ECONOMIC RESEARCH CENTERDISCUSSION PAPER
E-Series

No.E07-2

Impacts of Environmental Regulation on Technical
Efficiency and Productivity Loss in China
byDonglan Xu

March 2007

ECONOMIC RESEARCH CENTERGRADUATE SCHOOL OF ECONOMICSNAGOYA UNIVERSITY

Impacts of Environmental Regulation on Technical Efficiency andProductivity Loss in China

Donglan, XU

Abstract: This study employs radial efficiency measure of Data Envelopment Analysis(DEA) to compute output distance functions which can show technical efficiencies forthe strong and weak disposability of pollutants. We define the environmental efficiency
index (EEI) as the ratio of two technical efficiencies so as to calculate productivity loss
which can be estimated as GDP multiplied (1-EEI). The study focuses on 28 provinces
and municipalities in China, which are divided into three regions, including the East,
the Middle and the West. We find that the East paid the largest cost for environmental
regulation among three regions. The rate of productivity loss of the East forenvironmental regulation is 4.24% while those of the Middle and the West are 1.53%
and 1.72%, respectively. In addition, we also find that productivity loss from controlling
wastewater is larger that from controlling SO2 for all the country. The rate ofproductivity loss from environmental regulation for wastewater is 1.43% whereas thatfrom regulation for SO2 is 0.46%.

1. Introduction
Since its opening-up to the world economy, China has achieved astonishing economic
growth and industrialization. Undoubtedly, the continuous and rapid economic growth
has contributed to providing industrial infrastructure to improve the standard of livingof people powerfully, however, it has increased enormous pressure on China’s
environment.
Air and water pollution in China, especially in urban areas, are most serious in theworld. That the level of pollution exceeds international standards to a large extent hasbrought enormous burden to China. If air pollution level had fallen to the government’s
standard, China could avoid the death of 289,000 people per year according to the WorldBank Report (1997).

In other words, the current economic loss from air and waterpollution is estimated approximately 3−8% of GDP per year in China.
Needless to say, industrial pollution is a main source of environmental problems in
1

China. It is really fatal as much as life-threatening level in China. In fact, Chinese
government started environmental regulation to prevent industrial pollution in the
1980s, however, environmental laws and policies were not practically enacted so thatthey have not successfully regulated industrial pollutant emissions. Severe industrial
pollution has been continuing to harm China’s environment. Consequently, thereinforcement of environmental regulation has increasingly become indispensable.
Environmental regulation can result in reduction of pollutant emissions to improve
the living environment of people at the expense of economic growth. That is to say, theenactment of environmental regulation will bring negative influence on productivity. If
Chinastrictly

executes

environmental

regulations,

how

will

the

impact ofenvironmental regulation on productivity be? In this paper, we use the data of 1990s toinvestigate it.
That the eastern coastal regions developed first resulted in unbalanced
development in China, that is, there are gaps among provinces for technical efficiency
and productivity. Therefore, we connect the analysis of impacts of environmental
regulation on technical efficiency and productivity loss with China’s regional economy.
We focus on 28 provinces and municipalities which are divided into three regions,
including the East, the Middle and the West.
The purpose of this paper is to analyze the impacts of strict environmental
regulation on technical efficiency and productivity loss in 28 provinces andmunicipalities of China, especially to focus on the difference of impacts ofenvironmental regulation in three regions. We try to find which pollutant brings larger
cost due to environmental regulation between wastewater and SO2. In addition, we also
want to know the movement of industrial pollutions due to unbalanced development
among three regions.
We follow radial efficiency measure (Fare et al., 1986) of DEA approach based on
output distance function in this paper.1 We estimate technical efficiencies for the strong
and weak disposability of pollutants and compare the level of technical efficiencychange due to environmental regulation.2

In addition, we measure the productivity

There are two main methods used in measuring the impacts of environmental
regulation on technical efficiency and productivity. One is the radial efficiency measure
which considers the increase of desirable outputs and pollutants. Another is thehyperbolic efficiency measure that allows for the improvement of production and the
reduction of pollution simultaneously. China paid attention to economic development
policy more than environmental policy in the 1990s. It is considered that China could
not practically distinguish desirable outputs and pollutants and almost merely pursued
the increase of desirable outputs and pollutants. Therefore, the radial efficiency
measure will be used in this paper.
2 The strong disposability of pollutants refers to the ability to dispose of pollutants withno private cost. The weak disposability of pollutants refers to the ability to dispose of1

2

loss came from environmental regulation by technical efficiency change. In details, wetest technical efficiency change and productivity loss in three cases. One case is toinclude wastewater and SO2. Other two cases are only to consider one pollutant
respectively. Additionally, we compare the impacts of environmental regulation ontechnical efficiency and productivity loss under different pollutants.
The variables in this paper are desirable output, pollutants and inputs. The
desirable output is real GDP of the manufacturing industry. Pollutants include
industrial wastewater, SO2 and waste solids. Inputs are real capital stock and labor
force of the manufacturing industry. Data are taken from China’s statistical yearbook of
1994-1999.
The paper is organized as follows. Section 2 presents the model which includes thetheoretic and empirical model. The strong and weak disposability of pollutants will be
introduced in the theoretic model. According to different technical efficiencies for two
disposability, we can know the impacts of environmental regulation on technicalefficiency and productivity. The radial efficiency measure of DEA approach is used tomeasure technical efficiencies for two disposability. Section 3 contains a brief
description of the data and a discussion of the empirical results. Section 4 is summaryand conclusions of this paper.

2. The model
2.1 The theoretical model
Different technologies about production equipment and operation incur different
production performances. These performances can be measured as technical efficiency.
This is the premise of our model. We categorize production technology as the strong andweak disposability technology. The strong disposability technology is a production
technology without restriction of environmental regulation, whereas the weakdisposability technology is the one under the restriction of environmental regulation.
Technical efficiencies under two disposability technologies are different due toenvironmental regulation. Therefore, potential impact of environmental regulation on
production can be measured by the two different technical efficiencies.
We assume our model as joint-production in which producers produce desirable
outputs and undesirable pollutants simultaneously. Increase or reduction of desirable
outputs and pollutants is decided freely in the strong disposability assumption, whereas

pollutants at positive private cost, that is, all of pollutants are paid cost due toenvironmental regulation under the assumption of weak disposability of pollutants.
3

that is restricted in the weak disposability assumption. When desirable outputs are
reduced due to environmental regulation, a certain cost to production will beaccompanied with environmental regulation.
To describe the theoretical background of the model used, we suppose that the setof k = {1,..., K } activities produce y ∈ R+

N

using x ∈ R+

M

desirable outputs and w ∈ R+ pollutants
J

inputs. Let Y and W denote the ( K × N ) matrix of observed desirable

outputs and ( K × J ) matrix of observed pollutants, respectively. And let X denote the( K × M ) matrix of observed inputs. Finally, let z ∈ R+K

be a ( K × 1 ) vector of intensities

that are used to weight the different activities in constructing the reference frontier toevaluate producer k. This vector enables us to shrink or expand individual observed
activities, for the purpose of constructing unobserved but nonetheless feasible activities.
It thus provides weights which facilitate the construction of the linear segments of theboundary of the technology.
Assuming that technology satisfies both constant returns to scale and the strong
disposability of desirable outputs, pollutants and inputs, production set under thestrong disposability of pollutants is written as the following:

P S ( x) = {( y, w) : zY ≥ y, zW ≥ w, zX ≤ x, z ≥ 0}
where PS

(1)denotes output set under the strong disposability of pollutants. z ≥ 0means that production scale is under constant return to scale. This production set
shows how much output can be produced. The intensity vector z serves to construct theboundary of production set from the convex combinations of the observed inputs andoutputs. Thus, this set makes maximum desirable outputs achieved, given input vector
X. Y is the output vector, and W is the pollutants vector. y, w, x are actual desirable
output, actual pollutant emissions and actual inputs, respectively.
Similarly we need the production set under the weak disposability of pollutants.
The production set that satisfies weak disposability of pollutants and strong
disposability of desirable outputs and inputs is represented as the following:

P W ( x) = {( y, w) : zY ≥ y, zW = w, zX ≤ x, z ≥ 0}
where PW

(2)

denotes output set under the weak disposability of pollutants. The equality

zW=w implies that pollutants w cannot be freely disposable in the pollutants vector W,
and pollutant emissions which exceed the current discharge are not be allowed.
Therefore, it is possible to realize the reduction of pollutants to some extent. In Figure 1,
the equality allows for feasible radial contractions of pollutants to the origin. Intuitively,4Eq.(1) and Eq.(2) represent reference technologies relative to technical efficiency of eachproduction unit according to the observed inputs and outputs.
Figure 1 clearly shows difference between Ps(x)and Pw(x) when there are adesirable output and a pollutant. Figure 1 expresses the production frontier curve by an
output and a pollutant. Ps(x) includes an area bounded by region OFBCD. Pw(x) is
restricted by region OABCD. The region OABF represents production possibilities thatare feasible under the strong disposability of all outputs, but not feasible under the
weak disposability of pollutants. The line CD represents the strong disposability ofdesirable output in which the reduction of output is freely achieved without decreasing
inputs or w. If Pw(x) ⊆ Ps(x), the output may be lost due to the restriction of the weakdisposability. If disposal of pollutant w is costless, the FB segment is a technologicalpart explainable for this situation. In other words, the reduction of w can be realized
without the increase of inputs or decreasing desirable output.

If disposal of w is notcostless, some inputs like capital and labor would be pulled out of the production ofdesirable output in order to clean up w. Therefore, the FB segment is no longer feasible
for this situation. Inputs also must be expended in deleting the pollutants. Output OF is
impossibly given by the reduced inputs, that is, the OF segment can not be achieved
when w is not costless. Reducing w is achieved under a certain cost along line OA and
AB. Therefore, the region OFBCD Ps(x) represents the strong disposability of w,
desirable output and inputs. And the region OABCD Pw(x) represents the strong
disposability of inputs and desirable output as well as the weak disposability ofpollutants w. The production set, which can be attainable without environmental
regulation, cannot be reached under environmental regulation because production
frontier curve is restricted. Hence, using the gap between the two production sets, weestimate technical efficiency of production under the restriction of environmental
regulation.
yn(δy 0 , δw0 )F●

P S (x)B● n ∗ (θy 0 ,θw0 )A

P W (x)C● m( y 0 , w0 )

O

wr
5

D

w

Figure 1. Output sets for the strong and weak disposability of pollutants

2.2 The empirical model
We introduce distance function, which describes a multi-input, multi-output production
technology without the need to specify a behavioral objective (such as cost-minimizationor profit-maximization). One may specify both input distance functions and output
distance functions. An input distance function characterizes the production technology
by looking at a minimal proportional contraction of the input vector, given an output
vector. An output distance function considers a maximal proportional expansion of theoutput vector, given an input vector. It is considered that China pursued the maximumof output more than the minimum of input in the 1990s of China. Therefore, we will
focus on output distance function in this paper. Shephard (1970) defined the output
distance function and Fare et al. (1986) firstly proposed the radial efficiency measure
based on the output distance function. He defined the radial efficiency measure for
equiproportional increases of all outputs- desirable output and pollutants as thefollowing:

DO ( x, y, w) = min{λ : (( y, w) / λ ) ∈ P}

(3)

where Do means output distance function, λ is technical efficiency level, and P is
production set.3 Output distance function is defined as reciprocal of maximum output
vector for actual output vector given input vector.

A random point of production set within the frontier curve (D( x, y, w) p 1) isinefficient, while a point on the frontier curve (D( x, y, w) = 1) is efficient. Using this
output distance function, we estimate technical efficiency under the strong disposabilityof pollutants. At the same time, we also can measure the technical efficiency under the
weak disposability of pollutants. According to the two technical efficiencies, we can also
calculate the opportunity cost of environmental pollution.
Practically, linear programs are used in measuring technical efficiency of arandom point in the production set. First, a measure of technical efficiency that satisfies
the strong disposability of inputs, desirable outputs and pollutants is the same as
3 Technical efficiency means the effects of catching up with advanced technology, i.e.,

movement from an internal point to a point on the production frontier. It isdistinguished from technical progress, which means the consequence of innovation or
creation of new technology, indicating the shift of frontier.
6

follows:{D ( x, y, w)} = Maxθ
−1

S
O

S

zkY ≥ θ S ykz k W ≥ ϑ S wk(4)

z k X ≤ xk
zk ≥ 0where θ S is Farrell’s technical efficiency, which shows a reciprocal with output
distance function DOS under the strong disposability of inputs and outputs. Y is an
output vector which forms a maximum output combined with intensity vector z k . W isa pollutant vector which forms a maximum pollutant by intensity vector z k . Input
vector X with z k makes a combination of minimum input which is always less than
producer’s actual input. y k and wk are k producer’s actual output and actual
pollution emission. x k is actual input.
Next, the linear program of technical efficiency for the technology, which satisfies
the weak disposability of pollutants and the strong disposability of inputs and desirable
outputs, is the same as follows:{D ( x, y, w)} = Maxθ
W
O

−1

W

zkY ≥ θ W yk
z k W = θ W wk

(5)

z k X ≤ xk
z k Y ≤ wr7

zk ≥ 0where θ W is also Farrell’s technical efficiency, which shows a reciprocal with output
distance function DOW under the weak disposability of pollutants.
We can estimate DOS and DOW using Eq.(4) and Eq.(5), respectively. The linear
program in Eq.(4) maximizes potential output relative to the production set P (x) ,S

while Eq.(5) maximizes potential output relative to the production set P (x ) . A sign ofWinequality denotes the strong disposability of inputs and desirable outputs, and a sign ofequality represents the weak disposability of pollutants. DOS ( x, y , w) and DOW ( x, y , w)
have random values between 0 and 1. As seen in Figure1, one point on the frontier curve
has maximum efficiency 1, but an internal point in the frontier curve has inefficiency
less than 1.
Therefore, we can measure the actual levels of efficiency far away from maximum
efficiency with DOS and DOW . For example, when a random production level is thepoint m in Figure 1, the efficiency level at m may be compared with a radial point,n and n ∗ . The efficiency level at the point m is DOS = om / on p 1 under the strong
disposability technology, and that is DOW = om / on ∗ p 1 under the weak disposability
technology. The gap between two efficiency levels under the restriction of environmental
regulation occurs as

0 ≤ DOS ≤ DOW ≤ 1 . An inefficiency point n ∗under theunregulated condition can be an efficient point under the enforcement of regulation( n ∗ P S : on ∗ / on p 1 → n ∗ P W :on ∗ / on ∗ = 1 ). Though the desirable output y decreases
from n to n ∗ , pollutant emission w also decreases at the certain rate with the
reduction of desirable outputs. The movement of production point from n to n ∗implies the corresponding reduction of pollutants instead of decreasing constantdesirable outputs. The result shows that the reduction of pollutant emissions improves
due to environmental regulation, but production decreases at the same time. The
technical efficiency also improves because of environmental regulation. Hence, we canuse technical efficiency change to calculate productive loss due to environmental
8

regulation or the opportunity cost of pollutants based on the relative gap between two
output distance functions. For instance, if a government regulates polluters to be incompliance with environmental regulation, desirable outputs and pollutants are on
trade-off, and the opportunity cost of pollutants may be estimated as the loss ofdesirable outputs due to the reduction of pollutants.
For investigating the productivity loss from environmental regulation, we first
define environmental efficiency index (EEI) with the relative rate of two efficiencies as
the following:

EEI = DOS / DOW(6)

where if EEI = 1( DO = DO ) , there is no technical constraints followed by compliance ofSW

environmental regulation, i.e., the transformation of the strong disposability into theweak disposability do not require the opportunity cost at all, and environmental
regulation do not trigger on additional cost for polluters. Conversely, EEI<1 implies thatthe compliance of environmental regulation constraints production and require theopportunity cost of technical transformation or incur additional cost of producers.
Therefore, the additional cost due to environmental regulation, that is, productive loss
due to environmental regulation is measured by environmental efficiency index. That is,
the more EEI is close to 1, the less productivity loss due to environmental regulation is.
Therefore, we can calculate the productivity loss as (1−EEI)× output, and the rate ofproductivity loss as 1−EEI.

3. Data and results
We use China’s regional output, pollutants, and inputs for 1993-1998 as sample data inthe empirical test. All of data are taken from China’s statistical yearbook of 1994-1999.
Actual data variables in empirical test are GDP of the manufacturing industry,
industrial waste water discharge, SO2, solid wastes, labor force of the manufacturingindustry and capital stock, respectively.5

GDP and capital stock was transformed into

real values with GDP deflator (1978=100). With linear programs for optimal solution,
we measure two different technical efficiencies under the strong and weak disposability

5 Because of no capital stock of the manufacturing industry, we have to use total capitalstock of every province. It is the limitation of data in the paper.
9

of pollutants. As the production frontier curve is formed by sample data in the program,the best practice in the sample has maximum efficiency level 1. We use “What’s Best”
computer program available for optimal solution of linear program. The linear program
will be repeatedly computed for estimation of technical efficiencies under the strong andweak disposability of pollutants.
Before turning to empirical results, it is instructive to check the pattern ofsample data for 1993-1998. What we observe with sample data are as follows: First, we
will examine the change of desirable output, pollutants and inputs. Assuming that othervariables are generally constant, increase of desirable output will bring out the
improvement of technical efficiency and productivity, while increase of pollutants andinputs can deteriorate them. Second, sample data can show the connection of desirable
output, pollutants and inputs. If economic growth is not accompanied by technologicaldevelopment of pollution abatement, pollutants will also increase with desirable
output’s increase. Third, we can compare the different patterns of growth for 28
provinces and municipalities that are divided into three regions by sample data.
Table 1. presents average annual growth rate of output, industrial wastewater
discharge, waste gas discharge, waste solids discharge, capital stock and labor in the
manufacturing industry in 28 provinces and municipalities as well as three regions overthe 1993-1998 period. The national annual growth rate of output in the manufacturingindustry is 12.23%, the East 12.52%, the Middle 13.45%, the West 10.96% for
1993-1998.6

The growth rate of the Middle showed largest among three regions,followed by the East, at last the West. In the annual change rate of industrial
wastewater discharge, the national change rate is –1.12%, the East –0.43%, the Middle–2.96%, and the West –0.35%. Wastewater discharge of three regions decreased in the
1990s and the change rate of the Middle is largest. In the annual change of SO2
discharge, most of provinces showed an increasing pattern. The nation is 4.53%, theEast 4.12%, the Middle 6.22%, and the West 3.59%.

In the annual growth rate ofcapital stock, most of provinces kept high growth rate. The East is 11.86%, the Middle
10.99%, the West 10.96%. In the annual growth rate of labor, all of three regions showeda decreasing pattern. The Nation is –1.55%, the East –1.93%, the Middle –1.54%, theWest –1.19%. All of three regions show the minus growth rate of labor because many
provinces and cities in China used the control policy of labor force for enterprises in the
1990s.
Table 1. shows that China’s manufacturing industry had a high economic growth
supported by the increase of capital stock. Industrial wastewater discharge over the

6 The annual growth rate of GDP including industry, agriculture and service trade isthat, that of the East is 11.4%, the Middle 11.4%, and the West 9.2%.
10same period showed a decreasing trend, while SO2 and industrial waste solids
discharge increased by 4.64% and 6.60%, respectively. It implies that environmental
regulation for wastewater relatively fulfilled its function in the 1990s, however, theeffects of environmental regulation for SO2 and industrial waste solids were weak,
especially for waste solids.
Table 2. shows technical efficiency ignoring pollution emissions, technicalefficiencies of the strong and weak disposability with considering wastewater, SO2 andwaste solids discharge simultaneously, environmental efficiency index and rate ofproductivity loss in China’s 29 provinces and municipalities for 1993-1998. A linear
program for output efficiency measure is used to get technical efficiency. When ignoring
pollutants, technical efficiency is very low in China, the nation 0.794, the East 0.871,
the Middle 0.811 and the West 0.712. Under the strong disposability of pollutants whichindicates free disposability of pollutants without the burden of pollution control cost,
the technical efficiency overall became larger comparing with that without considering
pollutants. Its cause is that producers can improve the technical efficiency by including
pollution factor in decision-making of production. The national technical efficiency is
0.925, implying that some provinces had technical inefficiency as much as 7.5%. The
East shows the lowest efficiency, 0.908, and then the Middle 0.931, at last the West0.935. Due to technical efficiency, actual desirable output y and pollutants w will be
expanded i.e. y/λ, w/λ. Therefore, the smaller technical efficiency is, the larger
desirable output y and pollutants are. From the above result, we can know that both ofdesirable output and pollution for the East are largest among three regions. It
corresponds to the reality.
Measuring the technical efficiency for the weak disposability of pollutants basedon the assumption that producers should pay pollution control cost, we can examine theeffect of desirable output reduction due to control of pollution emission. Here, thetechnical efficiency under the weak disposability of pollutants means the efficiency
when environmental regulation is strictly enforced. Because China could not strictly
enforce environmental regulation in the 1990s, the results in this paper should be
different from actual technical efficiency and productivity loss in the presence ofenvironmental regulation to some extent. The technical efficiency and productivity loss
got from the paper will indicate those under the strict enforcement of environmental
regulation. Table 2. shows that maximum efficiency increased from 8 to 17 provinces
under the weak disposability of pollutants. As expected in Figure 1, it indicates that thefrontier curve was more shrunk under the weak disposability of pollutants. Its reason isthat the increase of technical efficiency under the weak disposability of pollutants
results from the reduction of output due to environmental regulation. The national
average technical efficiency under the weak disposability is 0.969, higher than that11

under the strong disposability, 0.926. Three regions’ technical efficiencies under the
weak disposability are respectively higher than those under the strong disposability.
The East is 0.984, the Middle 0.975, and the West 0.950. All of three regions keep ahigher technical efficiency under the weak disposability. The gap of technical efficiency
between the strong and weak disposability can show the effect of constraint due toenvironmental regulation. A large gap denotes that environmental regulation can bringmore production cost to provincial economies. The provinces or municipalities that show
a significant gap between two technical efficiencies are Beijing, Tianjin , Hebei and
Zhejiang in the East, Henan in the Middle, Qinghai and Xinjiang in the West. The
regions that have no any change between two technical efficiencies are Liaoning andShanghai in the East, Heilongjiang and Hunan in the Middle, Guangxi, Sichuan,
Guizhou, Yunnan and Tibet in the West. Other provinces and municipalities show somedifference between the two technical efficiencies under the strong and weakdisposability, though there exists the difference of extent.
Environmental efficiency index (EEI) computed by two technical efficiencies under
the strong and weak disposability of pollutants is used in measuring the extent of
impact of environmental regulation on productivity. EEI close to 1 indicates low
technical efficiency change and low impact of regulation, that is, it shows low rate ofproductivity loss due to regulation, while EEI far away from 1 means large technicalefficiency change as well as high production burden, that is, it shows large rate ofproductivity loss due to regulation. When three types pollutants are considered at thesame time, the national EEI is 0.958, very close to 1. The East’s EEI is lowest, 0.929,
then the West 0.970, the Middle 0.978. The rate of productivity loss for the whole nationis 4.24%. And that for the East is 7.05%, the West 3.03% and the Middle 2.22%. Hence,
we can know that the technical efficiency change due to environmental regulation in the
East is largest, that is, the East shouldered the largest burden for regulation among thethree regions. We also can say that the impact of environmental regulation on
productivity loss in the East is largest. In fact, the East relatively enacted
environmental regulation more than the other two regions in the 1990s. Thus, it isobvious that the East practically paid the largest cost for environmental regulation in
the three regions. Its main reason is that the East is depended on high pollution
emission, and its economic structure is not sound enough to absorb environmental
regulation. Generally, a high EEI means that a national economy or a provincial
economy has a sound economy structure enough to absorb environmental regulation.
But the Middle and the West’s higher environmental efficiencies index are not the result
from an advanced economic structure, but from a relatively low pollution emission
compared with the East over the same period. Due to the difference of impacts ofenvironmental regulation on technical efficiency change and productivity loss among12three regions, that is, the difference of the opportunity cost of pollutant emissions forthree regions, it appears that pollution was trending to move from the East to theMiddle and the West in China, especially wastewater. It became one serious issue for
China.
Table 3. shows the technical efficiencies of the strong and weak disposability,
environmental efficiency index and rate of productivity loss under only considering
wastewater. In this case, the technical efficiency of the East is 0.882, the Middle 0.895,
and the West 0.818 under the strong disposability of pollutants. Those under the weakdisposability of pollutants are 0.911, 0.896 and 0.824, respectively. Hence, the EEIs ofthree regions are 0.968, 0.999, and 0.993, respectively. The rate of productivity loss forthe three regions is 3.20%, 0.07% and 0.69%, respectively. It is evident that the EEI of
the East is lower than those of the Middle and the West. It indicates that the impact ofenvironmental regulation on technical efficiency change and productivity loss for the
East is largest in the three regions when only considering wastewater discharge, that is,
the East paid the largest cost due to environmental regulation for wastewater. Between
the Middle and the West, there is no large difference. The burden for the West is a little
larger than that of the Middle. Practically, the environmental regulation for wastewater
was relatively enacted in all of three regions. Hence, we can know that the East carried
the largest burden for controlling wastewater in reality. That is why the enterprises
whose water pollutions were serious tried to move to the Middle and the West so thatbackward technology and water pollution were moved from the East to the Middle andthe West.
Table 4. shows the technical efficiencies of the strong and weak disposability,
environmental efficiency index, the rate of productivity loss when only considering SO2
discharge. In this case, the technical efficiency of the East is 0.893, the Middle 0.843
and the West 0.856 under the strong disposability of pollutants. Those under the weakdisposability of pollutants are 0.904, 0.844 and 0.880, respectively. Thus, the EEIs ofthree regions are 0.988, 0.999, 0.972, respectively. The rate of productivity loss for three
regions is 1.19%, 0.11% and 2.83%, respectively. The result implies that the Middle did
pay less cost for environmental regulation for SO2. The East and the West also did nothave larger impacts due to environmental regulation for SO2. In fact, SO2 emissions in
the three regions increased in the 1990s, their growth rate is 4.12%, 6.22% and 3.95%
for the East, the Middle and the West. It indicates that Chinese Government could notstrictly control SO2 emission by environmental regulation. Even if Chinese government
had strictly enforced environmental regulation for SO2, China would not pay much costfor controlling SO2. Comparing with the increase of SO2 in each region, the rate ofproductivity loss is rather low. Therefore, it is feasible for China to strength theenvironmental regulation for SO2 in the future without corresponding large cost13

increase.
Table 5 shows the technical efficiencies of the strong and weak disposability,
environmental efficiency index and the rate of productivity loss when only considering
waste solids discharge. In this case, the technical efficiency of the East is 0.894, theMiddle 0.864, and the West 0.810 under the strong disposability. And those under the
weak disposability are 0.926, 0.885 and 0.845, respectively. Therefore, the EEIs of three
regions are 0.966, 0.976 and 0.959, respectively. And the rate of productivity loss of
three regions are 3.44%, 2.37% and 4.08%, respectively. All of three regions showed
higher technical efficiency change and productivity loss. In other words, all of them had
to shoulder larger burdens for controlling waste solids. This case is different from theabove-mentioned two cases. The result implies that China had to pay larger cost for
waste solids under the strict environmental regulation. The pollution of solids iscurrently the most difficult issue in China’s environmental problems. To resolve waste
solids’ problem, each region should introduce and develop the advanced technology toincrease technical efficiency in order to absorb the impact of environmental regulation.

4. Summary and conclusions
This paper applied the radial efficiency measure of DEA approach to measure technicalefficiencies for the strong and weak disposability in order to analyze the impacts ofenvironmental regulation on technical efficiency and productivity loss in China’s 29
provinces and municipalities from 1993-1998. According to economic growth level, 29
provinces and municipalities were classified into three regions. The most developed
region is the East, followed by the Middle and the West. We focused our research on
three regions. In three regions, technical efficiencies increased due to the enforcement ofenvironmental regulation. As for the impact of environmental regulation on
productivity loss, three regions showed different results respectively. The rate ofproductivity loss from environmental regulation for the East is 7.08%, while the Middle
is 2.22%, and the West is 3.05% when considering three pollutants including
wastewater, SO2 and waste solids simultaneously. We knew that the East paid thelargest cost for controlling environmental pollutions from our results. That is why backtechnology and pollutions were moved from the East to the Middle and the West from1990s.
In addition, we also estimated the impact of environmental regulation on
productivity loss considering different pollutants respectively. As a result, the impacts
came from wastewater and waste solids for the East are 3.08% and 3.29%, respectively.
For the Middle, only regulation for waste solids influenced productivity loss, that from14

others are very low. For the West, regulations for SO2 and waste solids brought larger
productivity losses that are 2.84% and 3.67%, respectively. From these results, we could
know that all of three regions did not pay large cost for controlling SO2 under theassumption of strict enactment of environmental regulation. It implies that China canstrength environmental regulation for SO2 without corresponding large cost increase in
the current situation. Except for the SO2, the productivity losses from other pollutants
are not low. Comparing to growth rate of each region that the East is 12.52%, theMiddle 13.45 and the West 12.37%, the output losses from regulation are not easily
ignored, especially for the East, larger than a half of growth rate of GDP when including
three pollutants. It is somewhat difficult for China to generally realize the strict
enforcement of environmental regulation in the current situation. Therefore, withstrengthening environmental regulation, China has to change its old production pattern,which merely purses the increase of outputs but does not distinguish desirable output
and pollutants, to sustainable production pattern which achieves the increase ofdesirable outputs and the reduction of pollutants simultaneously. In one word,
resource-saving, energy-saving and the development of environmental technology are
fundamental ways to decrease the impact of environmental regulation on productivity
loss in China.

References
1. Chao, Dong and Wang, Jinnan, China’s industrial pollution economics, China
Environmental Science Publishing Company, 1999
2. Fare, Rolf, S. Grosskopf, and Carl Pasurka, “Effects on Relative Efficiency inElectric Power Generation Due to Environmental Controls”, Resources and Energy,
No. 8, P167-184, 1986.
3. Farrell, M. J., “The Measurement of Productive Efficiency”, Journal of Royal

Statistical Society, No.120, P253-281, 1957.
4. Shephard, Rohald W., Theory of Cost and Production Functions, Princeton:
Princeton University Press, 1970.
5. Solow, R. M., “Technical Change and the Aggregate Production Functions”, Review
of Economics and Statistics, Vol.39, No.3, P312-320, 1957.

15

Table 1. Average annual growth rate of output, pollutants and input (1993-1998) (%)

GDP

Waste

SO2

Capital

Labor

water
Nation

12.23

-1.12

4.53

11.29

-1.55

Beijing

8.02

-2.72

-0.37

14.61

-4.42

Tianjin

11.09

-1.86

4.88

12.17

-5.10

Hebei

13.81

5.40

7.28

19.82

2.88

Liaoning

6.81

-3.85

-1.60

2.30

-6.99

Shanghai

9.71

-6.67

2.09

18.67

-6.18

Jiangsu

12.07

-0.65

6.24

9.72

-2.32

Zhejiang

16.48

3.36

9.45

11.55

0.16

Fujian

18.94

-1.33

1.86

16.22

-0.19

Shandong

14.57

6.67

5.34

9.34

1.08

Guangdong

13.73

-2.67

6.07

4.23

1.80

The East

12.52

-0.43

4.12

11.86

-1.93

Shanxi

12.70

3.17

14.73

7.57

-2.13

Jilin

5.68

-2.60

2.54

5.42

-6.78

Heilongjiang

12.44

-3.87

0.32

0.61

-5.3

Anhui

14.73

-6.37

2.67

12.44

0.55

Jiangxi

15.44

-8.20

4.94

8.48

-1.89

Henan

15.25

-0.18

13.98

14.77

4.06

Hubei

16.76

-2.34

5.67

16.54

-0.92

Hunan

14.64

-3.24

4.88

12.07

0.12

The Middle

13.45

-2.96

6.22

10.99

-1.54

Inner Mongolia

11.54

-1.01

5.72

2.48

-3.07

Guangxi

10.74

-0.92

5.18

9.63

1.40

Sichuan

14.01

-6.68

-8.24

8.18

-6.73

Guizhou

10.24

4.03

8.90

14.34

2.30

Yunnan

14.46

-0.08

3.62

13.39

1.40

Tibet

26.46

-0.46

7.60

12.31

6.15

Shaanxi

8.88

0.30

0.42

12.44

-1.63

Gansu

13.16

-1.15

-0.19

16.81

-1.60

Qinghai

7.42

-5.29

8.49

12.66

-3.03

Ningxia

10.00

3.96

2.63

11.57

1.40

Xinjiang

9.19

3.31

9.35

8.11

-2.35

The West

12.37

-0.36

3.95

11.08

-0.52

Source: China's Statistical Yearbook(1994-1999)
16

Table 2. Technical efficiency and rate of productivity loss under considering dischargeof wastewater, SO2 (1993-1998)

Dno(1)

Ds

Dw

EEI

RPL(%)

National

0.794

0.904

0.928

0.974

2.57

Beijing

0.710

0.716

0.768

0.932

6.81

Tianjin

0.844

0.865

0.981

0.882

11.84

Hebei

0.730

0.794

0.800

0.993

0.67

Liaoning

0.871

0.954

0.954

1.000

0.00

Shanghai

1.000

1.000

1.000

1.000

0.00

Jiangsu

0.896

0.929

0.931

0.998

0.23

Zhejiang

0.877

0.883

0.910

0.970

2.97

Fujian

0.921

0.926

1.000

0.926

7.38

Shandong

0.931

0.941

1.000

0.941

5.90

Guangdong

0.934

0.934

1.000

0.934

6.57

The East

0.871

0.894

0.934

0.958

4.24

Shanxi

0.827

0.944

0.971

0.972

2.76

Jilin

0.717

0.770

0.774

0.995

0.47

Heilongjiang

1.000

1.000

1.000

1.000

0.00

Anhui

0.844

0.901

0.904

0.997

0.30

Jiangxi

0.769

0.905

0.934

0.969

3.11

Henan

0.803

0.816

0.822

0.993

0.73

Hubei

0.824

0.951

1.000

0.951

4.90

Hunan

0.705

1.000

1.000

1.000

0.00

The Middle

0.811

0.898

0.917

0.985

1.53

Inner Mongolia 0.665

0.890

0.905

0.983

1.75

Guangxi

0.754

1.000

1.000

1.000

0.00

Sichuan

0.980

1.000

1.000

1.000

0.00

Guizhou

0.624

1.000

1.000

1.000

0.00

Yunnan

0.946

0.973

0.975

0.998

0.15

Shaanxi

0.612

0.781

0.793

0.984

1.56

Gansu

0.630

0.880

0.880

1.000

0.00

Qinghai

0.652

0.668

0.688

0.972

2.83

Ningxia

0.593

0.998

0.998

1.000

0.00

Xinjiang

0.815

0.882

0.990

0.891

10.89

The West

0.712

0.910

0.915

0.983

1.72

Note(1): Dno means technical efficiency without considering environmental
factor.

17

Table 3.

Technical efficiency, environmental efficiency index and the rate ofproductivity loss under only considering discharge of wastewater (1993-1998)

Ds

Dw

EEI

RPL(%)

Nation

0.863

0.876

0.986

1.43

Beijing

0.712

0.760

0.942

5.84

Tianjin

0.846

0.929

0.914

8.61

Hebei

0.732

0.738

0.992

0.80

Liaoning

0.937

0.937

1.000

0.00

Shanghai

1.000

1.000

1.000

0.00

Jiangsu

0.924

0.924

1.000

0.00

Zhejiang

0.878

0.889

0.987

1.26

Fujian

0.926

0.934

0.991

0.86

Shandong

0.931

1.000

0.931

6.87

Guangdong

0.934

0.999

0.935

6.53

The East

0.882

0.911

0.968

3.20

Shanxi

0.839

0.842

0.996

0.44

Jilin

0.768

0.768

1.000

0.00

Heilongjiang

1.000

1.000

1.000

0.00

Anhui

0.900

0.900

1.000

0.00

Jiangxi

0.897

0.897

1.000

0.00

Henan

0.806

0.807

0.998

0.15

Hubei

0.951

0.951

1.000

0.00

Hunan

1.000

1.000

1.000

0.00

The Middle

0.895

0.896

0.999

0.07

Inner Mongolia 0.690

0.691

0.998

0.22

Guangxi

1.000

1.000

1.000

0.00

Sichuan

1.000

1.000

1.000

0.00

Guizhou

0.739

0.739

1.000

0.00

Yunnan

0.962

0.962

1.000

0.00

Shaanxi

0.644

0.644

1.000

0.00

Gansu

0.796

0.796

1.000

0.00

Qinghai

0.667

0.672

0.993

0.69

Ningxia

0.689

0.689

1.000

0.00

Xinjiang

0.815

0.871

0.942

5.80

The West

0.800

0.807

0.992

0.78

18

Table 4. Technical efficiency, environmental efficiency index and the rate ofproductivity loss under only considering discharge of SO2 (1993-1998)

Ds

Dw

EEI

RPL(%)

Nation

0.876

0.880

0.995

0.46

Beijing

0.716

0.716

1.000

0.00

Tianjin

0.865

0.865

1.000

0.02

Hebei

0.791

0.791

1.000

0.00

Liaoning

0.922

0.922

1.000

0.00

Shanghai

1.000

1.000

1.000

0.00

Jiangsu

0.916

0.916

1.000

0.00

Zhejiang

0.883

0.887

0.995

0.50

Fujian

0.921

0.987

0.933

6.73

Shandong

0.983

0.983

1.000

0.00

Guangdong

0.934

0.971

0.963

3.69

The East

0.893

0.904

0.988

1.19

Shanxi

0.944

0.944

1.000

0.00

Jilin

0.729

0.729

1.000

0.00

Heilongjiang

1.000

1.000

1.000

0.00

Anhui

0.862

0.862

1.000

0.00

Jiangxi

0.797

0.797

1.000

0.00

Henan

0.816

0.816

1.000

0.00

Hubei

0.838

0.841

0.996

0.04

Hunan

0.763

0.763

1.000

0.00

The Middle

0.843

0.844

0.999

0.11

Inner Mongolia 0.888

0.888

1.000

0.00

Guangxi

0.894

0.894

1.000

0.00

Sichuan

1.000

1.000

1.000

0.00

Guizhou

1.000

1.000

1.000

0.00

Yunnan

0.972

0.972

1.000

0.00

Shaanxi

0.779

0.779

1.000

0.00

Gansu

0.795

0.795

1.000

0.00

Qinghai

0.658

0.671

0.984

1.65

Ningxia

0.986

0.986

1.000

0.00

Xinjiang

0.882

0.882

1.000

0.00

The West

0.885

0.887

0.998

0.23

19