Controlling SO2 emissions in China:
A panel data analysis of the 11th Five-Year Plan
Teng Ma and Kenji Takeuchi∗
Graduate School of Economics, Kobe University

February 29, 2016

Abstract
We investigate the impact of policy measures to reduce SO2 emissions during 11th
Five-Year Plan of China (2006–2010). By using a provincial-level panel data set, wefind that installation of the flue-gas desulfurization equipment and closure of small coalfired power plants was related to the reduction of SO2 in a statistically significant way.
While estimation results suggest that these two policy measures played an import antrole in reducing SO2 emissions in China during this period, the size of the estimatedcoeﬃcients shows that the eﬀects might have been weaker than those predicted by
ex-ante cost-benefit analysis.

Keywords: SO2 ; Air pollution; Panel data; China; 11th Five-Year Plan.
JEL Classification Numbers: L94, P28, Q53, Q56.

∗

E-mail: mt yx@hotmail.com (Ma), takeuchi@econ.kobe-u.ac.jp (Takeuchi).

11IntroductionMore than 20 million tons of SO2 were emitted in China in 2011, three times higher

than that in the United States in the same year (National Bureau of Statistics of China,2012).1 SO2 is a major source of atmospheric haze and acid deposition, and it has been
linked to a number of adverse eﬀects on the human respiratory system (Nielsen and Ho,
2007; World Bank., 1997; Venners et al., 2003). World Bank (2007) estimates that the costof air pollution in China was 157.3 billion RMB in 2003, or 1.16% of GDP2 . The largest
contributor of SO2 emissions in China is coal-fired power generation. Chinese thermal power
sector discharges 40% of total SO2 while supplying more than 80% of electricity (Departmentof Energy Statistics, National Bureau of Statistics of China, 2012). Among the electricity
produced by thermal power plant, 87% is produced by coal.
One challenge for the Chinese government over the past 30 years has been to reduceSO2 discharges. A limit on total national emissions of SO2 was first introduced in the 9th
Five-Year Plan (1996–2000) (JES 2007). The emissions target in 2000 of 27.1 million tons
was easily achieved, with total SO2 emissions accounting for 22 million tons. In the following
10th Five-Years Plan, the total emissions control target was set as a 10% reduction from2000 emission levels by 2005. However, actual emissions in 2005 increased by 27.8% fromthat in 2000 against a backdrop of rapid economic growth with the average GDP growth rateof 9.5%. Following this experience, the 11th Five-Year Plan (2006–2010) implemented more
ambitious policies to reduce SO2 emissions. Total SO2 emissions as well as industrial SO2
emissions began to decrease in 2006 (Figure 1). Between 2006 and 2010, total SO2 emissions
declined by more than 14%, despite the economy growing annually by more than 10% on
average. Hence, in terms of total SO2 emissions, the oﬃcial statistics suggests that China
has passed the turning point of the environmental Kuznets curve (Grossman and Krueger,
1995).
1

According to OECD (2015), there were 5.85 million tons of sulfur oxides emissions in the United Statesin 2011.
2
As of January 2003, 1 US dollar was 8.28 RMB.

2

[Figure 1]
Several studies have investigated air pollution in China during the 11th Five-Year Plan
period. JES (2007) provides a cost-benefit analysis of China’s energy saving and SO2 control
policies during this period, concluding that the abatement of SO2 emissions of the 11th
Five-Year Plan will lead to significant improvements in air quality in Chinese cities. The
estimation results show that the benefit-cost ratio is more than 5 to 1. Indeed, quantifiable
benefits reach 4.7 billion US dollars compared with costs of only 950 million US dollars.
Cao et al. (2009) extend JES (2007) by using a CGE model of the Chinese economy.
Their simulation results show that the overall impacts of the 11th Five-Year Plan’s SO2
reduction policies is a 0.48% increase in GDP in 2010 above the baseline. They find thatthe productivity improvement generated by the closure of small-unit coal-fired power plants
oﬀsets the decline in GDP resulting from the installation of the flue-gas desulfurization(FGD) equipment. While these studies suggest that these regulations might have been
eﬀective for reducing SO2 emissions in China, no study has thus far conducted a thoroughex-post evaluation of the policy measures taken during this period.3
Based on the foregoing, this study examines how policy measures might have played arole in reducing SO2 emissions in Chinese provinces between 2006 and 2010. We focus on
the policies promulgated in National Acid Rain and SO2 Pollution Control in 11th Five-Year
Plan and discuss how they contributed to control emissions from coal-fired power plants ineach province. Special attention is paid to the eﬀect of two policies: the installation of FGD
equipment and closure of small coal-fired power plants. We also control for the impact ofother policies introduced before the period (e.g., pollution levy and Two Control Zones (TCZ)
policy), regional variation in sulfur content in the coal used in coal-fired power plants, andimprovement in energy eﬃciency for the power generation sector that has occurred during
the period. In addition, we examine the eﬀect of economic development on the eﬀectiveness3

The 2008 Olympic Games in Beijing might have been a driving force behind the strict regulation
introduced in the 11th Five-Year Plan. Chen et al. (2013) evaluate the impact of policy measures on
improving air quality to prepare for the Olympic Games.

3

of the installation of FGD equipment, by using a panel threshold model.
The contribution of this study can be summarized as follows. Firstly, we investigate theimpact of SO2 control policies on the coal-fired power sector in China’s 11th Five-Year Plan.
Although several studies have evaluated the pollution control in the same period (JES, 2007;
Cao et al., 2009), econometric analyses are scarce. Since the SO2 emissions started to decrease during this period, empirical analysis of the role of policy instruments has significant
implications for understanding the mechanism behind the turning point of the environmental Kuznets curve. Secondly, we empirically examine the extent to which a technologicalintervention for pollution control is eﬀective. Since our dataset on the installation of control equipment contains information of the reduction capacity, we can examine whether the
installation of FGD equipment successfully led to actual reductions. Thirdly, we investigate
the role of policies for small-scale power plants. The government issued requirements to close
small, ineﬃcient electric power generating units and replace them with larger, more eﬃcientplants. This policy improves energy eﬃciency as well as reduces SO2 emissions, and thus
might be eﬀective as an eﬃcient command-and-control policy instrument. Fourthly, we showthat the level of economic development might influence the eﬀectiveness of pollution control technology. Our results suggest that as the Chinese economy develops, more stringent
monitoring and enforcement activity can be expected.
The remainder of the paper structured as follows. In Section 2, we explain the Chinese
policy for controlling SO2 during the 11th Five-Year Plan period. Section 3 introduces theempirical model and data. The empirical results are presented in Section 4. Section 5 employsa threshold model and investigates possible structural breaks in terms of the installation of
FGD equipment. Section 6 concludes.

42

Chinese policies for controlling SO2 emissions
As mentioned in the Introduction, China’s 11th Five-Year Plan included two major poli-cies to control SO2 emissions: the installation of FGD equipment and closure of small power
plants. In 2007, the National Acid Rain and SO2 Pollution Control program was issued(SEPA and NDRC, 2007; Cao et al., 2009). The plan specified emissions targets for 2010
for each province that ranged from a reduction of 25.9% from the 2005 level for Shanghai tosimply maintaining the 2005 levels in Hainan and other provinces. It also included schedules for installing 167 GW of new FGD equipment at coal-fired power plants and listed 679
small power plants targeted for closure in order to remove more than 50 GW of electricity
generation capacity.
According to JES (2007), the expected net reduction in SO2 emissions was 5.4 million
tons from the installation of FGD equipment and 2.1 million tons from the closure policy.
The annualized benefits were estimated to be 35.4 billion RMB. The annualized cost estimate
for the FGD equipment was 7.15 billion RMB and that of the closure policy was assumed
to be zero. Since these small power plants are ineﬃcient and highly pollution-intensive, theeﬀectiveness of closure policy is expected to be high.
Many power plants installed FGD equipment during this period (Schreifels et al., 2012).
The percentage of coal-fired power plants that had FGD rose from 14% in 2005 to 86% by theend of 2010. However, the operation and performance of FGD equipment was insuﬃcient.
Field inspections in early 2007 by the State Environmental Protection Agency (SEPA), which
was replaced by the Ministry of Environmental Protection in 2008, revealed that less than
40% of the installed FGD equipment was running continuously and reliably (Xu, 2009). High
operation costs might have been the main reason for this inactiveness.
Despite these limitations, SO2 emissions in 2010 declined by 14% from 2005 levels, exceeding the reduction target of 10%. Schreifels et al. (2012) point out that several factors
contributed to this reversal from the environmental policies in the 10th and 11th Five-Year
Plans. Firstly, the reduction target was taken more seriously in the political process. The
5

goal of a 10% reduction was included in the Outline of the National 11th Five-Year Plan on
Economic and Social Development issued by the the National People’s Congress, whereas
the previous goal was only listed in the National 10th Five-Year Plan for Environmental Protection issued by SEPA. Secondly, the attainment of these environmental goals was linked
to the performance evaluation of local leaders.4 Indeed, the energy conservation target (energy consumption per unit of GDP) accounted for 40 points out of the 100 points available.
Thirdly, the monitoring and enforcement of environmental policy was strengthened. In 2006,
SEPA established six regional supervision centers to supervise local governments and environmental protection bureaus in order to prevent inadequate activities (Moore, 2011). It
also established two new departments to oversee the implementation and enforcement of
SO2 emission targets.
Economic incentives were also strengthened. The pollution levy for SO2 rose from 0.42
RMB/kg in 2004 to 1.26 RMB/kg in 2007 (OECD, 2007; JES, 2007). Further, the green
pricing program for electricity from FGD-equipped power plants was revised to focus on FGD
operation and performance. Since 2003, the Chinese government has applied an electricity
price premium for the electricity sector to encourage coal-fired power plant to install the
FGD equipment. In 2007, it even started to provide an incentive of 15 RMB/kWh for power
plants operating FGD equipment for 90% or more of total electricity generated, a penalty of
15 RMB/kWh for plants operating FGDs between 80% and 90% of total generation, and apenalty of 75 RMB/kWh for plants operating FGDs less than 80% of the time (Cao et al.,2009).
Another important measure for controlling SO2 emissions in China is the TCZ policy
implemented in 1998. The objective of this policy is to reduce SO2 emissions in cities andareas with particularly high air pollution. Cities exceeding specific standards were designatedas either acid rain or SO2 pollution control zones, based on their records in past years. The
4Zheng et al. (2014) investigates how proxies for greenness is included in promotion evaluation. Theirresults suggest that facility expenditure variable is highly significant while air quality measures is less significant. It supports the claim that the promotion criteria emphasize input-based performance rather than
output such as level of air quality.

6

area covers 175 cities across 27 provinces in China, and it accounted for around 60% of SO2
emissions in 1995 (Hao et al., 2001). The three main policy measures embodied in the TCZ
policy were closing the biggest polluters, reducing the sulfur-content of coal, and burning
cleaner coal-burning. While SO2 emissions in the power sector increased by 1.5 times from2000 to 2005 (Zhao et al, 2008), Hering and Poncet (2014) find that the TCZ policy might
have been eﬀective for reducing SO2 emissions. Other studies have also pointed out theeﬀectiveness of the measures in the TCZ policy (He et al., 2002; Xu et al., 2004).

3

Empirical strategy and data

3.1

Empirical model

In this study, SO2 emissions are regressed on two policies examined herein as well as onother variables as follows:

Emissionit = α + βP oliciesit + γXit + ζi + δt + ϵit ,(1)

where ζi and δt are the province-specific eﬀects and year-specific eﬀects, respectively.
Emissionit is total SO2 emissions in province i in year t. In some of the estimation
models, we use SO2 emissions from the process of fuel burning in the industrial sector inprovince i in year t as an explained variable (EmissionIndit ).
P oliciesit summarizes the installation of FGD equipment (F GDit ) and closure of small
coal-fired power plants (Closureit ). F GDit is the cumulative amount of planned reduction
in SO2 emissions owing to the installation of FGD equipment in province i in year t. SEPA
planned to install FGD equipment that can reduce 4.4 million tons of SO2 in 31 provincesfrom 2006 to 2010. Closureit represents the closure of small coal-fired power plants to
reduce SO2 emissions in province i, measured in terms of electricity generation capacity.
Small plants have low fuel eﬃciency, and it is economically unreasonable to install expensive

7

equipment in them. Indeed, units smaller than 300 MW consume 25% of total coal fuelin the electricity sector, but emit 54% of total SO2 emissions (Chen et al., 2014). Since
we cannot identify when each plant actually closed during the period, we assume that all
closures occurred in 2006, the starting year of the policy. This means that Closureit takes
the same value in all periods after 2006.
Xit summarizes the control variables of five factors: pollution levy, fuel eﬃciency, grossregional product (GRP) per capita, sulfur content in coal, and TCZ policy. The pollution levy
was among the earliest policy instruments adopted to address SO2 emissions from industrial
sources. We assume that the total pollution levy paid to the government in a province
in the prior year (Levyi,t−1 ) influences SO2 emissions. Fuel eﬃciency (Eﬃciency it ) is also
related to SO2 emissions. When old and small plants close, average fuel eﬃciency improves,
thereby reducing SO2 emissions as long as total production remains the same. Therefore,
we can expect a negative relation between fuel eﬃciency and SO2 emissions. We calculateEﬃciency it , fuel eﬃciency in province i in year t, by dividing total electricity production by
total coal consumption in each province. GRP per capita (GRPit ) represents the level of
economic development in each province. We assume that developed regions have higher SO2
emissions than others. Contenti represents the percentage of average sulfur content in coalin province i, which is used in coal-fired power plants. SO2 emissions are strongly related tothe quality of coal used in each province. Coal with a high sulfur content is widely used inprovinces in the southeast of China, whereas that with relatively lower sulfur content is usedin northeast China. The weighted average of sulfur content in all provinces is about 1.10%(Fridley et al., 2013). World Bank (2003) estimates that low-sulfur coal increases firms’ total
operating costs, as it is 40–50% more expensive than high-sulfur coal. To control for theeﬀect of the TCZ policy on SO2 emissions, we add a dummy variable T CZi into our models.
T CZi takes a value of 1 when the province has a city that is under the TCZ policy and 0
otherwise.

8

3.2

Data

Table 1 presents the descriptive statistics. We collect data for 31 provinces from 2004 to
2010. Our data set includes annual province-level information from 2004 to 2010 collectedfrom the China Energy Statistical Yearbook (CESY), China Environment Yearbook (CEY),
China Energy Databook (CED), China Statistical Yearbook on the Environment (CSYE),
China Statistical Yearbook (CSY), and National Acid Rain and SO2 Pollution Control in
the 11th Five-Year Plan (Department of Energy Statistics, National Bureau of Statistics ofChina, 2004–2010; Editorial Committee of China Environment Yearbook, 2004–2010; Fridley
et al., 2013; National Bureau of Statistics and Ministry of Environmental Protection, 2004–
2010; National Bureau of Statistics of China, 2004–2010; SEPA and NDRC, 2007).
This study uses total SO2 emissions and SO2 emissions from the process of fuel burningin the industrial sectors in each province from CEY. According to CESY, coal accounted forover 80% of the primary energy supply in China in 2010 (Department of Energy Statistics,
National Bureau of Statistics of China, 2011). The planned reduction through the installationof FGD equipment and capacity reduction through the closure policy in each provinces aretaken from SEPA and NDRC (2007). Data for the pollution levy in each province aretaken from CEY for the corresponding year. In all major sources, the pollution levy of SO2
emissions increased from 0.42 RMB/kg in 2004 to 1.26 RMB/kg in 2007 (OECD, 2007; JES,
2007). GRP per capita is taken from CSY. The generating capacity of thermal plants andcoal consumption of coal-fired power plant in each province are derived from CESY. Owing
to data availability, we use the generating capacity of all thermal power plants instead ofthat of coal-fired power plants. In 2012, approximately 66% of installed capacity for power
generation was at coal-fired power plants (Zhang, 2014). The sulfur content level of coalis measured at the regional level: North, Northeast, East, South Central, Southwest, andNorthwest. We calculate the market share of coal that has a sulfur content lower than 3% ineach region (Fridley et al., 2013). Finally, we take the TCZ policy from Law of the People’s
Republic of China on the Prevention and Control of Atmospheric Pollution.
9

[Table 1]

4

Empirical results
Firstly, we estimate a linear fixed eﬀects model, assuming the province eﬀects vi areunobserved. Table 2 presents the estimation results for the fixed eﬀects models.5 The sulfur
content and TCZ variables are not included in these models because these are time-invariant
and thus they were removed from the time-demeaned equation. The results suggest that the
cumulative installation of FGD equipment and closure of small power plants are negatively
related to total emissions and industrial emissions at a statistically significant level.
[Table 2]
The coeﬃcient of fuel eﬃciency is negative and statistically significant at 10% in themodel in column (3). Since higher fuel eﬃciency means lower SO2 emissions per energy
production, this is theoretically reasonable. In the estimation models for industrial sector
emissions, the coeﬃcient of the pollution levy is negative but not statistically significant.
Since Contenti and T CZi are time-invariant, they can not be included in the fixed eﬀects
model. Therefore, to estimate the impact of these variables, we use a hybrid method instead(Mundlak, 1978), namely the correlated random eﬀects model. Let be ζi = ϕ+ηP oliciesi +ξi .
We present the estimation model in question as follows:

Emissionit = α + βP oliciesit + γXi + ϕ + ηP oliciesi + ξi + δt + ϵit ,(2)

By averaging across t, we obtain a cross-sectional equation:

Emissioni = (α + ϕ) + βP oliciesi + γXi + ηP oliciesi + ξi + δt + ϵ̄i ,By subtracting (4) from (3), we have:
5

The Hausman test rejected the use of the random eﬀects model.

10

(3)

Emissionit − Emissioni =β(P oliciesit − P oliciesi ) + (ϵit − ϵi )

(4)

¨
¨
⇒ Emission
it =β P oliciesit + ϵ̈it
By summation, we obtain:¨¨Emissioni + Emissionit =(α + ϕ) + β P oliciesit + βP oliciesi(5)+γXi + ηP oliciesi + ξi + δt + ϵ̄i + ϵ̈itFinally, we obtain the following expression:¨
Emissionit = (α + ϕ) + β P olicies
it + (β + η)P oliciesi + γXi + ξi + δt + ϵit(6)

If β + η = β, it is a random eﬀects model. If β + η ̸= β, it is a fixed eﬀects model.
[Table 3]
The estimation result with the hybrid model are presented in Table 3. With regard to
the coeﬃcient of F GDit and Closureit , we obtain a similar result to that in fixed eﬀects
model, that is they are negatively correlated with SO2 emissions in a statistically significant
way in most models.
The coeﬃcient of Contenti is not statistically significant, perhaps because coal-fired
power plants do not use local coal. In China, coal production is concentrated in several
provinces (Shanxi, Shaanxi, Inner Mongolia and Henan produced 62.1% of coal in 2012).
Moreover, imported coal has increasingly been used for thermal power plants in recent years.
Indeed, the use of imported coal increased 10-fold from 9.82 million tons in 2008 to 101 million
tons in 2012 (Wang and Ducruet, 2014).
Total and industrial SO2 emissions are higher in the TCZ, although in a statisticallyinsignificant manner in every model. Here, new coal-fired power plants have typically been
constructed outside the TCZ in recent years. Further, regional structure of Chinese SO2

11emissions might be gradually changing, since SO2 controls have been expanded to the national level (SEPA and NDRC, 2007).
The results of our model thus suggest that the policy to install FGD equipment helped
reduce total and industrial SO2 emissions from 2006 to 2010. The estimated coeﬃcient canbe interpreted as that a 10, 000-ton increase in the control capacity of FGD equipment isassociated with a 5,700−6,600-ton reductions in SO2 discharge. The actual reduction might
have been smaller than the installed capacity because some installed FGD equipment was
not properly operated. According to Xu (2009), less than 40% of the available capacity forthe reduction of SO2 in coal-fired power plants was utilized in 2007. Our estimation results
therefore suggest that the utilization of installed FGD equipment might have been better
than that reported.
Our results also suggest that the closure of small coal-fired power plants might havehelped reduce SO2 . The size of the estimated coeﬃcient can be interpreted as that theclosure of coal-fired power plants with a capacity of 10 MW can reduce roughly 130 tonsof total SO2 emissions, or 150 tons of SO2 emissions from the industrial sector. From thisresult, we can expect that the closure of 50 GW small coal-fired power plants can reduce 0.6
million tons of SO2 . This is considerably smaller than the assumption of JES (2007) thatthe closure of 50 GW small coal-fired power plants can reduce 2.1 million tons of SO2 . Whensmaller power plants are closed, they are typically replaced with larger and more eﬃcient
power generation units.6 Hence, the emissions from these new units with larger capacity
might oﬀset some of the reductions of the closure policy.

5

Panel threshold estimators
This section extends the analysis presented in the previous section by considering a factor

that might influence the eﬀectiveness of the installation of FGD equipment. Several scholars
6

According to Li (2011), the guideline issued by the government in 2007 required that the capacity of
new coal-fired power units should be larger than 300MW and that in major grids should be larger than 600
MW.

12have pointed out that the the large diﬀerence in economic development among Chinese
regions should be considered when analyzing environmental policy in China. For example,
Song et al. (2013) use data envelopment analysis and find that environmental eﬃciency ishigher in aﬄuent regions and lower in poor regions. Xu and Lin (2016) also find that energy
eﬃciency improvement has greater potential to mitigate PM2.5 emissions in the central andwestern regions than those in the eastern region because of the lower technology levels.
Therefore, we investigate if the performance of pollution abatement technology depends
on the level of economic development. Specifically, we use GRP to measure of economic development and ensure that the eﬀectiveness of the installation of FGD equipment is significantly
diﬀerent between provinces above and below a certain GRP threshold. Our hypothesis isthat the coeﬃcient of FGD indicates the utilization of installed capacity, which decreases
because of insuﬃcient monitoring and enforcement by the regulator. Further, when there isa large disparity in enforcement activity between a rich province and a poor province, thecoeﬃcient of FGD might be higher in the former province than in the latter.
To find the threshold between SO2 emissions and the installed ability of FGD equipment
in coal-fired power sector, we use a non-dynamic panels with individual-specific fixed eﬀects(Hansen, 1999). This technique has been used to analyze the relationship between pollution
and income (Aslanidis and Xepapadeas, 2006), the relationship between energy consumption
and economic growth (Huang et al., 2008), and the impact of exports on energy intensity(Zheng et al., 2011). We treat F GDit as the regime-dependent variable and GRPit as thethreshold variable:

Emissionit =α + β1 F GDit I(θ1 ≤ GRPit < θ2 )(7)

+ β2 F GDit I(GRPit ≥ θ2 ) + γXit + ζi + δt + ϵit ,In this model, β1 captures the eﬀectiveness of FGD for provinces below the thresholdand β2 captures that for provinces above the thresholds. We expect that β2 is lower than13

β1 , since a higher level of economic development would lead to the better enforcement ofenvironmental regulations. Figure 2 shows the scatter plot between GRPit and F GDit . In
our estimation, data for Beijing, Tianjin, Shanghai and Guizhou are excluded as outliers.
[Figure 2]
The estimation results are presented in Table 4. The coeﬃcients of F GDit (θ1 ≤ GRPit <θ2 ) and F GDit (GRPit ≥ θ2 ) are statistically significant in all models.7 In columns (1) and(3), we find that the threshold estimator is 23.34. The result means that the eﬀectivenessof the installtion of FGD equipment diﬀers for provinces whose per capita GRP is below
or above 23,000 RMB. The estimated coeﬃcients also suggest that a 10, 000-ton increase in
FGD capacity is associated with a 6, 000-ton reduction in total SO2 emissions when GRP per
capita is less than 23,000 RMB in a province. The eﬀectiveness of the same FGD capacity
is a 9, 300-ton reduction in total SO2 emissions when GRP per capita is more than 23,000
RMB.
In 2005, only six provinces were above the threshold value, namely Beijing, Tianjin,
Shanghai, Jiangsu, Zhejang, and Guangdong (Figure 3), and there were large disparities
between those provinces whose GRP was the highest and lowest. The lowest and the highest
GRP per capita was about 5,000 RMB and 48,000 RMB, respectively. Indeed, because of therapid economic development in China at that time, 12 provinces were above the thresholdin 2010 and 19 were below. Further, between 2008 and 2009, average GRP per capita in
China rose above the threshold. Higher GRP per capita leads to higher monitoring andenforcement, thereby increasing the eﬀectiveness of the installation of FGD equipment.
[Figure 2][Table 4][Figure 3]
7

The threshold eﬀect test shows that the threshold is not significant for the models of emissions from theindustrial sector. The LR test statistics also reject the existence of two thresholds for the models of total
emissions.

146Conclusion
This study investigated the impact of policies for controlling SO2 emissions in the 11th

Five-Year Plan period, using a provincial-level panel dataset in China. First, the results ofour model suggest that the policy to install FGD equipment might have reduced total andindustrial SO2 emissions from 2006 to 2010. The estimated coeﬃcient can be interpreted asthat an increase in the 10, 000-ton capacity of FGD equipment is associated with a 6, 000-ton
reduction in SO2 discharge. Second, the closure of small coal-fired power plants might havehelped reduce SO2 emissions, too. The size of the estimated coeﬃcient suggests that theclosure of coal-fired power plants with a capacity of 10 MW can reduce roughly 130 tons of
total SO2 discharge, or 150 tons of SO2 from the industrial sector. These results suggest thatthe two policy measures examined herein played a significant role in reducing SO2 emissionsin China during that period, although the size of the estimated coeﬃcients shows that theeﬀects might have been weaker than those predicted by ex-ante cost-benefit analysis.
I addition, we used a non-dynamic panel threshold eﬀects model and found significant
diﬀerences in the eﬀectiveness of the installation of FGD equipment between provinces aboveand below a GRP per capita threshold. As the Chinese economy develops, the gap between
provinces will converge, leading to better monitoring and enforcement by the environmental
departments in each provinces. As of 2010, FGD equipment has already been installed in
86% of coal-fired power plants in China. The next Five-Year Plan, should aim to emphasize
controlling pollution by monitoring and enforcing the use and operation of this installed
equipment.

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1925Million ton
20
15

2000

2005
Year

2010

Total SO2

Industrial SO2

Figure 1: Trend of SO2 emissions in China (millions of tons)

20

50
40
FGD (10,000 ton)
20
30
10
0

0

20
Other Provinces40
GRP (10,000RMB)

60

Beijing, Tianjin, Shanghai and Guizhou

Figure 2: GRP and FGD capacity

21

80

Provinces below threshold05

Number of provinces
10
15
20

25

Provinces above threshold

2005

2006

2007

2008

2009

2010

Figure 3: Number of provinces below and above the threshold value

22

Table 1: Descriptive statistics
Unit
N Mean SD
10,000 t
217 75.78 46.88

Min
0.10

EmissionIndit

10,000 t

217

58.13

36.53

0.041 156.6

F GDit10,000 t

217

5.670

9.370 0.000 50.09

Closureit

10 MW

217

118.4

162.4 0.000 899.9

Levyi,t−1

10 million RMB

186

42.73

39.80

Eﬃciency it

%

217

0.200

0.040 0.070 0.380

GRPit

1,000 RMB

217

20.04

13.18

Contenti

%

217

0.850

0.150 0.520 0.940

Dummy

217

0.450

0.500 0.000 1.000

Variable
Emissionit

T CZi

Max
200.3

0.374 252.9

4.150

65.78

Note: Levy and per capita GRP are adjusted to 2004 value by using the CPI.

23

Table 2: Estimation results: Fixed eﬀects model
(1)
(2)
(3)
(4)
Dependent variable Emissionit EmissionIndit Emissionit EmissionIndit
F GDit-0.674∗∗∗-0.567∗∗∗-0.663∗∗∗-0.578∗∗∗(0.092)(0.104)(0.093)(0.108)-0.012∗∗(0.005)-0.014∗∗∗(0.004)

Levyi,t−1-0.010(0.029)0.016(0.028)Eﬃciency it-15.11∗(7.965)9.954(8.999)GRPit-0.116(0.259)-0.123(0.264)91.66∗∗∗(6.762)180
0.844

66.97∗∗∗(7.761)180
0.644

Closureit

Constant
N
Rw2-0.013∗∗(0.006)84.97∗∗∗(0.684)180
0.840-0.015∗∗∗(0.004)65.11∗∗∗(1.073)180
0.641

Note: ∗ ∗ ∗, ∗∗, and ∗ denote statistical significance at the 1%, 5%, and 10% levels, respectively.
All models include the year dummy variables from 2005 to 2010. Standard errors are clusteredby province and written in parentheses.

24

Table 3: Estimation results: Hybrid model
(1)
(2)
(3)
Dependent variable Emissionit EmissionIndit Emissionit
dF GDit-0.674∗∗∗-0.566∗∗∗-0.662∗∗∗(0.093)(0.104)(0.095)-0.012∗∗(0.005)-0.014∗∗∗(0.004)dLevyi,t−1-0.011(0.029)0.014(0.028)dEﬃciencyit-14.98∗(8.132)

10.11(9.174)dGRPit-0.117(0.265)-0.130(0.272)dClosureit-0.013∗∗(0.006)-0.015∗∗∗(0.004)(4)
EmissionIndit-0.575∗∗∗(0.111)mF GDit4.197∗∗∗(0.766)2.680∗∗∗(0.617)2.630∗∗∗(0.703)1.604∗∗(0.630)mClosureit

0.0596(0.043)0.0743∗∗(0.033)0.0667∗∗(0.029)0.073∗∗(0.036)mLevyi,t−1

0.432∗∗(0.206)0.298(0.199)mEﬃciency it-296.0(182.0)-62.54(211.3)mGRPit-0.288(0.369)-0.481(0.447)

Contenti-46.46(35.28)-5.297(27.25)

T CZi

6.354
(9.550)5.034(7.505)131.9∗∗∗(33.56)
180
0.844

50.63
(36.21)180
0.644

Constant
N
Rw2

0
(.)
180
0.840

0
(.)
180
0.641

Note: All variables starting with a small d indicate the deviations from the mean value and all
variables starting with a small m indicate the average among periods. ∗ ∗ ∗, ∗∗, and ∗ denotestatistical significance at the 1%, 5%, and 10% levels, respectively. All models include the year
dummy variables from 2005 to 2010. Standard errors are clustered by province and written inparentheses.

25

Table 4: Results of the threshold eﬀects (cleaned sample)
(1)
(2)
(3)
(4)
Dependent variable
Emissionit EmissionIndit Emissionit EmissionIndit
F GDit (θ1 ≤ GRPit < θ2 )-0.605∗∗∗-0.486∗∗∗-0.587∗∗∗-0.484∗∗∗(0.067)(0.123)(0.081)(0.142)F GDit (GRPit ≥ θ2 )-0.914∗∗∗(0.150)-0.783∗∗∗(0.120)-0.929∗∗∗(0.218)-0.862∗∗∗(0.131)

Closureit-0.008∗∗(0.004)-0.010∗∗(0.004)-0.008∗∗(0.004)-0.010∗∗(0.004)

Levyi,t−1-0.014(0.025)-0.002(0.025)Eﬃciency it-14.14∗(7.339)9.945(9.888)

GRPit

0.107
(0.442)0.306(0.393)89.91∗∗∗(9.214)
156
0.876
1
23.34
0.063

61.68∗∗∗(9.451)
156
0.715
1
23.70
0.227

ConstantN
Rw2
Threshold number
Threshold value
Threshold test

88.95∗∗∗(1.478)
156
0.874
1
23.34
0.010

70.72∗∗∗(1.706)
156
0.711
1
23.70
0.173

Note: ∗ ∗ ∗, ∗∗, and ∗ denote statistical significance at the 1%, 5%, and 10% levels, respectively. All
models include the year dummy variables from 2005 to 2010. Robust standard errors are written inparentheses.

26