Cleaning up the air for the 2008 Beijing Olympic
Games: Empirical study on China’s thermal power
sectorTeng Ma
Kenji Takeuchi

September 2017
Discussion Paper No.1718

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANCleaning up the air for the 2008 Beijing Olympic Games:
Empirical study on China’s thermal power sector∗
Teng Ma and Kenji Takeuchi†
Graduate School of Economics, Kobe University

September 22, 2017

Abstract
This study examines the eﬀects of air pollution control within the thermal power sector during the 2008 Beijing Olympic Games (BOG08). Using data on pollution control
equipment and energy intensity, we investigate for significant diﬀerences in their levels
between provinces under the regional control policy for BOG08 and other provinces.
The results suggest that the energy intensity of thermal power plants improved in 2007
and 2008 in provinces designated as areas requiring coordinated air pollution control
for the Olympic Games. On the other hand, we found weaker statistical evidence fortreatment eﬀects on pollution control equipment.

Keywords: Air pollution; China; Beijing Olympic Games; Thermal power sector
JEL Classification Codes: Q52, L51, L94∗

Earlier versions of this paper has been presented at the Workshop on Energy Conservation in Keihanna, the 7th Congress of EAAERE, and the 2017 Annual Meeting of SEEPS. The authors thank Toshi
Arimura, Hajime Katayama, and Michikazu Kojima for helpful comments. This work was supported by
JSPS KAKENHI Grant Numbers JP16H03006, JP26241033.
†
E-mail: mt yx@hotmail.com (Ma), takeuchi@econ.kobe-u.ac.jp (Takeuchi).

11Introduction
Air pollution is a global risk factor for various diseases. According to the Health Eﬀects

Institute (2017), exposure to PM2.5 is the fifth largest risk factor leading to death and is
responsible for 4.2 million deaths caused by a heart disease, stroke, lung cancer, chroniclung disease, or respiratory infections. The pollution level is particularly serious in Asian
countries: 86% of the most extreme concentrations (above 75µg/m3 ) are experienced bypopulations in China, India, Pakistan, and Bangladesh. Although stringent environmental
policies are needed to reduce harmful health risks in these countries, it is often diﬃcult to
enforce eﬀective regulations because of rapid economic growth and population increases.
In this study, we examine the eﬀect of the 2008 Beijing Olympic Games (BOG08) on air
pollution control in Beijing and neighboring provinces. As an event attracting international
attentions, the Olympic Games serve as an opportunity for a country to promote an ambitious
policy for environmental improvement. The Beijing Olympic Committee for the Games of
XXIX Olympiad (BOCOG) and the Beijing Municipal Government launched the concept of“Green Olympics” and integrated several environmental targets in the bid with accelerated
deadlines. In particular, air quality improvement was of high priority in the planning for the
Games. The resultant air pollution control reduced the air pollution index (API) in Beijing
by 24.9% during the Games, compared with the index value reported one year prior to any
Olympic-motivated actions (Chen et al., 2013).
Several studies have investigated the impact of air quality control during the BOG08.
Chen et al. (2013) use oﬃcially reported API values for 2000–2009 and show that policy
measures improved the Beijing’s API during and shortly after the Games. In addition,
they find that most of the improvement in air quality dissipated one year after the Games,suggesting that the improvement was temporary. He et al. (2016) estimate the eﬀects of air
pollution on mortality in China using exogenous variations in air quality during the BOG08.
and show that monthly PM10 concentrations in Beijing were reduced by approximately 30%.
In addition, the authors find that a 10% reduction in PM10 concentrations is associated with2

an 8% decrease in the overall mortality rate. Viard and Fu (2015) evaluate the pollution
and labor supply reductions from driving restrictions, including the period of the BOG08.
Employing daily data from multiple monitoring stations, the authors find that the aggregate
API fell 18% during the implementation of the odd–even policy, which restricted cars tobeing driven only every other day. However, these studies do not fully address the reductionof air pollution in stationary sources, particularly in the thermal power sector.
This study investigates the pollution reduction within the thermal power sector during
the BOG08. Using data on pollution control equipment and energy intensity, we investigate
for significant diﬀerences in their levels between provinces under the regional control policy
for BOG08 and other provinces. The results of this study suggest that installation level ofpollution control equipment and energy intensity improved in Beijing and the neighboring
provinces in 2007 and 2008, although the statistical evidence for the control equipment is
not very strong.
This study makes three contributions to the literature. First, in contrast to previous
studies that examine the impact of BOG08 on air quality improvement (Chen et al., 2013;
He et al., 2015), we examine the impact of the Games on control measures for air pollution.
Because the control measures are directly related to the behavior of emission sources, they
can reveal the impact of various policies enforced during the period on emission. Second, thisstudy considers two activities as control measures: installation of pollution control equipment
and improvement in energy intensity. Both are important measures determining emissions
from thermal power plants, where the former relates to end-of-pipe control and the latter tocleaner production processes. Comparing the Olympic eﬀect on these two measures, we canidentify the stage of pollution reduction that played a more important role in reducing air
pollution in the Beijing. Finally, this study focuses on pollution control in the thermal power
sector, a major contributor to air pollution. Wang et al. (2010) analyze pollution emission
reductions during the BOG08 and find that in the pre-Games period, power plants were thelargest contributors to SO2 emissions, which accounted for 11% of total SO2 emissions in3

Beijing. Thus, this study complements previous studies that focus on other sectors (Viard
and Fu, 2015; Sun et al, 2014) and can help understand air quality control during the Olympic
Games in a rapidly growing economy.
The remainder of this paper is organized as follows. Section 2 presents the backgroundof this study. We review various air pollution control policies implemented for the BOG08 in
Beijing and surrounding areas. Section 3 analyzes the eﬀect of the BOG08 on the installationof pollution control equipment. Section 4 examines the eﬀect of BOG08 on energy intensity.
Section 5 concludes.

2

Background
The BOCOG was established in December 2001 to bid for China’s Olympic Games.

To promote the environmental sustainability of the BOG08, the BOCOG and the Beijing
Municipal Government proposed the concept of the “Green Olympics” (UNEP 2009). Air
quality, in particular, was considered a concern because of the direct impact of poor air
on the health and performance of athletes. The measures taken to reduce air pollution for
the Games can be classified under the following five objectives: reduce energy consumption
growth and improve energy eﬃciency; close and relocate production lines in major industrial
sectors; reduce emission in transportation sector; control dust from construction; and otherspecial short-term measures.
Reduced air pollution from the thermal power sector was achieved through various options, which can be classified into adoption of end-of-pipe technology (installation of desulfuration, denitration, and dust removal equipment for boilers) and improvement in energy
intensity (use of clean fuels to replace coal fuels; reduce industrial use of coal; and shut
down of ineﬃcient power plants, steel companies, and other petrochemical plants). Beijing’s
eﬀort to reduce air pollutants in the thermal power sector for the BOG08 began in late 2002
(Chen et al., 2013). In 2003 and 2004, Beijing reduced its industrial use of coal by 10 million

4tons and shut down coal-fired generators at the Capital Steel Company and Beijing Coking
Plant. Between 2005 and 2006, desulfuration, denitration, and dust removal facilities wereconstructed at the Beijing Thermal Power Plant and the power plant at Capital Steel. Further, Beijing renovated 100% of its boilers for clean fuel in five districts and an additional
50% in three other districts by late 2006.
As Streets et al. (2007) suggest, neighboring provinces and municipalities, such as Hebei,
Shandong, and Tianjin, significantly contribute to air pollution in Beijing. Acknowledging
this viewpoint, in October 2007, the State Council of China issued Measures to Ensure GoodAir Quality in the 29th Beijing Olympics and Paralympics (MEGA policy), which is a wider
regional control policy for six provinces, including Beijing and the neighboring provinces of
Tianjin, Hebei, Shandong, Shanxi, and Inner Mongolia (He et al., 2016). Beijing and theseneighboring provinces and cities were required to retire outdated production facilities in
power plants and to install desulfurization facilities. The Chinese Ministry of Environmental
Protection coordinated with the governments of these provinces and cities to improve air
quality through cooperation (Chinese Ministry of Environmental Protection, 2012).
Provinces and cities included under the MEGA policy made various attempts to reduce
air pollution. For instance, Tianjin’s municipal government required all thermal power plants
to install desulfurization equipment by June 2008 (People’s Daily, 2008). Furthermore, allcoal-fired boilers were asked to adopt clean coal (Tianjin Municipal Government, 2008).
In Hebei Province, a list was compiled of small thermal power plants that were required
to shut down in 2007. In Shijiazhuang, all coal-fired boilers with a capacity of more thanfour tons in Tangshan, Langfang, and Baoding, which are key regions designated by Hebei
Provincial Government, were required to install desulfurization and dust removal equipment. In addition, all boilers for electricity generation in the key regions were ordered to use
clean coal (Hebei Provincial Government, 2007). Shanxi Province is a major coal producer
and the largest electricity provider in China. Shanxi’s provincial government requested
several thermal power plants to install desulfurization equipment during the pre-Olympic

5Games (i.e., from November 2007 to July 2008). In addition, during the Olympic Games,
the SO2 and NOx discharges in thermal power plants were strictly controlled. Furthermore,
the monitoring of air pollution was reinforced during the period (Environmental Protection
Bureau of Shanxi Province, 2007). By 2007, Shandong reduced SO2 emissions by 1.82
million tons, which is a 7.12% reduction compared with the emission level in 2006. Thermal
power plants that failed to achieve the SO2 standards in the province were required to shut
down during the BOG08 (Shandong Provincial Government, 2008). In Inner Mongolia, theProvincial Government implemented a regional control policy for Huhhot, Baotou, Chifeng,
Xilingol, and Ulanqab. In these area as well, thermal power plants were required to install
desulfurization and denitration equipment or shut down from January 1, 2007, to September
20, 2008 (the Government of Inner Mongolia Autonomous Region, 2007). These policy
measures in neighboring provinces were crucial to reducing air pollution in Beijing. Xu etal. (2016) analyze the impact of air pollution controls in the North China Plain during
the BOG08 and find that a large reduction of PM2.5 can be mainly attributed to regionaltransportation within the area beyond Beijing.
We estimate the eﬀects of BOG08 on air pollution controls in the thermal power sector
using a sample of six provinces in the treatment area and nine provinces in the control
area (Table 1). The treatment area includes provinces under the MEGA policy: Beijing,
Tianjin, Hebei, Shandong, Shanxi, and Inner Mongolia. The control area comprises provinces
neighboring the treatment area: Heilongjiang, Jilin, Liaoning, Gansu, Ningxia, Shaanxi,
Henan, Anhui, and Jiangsu.
[Figure 1]

3

Data
We use two primary datasets for dependent variables throughout our analysis. First, we

employ installation of pollution control equipment in the thermal power sector. We examine
6

two types of pollution control equipment: a desulfurization system to remove sulfur and adenitration system to remove nitrogen oxides. Both data are adopted from the website forthe data center of the Chinese Ministry of Environmental Protection. Second, we use energy
intensity of the thermal power plants, which is adopted from the Compilation of Statistics on
Chinese Electric Power Industry (China Electricity Council, respective years). In addition,
we employ control variables that possibly aﬀect the installation of control equipment andenergy intensity.

3.1

Pollution control data

Data on pollution control equipment are obtained from the List of Pollution Control
Equipment of Coal-Fired Boilers published by the data center at the Chinese Ministry ofEnvironmental Protection (2014a, 2014b, 2015).1 The list contains information on 4,659 coalfired boilers that have installed desulfurization equipment and 1,135 coal-fired boilers with
denitration equipment. More specifically, it includes the name of each boiler, the province
where the boiler is located, year in which the boiler began operation, boiler capacity, typeof desulfurization and denitration technology, year of equipment installation, and name ofcompany that manufactured pollution control equipment. We use the year of equipment
installation and aggregate the capacity of boilers with installed equipment by province foreach year from 2003 to 2012. Dividing this number by the total capacity of boilers operatingin each province per year, for which data are taken from the China Energy Statistical Yearbook, we construct provincial-level panel data for the share of boilers that installed pollution
control equipment among all boilers.
Table 1 provides the summary statistics of the pollution control data. During the study
period, the average share of boilers with pollution control equipment is 55.4% in the treatment area and 53.2% in the control area for desulfurization equipment and 17.3% in thetreatment area and 10.6% in the control area for denitration equipment. Because the policy
1

http://datacenter.mep.gov.cn

7

for SO2 control was implemented before that for NOx control, the average share of boilers
with desulfurization equipment is higher than that of boilers with denitration equipment.
[Table 1]

3.2

Energy intensity data

We obtained energy intensity data for 4,568 thermal power plants between 2003 and 2012
from the Compilation of Statistics on Chinese Electric Power Industry published by the
China Electricity Council.2 Our sample includes plants whose main purpose is to generate
electricity and those in other industries such as steel and chemicals. The mean value ofenergy intensity for all thermal power plants in China is 409 grams of coal equivalent (gce)per kWh. Approximately 92% of thermal power plants in our sample are coal-fired. Energy
intensity in a coal-powered plant is generally higher than that in an oil- and gas-fired power
plant.
Table 2 reports the descriptive statistics for the energy intensity data. Our sample
comprises 6,265 plants in the treatment area and 7,010 plants in the control area. Averageenergy intensity in the treatment area is 8.9% higher than that in the control area.
[Table 2]

3.3

Control variables

The control variables for the pollution control regressions include gross regional production (GRP) per capita, pollution levy, and electricity price. Data for GRP per capita
are adopted from the China Statistical Yearbook. Pollution levy data for each province
are taken from the China Environment Yearbook (Editorial Committee of China Environment Yearbook, respective years). The on-grid electricity prices are from the CEIC (2015)
database.
2

We use “gross coal consumption rate” to measure the energy intensity of each plant.

8For the energy intensity regressions, we include self-use, generation, and utilization as
control variables. Self-use is a variable that represents percentage of electricity used by aplant in each year. Generation is the amount of electricity generated by the plant in eachyear. Utilization is the plant’s total operational hours in each year. All data are taken fromthe Compilation of Statistics on Chinese Electric Power Industry.

4Eﬀect of BOG08 on pollution control
We use a diﬀerence-in-diﬀerence (DID) model to estimate the impact of the BOG08 on air

pollution controls by thermal power plants. The estimation model is represented as follows:

Si = α + γArear + λT imek + βAreaT imerk + ϕXi + εi ,(1)where Si is the installation share of desulfurization (Share sulf uri ) or denitration(Share nitrogeni ) equipment by generation capacity in province i. The share is calculated by dividing the total capacity of boilers that installed end-of-pipe equipment by the
total capacity of thermal power boilers in each province. Arear is the dummy variable fortreatment area, which takes the value of 1 for six provinces in which the MEGA policy wasapplied and 0 otherwise. T imek is the dummy variable for treatment period, which takes
the value of 1 for the period 2007–2008 and 0 otherwise. Because the MEGA policy wasissued in October 2007 and the Games were completed on September 17, 2008, we define
the treatment period as 2007–2008.3 AreaT imerk is the cross-term for Arear and T imek tocapture the impact of MEGA policy on the installation level of end-of-pipe equipment.
In addition, we include several control variables (Xi ) in the model to not only control for
confounding trends but also reduce the variance of εi . We use three control variables in themodel. First, Levyi denotes the pollution levy paid for total pollutants discharged in province3

As noted in the previous section, the preparation for the BOG08 in Beijing began in late 2002. Thus,
our definition of the treatment period might be narrow to capture the total eﬀect of policies for the BOG08
implemented in the preparation period.

9

i. China has a pollution levy system since 1979 and significantly increased the levy rates in2003 (Lin, 2013). The benefits of pollution control are expected to increase with higher levelsof pollution levy. This provides stronger incentives for the thermal power sector to install
end-of-pipe equipment. The pollution levy of SO2 emissions increased several times from
0.21 RMB/kg in 2002 to 1.26 RMB/kg in 2007 (JES, 2007; OECD, 2007). Second, GRPi
indicates the GRP per capita in province i and captures the level of economic developmentin each province. We assume that the share of boilers with control equipment is higher indeveloped provinces. Third, P ricei represents the on-grid electricity price in province i. In
July 2003, a market-based pricing system was introduced in the thermal power sector. We
assume that a higher on-grid tariﬀ is associated with a greater capacity share of end-of-pipe
equipment owing to higher profit.
As of 2013, among 1,835 China’s independent power producer, approximately 74% of
thermal power boilers installed desulfuration equipment and 22% had denitration equipment(Chinese Ministry of Environmental Protection, 2015).4 Figure 2 illustrates the trends ofdesulfuration equipment installation in the thermal power sector. The Chinese government
initiated eﬀorts to reduce SO2 emission much before the preparation period of the Beijing
Olympic Games. The central government reinforced a policy to control SO2 discharge fromthe thermal power sector in the Tenth (2001–2005) and Eleventh Five-Year Plan (2006–2010)
periods. In the Tenth Five-Year Plan period, a total of 75 power plants (165.85 MW) installed
desulfurization equipment. Following this period, a larger-scale desulfurization plan5 wasissued in Eleventh Five-Year Plan period. The plan defines targets for desulfurization at
the provincial level to reduce SO2 . That is, 248 power plants (126.19 GW) were ordered to
install desulfurization equipment and 679 power plants (51.48 GW) were instructed to shut
down within the period.
The capacity share for desulfurization equipment in the treatment area was higher than
4

Note that our calculation of the share of boilers that installed control equipment is based on the capacityof boilers and does not coincide with these figures.
5
In 2007, the State Environmental Protection Administration and National Development and Reform
Commission issued National Acid Rain and SO2 Pollution Control in the 11th Five-Year Plan

10

that in the control area till 2009, but decreased following 2010. The SO2 emissions control
policy in China began during the Tenth Five-Year Plan period (2001–2005). Until the endof 2007, 17 GW of thermal power boilers cumulatively installed desulfurization equipmentin the treatment area. We hypothesize that, although the SO2 emissions control policies
were implemented before the BOG08 preparation period, the installation of desulfurizationequipment accelerated because of the greater demand for cleaner air during the BOG08
period in the treatment area than in the control area.
[Figure 2]
In contrast to the regulation of SO2 emissions, the government began controlling NOx
emissions from the thermal power sector in the Eleventh Five-Year Plan (2006–2010) andseriously implemented controls in the Twelfth Five-Year Plan period (2011–2015). Figure 3
presents the trends in the denitration equipment installation in the thermal power sector.
The capacity share of installed denitration equipment in the treatment area is higher thanthat in the control area from 2007 to 2013.
[Figure 3]
The results of the simple DID model are presented in Table 3. Columns (1) and (2) list theresults for desulfurization equipment and columns (3) and (4) present those for denitrationequipment. The models in columns (2) and (4) are estimated with control variables. The
cross-term AreaT imerk are statistically insignificant in both desulfurization and denitration
models. This suggests no significant impact by the BOG08 on the level of end-of-pipe control.
[Table 3]
Next, we estimate a fixed-eﬀects model to explore the panel structure of the data (15provinces for 10 years). The dummy variables for provinces and year are included in themodel: Dum proi denotes the province dummy and Dum yearj is the year dummy that uses
2006 (j = −1) as the base year.
11Sit = α+βAreaT imerk +

15
∑
i=2

ξi Dum proi +5∑

ηj Dum yearj (t = m+j)+ϕXit +υi +εit , (2)j=−4where υi is an unobservable individual eﬀect and εit is idiosyncratic errors. Table 4 reports the results for the fixed-eﬀects model. The coeﬃcients of AreaT imerk are positive andstatistically significant at 10% in the models for desulfurization equipment without control
variables; however, they are insignificant in models with control variables. As for the models for denitration equipment, the coeﬃcients of AreaT imerk are positive and statisticallysignificant at 10% without control variables and at 5% with control variables.6[Table 4]
To further investigate the eﬀects of a regional control policy in each year, we use a
multiple-period fixed-eﬀects DID model. We introduce AreaDum yearrt , which are interaction terms between Arear and Dum yeart . The coeﬃcient of AreaDum yearrt demonstrates
the eﬀects of a regional control policy on the installation of end-of-pipe equipment by eachyear.

Sit =α +5∑

βj AreaDum yearrt (t = m + j) +

15
∑

j=−4

+

5
∑

i=2

ξi Dum proi(3)

ηj Dum yeart (t = m + j) + υi + εit ,j=−4where the excluded time category is 2006 (j = −1) such that the eﬀects are measured relative
to the year prior to the implementation of the MEGA policy. Figure 4 shows the treatmentarea eﬀect on desulfurization equipment installation for each year. The installation eﬀects
of desulfurization equipment are negative but statistically insignificant during 2007–2008.
The result suggests that the installation of desulfurization equipment in the treatment area
6

When using a random eﬀects model, the coeﬃcient of AreaT imerk is positive but statistically insignificant. The chi-square statistics of Hausman test is negative, suggesting that the test is invalid for thesemodels. Considering the small sample size, here, we report the results for fixed eﬀects model.

12does not significantly diﬀer from that in the control area in these periods compared to 2006.
In addition, the figure shows that the impact is negative and statistically significant after
2010. This suggests that, after the BOG08 period, the numbers of installations of control
equipment increased in the control area compared to the treatment area.
[Figure 4]
Figure 5 reports the estimation results for denitration equipment for each year. In particular, the eﬀects on denitration equipment in each year are statistically insignificant, suggesting that the treatment area eﬀect in each year is not significantly diﬀerent from that in2006.
[Figure 5]
To further compare the treatment eﬀects by provinces, we use a multiple-region fixedeﬀects DID model. Dum AreaproT imeik is the cross-term for Dum Areaproi and T imek ,where Dum Areaproi is a dummy variable for each province in the treatment area andBeijing (i = 1) is used as a base category. It captures the diﬀerence in the installationlevel of end-of-pipe equipment in each province of treatment area during the BOG08 by thecoeﬃcient βi for the ith province.

Sit =α +
+

6
∑

i=2
5
∑

βi Dum AreaproT imeik +6∑

ξi Dum proi

i=2

(4)

ηj Dum yearj (t = m + j) + υi + εit .

j=−4Figure 6 summarizes the results. We find that the eﬀects of the BOG08 on desulfurizationequipment are statistically significant only in Tianjin. The installation level of desulfurization equipment in Tianjin is 37% lower than that in Beijing in both 2007 and 2008. The
coeﬃcients for other provinces are statistically insignificant, suggesting that the installationlevel of desulfurization equipment in other provinces does not significantly diﬀerent fromthat in Beijing.
13

[Figure 6]
Figure 7 presents the results for the multiple-region fixed-eﬀects DID model for denitration equipment installation. Compared to Beijing, the eﬀects of the BOG08 are negative andstatistically significant in other provinces. The installation level of denitration equipment
in Beijing is 30% higher than that in other provinces under the MEGA policy during the
BOG08 period. In other words, the positive installation eﬀect for denitration equipment
might be due to the early introduction of the equipment in Beijing, not the MEGA policy,.
[Figure 7]
In sum, our results suggest that the BOG08 promote the installation of pollution equipment, although the statistical evidence is weak. The treatment eﬀect during the BOG08
is not significant in the simple DID model, but it is significant in some of the fixed eﬀects
models. Following the Olympic Games period, the capacity share of desulfurization equipment in the treatment area becomes lower than that in the control area. The result for themultiple-period fixed-eﬀects DID model indicates that eﬀects of a MEGA policy no longer
exist after the Olympic Games period. Furthermore, the BOG08 eﬀect on denitration might
be attributed to the early introduction of equipment in Beijing, not the regional control policy for the Games. The installation of denitration has been promoted at the national level
since the Twelfth Five-Year Plan (2011–2015), which is the period after the BOG08. Of the
1,135 thermal power boilers examined in this study, only 37 thermal power boilers installed
denitration equipment during the Beijing Olympic Games period (2007–2008), where mostof the thermal power boilers (958 boilers) did so during 2011–2013.

5

Eﬀect of BOG08 on energy intensity
First, we estimate the eﬀects of the BOG08 on energy intensity using the following simple

DID model:
14

EIi = α + γArear + λT imek + βAreaT imerk + ϕXi + εi ,(5)where dependent variable EIi denotes energy intensity in thermal power plant i and measures
the amount of energy (in coal equivalent) used to generate one kWh of electricity. Higher
energy intensity generally means lower energy eﬃciency of a power plant. The averageenergy intensity in our data is 428 gce/kWh in the treatment area, and 392 gce/kWh in thecontrol area. Arear is a dummy variable that represents plants located in provinces under
the MEGA policy. Our sample comprises of 1,246 thermal power plants (approximately
27.28% of all thermal power plants in China) in the treatment area and 1,865 thermal power
plants (approximately 40.83% of all thermal power plants in China) in the control area. The
dummy variable T imek takes the value of 1 if the period is 2007 or 2008, and 0 otherwise.
AreaT imerk indicates the cross-term of Arear and T imek . Xi represents the control variables
that include Self usei , Generationi , and U tilizationi . Self usei indicates the rate of selfconsumption of electricity in power plant i. A higher number for Self usei means reductionof the electricity transmitted to grid.7 The reduction in self-consumption improves the net
unit heat. Generationi is the electricity generation of power plant i. In our sample, 40%of the thermal power plants have generation capacity greater than 300 GWh.8 U tilizationi
represents the annual utilization hours in plant i. Longer utilization time means greater
electricity generation. The average annual utilization hour in the treatment area is 4,848
hours, which is slightly lower than that in the control area (5,045 hours).
We also estimate the models with panel data settings, as follows:

EIit = α + γArear + λT imek + βAreaT imerk + ϕXit + υi + εit ,(6)assuming that counterfactual outcomes in the absence of treatment are independent of treat-

7

The self consumption of electricity includes a feed-water system, cooling water system, pollution control
system, combustion air and fuel gas, fuel handing, and other loads.
8
In China, a plant with generation capacity larger than 300 GWh is defined as a large power plant.

15

ment, and conditional on an unobservable individual eﬀect υi and covariates Xit . The Hausman test rejected the null hypothesis that the individual-level eﬀects are adequately modeledby a fixed eﬀects model (p-value=0.466). We use a random eﬀects model, because the sample
size is large enough. Under large N and fixed T asymptotics, any kind of serial dependence
is allowed in the observables and unobservables.
Figure 8 shows changes in energy intensity during the study period. The energy intensity
level in both areas is steadily decreasing, and although the level in the treatment area isalways higher than that in the control area.9 The figure shows that energy intensity in thetreatment area sharply declined in 2007 and was close to that in the control area. In Beijing,several large thermal power plants and 16,000 boilers located in the central area of Beijing
were requested to use cleaner fuels during the BOG08 period (Beijing Daily, 2008). Following
the environmental standard DB12/151-2003, Tianjin’s municipal government requested allcoal-fired boilers use low-sulfur coal. The Hebei Provincial Government asked all coal-fired
boilers in Shijiazhuang, Tangshan, Langfang, and Baoding to use clean coal (Hebei Provincial
Government, 2007). This variety of policies in MEGA policy areas could have been eﬀective
in reducing energy intensity during the BOG08 period.
[Figure 8]
Table 5 reports the results for pooled ordinary least squares in columns (1) and (2) andthose for the random eﬀects models in columns (3) and (4). Models in columns (2) and (4)9Several national-level polices have contributed to the steady decrease of energy intensity. For example,
in November 2004, the National Development and Reform Commission issued the China Medium and Long
Term Energy Conservation Plan (Ke et al., 2012). In terms of the thermal power sector, the plan targeted
the decrease of the energy consumption index (ECI) per unit in the power supply sector from 392 gce/kWhin 2000 to 320 gce/kWh in 2020. In addition, under the Coal-fired Industrial Boiler Retrofit Projects, anadditional 5% of coal use was reduced for coal-fired boilers. Furthermore, the central government planned toreduce 25 million tons of coal by using high quality coal and by adopting advanced technologies during the
Eleventh Five-Year Plan period (National Development and Reform Commission, 2006). In 2006, China’s
central government launched the Top-1000 Enterprises Project, which aimed to improve the industrial energy
eﬃciency of the largest energy users in China’s industrial sector. The project includes 1,008 enterprises witha minimum annual energy consumption of 180,000 tce and set a 100 million tce energy-saving target during
the Eleventh Five-Year Plan (National Development and Reform Commission, 2006). A total of 132 thermal
power plant can be found in the list (40 thermal plants are observed in the treatment area and 51 thermal
plants in the control area). The target was achieved by the end of the Eleventh Five-Year Plan (Ke et al.,2012).

16

are estimated with control variables. Arear is positive and statistically significant, whichmeans that the energy intensity in the treatment area is significantly higher than that in
the control area. T imek is negative and statistically significant, which suggests that energy
intensity is significantly improved during the BOG08 period. The cross-term AreaT imerk
is negative and statistically significant in the random eﬀects models, suggesting that energy
intensity in the treatment area improved during the BOG08 period.
[Table 5]
Next, we use a multiple-period random-eﬀects DID model to estimate the year eﬀects.

EIit =α + γArear +5∑

βj AreaDum yearrt (t = m + j) +

j=−4

5
∑

ηj Dum yeart (t = m + j)j=−4

+ υi + εit ,(7)
where AreaDum yearrt are the cross-terms of Arear and Dum yeart . These variables capture the eﬀects of the MEGA policy on energy intensity in each year. βj is the coeﬃcient on
the jth lead or lag.
Figure 9 shows the eﬀects of the BOG08 on energy intensity in each year. Compared tothe base year (2006), the eﬀect is negative and statistically significant in 2007, 2008, and 2009.
Energy intensity in the treatment area shows a declining trend since 2006, with a decrease of
32.52 gce/kWh, 30.28 gce/kWh, and 20.09 gce/kWh in 2007, 2008, and 2009, respectively.
From this, it can be interpreted that the eﬀect of the BOG08 on the improvement of energy
intensity was maintained for three years. The reductions are 7.5%, 7%, 4.6% of the averageenergy intensity in the treatment area. These are sizable numbers compared to the estimate
presented in Chan et al. (2017), who show that electricity restructuring led to a 1.4%
improvement in China’s energy eﬃciency.
[Figure 9]17

To evaluate the eﬀects of the BOG08 on energy intensity in each province, we use a
multiple-region random-eﬀects DID model as follows:

EIit =α + γT imek +6∑βi Dum AreaproT imeik +

i=2

6
∑

ηDum proi

i=2

(8)

+ υi + εit .
Figure 10 summarizes the eﬀects of the BOG08 on energy intensity in each province
under the MEGA policy. The results show that, compared to Beijing, the reduction ofenergy intensity in the other five provinces are statistically insignificant during the BOG08
period. This suggests that the impact of the MEGA policy does not diﬀer by the provinces
involved.
[Figure 10]
The improvement of energy intensity can be attributed to several factors. First, the useof cleaner coal is relatively easier measure to reduce energy intensity. Although the diﬀerenceby the provinces are not statistically significant (Figure 10), a large improvement of energy
intensity in Shanxi compared to that in Beijing suggest that there is a higher impact of the
MEGA policy in the coal rich province. Second, the closure of small thermal power plants
directly aﬀects energy intensity by reducing operation of boilers with lower eﬃciency. Figure
11 compares the Kernel density for generation capacity of power plants in the treatmentarea between 2006 and 2008. It shows that the density of smaller power plants are higher
in 2006. While the total number of power plants in operation does not change during thestudy period, there is a shift in the distribution of generation capacity.
[Figure 11]
The results can be summarized as follows. First, energy intensity significantly reducedin the treatment area during the BOG08 period. Under the stringent policy, thermal power
plants began using cleaner fuel and made other eﬀorts to reduce coal consumption. These18

eﬀorts might contribute to the improvement of energy intensity in the treatment area withheavily polluting thermal power plants. Second, the eﬀects of the BOG08 on the energy
intensity of thermal power sector were sustained until 2008. This finding is in line with
Chen et al.’s (2013) result that the eﬀect of the BOG08 on air pollution reduction dissipated
one year after the Games.

6

Robustness checks
To confirm the robustness of the main results, we conduct estimations using an alternative

definition of the control area and treatment period. First, we extend the control area fromnine provinces neighboring the treatment area to all provinces in China other than thetreatment area. We also estimated the model using 2008 as the treatment period instead of2007 and 2008.
Table 6 reports the estimation results. The numbers presented are the eﬀects of the
MEGA policy on the installation of desulfurization equipment and denitration equipment
and energy intensity during the BOG08 period. The estimated coeﬃcients for desulfurization equipment are statistically insignificant at all conventional significance levels. The
coeﬃcients for denitration equipment are statistically significant in columns (3) and (4);however, they are insignificant in columns (5) and (6). The coeﬃcients for energy intensity are negative and statistically significant in all alternative models, except in column (6),where the eﬀect is not statistically significant. In summary, the estimation results indicatethat the main results are robust to alternative definitions of the control area and treatment
period.
[Table 6]197Conclusions
This study examined the impacts of the BOG08 on pollution control in the China’s

thermal power sector. Our results indicate that the energy intensity of thermal power plants
improved in 2007 and 2008 in provinces designated as areas requiring a coordinated air
pollution control policy for the Olympic Games. On the other hand, evidence of such atreatment eﬀect is weak for the eﬀect on pollution control equipment.
The reason for the slightly contrasting results might be attributed to diﬀerences in characteristics between end-of-pipe controls and improvement in energy intensity. Typically, theend-of-pipe control strategy entails considerable amount of investment and requires considerable fixed costs. The reduction of energy intensity, such as using cleaner fuel or reducing
waste heat, is relatively cheaper and aﬀordable even for power plants that do not have sufficient financial resources. Hammar and Löfgren (2010) investigate drivers for investments
in end-of-pipe solutions and clean technologies and find that the expenditures for environmental R&D played an important role in the former type of investment, while energy priceis important for the latter.
Our results complement those of Chen et al. (2013) and He et al. (2016), who present
a significant but temporal eﬀect of the BOG08 on air quality improvement. Our findings
suggest that energy intensity improved during the studied period in the treatment area.
Because the improvement in energy intensity has persistent eﬀects over the long term, theobserved change in the thermal power plants has significant policy implications in contrast
to the temporal eﬀects caused by reduced traﬃc volume (Chen, 2013). Although we could
not examine the impact of the improvements in energy intensity on reduced air pollution in
the area, it is reasonable to expect that there is a considerable linkage between them. To
sustain good air quality even after the Beijing Olympic period, policies with a long-term
impact are crucial.

20References
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24

Table 1: Descriptive statistics for pollution control equipment
Treatment Area

Control Area

6 provinces

9 provinces

Unit

N

Mean

SD

N

Mean

SD

Share sulf uri

%

60

55.4

0.315

90

53.2

0.365

Share nitrogeni

%

60

17.3

0.279

90

10.6

0.174

T imek

Dummy

60

0.200

0.403

90

0.200

0.402

GRPi

10,000 RMB

60

40.65

22.75

90

24.75

14.22

Levyi

Billion RMB

60

0.744

0.583

90

0.529

0.434

P ricei

RMB

60

0.585

0.088

90

0.628

0.083

Note: The province-level data are from 2004 to 2013.

Table 2: Descriptive statistics for energy intensity

Unit

Treatment Area

Control Area

6 provinces

9 provinces

N

Mean

SD

N

Mean

SD

EIi

gce/kWh 6,264

427.9

302.3

7,010

392.8

184.5

T imek

Dummy

6,265

0.219

0.413

7,010

0.218

0.413

U tilizationi

h

6,265

4,836

1,987

7,010

4,936

1,921

Self usei

%

6,077

9.892

4.948

6,862

8.382

4.968

Generationi

GWh

6,265

1,271

2,872

7,008

1,239

2,232

Note: Our sample includes unbalanced panel data from 2003 to 2012. There are 1,206 thermal power
plants in Beijing and its surrounding regions.

25

Table 3: Simple DID results for pollution control equipment(1)
Share sulf uri(2)
Share sulf uri(3)
Share nitrogeni(4)
Share nitrogeni

T imek

-0.091(0.068)-0.021(0.068)-0.124∗∗∗(0.022)-0.061∗∗∗(0.018)

Arear

0.003
(0.068)-0.215∗∗∗(0.058)0.052(0.045)-0.079∗∗(0.034)

AreaT imerk

0.098(0.097)0.063(0.094)0.078(0.098)0.086(0.077)Si

GRPi

0.010∗∗∗(0.001)0.008∗∗∗(0.001)Levyi

0.219∗∗∗(0.043)-0.057∗(0.030)P ricei-0.429(0.266)-0.225(0.139)Constant
N
Rw2

0.550∗∗∗(0.047)150
0.008

0.442∗∗(0.178)150
0.346

0.130∗∗∗(0.022)150
0.055

Note: Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

26

0.083
(0.086)150
0.491

Table 4: Fixed eﬀects DID results for pollution control equipment
Sit
AreaT imerk(1)
Share sulf uri(2)
Share sulf uri(3)
Share nitrogeni(4)
Share nitrogeni

0.098∗(0.050)0.077(0.055)0.078∗(0.046)0.115∗∗(0.049)GRPit-0.005∗(0.002)0.005∗∗(0.002)

Levyit0.067(0.049)-0.199∗∗∗(0.070)P riceit-0.227(0.144)0.167(0.127)

Constant
Year dummy
Province dummyN
Rw2-0.045(0.104)
Yes
Yes
150
0.902

0.333
(0.211)
Yes
Yes
150
0.906

0.438∗∗∗(0.118)
Yes
Yes
150
0.804

Note: Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

27

0.051
(0.221)
Yes
Yes
150
0.833

Table 5: Simple and random eﬀects DID results in energy intensity
(1)
POLS

(2)
POLS

(3)
RE

(4)
RE

Arear

37.54∗∗∗(5.330)16.65∗∗∗(5.211)52.71∗∗∗(13.27)39.97∗∗∗(13.21)

T imek

-9.621∗∗(4.813)-10.05∗∗(4.226)36.50∗∗∗(5.107)33.08∗∗∗(4.475)

AreaT imerk-11.06(8.490)-1.522(7.989)-20.27∗∗∗(6.873)-15.85∗∗(6.792)

EIi

Self usei

12.66∗∗∗(0.797)5.309∗∗∗(0.863)Generationi-0.011∗∗∗(0.001)-0.002∗∗(0.001)

U tilizationi

0.001(0.002)-0.0001(0.002)394.9∗∗∗(2.579)Year dummyNo
Province dummyNoN
13,274
2
R
0.006
Rb2

296.2∗∗∗(11.69)NoNo
12,9360.094

Constant

352.5∗∗∗(5.237)
Yes
Yes
13,274314.7∗∗∗(11.83)
Yes
Yes
12,936

0.043

0.074

Note: Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, ***p < 0.01.

28

Table 6: BOG08 eﬀects: alternative definition of control(1)
Baseline(2)
Baseline(3)
All Provinces(4)
All Provinces

(5)
2008

(6)
2008

Desulfurization

0.098∗(0.050)0.077(0.055)0.038(0.048)0.030(0.057)0.063(0.054)0.051(0.055)N
2
Rw
Denitration

150
0.902
0.078∗(0.046)150
0.906
0.115∗∗(0.049)310
0.752
0.167∗(0.098)310
0.779
0.132∗∗(0.064)

150
0.899
0.074
(0.065)

150
0.905
0.091
(0.065)

N
2
Rw
Energy intensity

150
0.804
-20.27∗∗∗(6.873)150
0.833-15.85∗∗(6.792)310
0.558-18.62∗∗∗(6.432)310
0.710-11.75∗(6.776)150
0.802-16.71∗∗(7.772)150
0.826-9.701(7.189)N
Rb2

13,2740.043

12,9360.074

18,9350.055

18,4240.120

13,2740.043

12,9360.074

Note: Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. The numbers denotethe coeﬃcient of the interaction term between the treatment area and treatment period. In columns (3) and(4), the control area includes all provinces in China except the treatment area. In columns (5) and (6), thetreatment period is defined as 2008 instead of 2007 and 2008. Control variables are used in column (2), (4),and (6). The year dummy variables and province dummy variables are used in the analysis of energy intensity.

29

Figure 1: Treatment area and control area30

Installation share of desulfurization equipment (%)
0
20
40
60
80
100

2004

2005

2006

2007

2008 2009
year

2010

2011

2012

Mean of share in treatment area
Mean of share in control area

Figure 2: Share of desulfurization equipment
31

2013

Installation share of denitration equipment (%)
0
20
40
60
80
100

2004

2005

2006

2007

2008 2009
Year

2010

2011

2012

Mean of share in treatment area
Mean of share in control area

Figure 3: Share of denitration equipment
32

2013

20
Effects on desulfurization equipment (%)
-20
0
-40

2004

2005

2006

2007

2008 2009
Year

2010

2011

2012

2013

Note: Dashed lines indicate 90% confidence intervals.

Figure 4: Multiple-period model for desulfurization equipment33

30
Effects on denitration equipment (%)
-10
0
10
20
-20

2004

2005

2006

2007

2008 2009
Year

2010

2011

2012

2013

Note: Dashed lines indicate 90% confidence intervals.

Figure 5: Multiple-period model for denitration equipment
34

Regional effects on desulfurization equipment (%)
-60
-40
-20
0
20

Beijing

Tianjin

Hebei

Shanxi
Province

Inner Mongolia Shandong _Note: Dashed lines indicate 90% confidence intervals.

Figure 6: Multiple-region model for desulfurization equipment
35

Regional effects on denitration equipment (%)
-80
-60
-40
-20
0

Beijing

Tianjin

Hebei

Shanxi
Province

Inner Mongolia Shandong _Note: Dashed lines indicate 90% confidence intervals.

Figure 7: Multiple-region model for denitration equipment36

600
Mean of energy intensity (gce/kWh)
400
500
300

2002

2004

2006

2008

2010

Year
Treatment Area

Control Area

Figure 8: Energy intensity
37

2012

Effects on energy intensity (gce/kWh)
0
50
-50

2003

2004

2005

2006

2007 2008
Year

2009

2010

2011

2012

Note: Dashed lines indicate 90% confidence intervals.

Figure 9: Multiple-period model for energy intensity38

100
Effects on energy intensity (gce/kWh)
-100
-50
0
50
-150

Beijing

Tianjin

Hebei

Shanxi
Province

Inner-mongoila Shandong _Note: Dashed lines indicate 90% confidence intervals.

Figure 10: Multiple-region model for energy intensity
39

.003
.002
Density
.001
0

0

200

400
600
Capacity (MW)
2006

800

1000

2008

Figure 11: Multiple-region model for energy intensity40