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Impact of environmental regulations on the efficiency and CO
2
emissions
of power plants in China
Xiaoli Zhao
a
, Haitao Yin
b,
⇑
, Yue Zhao
c
a
School of Business Administration, China University of Petroleum-Beijing, Beijing, China
b
Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China
c
China Resources Power Holdings Company Limited, Shenzhen, China
highlights
An empirical study with 137 power plants in China.
Market based regulations help improve efficiency and reduce CO
2
emission.
Government subsidies help improve efficiency and reduce CO
2
emission.
Command and control regulations do not have a clear impact.
China should utilize more market based regulations in its power industry.
article info
Article history:
Received 15 October 2014
Received in revised form 24 February 2015
Accepted 22 March 2015
Keywords:
Environmental regulations
Operational efficiency
CO
2
emissions
Power plants
China
abstract
The power industry is the largest air polluter in China, contributing nearly 40% of CO
2
emissions and 60%
of SO
2
emissions. Under mounting pressure to improve standards of environmental protection, it is
imperative that the industry increases the efficiency and environmental performance of power plants
in China. We investigate the impacts of three different environmental regulations on efficiency improve-
ment and CO
2
reduction: command and control regulations (CCR), market-based regulations (MBR), and
government subsidies (GS). We find that MBR and GS have a positive impact on efficiency improvement
and CO
2
reduction. However, CCR have no significant impacts. This finding has important implications
since CCR dominates China’s environmental policy. We discuss the policy implications of these findings,
such as China should further release the potential of MBR in the power industry, instead of solely relying
on CCR; and pay more attention to the coordination of different policy instruments.
Ó2015 Elsevier Ltd. All rights reserved.
1. Introduction
In response to the economic and environmental challenges
posed by energy use, Chinese policy makers have formulated tar-
gets for reducing energy consumption and CO
2
emissions in terms
of not only intensity, but also amount. Besides the target set for
China at the Copenhagen International Climate Change
Conference in 2009, in 2011 the State Council issued The Twelfth
Five-year Energy Saving and Emissions Reduction Comprehensive
Plan and set a goal of reducing energy consumption by 16% per
10,000 Yuan GDP by 2015 from 2010 levels and 32% from 2005
levels. Furthermore, the plan established goals based on absolute
amounts: for example, saving 670 million tonnes of coal equivalent
(TCE) during the five-year period 2010–2015, reducing chemical
oxygen demand (COD) and SO
2
emissions by 8%, and reducing
ammonia and nitrogen oxide emissions by 10% during the same
period.
Without question, it will require significant policy intervention
to achieve these environmental goals. Chinese policy makers face a
critical question: should they fall back on the traditional command
and control approach, or intensify the use of environmental
economic instruments? Research has shown that firms behave dif-
ferently under different policy regimes, which in turn determines
policy outcomes [1,2]. Our central task is to study how different
types of regulations affect Chinese power plants’ operational effi-
ciency and environmental performance. We chose the power
industry for the following reasons. First, it is a primary pollution
source, accounting for around 40% of total CO
2
emissions and
60% of total SO
2
emissions in China in the past thirty years.
Second, relative to other energy sectors the power industry is flexi-
ble when it comes to fuel choices and technological solutions for
http://dx.doi.org/10.1016/j.apenergy.2015.03.112
0306-2619/Ó2015 Elsevier Ltd. All rights reserved.
⇑
Corresponding author.
E-mail address: haitao.yin@gmail.com (H. Yin).
Applied Energy 149 (2015) 238–247
Contents lists available at ScienceDirect
Applied Energy
journal homepage: www.elsevier.com/locate/apenergy
environmental problems, and as a result, shows wide variation
between firms in terms of environmental improvement. Third,
power plants are relatively concentrated and thus easy to regulate
[3].
We created a plant-level database based on data collected from
China Electric Power Statistical Compilation (CEPSC) (2011 and
2012) and surveys of power plant managers. Based on this data-
base, we analyzed the impact of various environmental policies
on the operational efficiency and CO
2
emissions of power plants.
The policy variables are captured by the perception of plant
managers. We found that power plants that perceive a stronger
influence from market-based regulations (MBR) perform better
on operational efficiency and CO
2
emissions than the plants
that perceive a stronger influence from command and control
regulations (CCR)
1
. In contrast, power plants that perceive a
stronger influence from CCR do not perform significantly better or
worse in terms of operational efficiency and CO
2
emissions.
The rest of our paper proceeds as follows. In Section 2we
review the related literature, before going on to describe the
Chinese environmental regulations that affect the power industry
in Section 3. We discuss our data collection, measurement, and
analytical models in Section 4, and in Section 5we present and
discuss our empirical findings. In Section 6, we conclude our paper.
2. Literature review
2.1. CO
2
mitigation in China
Because of China’s prominent role in combating global warm-
ing, factors affecting CO
2
emissions in China have recently become
an actively researched topic [4–8]. Some of the literature has
explored the link between CO
2
emissions and China’s economic
growth [4,6,7], however, these papers found mixed results. For
example, Zhang and Cheng [6] argued that neither CO
2
emissions
nor energy consumption affect economic growth. However, Bloch
et al. ag Bloch et al. [7] found that energy consumption affects
economic growth positively. Su and Ang [8] investigated the
impact of inter-regional trade and international trade on CO
2
emissions.
Some other papers have studied CO
2
emissions in specific sec-
tors, for instance, electricity generation [5,9,10,11], transportation
[12], construction [13], cement production [14], and iron and steel
sector [15,16]. Xu et al. [17] investigated the changes of energy-
related GHG emissions in five sectors in China (agricultural sector,
industrial sector, transportation sector, commercial & service
sector, and the residential sector). To the best of our knowledge,
only four papers have examined CO
2
reduction in the sector of
electricity generation in China. Gnansounou et al. [5] analyzed
the strategic technology options for mitigating CO
2
emissions in
Shanghai’s power sector. Cong and Wei [9] studied the impact of
carbon emissions trading (CET) on China’s power sector. Zhao
et al. [10] studied the major factors that have influenced CO
2
emissions within China’s power industry from 1980 to 2010.
Zhou et al. [11] studied the impact of CDM on the carbon emission
in power industry in the city of Shenzhen in China.
In contrast to these previous studies of CO
2
mitigation in China,
this study explores how to promote CO
2
reduction in China’s
power sector based on a plant-level database. Specifically, we
investigate the role of environmental regulations, which are an
important instrument in motivating power plants to save energy
and reduce emissions.
2.2. Environmental regulations and operational efficiency
The relationship between environmental regulations and opera-
tional efficiency is important because firms are unlikely to
voluntarily comply with and exceed environmental regulations
unless they can simultaneously achieve efficiency gains by doing
so. Two radically different views exist on the relationship between
environmental regulation and corporate performance. The classical
school [18]; 1992; [19] argues that economic and environmental
goals often conflict with each other and environmental regulation
may negatively impact corporate efficiency. In contrast, the
revisionist school, notably the ‘‘Porter hypothesis’’, considers
environmental regulation to have a positive impact on technologi-
cal innovation and, therefore, on corporate efficiency [20,21].
Scholars have investigated whether the impact of environmen-
tal regulation on corporate performance is contingent upon the
type of environmental regulations and the type of firms. Zerbe
[22] argued that a pollution tax seems to have the most positive
impact on corporate innovation, while a production tax has the
weakest. Downing and White [23] found that, compared to CCR,
MBR promote corporate innovation and thus efficiency more effec-
tively. Milliman and Prince [24] concluded that direct controls usu-
ally provide the fewest incentives to promote technological
change; on the other hand, market-based measures, such as emis-
sions taxes and permit auctions, can offer the greatest incentive to
promote technological change. Williams [25] pointed out that MBR
create more incentives to enhance efficiency than command-and-
control regulation. These studies are all based on theoretical
analysis. Some other studies explored the issue from an empirical
perspective. Testa et al. [26] found that direct regulation has the
most effective role on corporate performance improvement, while
MBR (economic instruments) has negative effects on business
performance.
Several researchers have investigated empirically the impact of
environmental regulations on corporate performance in China
[28,27,29]. However, most of these studies are built on industry-
or provincial-level data [27,29]. Such data neglect variation across
firms, the primary units that face environmental regulation and
make decisions on efficiency and environmental management
[30]. Zhang et al. [28] based their study on firm-level data, but
the sampled firms were from Wujin county of the Jiangsu province,
which limits our ability to generalize the findings of the study.
2.3. Environmental regulations and environmental performance
Scholars have also investigated the impact of different types of
environmental policy on corporate environmental performance.
For instance, Ellerman et al. [31] reported that SO
2
emissions in
the U.S. fell dramatically relative to previous levels and also
relative to levels that likely would have been obtained in the
absence of a cap and trade program. Knoar and Cohen [32] found
that firms with the largest stock price decline on the day that the
Toxic Release Inventory became public subsequently reduced
emissions more than their industry peers, providing evidence that
a mandatory information disclosure program is an effective instru-
ment for environmental management. Cui et al. [33] explored the
cost-saving effect of China’s emission trading scheme on CO
2
emission reduction. They argued that the cost-saving effects of
the eastern and western provinces are more pronounced than the
central provinces. Zhou et al. [10] concluded that CDM effectively
facilitates the development of renewable energy and CO
2
emission
reduction.
More closely related to our study, several scholars compared the
impact of different types of regulations on corporate environmental
performance. Mao et al. [11] examined and compared the impact of
various environmental policies – carbon tax, energy tax, clean
1
We classify the perceived influence of environmental regulations (for example,
CCR) by power plants into 7 levels (1–7), with 7 for the strongest influence and 1 for
the least.
X. Zhao et al. / Applied Energy 149 (2015) 238–247 239
energy vehicle subsidy, and reduction on ticket price – on the CO
2
emissions in China’s transportation sector. Fischer and Newell
[34] assessed different policies for reducing CO
2
and found that
the most cost-effective policy is emissions price, followed by
emissions performance standard, fossil power tax, renewable share
requirement, renewable subsidy and R&D subsidy.
Building on these papers, we studied a sample of power plants
scattered across different regions of China. We distinguish three
types of environmental regulations: command and control (CCR),
market-based (MBR), and subsidies (GS), and investigate how
these three types of environmental regulation affect corporate
operational efficiency and CO
2
emissions differently. We measure
operational efficiency with Malmquist Index, in which power
capacity, coal consumption and employee number are used as
input variables and power generation as the output variables.
CCR have dominated China’s environmental regulation. Studying
the different impacts that various types of regulations have on effi-
ciency and CO
2
emissions may help inform future environmental
regulation reform in China and improve the effectiveness of its
environmental regulation.
3. Environmental regulation in China’s power industry
As a country with a long history of strong, centralized govern-
ment, most of China’s environmental regulations follow a com-
mand and control approach. Specifically on improving energy
efficiency, the regulations include The Notice on Several Issues of
Improving China’s Energy Efficiency in 1979, The Notice on
Progressively Setting up the Evaluation Institute of Integrated Energy
Consumption in 1980, and The Specific Requirements of Energy
Saving for Industrial and Mining Enterprises and Cities (Trial) in 1981.
With regard to environmental protection, The Managerial
Guidelines for Standards of Environmental Protection in China was
promulgated in 1983, which set standards for air, water and soil
quality as well as standards for pollutant discharge and environ-
mental monitoring. In the 1990s, as concern for environmental
protection increased, China’s regulators paid more attention to pol-
lutant control in the power industry and other pollution-intensive
industries. In 1991, China published Standards for Air Pollutant
Discharge from Thermal Power Plants (GB 13223-1991), which was
revised in 1996, 2003, and 2011. This is a performance-based reg-
ulation and the latest revision established the following standards
for pollutant discharge. The upper limit for soot emissions is 30 mg
per cubic meter, 100 mg per cubic meter for SO
2
from newly
installed boilers, 200 mg per cubic meter for SO
2
from existing boi-
lers, 100 mg per cubic meter for NO
x
, and 0.03 mg per cubic meter
for mercury and its compounds. Technology-based regulations
were also ratified during this time to curb power industry
pollution. For example, The Division Program of Acid Rain and SO
2
Control Zones, published in January 1998, required all thermal
plants under construction to install desulfurization facilities [35].
Although not a primary approach, MBR developed to promote
environmental protection in the power industry. For example, in
1982, The Interim Measures of Pollution Charges stipulated that firms
should pay 40 CNY for every ton of SO
2
or NO
x
emissions above
specified standards, and 3 to 10 CNY per thousand cubic meters of
sulfuric acid mist, lead and mercury exceeding specified standards.
This is a very typical pollution charge system. Meanwhile, GS were
often implemented by increasing regulated electricity prices. For
example, to support power industry desulfurization and denitra-
tion, China increased the tariff by 15 CNY per MWH for desulfuriza-
tion in 2004 and by 8 CNY per MWH for denitration in 2013.
Although these subsidies are not sufficient to cover all pollution
abatement costs [36], they serve as an important economic support
for corporate efforts to reduce SO
2
and NO
x
emissions.
Besides pollution charges and subsidies, the Clean Development
Mechanism (CDM) and Cap & Trade system, two other marked-
based policies, have been implemented or tried in China. To pro-
mote CDM development, China’s government promulgated The
Operation and Management Measures of CDM projects (Trial) in
2004. In the same year, the first CDM project, HuiTengXiLe wind
farm in Inner Mongolia, began operations. China’s CDM projects
focus mainly on the power industry. By the end of 2010, renewable
energy sector CDM projects accounted for 71% of total approved
projects [37].
As for Cap & Trade, the Chinese Ministry of Environmental
Protection (MEP) set it as an important task to promote the emis-
sion rights trading of key pollutants during the ‘‘Eleventh Five
Year’’ period (2006–2010). The MEP selected eight provinces and
cities, including Jiangsu, Zhejiang, Tianjin, Hubei, Hunan, Shanxi,
Inner Mongolia and Chongqing, as pilot areas for SO
2
emission
rights trading. However, in reality there is little substantial and
voluntary trading of SO
2
emission rights, and most of the few
transactions that have occurred have been government facilitated.
As for the trading of carbon emission rights in China, the first pilot
program was setup in Shenzhen in June 2013. By the end of April
2014, six provincial entities had followed in the footsteps of
Shenzhen, including Shanghai, Beijing, Tianjin, Chongqing,
Guangdong, and Hubei. However, the trading volume of CO
2
emis-
sions only accounts for a very limited proportion of national CO
2
emissions.
In sum, CCR still dominate China’s power industry environmen-
tal regulations, although MBR and GS are burgeoning. As discussed
previously (Section 2), generally, various types of environmental
regulations could have different influences on corporate efficiency
and environmental performance. In the following sections, we will
empirically analyze the impact of various policies on the corporate
efficiency and environmental performance of China’s power plants,
and provide policy implications on how to promote the sustainable
development of China’s power industry with the consideration of
environmental issues.
4. Methodology
In this section, we introduce the sample and data collection
first, then discuss how we measured plants’ operational efficiency,
CO
2
emissions, and the influence firms perceived from different
types of environmental regulations.
4.1. Sample and data collection
We collected our data from two major sources: China Electric
Power Statistical Compilation (CEPSC) (2011 and 2012) and our
survey of power plant managers (Table 1). The CEPSC provided
information on power capacity and coal consumption in 2011
and 2012. To the best of our knowledge, plant-level employee
numbers are not publicly available. Therefore, we estimated the
number of employees based on the Labor Quota used in the power
industry. Specifically, we followed Labor Force Quota for Thermal
Power Plants (SPC, 1998), Labor Force Quota for General Thermal
Power Plants and Labor Force Quota for New Thermal Power
Plants (CHC, 2008) to estimate employee numbers for all power
plants. Three factors play significant roles in the estimation: power
plant capacity, when the power plants were established, and the
capacity of each unit in a power plant. We also collect labor force
information in the questionnaire survey as a complement. More
specifically, we ask the respondents to mark his or her power plant
in four categories: less than 100, 100–500, 501–2000, and more
than 2000. Labor force information from the survey is used in the
regression analyses.
240 X. Zhao et al. / Applied Energy 149 (2015) 238–247
Our survey asked plant managers to assess their perceived
influence from three policy types – CCR, MBR, and GS (The items
of capturing the three types of policies are shown in Appendix
Table A) – and report when the power plant was established. We
sent the questionnaires to 308 power plants in 22 of China’s 31
provincial entities in 2012. A total of 172 were returned, and 137
were valid (for a 55.84% response rate and 79.65% validity rate).
Questionnaires were deemed invalid mainly because of non-
response to a significant portion of the survey questions or having
the same answer for all questions. Invalid questionnaires were
distributed randomly across firms, implying that invalid question-
naires do not bring a systematic bias into the evaluation.
In order to measure the perceived influence from CCR, MBR, and
GS, we develop a set of questions using Likert scale [38], the most
commonly used method for perception measurements. Oaster [39]
indicated that a 7-point scale showed the highest reliability.
Therefore, the 7-point Likert scale is used in this study.
The survey was conducted in two formats: through email and
through on-the-spot surveys. We selected firms with which we
had personal contacts and distributed the questionnaires to the
leaders in these firms who were responsible for making strategy
and other types of management decisions. The second method
was on-spot survey. We made use of the opportunity provided
by training courses for leading cadres (who came from all over
China) of power firms in the North China Electric Power
University. The topic of the training courses focused on the issues
of firm strategy and management. Hence, participants of the train-
ing courses were leaders responsible for making strategy and other
types of management decisions in their firms. The sample covers
power plants across most regions of China (Fig. 1), including 22
provinces. Moreover, the sample covers various sizes of power
plants (Fig. 2) and all the five Power Corporations in China.
Hence, the sample does not bias toward a certain type, but cover
a full range of power plants in China.
To develop an accurate survey, we not only reviewed the
related literature [24,40] and so on), but also interviewed nine
leaders from the China Guodian Corporation, the China Huadian
Corporation, and the China State Grid Energy Research Institute,
who specialize in issues related to energy savings and emissions
reductions. Their suggestions and comments helped us refine our
questionnaire.
4.2. Efficiency
We used the Malmquist Index to capture efficiency change in
our study. The Malmquist Index is a nonparametric approach to
describe efficiency, which needs not assume particular functional
forms the way that parametric approaches do. Following Färe
et al. [41], we assumed a power plant uses ntypes of inputs X
t
k;n
and produces mtypes of outputs Y
t
k;m
. Then, the output-oriented
Malmquist Index can be defined as:
M
t
0
¼D
t
0
ðx
tþ1
;y
tþ1
Þ
D
t
0
ðx
t
;y
t
Þ;M
tþ1
0
¼D
tþ1
0
ðx
tþ1
;y
tþ1
Þ
D
tþ1
0
ðx
t
;y
t
Þð1Þ
M
t
0
measures technical efficiency change from period tto t+1 in
relation to the technology at t(the reference technology). M
tþ1
0
mea-
sures technical efficiency change from period tto t+ 1 in relation to
the technology at t+1.
To avoid arbitrariness in choosing a benchmark, Färe et al. [41]
specified an output-based Malmquist productivity change index as
the geometric mean of two types of Malmquist productivity
indexes (this is typical of Fisher ideal indexes), which is shown
as Eq. (2).
M0ðxt;yt;xtþ1;ytþ1Þ¼Dtþ1
0:
v
rsðytþ1;xtþ1Þ
Dt
0:
v
rsðyt;xtÞ:Dt
0:crsðytþ1;xtþ1Þ
Dtþ1
0:crsðytþ1;xtþ1Þ:Dt
0:crsðyt;xtÞ
Dtþ1
0:crsðyt;xtÞ
"#
1
2
ð2Þ
where M
0
(x
t
,y
t
,x
t+1
,y
t+1
) represents the change in productivity
between years tand t+1;D
t
0:crs
ðy
tþ1
;x
tþ1
Þrepresents the technical
efficiency of an observation from period t+ 1 in relation to the tech-
nology at t;D
t
0:crs
ðy
t
;x
t
Þrepresents the technical efficiency of an
observation from period tin relation to the technology at t;
Table 1
Variables and the data source.
Variable
descriptions
Variable
name
Data source
Dependent
variables
Efficiency change EF Calculated by authors
CO
2
emission CO
2
Calculated by authors
Independent
variables
Command and
control regulation
CCR Questionnaires
Market based
regulation
MBR Questionnaires
Government subsidy GS Questionnaires
Other
variables
Power capacity PC CEPSC
Employee number SC Questionnaires and
calculated by authors
Power plant age T Questionnaires
Coal consumption CC CEPSC
Power generation PG CEPSC
23%
37%
32%
8%
Northeast areas Eastern coastal areas
Middle areas West areas
Fig. 1. Region distribution of sampled power plants.
3%
33%
44%
20%
Less than 100 100-500
501-2000 More than 2000
Fig. 2. Size distribution of sampled power plants.
X. Zhao et al. / Applied Energy 149 (2015) 238–247 241
D
tþ1
0:crs
ðy
tþ1
;x
tþ1
Þrepresents the technical efficiency of an observation
from period t+ 1 in relation to the technology at t+1; D
tþ1
0:crs
ðy
t
;x
t
Þ
represents the technical efficiency of an observation from period t
in relation to the technology at t+1.
If the value of M
0
(x
t
,y
t
,x
t+1
,y
t+1
)is greater than 1, it indicates
increased productivity, while an equal value indicates no change
and a value lower than 1 reflects declining productivity.
D
tþ1
0:
v
rs
ðy
tþ1
;x
tþ1
Þ
D
t
0:
v
rs
ðy
t
;x
t
Þ
measures the efficiency change between years tand
t+ 1, and
D
t
0:crs
ðy
tþ1
;x
tþ1
Þ
D
tþ1
0:crs
ðy
tþ1
;x
tþ1
Þ
:
D
t
0:crs
ðy
t
;x
t
Þ
D
tþ1
0:crs
ðy
t
;x
t
Þ
1
2
measures the technical change
between years tand t+1.
We calculate a Malmquist productivity index for our sample of
137 electric plants in China, part of which is shown in Table 2.We
chose three input variables: (1) power capacity, which is a proxy
for fixed assets; (2) the number of employees, which is calculated
based on Labor Quota Standards for Thermal Power Plants by the
National Power Company in 1998, and similar documents by the
China Huadian Corporation in 2004 and 2008 depending on the
establishment year of each power plant; and (3) coal consumption.
The output variable is the amount of power generation.
In Table 2, the average score of efficiency change is greater than
2, which seems to be a little bit high. However, it is reasonable
when considering the fact that China’s government made a 4000
billion Yuan investment in 2009 to stimulating the economy. One
of side effects of this huge investment is the rapid growth of
China’s energy intensive industries. The demands for electricity
and coal increase significantly. The demand and price for coal
demand reached a record high in 2011 [42]. This has led that coal
based power plants took a series of measures to promote
production efficiency, such as employing mixed coal combustion
technology, coal on-line inspection technology, and improving coal
inventory management, and so on. As a result, we witness a large
increase of production efficiency among thermal power plants over
this period in China.
4.3. CO
2
emissions
Power plant-level CO
2
emissions data is not publicly available.
Hence, we calculated the data based on the Intergovernmental
Panel on Climate Change (IPCC) recommended method.
CO
2i
¼fðE
i
Þ
fðE
i
Þ¼P
a
E
i
a¼
44
12
bd
P
ð3Þ
where CO
2i
is plant CO
2
emissions i;E
i
is plant energy consumption
i,ais the CO
2
emissions coefficient of coal from the IPCC Report
(2006), and 44/12 is the CO
2
conversion coefficient of carbon. bis
the carbon factor embodied in standard coal, dis the carbon oxida-
tion factor of standard coal, and Pis the calorific value of coal. We
calculated the CO
2
emissions from the 137 power plants, part of
which is shown in Table 3.
4.4. Types of environmental regulations
In line with the existing research [24,40,21] and based on our
discussion in the last paragraph of Section 2, our paper categorizes
environment regulations into three types: CCR, MBR, and GS.
CCR is mandatory by nature. It allows managers very little
freedom. Based on prior studies [19,40,43] and expert interviews,
we focus on five types of CCR in this study, including emissions
standards, fines, supervision, environmental assessment system,
and production technology standards (Appendix Table A). We
asked managers to assess the extent to which they are strongly
influenced by these five types of regulations.
MBR sends market signals and firms have flexibility in deciding
on an appropriate level of pollution abatement in response to these
signals. Based on prior research [44,23,24,19] and interviews with
experts who are familiar with the power industry in China, we
focused on three types of marked-based environmental policies:
tax credits (tax-exempt financing), clean development mechanism
(CDM), and emissions-trading systems (Appendix Table A). We
asked managers to assess the extent to which they are strongly
influenced by these three types of policies.
We distinguished GS from MBR because of the following two
reasons: first, GS has a few special features that other kinds of
MBR do not have. For example, GS has the revenue-recycling effect
to offset the impact of command and control or market-based poli-
cies [45]. Popp [46] also argued that GS would promote the effect
of carbon tax on emissions reduction. Second, some scholars have
expressed special concerns on GS role of controlling environmental
pollution [46–50]. Chau et al. [47] concluded that government sub-
sidy can be used to mitigate negative externalities. Bajona and
Kelly [48] stated that subsidies affect pollution in three ways:
pollution-causing capital accumulation, transferring of capital
and labor from more to less pollution-intensive firms, and increas-
ing output of production in more productive firms and thus
decreasing pollution. According to Maddison et al. [50], Beers and
Bergh [49] and expert interviews, GS, used as environmental
instruments, aim to compensate the extra cost that firms pay for
energy saving and emissions reduction. We differentiate three
types of GS: subsidy for new technological research and develop-
ment (R&D), subsidy for new technical production, and preferential
loan guarantees (Appendix Table A). We asked managers to assess
the extent to which they are strongly influenced by these three
types of GS.
Although most environmental regulations are enacted at the
national level, local governments have significant freedom in
deciding how to implement these policies that are often stipulated
in general terms. Therefore, if local governments have different
preferences toward market mechanisms or administrative tools,
power plants in some regions may perceive a stronger influence
from market-based environmental regulations, while those in
other regions may perceive a stronger influence from the
command and control approach. The development of a market
economy is significantly unbalanced across China. This is a result
of not only cultural differences, but, more importantly, a reflection
of the learn-by-doing nature of Chinese reform. The east coast pro-
vinces are where market reforms get started and take root, while
the northwest and northeast areas lag behind in moving from
strong government to market leadership. We divided China into
seven regions based mainly on Liu [40]
2
and looked at whether
power plants’ perception of different levels of influence from differ-
ent types of environmental regulations reflected a heterogeneous,
dominating administrative philosophy across different regions.
The differing perceptions relating to environmental regulations
across different regions are shown in Table 3. We categorize the
perception into seven levels from 1 to 7; 1 represents the percep-
tion of influence of a particular type of regulation is the weakest,
whereas 7 represents the strongest perception of influence of a par-
ticular type of regulation. Table 4 indicates that, in East-China,
power plants reported that market-based environmental reg-
ulations played a more significant role, while in the Northwest
and Northeast regions, command and control environmental reg-
2
Southwest region includes Sichuan, Chongqing, Guangxi, Yunnan, Guizhou, and
Tibet; Northwest region includes Shanxi, Xinjiang, Gansu, Ningxia, and Qinghai; South
China includes Guangdong, Fujian, and Hainan; Central region includes Henan, Hubei,
and Hunan; North China includes Shandong, Beijing, Tianjin, Hebei, Inner Mongolia,
and Shanxi; Northeast region includes Liaoning, Heilongjiang, and Jilin; and East
China includes Jiangsu, Shanghai, Zhejiang, Anhui, and Jiangxi.
242 X. Zhao et al. / Applied Energy 149 (2015) 238–247
ulations demonstrated a stronger influence. We performed an
ANOVA analysis which shows that the reported influence from
market-based environmental regulations is significantly different
between power plants in East-China and those in other regions
(Table 5).
We also performed an ANOVA analysis to test whether power
plants’ perceptions about the influence of GS differ significantly
across six regions of China: East-China, Central-China, North-
China, Northeast, Southwest, and Northwest
3
. The results show that
the null hypothesis is rejected at 1% level of confidence, because no
difference exists (Table 6). This implies that the reported influence
from GS is significantly different between different regions in China.
As for the CCR, a gap in perception exists only between East-
China and the Southwest (Table 7). Except for the Southwest, the
other regions have no statistical significance in the perception of
CCR influence on power plants. This may be explained by the fact
that hydropower dominates Southwest China. According to the
Annual Report of Electric Power Regulation (2011) data, hydro-
power constitutes 60.01% of Southwest China’s energy capacity.
Hence, CCR, which functions mainly against thermal power emis-
sions, plays a relatively weaker role in this region.
4.5. Control variables
We included age, size, and power demand in our regression
analyses as control variables. Joskow and Schmalensee [51]
pointed out that the technical profile of generation units, such as
age and size, could be potentially significant factors affecting effi-
ciency. Hence, we introduced the age of a power plant in our model
and defined it as the calendar year minus the year of initial opera-
tion. As for size, we divided enterprises into two groups: power
plants with 500 or fewer employees are defined as medium or
small enterprises, and those with more than 500 employees as
large ones. We coded the size variable as 0 for medium or small
enterprises and 1 for large enterprises.
With regard to power demand, some articles pointed out that
power demand had an impact on CO
2
emissions [52]; Zhao et al.,
2013). We used power capacity to measure this. One notable fea-
ture about China’s power market is that the power industry is
quickly upgrading due to increasing pressure for energy con-
servation and emissions reduction and, at the same time, the rapid
increase of power demand. The situation is prominently reflected
by ‘‘Build Large and Shut down the Small’’ policies implemented
in China’s power industry in 1999. In this context, China’s power
plants pay a lot of attention to replacing small power units with
large ones, which results in capacity change at plant level in line
with increasing power demand. As a result, we chose power capac-
ity as one of our control variables.
4.6. Model specifications
We used the Tobit model for our operational efficiency analysis.
As we have previously discussed, the Malmquist productivity
index is a chain index and captures the productivity change from
time tto t+ 1. Since the index is truncated at 0, the OLS regression
model would not provide consistent estimates. Instead, we employ
Tobit regression analyses. Tobin [53] put forward the Tobit model
for the first time, and it is frequently used by economists to analyze
incontinuous dependent variables, as well as to analyze the
Table 2
The efficiency change of the sampled power plants.
Summary statistics Efficiency change Technological progress change Pure technological efficiency change Scale efficiency change Total factor productivity
MEAN 2.106 0.583 2.004 1.080 1.076
Median 1.675 0.618 1.618 1.008 1.063
SD 2.311 0.155 5.234 0.235 0.390
Table 3
The CO
2
emissions of sampled power plants.
Summary
statistics
CO
2
emissions
(Thousands of
tons)
Power
generation
(100 GW h)
CO
2
emissions per power
generation (Thousands of
tons/100 GW h)
MEAN 8095.08 56.58 171.64
Median 4512.29 33.7 140.53
SD 13371.58 101.10 160.92
Table 4
Perception of environmental regulations is different across different regions
a
.
Regions Provinces CCR MBR GS
East-China JiangSu 4.44 4.67 6.44
ShangHai 3.97 6.05 5.53
JiangXi 4.67 6 5.67
Northwest NingXia 3.9 3.25 3.13
Gansu 4.83 4.75 5
XinJiang 5.67 2.75 2.33
Northeast HeiLongJiang 5.01 4.35 4.17
LiaoNing 4.49 4.08 4
JiLin 5.61 3.5 1.67
a
The table shows the mean values based on the Likert scale from 1 to 7.
Table 5
ANOVA analysis of perceptions to mbr between power plants in east china and those
in other regions.
Sum of squares df Mean square Fvalue Sig.
Between groups 6.002 1 6.002 5.652 0.037
Within groups 11.681 11 1.062
Total 17.683 12
Table 6
The ANOVA analysis of perception to the influence of gs across different regions of
China.
Sum of squares df Mean square Fvalue Sig.
Between groups 57.742 5 11.548 3.180 .010
Within groups 428.525 118 3.632
Total 486.267 123
Table 7
The ANOVA analysis of perception to the influence of GS between East China and the
Southwest China.
Sum of squares df Mean square Fvalue Sig.
Between groups 3.252 1 3.252 3.144 0.094
Within groups 17.581 17 1.034
Total 20.833 18
3
Since the data for South-China are from only one province, Fujian, the South-
China region is not included when performing this analysis.
X. Zhao et al. / Applied Energy 149 (2015) 238–247 243
dependent variables subject to a known upper or lower bound [54].
It is defined as the following two-stage function:
y
it
¼y
it
y
it
>0
0y
it
¼0
ð4Þ
In this study, y
it
¼aþbx
it
þe
it
can be expressed as follows:
EF ¼
a
þb
1
CCR þb
2
MBR þb
3
GS þb
4
PC þb
5
Tþb
6
CCR GS
þb
7
MBR GS þb
8
SC þb
9
CCR SC þb
10
MBR SC
þb
11
GS SC þ
e
ð5Þ
where EF stands for the change in power plants’ efficiency; PC
stands for power capacity; Tstands for power plant age; SC stands
for power plant size; and ecaptures other factors that affect the effi-
ciency but do not correlate with explanatory variables in the model.
The cross term between CCR and GS, as well as MBR and GS are
used to measure how the impact of CCR or MBR depends on the
availability and strength of GS. Generally, CCR or even MBR, such
as taxes, will increase companies’ compliance costs and this may
lead to companies having a negative attitude on environmental
regulation. However, if subsidies can be provided to companies
to encourage innovation and upgrades, this may help reduce
companies’ passive attitude toward abiding by environmental reg-
ulation, thus promoting efficiency improvement and CO
2
emissions
reduction. Popp [46] studied the combined roles of market-based
policies (carbon tax) and subsidies on CO
2
emissions reduction in
the U.S.; he concluded that a combination of a tax and R&D
subsidies was an optimal strategy for promoting CO
2
emissions
reduction in most cases.
Similarly, the cross term between environmental regulations
and size measures whether and how the impact of different types
of regulations is different for different-sized power plants.
We use an OLS model to analyze how selected factors affect CO
2
emissions intensity at the power plant-level. More specifically, the
meaning of cross term is similar to that in Eq. (5).
CO
2
¼
a
þb
1
CCR þb
2
MBR þb
3
GS þb
4
PC þb
5
Tþb
6
CCR GS
þb
7
MBR GS þb
8
SC þb
9
AP SC þb
10
MBR SC
þb
11
CCR SC þ
e
ð6Þ
where CO
2
denotes the CO
2
emissions per unit of power generation
and other variables are defined similarly as above.
5. Findings
5.1. Reliability and validity test
Since we collected our data on the perceived influence of
environmental regulations from a survey, reliability and validity
tests were necessary before performing regression analyses.
Reliability refers to the internal consistency of various items to
capture a latent variable (such as perceived influence from CCR).
Cronbach’s alpha (
a
) is often used for reliability testing. A
Cronbach’s
a
that is greater than 0.50 means relatively reliable, a
Cronbach’s
a
that is greater than 0.70 means reliable and a
Cronbach’s
a
that is greater than 0.90 means very reliable [55];
that is, the consistency of the items used to measure a latent
variable is high. We used SPSS 13.0 to analyze the reliability of
the environmental regulations. The results showed that the
Cronbach’s
a
of command and control and government subsidy
policies are all greater than 0.8 and the Cronbach’s
a
of MBR is
0.766. These results show that the internal consistency for our
measures for perceived influence of environmental regulations is
reliable or relatively reliable.
Validity refers to the extent that survey items measure the con-
cept that they aim to capture. Convergent validity tests, based on
principal component factor analysis, are often used to test the
validity of a variable. If a factor loading is more than 0.3, the vari-
able is considered to have an acceptable convergent validity. In
Yang and Wang [56] study, the factor loading of relative items ran-
ged from 0.36 to 0.81; in Garcı
´a-Bernal et al.’s study [57], the factor
loadings of relative items were between 0.428 and 0.934. Their
studies are considered to have a reasonable convergent validity.
The factor loadings of the items of command and control, mar-
ket-based, and government subsidy policies in our study are
between 0.458 and 0.864, showing that the validity of the variables
that measure the perception of environmental regulations is good.
5.2. Impact of environmental regulations on operational efficiency of
power plants
Table 8 presents the results from our regression analysis of
power plants’ operational efficiency (The descriptive statistics of
the variables in Tables 8 and 9 are shown in Appendix Table B).
It shows that power plants that report a stronger influence from
MBR and GS demonstrate a higher efficiency improvement. The
coefficients on these two variables were 0.2592 and 0.1935 respec-
tively and statistically significant. The impact of CCR on the effi-
ciency improvement of power plants was not statistically
significant. This shows that, under CCR, power plants do not have
flexibility to utilize measures that could be beneficial to efficiency
improvement, even when these opportunities exist. This result is
consistent with Zerbe [22], Downing and White [23], Milliman
and Prince [24], María et al. (2010), and Williams [25], all of which
argued that MBR is more likely to have positive impact on corpo-
rate competitiveness than CCR. Furthermore, this result implies
that the ‘‘crowding-out effect’’ and ‘‘constraint effect’’ caused by
environmental regulations, put forward by Jaffe and Palmer [58],
could form only in the context of CCR.
The power plant size coefficient was positive and statistically
significant, which shows that larger power plants achieve a greater
efficiency improvement compared to small and medium-sized
ones. This result is consistent with Brian [59], who argued that
enterprises with greater ability to adapt to environmental reg-
ulations are more competitive than their competitors who lack
that ability. Large power plants are often under close supervision
from government, society, non-government organizations (NGO)
and consumers, and therefore, have stronger motivation to pursue
a higher operational efficiency and better environmental perfor-
mance. They also have greater ability to do so, because of the
Table 8
Impact of environmental regulations on efficiency of power plants.
Variable Coefficient Std. error z-Statistic Prob.
CCR 0.0010 0.1212 0.0821 0.9346
MBR 0.2592
***
0.0845 3.0666 0.0022
GS 0.1935
**
0.0853 2.2676 0.0234
CCRGS 0.0038 0.0650 0.0580 0.9538
MBRGS 0.0326 0.0517 0.6293 0.5292
TIME 0.0085
***
0.0028 3.0990 0.0019
PC 0.0035
***
0.0006 6.1523 0.0000
SC 0.4152
***
0.1018 4.0780 0.0000
CCRSC 0.0323 0.1436 0.2247 0.8222
MBRSC 0.1660 0.1058 1.5691 0.1166
GSSC 0.2162
*
0.1061 2.0368 0.0417
C 1.0319 0.0982 10.5104 0.0000
Log likelihood 85.6717
Avg. log likelihood 0.8082
*
Significant at 10% respectively.
**
Significant at 5% respectively.
***
Significant at 1% respectively.
244 X. Zhao et al. / Applied Energy 149 (2015) 238–247
availability of financial resources and human resources. By con-
trast, small and medium-sized power plants have less motivation
and fewer resources to pursue higher efficiency levels when facing
environmental regulations. It is worth noting that the interaction
term between government subsidy policy and size has a positive
coefficient which is statistically significant. This may be due to
the fact that large power plants have more possibilities to take
the innovation actions that would help them obtain GS because
of their strong internal resources, which in turn leads to greater
achievement of their efficiency improvements.
It seems strange that the time coefficient, which represents
power plant age, was positive. This suggests that older plants tend
to have a higher efficiency than younger ones. This seemingly
counterintuitive observation actually makes sense considering
the three facts. First, power capacity increase has outpaced the
increase of demand in recent years in China, so most power plants
are not able to run at full capacity. China’s current power dis-
tribution model is based, simply speaking, on the principal of equal
distribution of power generation. The power capacity of older
power plants is generally less than that of younger. Therefore,
older power plants are more likely to run at a comparatively higher
level, closer to capacity than younger ones. For power units, the
closer to capacity they are running, the higher their level of effi-
ciency. Hence, it is found that older power plants have a higher
efficiency than younger ones. Second, as a large number of power
plants have been built in China in recent years, most of the com-
paratively ‘‘older’’ plants are not really old. Third, during the period
2002 to 2012, the price of coal increased very quickly in China. In
order to control costs, China’s power generators paid special atten-
tion to technically upgrade old power plants to improve their
energy efficiency.
Another finding that is different from what we expected is that
the coefficients on the cross terms between subsidies and other
types of regulations were not statistically significant. Therefore,
we did not find support for GS and other policies simultaneously
having a better effect on efficiency improvement. We have two
comments following this observation. First, the MBR approach,
more particularly tax policy, has been shown to have a larger posi-
tive impact when applied with GS [46]. However, since carbon tax
has not yet been put in place in China’s power market at present,
the MBR in this study do not include tax. This might explain why
we did not have a significant result. Second, GS do not seem able
to mediate the impact of environmental regulations on operational
efficiency. This could result from government being concerned
only with its own subsidy objectives and lacking consideration
for policy coordination. Such isolated policy making is not con-
ducive to efficiency improvements.
5.3. Impact of environmental regulations on power plants’ CO
2
emissions
Table 9 presents the results from our regression analysis of how
various environmental regulations affect power plants’ CO
2
emis-
sions. CCR had no significant impact on it. This may be due to
the fact that China’s command and control environmental reg-
ulations on power plants focus primarily on the installation of
desulfurization and denitration equipment, as well as on the emis-
sions standard of SO
2
, soot and NO
x
. To date, no command and con-
trol policies on CO
2
emissions reduction exist.
Table 9
Impact of environmental regulations on CO
2
emissions of power plants.
Variable Coefficient Std. error t-Statistic Prob.
CCR 0.3623 0.4606 0.7865 0.4335
MBR 1.0118
***
0.3211 3.1517 0.0022
GS 1.1396
***
0.3241 3.5163 0.0007
CCRGS 0.1529 0.2468 0.6195 0.5370
MBRGS 0.1193 0.1965 0.6070 0.5453
TIME 0.0433
***
0.0105 4.1315 0.0001
PC 0.0162
***
0.0021 7.5493 0.0000
SC 2.5187
***
0.3867 6.5126 0.0000
CCRSC 0.4317 0.5454 0.7916 0.4306
MBRSC 0.7416
*
0.4019 1.8454 0.0681
GSSC 1.1480
***
0.4032 2.8476 0.0054
C 5.1898
***
0.0237 219.1719 0.0000
R-squared 0.2734
Adjusted R-squared 0.1884
F-statistic 3.2160
Prob (F-statistic) 0.0009
*
Significant at 10% respectively.
***
Significant at 1% respectively.
Table A
Descriptive statistics of manager perceptions of environmental regulations.
Items Mean SD Corrected item-total correlation Cronbach’s alpha (
a
) Cumulative variance (%)
a
CCR Emission standards 4.38 1.687 0.678 0.887 68.990
Fines 4.53 1.778 0.752
Supervision 4.87 1.726 0.793
ESS
b
5.04 1.750 0.712
PTS
c
5.01 1.646 0.701
MBR Tax credits 4.42 1.827 0.500 0.766 82.312
CDM 4.34 2.016 0.729
Cap & trade 4.04 2.054 0.574
GS Subsidy for new technological R&D 3.72 2.135 0.903 0.941 89.543
Subsidy for new technical production 3.87 2.050 0.897
Preferential loan guarantees 3.84 2.166 0.942
a
If the value of the cumulative variance is more than 60%, it is within the scope of what can be considered acceptable.
b
ESS: environmental assessment system.
c
PTS: production technology standards.
Table B
Descriptive statistics of variables used in Eqs. (5) and (6).
Variable description Variable
name
Mean Medium Standard
deviation
Efficiency change EF 1.076 1.063 0.390
CO
2
emission per unit power
generation (ton)
CO
2
171,647 140,529 160,920
Command and control
regulation
CCR 4.76 4.67 1.20
Market based regulation MBR 4.22 4.50 1.46
Government subsidy GS 3.71 3.67 1.99
Power plant age (years) T 21 15 19
Power plant capacity (MW) PC 935.75 600 850.07
Note: Power plant size is a dummy variable, hence, no mean, medium and Standard
deviation.
X. Zhao et al. / Applied Energy 149 (2015) 238–247 245
Table 9 also shows that the coefficients on market-based and
government subsidy policies are negative and statistically
significant. This tells us that power plants that perceive a stronger
influence from MBR and GS have a lower CO
2
emissions intensity
compared to others.
It is interesting to find that larger power plants generally have a
higher CO
2
emission intensity than smaller ones. More impor-
tantly, the impact of market-based environmental and subsidy
policies was somewhat smaller for larger power plants. In this
study, we measured power plant size based on the number of
employees. Power plants with more employees, in general, had a
longer history. With old facilities, higher CO
2
emissions intensity
is common and they often find it difficult to lower those emissions
in response to market-based environmental or subsidy policies.
This is consistent with the time coefficient, which was statistically
positive and showed that older power plants have a higher CO
2
emissions intensity.
6. Conclusion and policy implications
This study investigates the impact of various environmental
regulations, namely CCR, MBR and GS, on the efficiency and CO
2
emissions of power plants in China. Our main conclusions from this
study are that: MBR and GS have positive impacts on efficiency
improvement and CO
2
emissions reduction among China’s power
plants. This conclusion implies that even in China, where CCR
dominates in many fields, MBR plays an irreplaceable role in
promoting green development among power plants.
However, CCR had no statistically significant impacts on effi-
ciency improvement and CO
2
emissions reduction. This conclusion
reflects that China’s CCR currently pay little attention to CO
2
emissions. For example, emission standards for soot, SO
2
,NO
x
,
mercury and its compounds exist in the power industry; however,
there is no emission standard for CO
2
emissions in the power
industry.
Another important conclusion is that larger power plants have
greater capacity to capitalize on environmental regulations that
require efficiency improvements. This is consistent with Brian
[59], Hitchens et al. ch Hitchens et al. [60] and Aragón-Correa
et al. [61].
The last conclusion is that the revenue-recycling effect of GS
does not exist in China’s power industry. This result implies that
the efficiency and environmental benefits of combining GS and
other types of environmental regulations have not been
materialized.
The empirical findings that different types of environmental
regulations have heterogeneous impacts on power plant efficiency
and CO
2
emissions has important policy implications for China’s
environmental regulations in the power industry. The findings
suggest that China should further release the potential of MBR in
the power industry, instead of solely relying on CCR. As previously
mentioned, China has implemented ‘‘Cap & Trade’’ for CO
2
emissions in seven pilot provinces and cities. However, some
improvements have to be made to help the system function well.
For example, further study is required on how to rationally identify
the carbon emission factor (CEF), which is a crucial factor in
measuring CO
2
emissions and deciding the trading amount.
Another challenge is to appropriately determine a CO
2
emissions
quota for power generators, which is critical for controlling the risk
of buying CO
2
emission rights for some coal-based plants. In sum,
MBR in China’s power industry is still in a nascent stage, and our
study shows that we need to pay more attention to the use of this
type of policy instrument in developing future regulation.
The second policy implication is that China’s policy makers
need to pay more attention to the coordination of different policy
instruments. It is shown in this study that both MBR and GS have a
positive impact on efficiency improvement and CO
2
emission
reduction in China’s power sector. But the theoretical prediction
that the effectiveness of MBR could be further enhanced by using
subsidies, which has received empirical support in earlier studies,
does not seem to be a reality in China. It would be interesting
and pertinent for scholars and policy makers in China to explore
how to better coordinate different policies to improve outcomes
in future. For example, the ‘‘Cap & Trade’’ system of CO
2
emissions
would increase the cost of CO
2
emission, and therefore further
boost the attractions of GS that aims to encourage the development
and deployment of technological innovations for reducing CO
2
emission.
In this study we did not consider CO
2
emission as a negative
output when evaluating the operational efficiency of power plants.
This is because no CO
2
emission data are publicly available at the
level of power plants in China. Although we can calculate CO
2
emission based on coal consumption, the data of coal consumption
have been used as an input variable in the DEA model. In order to
avoid double accounting, we have to give up considering bad out-
puts in this study. We believe that this has little impact on the
credibility of our study. First, the study period spans only from
2011 to 2012. For such a short period of time, we can assume with
adequate confidence that power plant investment in CO
2
emission
control would change very little if no special environmental reg-
ulation policies are issued. Second, before the year 2012, no CO
2
emission trading existed in China and the policies for controlling
pollutant emissions were dominated by CCR, which normally has
a unified emission standard for all power plants. This means that
even if investment in emission control among power plants
increased in 2012 compared with 2011, the increase would be
similar across power plants, and therefore the impact on power
plant efficiency change could be ignored.
Acknowledgments
We are grateful for the support of the National Natural Science
Foundation of China (Projects Nos. 71073053; 71373078;
71202071; 71322305; 71421002) and the Humanities and Social
Sciences Research Foundation at the Ministry of Education of
China (Project No. 10YJC630355). We thank the editor and the
two anonymous reviewers for very constructive comments.
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