contracting from Nunn (2007) to control for thesepolicy changes. We then aggregate the industry-level export licenses at the prefecturelevel. Prefecture average import tariff rates are weighted by import value, whereas allother variables are weighted by export value for each prefecture.
Specifically, for export licenses, we match the customs data with the ASIF dataset, inwhich firms reporting positive export values in both data sets in a specific year are classified as having an export license. Firms reporting positive export values only in the ASIF
data set are defined as indirect exporters.14 We calculate the ratio of direct exporters in agiven industry to measure the extent to which an industry was affected by export licenses.
We then aggregate the industry-level export licenses at the prefecture level, weighted byp

13 For the depreciation rate, we use each firm’s real depreciation rate provided by the NBSC firm-

production database.
14 Prior to China’s joining of the WTO, firms exported either by obtaining export licenses or trade intermediaries. Export licenses were gradually phased out, and eventually, all firms became eligible to export
directly by 2004 (Bai et al., 2017).

17

industry-level export value in each prefecture. We use data from Brambilla et al. (2010)
and Handley and Limao (2017) to construct an HS6 digit-level indicator for Multi-Fiber
Agreement on Textiles and Clothing (MFA) quotas. In particular, products are classified
into 2 groups, one with a binding export quota and one without. We use their data andcalculate the ratio of the products that were bound by quotas in each industry. We aggregate the industry-level index at the prefecture level. For the import tariffs imposed by
China, we first collect the HS 6-digit-level MFN tariff from the WITS data set and calculate the import tariff rates at the prefecture level. Foreign direct investment (FDI) plays
an important role in promoting Chinese local development, and it is necessary to accountfor this policy change. Following Facchini et al. (2019), we use the contract intensity measure proposed by Nunn (2007). Since the contract intensity measure is at the 3 digit ISIC
(revision 2) industry level, we first convert these classifications to the HS 6-digit productlevel using the concordance provided by WITS and then calculate the average contract
intensity at the prefecture level.
Table 1 reports the summary statistics for a set of key variables used in our paper.

5

Empirical Results

5.1

Average effects

5.1.1

Main results

Table 2 shows the main effects of the reduction in uncertainty on firm production andSO2 emissions. Columns (1) to (2) present the effects on firm output; columns (3) to (4)show the effects on SO2 emissions; columns (5) to (6) focus on SO2 emission intensity. All
regressions control for firm and year fixed effects. Odd columns add no further controls,
whereas even columns additionally control for firm initial output and SO2 emissions interacted with year dummies, which addresses the concern that our estimated effects areconfounded by different time paths in output and emissions by firms with different initial
sizes or emission levels. Consistent with our model predictions, we find that the reduction
in export policy uncertainty leads to higher levels of firm output. The difference impact
between the regions at 90th percentile and the regions at 10th percentile of export policy
uncertainty reduction is about 5.6 percent.15 Moreover, a one standard deviation increasein pre-WTO export policy uncertainty leads to a 4.2 percent increase in output after WTO
15 The pre-WTO uncertainty difference between the 90th and 10th percentiles are 0.087 (as reported inTable 1), and the marginal effect of uncertainty on output is 0.639 (as reported in Table 2).

18

entry.16 This is comparable with our simulated results in Section 3.5. The effect on SO2emissions is negative but statistically insignificant. As a result of the positive effects onfirm output and the insignificant effects on SO2 emissions, we find that the reduction inexport policy uncertainty causes lower levels of SO2 emission intensity for firms. A onestandard-deviation increase in pre-WTO export policy uncertainty leads to an 8.6 percent
decrease in emission intensity in the post-WTO period. These results are consistent withthe model when firms face an overall emission cap.
The identification strategy for equation (33) relies on the parallel trends assumptionthat the outcomes in different comparison groups would follow the same time trend in
the absence of treatment. Our estimate of β would be biased if firms located in prefectures
facing different extents of trade policy uncertainty prior to WTO accession follow different time paths. To support the parallel trends assumption, we conduct an event study
that takes the following form:
2007

yit =

∑ βt Uncertainty p × δt + γXct + ∑ θt Xi × δt + δt + γi + eit ,

(36)

t

t=1998All variables in equation (36) follow the same definition as those in equation (33). Rather
than interacting Uncertainty p with a Postit dummy, we interact Uncertainty p with each of
the year dummy variables, with 2001 being the benchmark year. The event study specification allows us to examine changes in the correlation between a prefecture’s pre-WTO
uncertainty and the outcome of interest over time, with each β t measuring the differencein this correlation in year t relative to the year 2001. If the parallel trends assumption
holds, then we expect the estimated β t s to be approximately zero throughout the WTO
period but experience a sharp change after WTO accession.
We test this assumption separately for firm output and emission intensity following
equation (36). We present the estimated coefficients and the 95 percent confidence intervals for firm output and emission intensity in Figures (5a) and (5b), respectively. In all
figures, the benchmark year is 2001. We find that before 2001, the estimated coefficientsare indistinguishable from zero. Moreover, the effect on output and SO2 intensity starts
in 2001.
5.1.2

Robustness

In this subsection, we employ several empirical exercises to examine the sensitivity of thebaseline estimates. Because our main variation is at the prefecture level, one concern is
16 The standard deviation of uncertainty is 0.06545. We multiply the estimated coefficients by this num-

ber.

19

that our results are confounded by other concurrent events that occurred at the prefecturelevel. To address this concern, we further control for a set of prefecture variables. First,
at the time of the WTO entry, China also changed its import tariffs, eliminated export licensing requirements, and faced different export quotas, and firms with different contract
intensities also responded differently to WTO entry. We control for the value of these variables in 2000 at the prefecture level and interact them with the post dummy to address
the concern that prefectures were affected differently by WTO accession along these dimensions. Second, we also control for contemporaneous prefecture GDP and population
density. We present the results in Table 3 for firm output, SO2 emissions, and emission
intensity. The results are highly consistent. In columns (1), (3), and (5), we control forthe initial prefecture value of other policy changes interacted with the post dummy; incolumns (2), (4), and (6), we further control for contemporaneous prefecture GDP per
capita and population density. The effects on firm output, SO2 emissions and emission
intensity remain largely consistent with those in Table 2, and the estimated coefficientsare not influenced by additional contemporaneous prefecture controls, which suggeststhat our results are highly unlikely to be driven by other concurrent prefecture events.
Relatedly, firms with different ownership status or located in different provinces may
experience different changes in production or SO2 emissions. One specific concern is thatthese differences are confounded by our uncertainty measure. We therefore address thisconcern by controlling for ownership-year fixed effects and province-year fixed effects
and present the results in Table 4. Our results remain robust.
Another concern is that our main results are driven by the specific construct of the prefecture uncertainty measure. To show that our results are robust to the construction of theuncertainty measure, we use four alternative methods to construct the uncertainty measure. First, we use labor instead of exports as our weights and then compute the laborweighted average value of the NTR gap at the prefecture level. To reflect the fact that Chinese exports to the U.S. experience a reduction in tariff uncertainty, we use product-level
Chinese exports to the U.S. as our weights in the main exercise. This exercise, however,has two concerns. First, the Chinese exports to the U.S. are endogenous to U.S. policy,
and our constructs using endogenous weights may cause biases. Second, this measureomits those exports from China to the U.S. via a third country or region such as HongKong. To demonstrate the robustness of our results, we use all product-level Chinese exports in our second and third measures. Our second measure uses the simple average
NTR gap of all exports from a prefecture; the third measure uses the export-weighted
average value of the NTR gap of a prefecture. We also calculate the firm-level trade policy uncertainty shock as fourth measures. As in the main exercise, we use the Chinese
20

exports to the U.S. as our weights when we measure uncertainty at the firm level. Table
5 presents the results using different proxies for uncertainty. The first three columns ofpanel A use the first alternative uncertainty measure; the last three columns of panel A
use the second alternative uncertainty measure; the first three columns of panel B use
the third alternative uncertainty measure. In the last three columns of panel B, we define uncertainty at firm levels, which measures the uncertainty shock exerted on firms in
more precise ways.17 Notice that we have lost a significant number of observations using
firm-level uncertainty measures. The results are highly consistent. The reduction in trade
policy uncertainty significantly increased firm production, reduced firm overall emissionof SO2, and reduced firm SO2 emission per unit of output.
5.1.3

Falsification Test

To rule out the possibility that our main results are spurious and driven by chance, we
perform a falsification test. We follow equation (33) to conduct regression analyses basedon randomly assigned false prefecture-level uncertainty to firms, in proportion to theactual uncertainty distribution. We then conduct 500 rounds of randomization and plot
the distribution of estimated coefficients in Figure 6, separately for firm output in subfigure (a) and firm SO2 emission intensity in sub-figure (b). For firm output, the meanvalue of the estimated coefficient is 0.0069, the standard deviation is 0.2355, and the actual
estimate for firm output is depicted by the red vertical line valued at 0.639. For firm SO2
emission intensity, the mean value of the estimated coefficient is 0.0626, the standard
deviation is 0.5817, and the actual estimate for firm output is depicted by the red verticalline valued at -1.313. Therefore, it is unlikely that the increases in firm output and declines
in firm SO2 emission intensity reported in Table 2 are driven by chance.

5.2

Heterogeneous effects

To test Predictions 2 and 3, we examine the heterogeneous effects of reduced uncertainty
on firm output and SO2 emissions varied by the strength of SO2 emission control. As we
mention in Section 2, firms that are located in TCZs face more stringent SO2 emissions
control than firms in non-TCZs. Thus, we expect that the reduction in export policy uncertainty should increase firm output in both TCZs and non-TCZs, but it would reduce
firm emission intensity only for firms in TCZs.
17 A prefecture may have firms that do not export to the U.S. Thus, defining uncertainty at prefecturelevels is a less precise way to measure the policy uncertainty shock exerted on each firm.

21

We first separately study the average effects for firms in TCZs and non-TCZs following
the main specification in equation (33). To further test the statistical significance of theeffect difference, we conduct a triple-difference specification following equation (34).
Table 6 presents the results. Columns (1) to (3) study the effects on firm output. Column (1) focuses on firms in TCZs, and column (2) focuses on firms in non-TCZs. We
find that the reduction in export policy uncertainty leads to increases of firm output inboth TCZs (as shown in column (1)) and non-TCZs (as shown in column (2)). The tripledifference results presented in column (3) suggest insignificant differences in firm output
increases in TCZs and non-TCZs.
Columns (4) to (6) rerun the exercises in columns (1) to (3) but replace the dependent
variable with log value of firm SO2 emissions. We find that firm-level SO2 emissions in
TCZs are significantly reduced when export policy uncertainty decreases (as shown incolumn (4)), whereas the firm-level SO2 emissions are unaffected in non-TCZs (as shownin column (5)). The triple-difference results presented in column (6) suggest that the effect difference in SO2 emissions between firms in TCZs and non-TCZs is significant at 5
percent.
Columns (7) to (9) further repeat the exercises with log values of firm SO2 emission
intensity as the dependent variable. Consistent with the effects on firm SO2 emissions,
we find that the reduction in export policy uncertainty significantly lowers SO2 emission
intensity in TCZs but does not affect firms in non-TCZs. The difference of the effects on
SO2 emission intensity is statistically significant at the 1 percent level.
We have also conducted the effects of reduced uncertainty on other pollution outcomes as our placebo check. Since the emission gap specifically targets SO2, the effects
should not exist for other types of firm pollution. We use wastewater, fumes, and nitrogen oxides (NOx ) for our placebo check, as our dataset also covers these variables. Table
7 presents the results. All regressions control for firm and year fixed effects. In contrast
to the results on SO2 in Tables 2, we do not find significant effects on the emission intensity of these types of pollution. Moreover, the trade policy uncertainty reduction exerts
no heterogeneous effects on these alternative pollutants across regions with different SO2
emission control.
These results support our model predictions. When export policy uncertainty decreases, the overall emission control imposed on firms reduced per firm SO2 emissions
and per firm SO2 emission intensity, but it does not affect firm output. One plausible
explanation for why firm production (measured by firm output) is not influenced by theemission cap is that firms could improve production efficiency more in the regions withemission caps. We test the mechanisms in greater detail in the sections below.
22

5.3

Mechanism

Emission intensity declined following the reduction in export policy uncertainty, as shownin our previous analyses. It thus becomes natural to ask, what inherent mechanisms areaccounting for this change? In particular, what accounts for the greater emission reduction for firms in TCZs? According to Liu et al. (2018), SO2 generated per unit of fossil
fuel during the production process depends on conversion efficiency, desulfurization efficiency, and average sulfur content.18 Therefore, we will analyze the underlying mechanisms from the perspective of the relative usage of fossil fuel (coal and fuel) and its sulfur
content, pollution-control facilities and firm productivity in this section.
5.3.1

Effects on inputs

First, we study firms’ use of energy in Table 8, separately for firms in TCZs and nonTCZs. Column (1) studies the average effect on coal use for all firms, whereas columns(2) and (3) split the sample and study the effect for firms in TCZs and non-TCZs. We findthat, although the reduction in export policy uncertainty on average has a negative but
statistically insignificant effect on coal use, the effects are drastically different for firms in
TCZs and firms in non-TCZs. The reduction in uncertainty leads to a decline in coal use
for firms in TCZs but an increase in coal use for firms in non-TCZs. The differences inthe effects are statistically significant, as shown by the coefficient of the triple-interaction
term in column (4).
We then repeat the exercises for fuel use. Column (5) studies the average effect onfuel use for all firms, whereas columns (6) and (7) split the sample and study the effectfor firms in TCZs and non-TCZs. Consistent with the exercise on coal, we find that the
reduction in uncertainty has an insignificant effect on fuel use on average, and the effects
are different for firms in TCZs and firms in non-TCZs. The reduction in uncertainty leads
to an insignificant decline in fuel use for firms in TCZs but a significant increase for firms
in non-TCZs. The effect difference is statistically significant (column (8)).
Firms may also substitute away from fossil fuel to other inputs, such as non-polluting
materials and labor. We then study the effect on other firm inputs in Table 9. Our dataset
reports the total amount of intermediate inputs, which include energy input and material.
For firms in TCZs, the reduced uncertainty causes a lower usage of energy but a possibly
18 The emissions of SO2 can be expressed as E
SO2 = ∑i,j 2 × C j × Ai,j × Si,j × (1 − ηi ) based on the massbalance method in Liu et al. (2018)), where ESO2 represents the total emissions of SO2 ; i and j represent
power plant i and fuel type j, respectively; Cj is the conversion efficiency to sulfur dioxide from fuel typej; and Ai,j is the annual consumption of fuel type j by power plant i. ηi is the desulfurization efficiency
that varies across different de-SO2 processes; Si.j represents the sulfur content in fuel j of plant i; and 2
represents the molecular weight of SO2 that is twice the atomic weight of Si,j .

23higher use of material, while the effect on total intermediate inputs is ambiguous; firms
in non-TCZs, the reduction in uncertainty promotes firm production and does not reduceenergy use, and therefore, we expect the use of total intermediate inputs to increase. We
present the results in columns (1) to (4). Columns (1) to (3) focus on all firms, firms locatedin TCZs and firms located in non-TCZs, respectively. The average effect on intermediate
input use is statistically insignificant, as reported in column (1). Consistent with our expectation, we find that firms in non-TCZs increased their intermediate input use, whereas
firms in TCZs did not significantly change intermediate input use.
The dataset also reports firm total labor employment. If labor is a substitute for fossil
fuel, then reduced export policy uncertainty should cause firms in TCZs to increase theirlabor employment. For firms in non-TCZs, however, the expected effect is ambiguous.
On the one hand, increases in production scale lead to a higher demand for labor; on theother hand, the higher labor demand in TCZs may drive up labor wages, which reduces
demand for labor.19 We report the results on labor in columns (5) to (8). We find thatthe reduction in uncertainty leads to higher overall labor employment for firms in our
sample. The increases in labor are driven solely by firms in TCZs. There is no significant
effect on firms in non-TCZs.
5.3.2

Effects on sulfur content and pollution abatement equipment

Firms may also change the types of coal and fuel to reduce emissions intensity. A more
direct test would be to test the sulfur content used by firms. Moreover, the reduction inemission intensity can be also caused by more abatement equipment. We then examine
the effects of reduced uncertainty on energy sulfur content and abatement equipment,
which are two variables available in our dataset.
Table 10 presents the results. Columns (1) to (4) focus on the sulfur content of energyuse, and columns (5) to (8) focus on pollution abatement equipment. We find that thefirms in TCZs significantly reduced the sulfur content when they increased firm output
as a response to the reduction in export policy uncertainty, and they also adopted morepollution-control facilities to remove sulfur emissions. These effects only exist for firms in
TCZs and not for firms in non-TCZs. The significant heterogeneous effects are highlighted
in the estimated coefficients of the triple-difference exercise in columns (4) and (8).
19 Higher demand for labor in TCZs induces labor to be reallocated from non-TCZs to TCZs.

24

5.3.3

Effects on firm productivity

According to the Porter hypothesis, strict environmental regulations can induce firms to
upgrade their productivity. We examine this by testing whether firms in TCZs improve
productivity more when export policy uncertainty decreases. In Table 11, we use two different measures of TFP. Columns (1) to (4) use the Olley-Pakes method to compute firm
TFP, while columns (4) to (8) use the ACF method to compute firm TFP. We follow theprevious exercises and separately examine the average policy effects on firm TFP, the effects on firm TFP in TCZs and effects on firm TFP in non-TCZs. In columns (1) and (5), weshow the average effects. We find that the reduction in export policy uncertainty leads toa higher level of firm TFP.20 We also find that the productivity-improving effects of lower
uncertainty are greater for firms in TCZs, which is consistent with our results in Table
6 that after export policy uncertainty decreases, firms in TCZs and non-TCZs increase
firm output by comparable magnitudes, but only firms in TCZs reduce SO2 emissions
intensity.

6

Conclusion

This study examines the effects of reducing the uncertainty of trade policy on firms’ pollution behavior. We first develop a model to show that the impacts of reducing trade policy uncertainty on firms’ pollution behavior depend on whether an emission cap exists.
When a cap exists, reduced uncertainty leads to higher output but lower emission intensity. When no cap exists, however, reduced uncertainty increases firm output but has noeffect on emission intensity. We exploit spatial variations in the reductions in trade policy
uncertainty caused by U.S. conferral of PNTR status to Chinese exporters and variations
in emission control caused by the TCZs to test the hypotheses. Our empirical evidence isconsistent with the model predictions. We find that the reduction in uncertainty increases
firm output by comparable magnitudes across regions with different extents of emission
control, but it reduces firm SO2 emission intensity and total firm SO2 emissions only in regions with stringent emission control. The decline in SO2 emissions is caused by reduced
use of fossil fuel, less sulfur content in energy use, and more abatement equipment, andfirms substitute away from energy use with more labor. We also find that the reductionin uncertainty and emission control jointly improve firms’ productivity, consistent with
the Porter hypothesis. One implication is that it is not imperative for developing countries to adopt lax environmental regulations to capture gains from globalization. Instead,
20 This is consistent with the literature on trade-induced technological upgrading; see, e.g., Bustos (2011).

25

strengthening environmental regulations may promote production efficiency for firms indeveloping countries in times of globalization.

26

References
Acemoglu, D., U. Akcigit, D. Hanley, and W. Kerr (2016). Transition to clean technology.
Journal of Political Economy 124, 52–104.
Ackerberg, D., K. Caves, and G. Frazer (2015). Identification properties of recent production function estimators. Econometrica 83, 2411–2451.
Aghion, P., R. Benabou, R. Martin, and A. Roulet (2020). Environmental preferences andtechnological choices: Is market competition clean or dirty? CEPR Discussion Paper No.
DP14581.
Aghion, P., A. Dechezlepretre, D. Hemous, R. Martin, and J. Van Reenen (2016). Carbon
taxes, path dependency, and directed technical change: Evidence from the auto industry. Journal of Political Economy 124(1), 1–51.
Aichele, R. and G. Felbermayr (2015). Kyoto and carbon leakage: An empirical analysisof the carbon content of bilateral trade. Review of Economics and Statistics 97, 104–115.
Alpert, P., O. Shvainshtein, and P. Kishcha (2012). Aod trends over megacities based on
space monitoring using modis and misr. American Journal of Climate Change 1(3), 117–31.
Amiti, M. and J. Konings (2007). Trade liberalization, intermediate inputs, and productivity: Evidence from indonesia. American Economic Review 97, 1611–1638.
Bai, X., K. Krishna, and H. Ma (2017). How you export matters: Export mode, learningand productivity in china. Journal of International Economics 104, 122–137.
Banerjee, A., E. Duflo, and R. Glennerster (2008). Putting a band-aid on a corpse: Incentives for nurses in the indian public health care system. Journal of the European Economics
Association 6(2-3), 487–500.
Barrows, G. and H. Ollivier (2016). Emission intensity and firm dynamics: reallocation,
product mix, and technology in india. GRI Working Papers 245.
Brambilla, I., A. Khandelwal, and P. Schott (2010). China’s experience under the multifiber
arrangement (mfa) and the agreement on textiles and clothing (atc). NBER Working
Papers 13346.
Brandt, L., J. Biesebroeck, and Y. Zhang (2009). Creative accounting or creative destruction? firm-level productivity growth in chinese manufacturing. Journal of Development
Economics 97(2), 339–351.
27

Broda, C. and D. E. Weinstein (2006). Globalization and the Gains From Variety. The
Quarterly Journal of Economics 121(2), 541–585.
Broner, F., P. Bustos, and V. M. Carvalho (2012). Sources of comparative advantage inpolluting industries. NBER Working Papers 18337.
Bustos, P. (2011). Trade liberalization, exports, and technology upgrading: Evidence on
the impact of mercosur on argentinian firms. American Economic Review 101(1), 304–340.
Cherniwchan, J. (2017). Trade liberalization and the environment: Evidence from nafta
and u.s. manufacturing. Journal of International Economics 105, 130–149.
Crowley, M., N. Meng, and H. Song (2018). Tariff scares: Trade policy uncertainty andforeign market entry by chinese firms. Journal of International Economics 114, 96–115.
Cui, J., H. Lapan, and G. Moschini (2016). Productivity, export, and environmental performance: Air pollutants in the united states. American Journal of Agricultural Economics 98(2), 447–467.
Dekle, R., J. Eaton, and S. Kortum (2007). Unbalanced Trade. The American Economic
Review 97(2), 351–355.
Dekle, R., J. Eaton, and S. Kortum (2008). Global Rebalancing with Gravity: Measuring
the Burden of Adjustment. IMF Staff Papers 55(3), 511–540.
Erten, B. and J. Leight (2019). Exporting out of agriculture: The impact of wto accession
on structural transformation in china. Review of Economics and Statistics.
Facchini, G., M. Y. Liu, A. M. Mayda, and M. Zhou (2019). China’s ”great migration”: The
impact of the reduction in trade policy uncertainty. Journal of International Economics 120,
126–144.
Fan, H., Y. A. Li, and S. R. Yeaple (2015). Trade liberalization, quality, and export prices.
Review of Economics and Statistics 97, 1033–1051.
Fan, H., Y. A. Li, and S. R. Yeaple (2018). On the relationship between quality and productivity: Evidence from china’s accession to the wto. Journal of International Economics 110,
28–49.
Feenstra, R. C., J. Romalis, and P. K. Schott (2002). U.s. imports, exports, and tariff data,
1989-2001. NBER Working Papers 9387.

28

Forslid, R., T. Okubo, and K. H. Ulltveit-Moe (2018). Why are firms that export cleaner?
international trade, abatement and environmental emissions. Journal of Environmental
Economics and Management 91, 166–183.
Gan, L., M. A. Hernandez, and M. Shuang (2016). The higher costs of doing businessin china: Minimum wages and firms’ export behavior. Journal of International Economics 100, 81–94.
Garred, J. (2018). The persistence of trade policy in china after wto accession. Journal ofInternational Economics 114, 130–142.
Greenstone, M. and R. Hanna (2014). Environmental regulations, air and water pollution,and infant mortality in india. American Economic Review 104(10), 3038–3072.
Gutierrez, E. and K. Teshima (2018). Abatement expenditures, technology choice, andenvironmental performance: Evidence from firm responses to import competition inmexico. Journal of Development Economics 133, 264–274.
Handley, K. and N. Limao (2017). Policy uncertainty, trade and welfare: Theory andevidence for china and the u.s. American Economic Review 107(9), 2731–83.
Hanna, R. (2010). Us environmental regulation and fdi: Evidence from a panel of us-based
multinational firms. American Economic Journal: Economic Policy 2, 158–189.
Holladay, J. S. (2016). Exporters and the environment. Canadian Journal of Economics 49,
147–172.
Hsieh, C.-T. and R. Ossa (2016). A global view of productivity growth in China. Journal ofInternational Economics 102, 209–224.
Imbruno, M. (2019). Importing under trade policy uncertainty: Evidence from china.
Journal of Comparative Economics 47(4), 806–826.
Levinson, J. and S. Taylor (2008). Unmasking the pollution haven effect. International
Economic Review 49, 223–254.
Liu, H., B. Wu, P. Shao, X. Liu, C. Zhu, Y. Wang, Y. Wu, Y. Xue, J. Gao, Y. Hao, and H. Tian(2018). A regional high-resolution emission inventory of primary air pollutants in 2012
for beijing and the surrounding five provinces of north china. Atmospheric Environment 181, 20–33.

29Melitz, M. J. (2003). The Impact of Trade on Intra-Industry Reallocations and Aggregate
Industry Productivity. Econometrica 71(6), 1695–1725.
Nunn, N. (2007). Relationship-specificity, incomplete contracts, and the pattern of trade.
The Quarterly Journal of Economics 122(2), 569–600.
Olley, G. S. and A. Pakes (1996). The dynamics of productivity in the telecommunications
equipment industry. Econometrica 64, 1263–1297.
Pierce, J. R. and P. K. Schott (2016). The surprisingly swift decline of u.s. manufacturing
employment. American Economic Review 106(7), 1632–62.
Porter, M. E. and C. Van der Linde (1995). Toward a new conception of the environmentcompetitiveness relationship. Journal of Economic Perspectives 9(4), 97–118.
Shapiro, J. S. and R. Walker (2018). Why Is Pollution from US Manufacturing Declining?
The Roles of Environmental Regulation, Productivity, and Trade. 108(12), 3814–3854.

30

50°N

40°N

16°N
30°N
10°N

4°N

20°N

108°E

70°E

80°E

90°E

100°E

110°E

Non−Two−Control Zones

120°E

130°E

113°E

118°E

140°E

Two−Control Zones

Figure 1: Geographic Locations of Two-Control Zones and Non-Two-Control Zones
Notes: This figure presents the geographic locations of prefectures in the two-control zones (colored in red)and prefectures in the non-two-control zones (colored in blue).

31

Non−Two−Control Zones

Two−Control Zones●●

Scaled value (year 1999 =100)

250
●

●

200
●

●

●

●

150
●

●

●
●
●
●

100

●

1999

●

2000

2001

2002

2003

2004

2005

2006

1999

2000

2001

2002

2003

2004

2005

2006

Year
●

GDP

SO2 emission

Figure 2: Total Output and SO2 Emission in Two-Control Zones and Non-Two-Control
Zones
Notes: This figure plots the total GDP and total SO2 emission in two-control-zones (TCZs) and non-twocontrol-zones (non-TCZs). The data for GDP are drawn from CEIC database, and the SO2 emission data arefrom China Statistical Yearbook on Environment, published by the Ministry of Ecology and Environmentof China. We treat 1999 as the benchmark year (value=100). Although GDP has been rising rapidly in bothregions, the SO2 emission in TCZs remains relatively stagnant between 1999 and 2006, which reveals a
relatively more stringent SO2 emission control.

32

Output

0.07

Emission

0.07
0.06

0.06

0.05
0.05
0.04

Emission

Output

0.04
0.03
0.02

0.03
0.02
0.01
0

0.01
-0.01
0

-0.02

-0.01

-0.03
0

0.2

0.4

0.6

0.8

1

0

Uncertainty Reduction ( 2 )

0.4

0.6

0.8

1

Uncertainty Reduction ( 2 )Emission Intensity

00.2(1- 3 )^(1/ , )

0.06
;=0
;=0.25;=0.5;=0.75
;=1

0.05

-0.01

0.04
-0.02

(1- 3 )^(1/ ,)Emission intensity

0.03
-0.03
-0.04
-0.05

0.02
0.01
0
-0.01

-0.06
-0.02
-0.07

-0.03

-0.08

-0.04
0

0.2

0.4

0.6

0.8

1

0

Uncertainty Reduction ( 2 )

0.2

0.4

0.6

0.8

1

Uncertainty Reduction ( 2 )Figure 3: The Simulated Effects of Uncertainty Reduction on Production and Emission(Emission Cap κ = 0.975)
Notes: This figure presents the simulated effects of a reduction in export policy uncertainty on firm total
output, total emission, emission intensity, and inputs used for production, respectively. We set the emission
cap κ to be 0.975. We plots the effects for different levels of environment regulation stringency, characterizedby parameter ρ. A higher value of ρ corresponds to more stringent emission control.

33

Emission Costs

0.09
;=0
;=0.25;=0.5;=0.75
;=1

0.08

0.07

Emission costs

0.06

0.05

0.04

0.03

0.02

0.01

0
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Uncertainty Reduction ( 2 )Figure 4: The Simulated Effects of Uncertainty Reduction on Emission Costs (Emission
Cap κ = 0.975)
Notes: This figure presents the simulated effects of a reduction in export policy uncertainty on firm emission
costs. We set the emission cap κ to be 0.975. We plots the relations between uncertainty reduction andemission cost for different levels of environment regulation stringency, which is characterized by ρ. A
higher value of ρ corresponds to more stringent emission control.

34

1.8
1.2
.6

Coefficients

0
−.6
1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2004

2005

2006

2007

Year

−1.2
−2.4
−4.8

−3.6

Coefficients01.2(a) Firm Output

1998

1999

2000

2001

2002

2003

Year

(b) Firm SO2 Intensity

Figure 5: Dynamic Effects of Uncertainty Reduction
Notes: This figure show the dynamic effects of export policy uncertainty reduction on firm output,
and SO2 intensity, respectively in subfigure (a), and (b). We run the following regression, yit =∑2007
t=1998 β t Uncertainty p × δt + γX pt + ∑t θt Xi × δt + δt + γi + eit , where yit respectively refers to log sales
and log SO2 intensity of firm i in year t. Uncertainty p is the pre-WTO export policy uncertainty of prefecture. δt and δi are year and firm fixed effects, respectively. X pt include prefecture contemporaneous controls
and Xi denotes firm pre-WTO controls. We cluster standard error at the prefecture-year level. The solid line
connects all estimates of β t where 2001 is the benchmark year; the dashed lines represent the 95 percent
confidence intervals.

35

2
1.5
1
.5
0

−.5

0
Estimated Coefficient
Density.5

1

Actual estimate

0

.2

.4

.6

.8

(a) Firm Output

−2

−1

0
Estimated Coefficient
Density

1

2

Actual estimate(b) Firm SO2 Intensity

Figure 6: Distribution of estimates in 500 randomization tests
Notes: In this exercise, we follow equation (33) to conduct regression analyses based on randomly assigned
uncertainty. We assign false prefecture-level uncertainty to firms randomly in proportion to the actual uncertainty distribution. We then conduct 500 rounds of randomization and plot the distribution of estimatedcoefficients in the figures above, separately for firm output and firm SO2 emission intensity. For firm output, the mean value of estimated coefficient is 0.0069 and the standard deviation is 0.2355, and the actual
estimate for firm output is depicted by the red vertical line valued at 0.639. For firm SO2 emission intensity, the mean value of estimated coefficient is 0.0626 and the standard deviation is 0.5817, and the actual
estimate for firm output is depicted by the red vertical line valued at -1.313.

36

Table 1: Summary Statistics
Variable
Dependent Variables
Log Output
Log SO2 Emission
Log SO2 Emission Intensity
Log Wastewater Intensity
Log Fumes Intentsity
Log NOx
Log Coal
Log Fuel
Log Facilities
Sulfur Content
Log Employment
Log Intermediate Inputs
TFP(OP)
TFP(ACF)

Obs

Mean

Std.Dev.

58979 8.007
58979 10.050
58979 2.038
51623 2.909
54971 1.697
17957 -6.561
53607 6.715
13275 3.800
52398 1.143
13154 0.132
58836 5.951
58698 10.220
58495 0.781
58495 3.892

1.607
2.065
2.129
1.962
2.702
4.235
2.571
3.237
0.803
0.642
1.111
1.542
1.458
1.812

6.205 7.895 10.050
7.455 10.150 12.500-0.676 2.239 4.504
0.425 3.010 5.317-1.609 1.594 5.409-10.370 -7.871
1.216
3.714 7.080
9.398
0.000 4.078
8.075
0.000 1.099 2.197
0.000 0.000
0.580
4.605 5.886 7.378
8.465 10.100 12.200-1.373 1.045
2.399
2.203 3.487 6.303

Independent Variables
Uncertainty(Base)
58979 0.054
Uncertainty(All Exp. Weighted)
58979 0.346
Uncertainty(Employment Weighted) 65011 0.244
Uncertainty(Simple Avg.)
58979 0.392
Uncertainty(Firm Level)
8221 0.018
Initial Log Output
58979 7.910
Initial Log SO2 Emission
58979 10.180
SOE (=1 if SOE)
58979 0.389
Log GDP per capita
58522 9.295
Log pop58522 6.049
TCZ (=1 if TCZ)
58979 0.744
Import Tariff
58439 15.010
Export License
58699 0.166
Contract Intensity
58979 0.509
Export Quota
58979 0.156

0.047
0.115
0.069
0.084
0.065
1.480
2.033
0.487
0.810
0.677
0.437
8.205
0.273
0.113
0.141

0.005 0.049 0.092
0.182 0.371 0.490
0.142 0.256 0.320
0.297 0.405 0.472
0.000 0.000
0.030
6.215 7.824 9.810
7.659 10.290 12.600
0.000 0.000 1.000
8.335 9.223 10.390
5.233 6.118 6.775
0.000 1.000 1.000
8.464 14.130 20.790
0.009 0.042
0.597
0.344 0.528 0.630
0.006 0.139 0.333

37

P10

P50

P90

Table 2: Average Policy Effects on Firm Output and SO2 Emission
OutputSO2 Emission(3)(4)

SO2 Intensity
(5)
(6)

(1)

(2)

Uncertainty × Post

0.516∗∗∗(0.175)0.639∗∗∗(0.183)-0.538(0.420)-0.674(0.412)-1.054∗∗∗(0.397)-1.313∗∗∗(0.395)Firm-level Controls
Firm FE
Year FE
adj. R-square
Observation

No
Yes
Yes
0.853
58979

Yes
Yes
Yes
0.855
58979

No
Yes
Yes
0.762
58979

Yes
Yes
Yes
0.774
58979

No
Yes
Yes
0.742
58979

Yes
Yes
Yes
0.756
58979

Notes: The dependent variable in columns (1)-(2) is the (log) output, in columns (3)-(4) is the(log) SO2 emission, and in columns (5)-(6) is the (log) SO2 emission intensity. Firm-level controls refer to the interactions between the year dummies and each firm’s initial output, as wellas their initial SO2 emission. Robust standard errors are clustered at the prefecture-year levelin parentheses. ∗∗∗ p<0.01 ∗∗ p<0.05 ∗ p<0.1.

38

Table 3: Average Policy Effects Controlling For Contemporaneous Prefectural Controls
OutputUncertainty × Post

SO2 Intensity

(1)

(2)

(3)

(4)

(5)

(6)

0.806∗∗∗(0.186)0.767∗∗∗(0.184)0.095∗∗∗(0.028)0.058∗∗(0.024)-0.218(0.417)-0.215(0.421)
0.008(0.075)0.036(0.048)-1.024∗∗(0.402)-0.982∗∗(0.405)-0.088(0.085)-0.022(0.047)

Yes
Yes
Yes
Yes
0.855
58439

Yes
Yes
Yes
Yes
0.855
58439

Yes
Yes
Yes
Yes
0.776
58439

Yes
Yes
Yes
Yes
0.777
58439

Yes
Yes
Yes
Yes
0.756
58439

Yes
Yes
Yes
Yes
0.756
58439

Log GDP per capita
Log pop

City-level Shocks
Firm-level Controls
Firm FE
Year FE
adj. R-square
Observation

SO2 Emission

Notes: The dependent variable in columns (1)-(2) is the (log) output, in columns (3)-(4) is the(log) SO2 emission, and in columns (5)-(6) is the (log) SO2 emission intensity. Firm-level controls refer to the interactions between the year dummies and each firm’s initial output, as wellas their initial SO2 emission. City-level shocks include import tariffs, export licensing, export
quotas, and contract intensities interacted with year dummies. Robust standard errors are
clustered at the prefecture-year level in parentheses. ∗∗∗ p<0.01 ∗∗ p<0.05 ∗ p<0.1.

39

Table 4: Robustness Checks: Policy Effects with More Fixed Effects
OutputSO2 Emission

SO2 Intensity

(1)

(2)

(3)

(4)

(5)

(6)

Uncertainty × Post

0.629∗∗∗(0.183)0.681∗∗∗(0.203)-0.845∗∗(0.413)-0.827∗∗(0.389)-1.474∗∗∗(0.396)-1.509∗∗∗(0.383)Firm-level Controls
Ownership-Year FE
Province-Year FE
Firm FE
Year FE
adj. R-square
Observation

Yes
Yes
No
Yes
Yes
0.855
58979

Yes
Yes
Yes
Yes
Yes
0.856
58979

Yes
Yes
No
Yes
Yes
0.775
58979

Yes
Yes
Yes
Yes
Yes
0.780
58979

Yes
Yes
No
Yes
Yes
0.756
58979

Yes
Yes
Yes
Yes
Yes
0.762
58979

Notes: The dependent variable in columns (1)-(2) is the (log) output, in columns (3)-(4) is the(log) SO2 emission, and in columns (5)-(6) is the (log) SO2 emission intensity. Firm-level controls
refer to the interactions between the year dummies and each firm’s initial output, as well as
their initial SO2 emission. Robust standard errors are clustered at the prefecture-year level inparentheses. ∗∗∗ p<0.01 ∗∗ p<0.05 ∗ p<0.1.

40

Table 5: Policy Effects using Alternative Constructs of Trade Policy Uncertainty
Panel A: Alternative Measures of Uncertainty (Employment Weighted and All Export Weighted)
Uncertainty (Employment Weighted)
Uncertainty (All Export Weighted)
Output(1)

SO2 Emission(2)

Intensity(3)

Output(4)

SO2 Emission(5)

Intensity(6)

Uncertainty × Post

0.222∗(0.124)-1.273∗∗∗(0.254)-1.495∗∗∗(0.260)0.125∗(0.075)-0.237(0.157)-0.362∗∗(0.160)Firm-level Controls
Firm FE
Year FE
adj. R-square
Observation

Yes
Yes
Yes
0.857
58979

Yes
Yes
Yes
0.855
58979

Yes
Yes
Yes
0.855
58979

Yes
Yes
Yes
0.750
58979

Yes
Yes
Yes
0.756
58979

Yes
Yes
Yes
0.755
58979

Panel B: Alternative Measures of Uncertainty (Simple Weighted and Firm-level Uncertainty)
Uncertainty (Simple Average)
Uncertainty (Firm-level)
(1)

(2)

(3)

(4)

(5)

(6)

Uncertainty × Post

0.180∗(0.102)-0.608∗∗∗(0.206)-0.787∗∗∗(0.208)0.460∗∗(0.230)-1.208∗∗(0.526)-1.668∗∗∗(0.526)

Firm-level Controls
Firm FE
Year FE
adj. R-square
Observation

Yes
Yes
Yes
0.766
58979

Yes
Yes
Yes
0.774
58979

Yes
Yes
Yes
0.774
58979

Yes
Yes
Yes
0.848
7705

Yes
Yes
Yes
0.826
7705

Yes
Yes
Yes
0.764
7705

Notes: The dependent variable in columns (1) and (4) is the (log) output, in columns (2) and (5) is the (log) SO2 emission, and in columns (3) and (6) is the (log) SO2 emission intensity. Firm-level controls refer to the interactions between
the year dummies and each firm’s initial output. Columns (1), (2) and (3) in panel A use alternative uncertainty measure 1; columns (4), (5) and (6) in panel A use alternative uncertainty measure 2; columns (1), (2) and (3) in panel
B use alternative uncertainty measure 3; columns (4), (5) and (6) in panel B use firm-level uncertainty measure. All
these three prefecture-year measures are constructed by using the year 2000 China export data across all destinations.
Robust standard errors are clustered at the prefecture-year level in parentheses. ∗∗∗ p<0.01 ∗∗ p<0.05 ∗ p<0.1.

41

42
Yes
Yes
Yes
0.860
43871

Yes
Yes
Yes
0.836
15108

0.508∗∗(0.232)0.767∗∗∗(0.284)

Yes
Yes
Yes
0.855
58979

0.495∗∗(0.231)0.266(0.368)0.003(0.026)

(3)

Yes
Yes
Yes
0.765
43871

-2.293∗∗∗(0.618)

(4)

Yes
Yes
Yes
0.756
15108

0.330
(0.402)

(5)

(6)

Yes
Yes
Yes
0.774
58979

0.301
(0.419)-1.896∗∗(0.781)
0.046(0.056)

SO2 Emission

Yes
Yes
Yes
0.780
43871

-1.526∗∗(0.659)

(7)

Yes
Yes
Yes
0.722
15108

-0.178(0.414)

(8)

(9)

Yes
Yes
Yes
0.756
58979

-0.194(0.425)-2.162∗∗∗(0.757)0.043(0.056)

SO2 Intensity

Notes: Columns (1), (4) and (7) use subsample of TCZ; columns (2), (5) and (8) use subsample of non-TCZ; columns (3), (6) and (9)use whole sample, The dependent variable in columns (1)-(3) is the (log) output, in columns (4)-(6) is the (log) SO2 emission, andin columns (7)-(9) is the (log) SO2 emission intensity. Firm-level controls refer to the interactions between the year dummies andeach firm’s initial output, as well as their initial SO2 emission. Robust standard errors are clustered at the prefecture-year level inparentheses. ∗∗∗ p<0.01 ∗∗ p<0.05 ∗ p<0.1.

Firm-level Controls
Firm FE
Year FE
adkj. R-square
Observation

Post × TCZ

Uncertainty × Post × TCZ

Uncertainty × Post

(2)

(1)

OutputTable 6: Heterogeneity Policy Effects by Two-Control-Zones (TCZ)

43
Yes
0.756
47602

Year FE

adj. R-square

Observation

35329

0.755

Yes

Yes

12273

0.757

Yes

Yes

Yes

(0.413)-0.278

non-TCZ(3)(0.533)

0.172

All

(8)

47602

0.756

Yes

Yes

50972

0.811

Yes

Yes

37578

0.820

Yes

Yes

13394

0.773

Yes

Yes

50972

0.811

Yes

Yes

Yes

(0.071)(0.046)
Yes-0.022-0.007(0.808)Yes(0.533)

0.197

non-TCZ(7)(0.638)Yes(0.606)-0.893TCZ(6)-1.065Yes(0.422)-0.404

All

(5)

0.365

(0.426)-0.242All(4)

Fumes Intensity

17840

0.883

Yes

Yes

Yes

(0.709)-1.017

All

(9)

14115

0.880

Yes

Yes

Yes

(1.039)-0.287

TCZ

(10)

3725

0.893

Yes

Yes

Yes

(0.910)-1.293

non-TCZ(11)

NOx Intensity

17840

0.883

Yes

Yes

Yes

(0.094)-0.249∗∗∗(1.398)1.051(0.938)-1.426All(12)

Notes: The specifications (2), (6) and (10) use subsample of TCZ; specifications (3), (7) and (11) use subsample of non-TCZ; specifications (1), (4),(5), (8), (9) and (12) use whole sample, The dependent variable in specifications (1)-(4) is the (log) wastewater emission intensity, in specifications(5)-(8) is the (log) fume emission intensity, and in specifications (9)-(12) is the (log) nitrogen oxides emission intensity. Firm-level controls refer tothe interactions between the year dummies and each firm’s initial output, as well as their initial SO2 emission. Robust standard errors corrected forclustering at the city-year level in parentheses. ∗∗∗ p<0.01 ∗∗ p<0.05 ∗ p<0.1.

Yes

Firm FE

Yes(0.475)(0.316)

Yes

0.241

-0.047TCZ

All

Firm-level Controls

Post × TCZ

Uncertainty × Post × TCZ

Uncertainty × Post

(2)

(1)

Wastewater Intensity

Table 7: Policy Effects on Other Firm Pollutants

44
Yes
Yes
Yes
0.932
53607

-0.293(0.515)

Yes
Yes
Yes
0.947
14442

-1.483∗(0.787)

Yes
Yes
Yes
0.927
39165

1.288∗∗∗(0.475)

(2)
TCZ

Yes
Yes
Yes
0.932
53607

1.198∗∗(0.529)-2.842∗∗∗(0.964)-0.097(0.062)

(4)
All

Yes
Yes
Yes
0.652
13275

0.973
(1.313)

(5)
All

Yes
Yes
Yes
0.661
11342

-1.333(1.569)

(6)
TCZ

Yes
Yes
Yes
0.584
1933

5.668∗∗∗(1.998)

Fuel(7)
non-TCZ

Yes
Yes
Yes
0.653
13275

5.401∗∗∗(2.000)-6.932∗∗∗(2.552)0.786∗∗∗(0.196)(8)
All

Notes: Columns (1), (4), (5) and (8) use whole sample; columns (2) and (6) use subsample of firms in TCZs; columns (3) and (7) use
subsample of firms in non-TCZs. The dependent variable in columns (1)-(4) is the (log) consumption of coal, in columns (5)-(8) is the(log) consumption of fule. Firm-level controls refer to the interactions between the year dummies and each firm’s initial output, as
well as their initial SO2 emission. Robust standard errors are clustered at the prefecture-year level in parentheses∗∗∗ p<0.01 ∗∗ p<0.05∗ p<0.1.

Firm-level Controls
Firm FE
Year FE
adkj. R-square
Observation

Post × TCZ

Uncertainty × Post × TCZ

Uncertainty × Post

(1)
All

Coal
(3)
non-TCZ

Table 8: Policy Effects on Energy Use

45
Yes
Yes
Yes
0.899
58698

0.193
(0.171)

Yes
Yes
Yes
0.905
43671

-0.104(0.258)

Yes
Yes
Yes
0.876
15027

0.553∗∗∗(0.199)

Yes
Yes
Yes
0.899
58698

0.537∗∗∗(0.199)-0.670∗∗(0.325)0.020(0.025)

(4)
All

Yes
Yes
Yes
0.912
58836

0.368∗∗∗(0.115)

(5)
All

Yes
Yes
Yes
0.914
43765

0.642∗∗∗(0.172)

Yes
Yes
Yes
0.904
15071

0.124
(0.144)

Employment
(6)
(7)
TCZ
Non-TCZ

Yes
Yes
Yes
0.912
58836

0.132
(0.147)0.491∗∗(0.226)-0.033∗∗(0.016)(8)
All

Notes: Columns (1), (4), (5) and (8) use whole sample; columns (2) and (6) use subsample of firms in TCZs; columns (3) and (7) use
subsample of firms in non-TCZs. The dependent variable in columns (1)-(4) is the (log) intermediate inputs, in columns (5)-(8) isthe (log) employment. Firm-level controls refer to the interactions between the year dummies and each firm’s initial output, as wellas their initial SO2 emission. Robust standard errors are clustered at the prefecture-year level in parentheses∗∗∗ p<0.01 ∗∗ p<0.05 ∗p<0.1.

Firm-level Controls
Firm FE
Year FE
adj. R-square
Observation

Post × TCZ

Uncertainty × Post × TCZ

Uncertainty × Post

(1)
All

Intermediate Inputs
(2)
(3)
TCZ
Non-TCZ

Table 9: Policy Effects on Employment and Intermediate Inputs

46
Yes
Yes
Yes
0.510
13154

-0.357∗∗(0.143)

Yes
Yes
Yes
0.494
9918

-0.643∗∗(0.281)

Yes
Yes
Yes
0.744
3236

-0.112(0.089)

Yes
Yes
Yes
0.510
13154

-0.132(0.112)-0.512∗(0.295)
0.061∗∗(0.025)

(4)
All

Yes
Yes
Yes
0.736
52398

0.343∗∗∗(0.129)

(5)
All

Yes
Yes
Yes
0.745
38819

0.671∗∗∗(0.210)

(6)
TCZ

Yes
Yes
Yes
0.703
13579

0.046
(0.147)(7)
non-TCZ

Facility

Yes
Yes
Yes
0.736
52398

0.042
(0.146)0.607∗∗(0.257)-0.026(0.021)(8)All

Notes: Columns (1), (4), (5) and (8) use whole sample; columns (2) and (6) use subsample of firms in TCZs; columns in (3) and (7)use subsample of firms in non-TCZs. The dependent variable in columns (1)-(4) is the sulfur content of coal, in columns (5)-(8) is the(log) number of off-gas treatment facilities. Firm-level controls refer to the interactions between the year dummies and each firm’s
initial output, as well as their initial SO2 emission. Robust standard errors are clustered at the prefecture-year level in parentheses.
∗∗∗ p<0.01 ∗∗ p<0.05 ∗ p<0.1.

Firm-level Controls
Firm FE
Year FE
adkj. R-square
Observation

Post × TCZ

Uncertainty × Post × TCZ

Uncertainty × Post

(1)
All

Sulfur content
(2)
(3)
TCZ
non-TCZ

Table 10: Policy Effects on Sulfur Content and Pollution-control Facility

47
Yes
Yes
Yes
0.852
58495

0.443∗∗∗(0.154)

Yes
Yes
Yes
0.856
43536

0.764∗∗∗(0.226)

Yes
Yes
Yes
0.839
14959

0.060
(0.202)

Yes
Yes
Yes
0.852
58495

0.051
(0.204)0.737∗∗(0.310)-0.004(0.022)

(4)
All

Yes
Yes
Yes
0.885
58495

0.584∗∗∗(0.166)

(5)
All

Yes
Yes
Yes
0.893
43536

0.739∗∗∗(0.257)

Yes
Yes
Yes
0.852
14959

0.357∗(0.196)

TFP (ACF)
(6)
(7)
TCZ
Non-TCZ

Yes
Yes
Yes
0.885
58495

0.352∗(0.199)0.410(0.329)0.014(0.024)(8)All

Notes: The specifications (1), (4), (5) and (8) use whole sample; specifications (2) and (6) use subsample of TCZ; specifications (3) and (7)use subsample of non-TCZ. The dependent variable in specifications (1)-(4) is the TFP calculated by OP method, in specifications (5)(8) is the TFP of ACF version. Firm-level controls refer to the interactions between the year dummies and each firm’s initial output, as
well as their initial SO2 emission. Robust standard errors are clustered at the prefecture-year level in parentheses∗∗∗ p<0.01 ∗∗ p<0.05∗ p<0.1.

Firm-level Controls
Firm FE
Year FE
adj. R-square
Observation

Post × TCZ

Uncertainty × Post × TCZ

Uncertainty × Post

(1)
All

TFP(OP)(2)(3)
TCZ
Non-TCZ

Table 11: Policy Effects on Firm Total Factor Productivity

48

Appendix

OutputEmission

0.07

0.06

0.06

0.05

0.05

Emission

Output

0.07

0.04

0.03

0.04

0.03

0.02

0.02

0.01

0.01

0

0
0

0.2

0.4

0.6

0.8

1

0

Uncertainty Reduction ( 2 )

0.6

0.8

1

(1- 3 )^(1/ , )

0.06
;=0
;=0.25;=0.5;=0.75
;=1

0.05

-0.01

0.04
-0.02

(1- 3 )^(1/ ,)Emission intensity

0.4

Uncertainty Reduction ( 2 )Emission Intensity

0

0.2

-0.03

0.03

0.02

-0.04
0.01
-0.05

0

-0.06

-0.01
0

0.2

0.4

0.6

0.8

Uncertainty Reduction ( 2 )

1

0

0.2

0.4

0.6

0.8

1

Uncertainty Reduction ( 2 )Figure A.1: The Simulated Effects of Uncertainty Reduction on Firm Production andEmission (Emission Cap κ = 1)
Notes: This figure presents the simulated effects of a reduction in export policy uncertainty on firm total
output, total emission, emission intensity, and inputs used for production, respectively. We set the emission
49environment regulation stringency, characterized bycap κ to be 1. We plots the effects for different levels ofparameter ρ. A higer value of ρ corresponds to more stringent emission control.

Output

0.07

Emission

0.08

0.06

0.06

0.05

0.04

Emission

Output

0.04
0.03

0.02

0

0.02
-0.02

0.01

-0.04

0
-0.01

-0.06
0

0.2

0.4

0.6

0.8

0

1

Uncertainty Reduction ( 2 )

0.6

0.8

1

(1- 3 )^(1/ , )

0.06

-0.02

0.04

-0.04

0.02

(1- 3 )^(1/ ,)Emission intensity

0.4

Uncertainty Reduction ( 2 )Emission Intensity

0

0.2

-0.06

0

-0.08

-0.02

-0.1

-0.04

-0.12

;=0
;=0.25;=0.5;=0.75
;=1

-0.06
0

0.2

0.4

0.6

0.8

1

0

Uncertainty Reduction ( 2 )

0.2

0.4

0.6

0.8

1

Uncertainty Reduction ( 2 )Figure A.2: The Simulated Effects of Uncertainty Reduction on Firm Production andEmission (Emission Cap κ = 0.95)
Notes: This figure presents the simulated effects of a reduction in export policy uncertainty on firm total
output, total emission, emission intensity, and inputs used for production, respectively. We set the emission
cap κ to be 0.95. We plots the effects for different levels of environment regulation stringency, characterizedby parameter ρ. A higer value of ρ corresponds to more stringent emission control.

50