1
The Pressures of Industry on the Chinese Environment: A Tale of Two South China Cities.
Christopher Brown, NSF-REU Scholar, University of Chicago
Daniel Gonzalez, NSF-REU Scholar, Trinity College
Elizabeth Medford, Lancy-NCUR Scholar, Central Washington University
Abra Murray, NSF-REU Scholar, University of Southern California
Richard Mack, Professor of Economics, Central Washington University1
Abstract
This paper combines statistical analysis and on-site interviews to assess the effects of
industrial location, industry mix, industry ownership, and environmental policies on urban
pollution levels in Shanghai and Xiamen, China. A regression model relates these variables to
an index of overall environmental quality for the two cities. Shift-share analysis is used to
determine the direction of recent sectoral shifts, and interviews and anecdotal evidence provide a
broader context for our conclusions. We found that industry type, ownership and size
significantly affect urban pollution levels. We conclude that although local enforcement of
environmental regulations in Shanghai and Xiamen has been effective, at the national level it is
still inadequate. Finally, we recommend that policies calling for a reduction in the role stateowned and certain small-scale industries be incorporated into the larger economic goals of the
central government.
Introduction
Environmental degradation in China is an issue that has received considerable attention
by both domestic and international analysts, and yet it is a problem far from resolution. Rapid
industrial growth and inadequate regulation in China’s cities have led to air and water pollution
levels that seriously threaten the health of urban residents. Outside the cities, rural China has also
been left with an environment ravaged by misguided Maoist era policies of massive relocations,
dam building and agricultural land transformation. Negative environmental impacts at the global
level are also commonly projected in the literature. As China simultaneously attempts to
develop its economy and restore its environment, those involved in policymaking must
understand the relationship between the nature of industry and urban pollution levels, as well as
the interaction among sectoral shifts, local policies, and environmental quality. This study
analyzes the major components of these relationships in the hopes of contributing a chapter on
South China to the growing body of works on this subject. Although localized in scope, the
1
The authors gratefully acknowledge the financial support for this undergraduate research project from the National
Science Foundation Office of International Science and Engineering, the Lancy Foundation, the National Council
for Undergraduate Research and Central Washington University. We also appreciate the helpful comments of the
anonymous reviewers of East-West Connections.
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conclusions of our work could act as a guide for policymakers across China who need to find a
balance between economy and environment.
The last half-century of Chinese history has been characterized not only by monumental
economic and ideological transformations, but also by environmental transformations. In the late
1950s, The Great Leap Forward left an unprecedented legacy of environmental degradation that
included deforestation, desertification, and soil erosion. The Cultural Revolution of 1966-1976
is best known for its political excesses; lesser known are the ecological transformations that
resulted from orders to fill lakes and plow grasslands in order to make more land available for
grain production. Under Deng Xiaoping, China began to focus attention on environmental
concerns with increased investment in environmental management, but the central government’s
first priority undoubtedly remained economic expansion. Now, contemporary China faces grave
environmental problems. Smil (1997, 192-193) blames air pollution from the production and use
of coal for “changing patterns of morbidity and mortality” in China; coal use is also a primary
culprit in producing acid rain in the southern provinces. Discharges from rural enterprises and
runoff from farms that use synthetic fertilizers are damaging water supplies at alarming rates. A
legacy of deforestation compels China to import lumber from Canada and the United States at
the cost of an estimated US$ 1 billion per year (Smil 1996, 47). Smil estimates that
environmental degradation costs the Chinese economy as much as 15% of its annual GDP, while
other estimates range from 4.5% to 18%. As China’s economy continues to grow, environmental
degradation is a critical concern. Accordingly, it is important to understand the interrelationship
between official policies, industrial activity, and pollution levels.
There is a large body of research that has been published on the topic of China’s
industrialization and its environmental consequences, but a few works deserve special mention
for their relevance to our research. The first is an extensive work by Xiaoyang Ma and Leonard
Ortolanob (2000), who attempt to ascertain which bureaucracies within the government are
responsible for regulating industrial pollution and the particulars of their policies. Most
important, however, is their description of interactions between these bureaucracies and firms of
different ownership models. In a World Bank Report, Dasgupta, Zhang, Wheeler and Huq
(1996) use data collected from factories in Beijing and Tianjin to evaluate the likely cost
effectiveness the levy system would have in abating industrial water pollution, if it were to
replace the current standard-based system, and conclude that it would be far more economical.
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Finally, Hanham and Robert (2000, 108-123) use the technique of shift-share analysis to evaluate
the relationship between manufacturing, employment, and spatial economies in Japan; their work
serves as a model for our own shift-share analysis of Shanghai and Xiamen. Yet, despite the
wealth of literature on the topic of China’s industrialization and its environment, there is a need
for research that addresses the effects of firm type and localized regulations on economic
behavior and environmental quality. Our study is also distinguished by its use of first-hand
interviews to support the findings of our statistical models.
The objective of this study is to analyze the effects that regulations, location of industry,
ownership structure and local industrial composition have on the environmental situation in
urban China. To this end, our study considers if and to what degree these variables determine
levels of compliance to environmental regulations, as well as the causes and effects of recent
sectoral shifts out of cities into the countryside. We also reflect on the implications of these
relationships and trends.
Three initial hypotheses are associated with this major objective: (1) sectoral shifts have
caused environmental degradation to migrate from within cities out to the hinterland; (2) close
ties to the government make state-owned enterprises less likely to be in compliance with
environmental regulations and, therefore, to be greater contributors to degradation, and (3) there
are significant, but explainable differences in compliance and emissions levels in Shanghai and
Xiamen.
Methods
Two south China cities, Shanghai and Xiamen, were chosen as case studies to analyze a
broad array of economic, locational, and environmental variables. Shanghai was chosen as a
major city that has undergone major shifts out of its Mao era heavy manufacturing into light
manufacturing and services. Xiamen, a Special Economic Zone, was chosen to represent a
smaller city that did not have a legacy of heavy industry and a city that has dedicated a
considerable effort to environmental enhancement.
Regression analysis and shift-share analysis are combined with a series of on-site
interviews to develop a comparative portrait of the industry structure, pollution policies, and
resultant pollution loads of the two cities2. Statistics were collected for Shanghai and Xiamen on
2
Notes from interviews are available from the authors.
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measures of industrial production, pollution levels, ownership structures, and sectoral shifts.
Most of the statistics are available in the statistical yearbooks for Shanghai and Xiamen. The
statistics form the foundation for understanding the data that we collected from our primary
research, that is, from interviews conducted with government officials, industry managers,
consultants and academicians.
Statistical Analysis
As for the regression analysis, due to the limited availability of time-series data for
Xiamen, we decided to create two separate regression models. The first is a multivariable
regression that examines the city of Shanghai in depth, incorporating a number of different
measures of the city’s industrial organization. The resulting coefficient estimates are then used
to examine the magnitude and significance of different factors in determining the city’s pollution
levels. The second is a simple regression relating industrial structure to pollution levels,
computed for both Shanghai and Xiamen. The results of the parallel regression are then used to
form a basis for comparison between the two cities.
Shanghai In-depth Regression
Using the Classical Linear Regression Model (Gujararti 1995, 718-725), we created a
log-linear model for environmental quality in urban China. Working from our hypotheses and
from information gleaned from our interviews, we formulated a model in which the level of
pollution in a city is a function of its industrial composition, ownership structure, and the
industrial mix of the city in relation to national trends. Industrial composition is a broad measure
that includes the magnitude of primary and secondary industries as a share of total GDP and the
magnitude of heavy industry as a share of total industrial output, under the assumption that major
polluters generally fall into these two categories. From the assumption that close ties with the
government allow state-owned enterprises to be less compliant with environmental regulations
than other firms, we created a measure showing the output of state-owned enterprises as a share
of total industrial output. Finally, in order to place the issue in a broader context, we created a
measure of “sectoral shift” by taking the ratio of industrial output in the city to national output,
which illustrates the trend of industrial decentralization in China.
To avoid the problem of spurious regression, we included time as a regressor. The
incorporation of time as an independent variable is a way to detrend the data – to separate the
effect of the experimental variables from general, time-dependent trends. In addition, we heed
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Granger and Newbold’s rule of thumb, using a Durbin-Watson statistic that is lower than R2 as a
good indicator of spurious regression (Gujarati 1995, 718-725.)
The formulation of our dependent variable, the index of environmental quality, was based
upon Likert’s methodology for index formulation; it incorporates the assumption that there is
some scale of general pollution that determines a city’s environmental quality (Carmines, 22-28.)
Such a scale of general pollution cannot be directly observed, but we estimated its value by
sampling its constituent parts. In this case, we were limited by the availability of data, but were
able to use data on sulfur dioxide, total suspended particulates (TSP), and industrial wastewater
emissions to estimate general levels of air and water pollution. Faced with the challenge of
synthesizing two fundamentally different kinds of data, we emulated the methodology of
Environmental Resource Management’s “Quality Index of City Life” (Environmental Resources
Management China 1999, 3) by creating two separate indices for air quality and industrial
wastewater, which were then added with equal weight. By dividing each summative scale by it
highest value, we created a unitless index of pollution ranging in value from 0 (no pollution) to 1
(the maximum observed value). In order to make the numbers meaningful on an intuitive level,
we then added the two indices for air and water pollution, and multiplied the resulting sum by 50
to create a standardized scale ranging from 0 to 100. We evaluated the internal consistency of the
resulting scale – “the ability of the individual items to measure the attribute measured by the total
scale” – through correlation analysis, as proposed by Likert (Carmines, 22-38, see Appendix C).
The Shanghai model takes the following form:
ln YSt = β0 + β1 ln PSIt + β2 ln HEVt + β3 ln SOEt + β4 ln SHARt + β5 ln t + ut
Where:
  SO 2  TSP
industrial watewater volume
Yt = 50

  max{SO 2  TSP} max{indutr ial wastewate r volume}

 i.e. “index of environmental
 t
quality”
 tertiary output 
PSIt = 1 
 i.e. “primary and secondary industry”
GDP

t
 heavy industrial output 
 i.e. “heavy industry”
HEVt = 
 total industrial output  t
 state - owned industry output
SOEt = 
total industrial output


 i.e. “state-owned enterprises”
t
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 total industrial output wit hin the city 
 i.e. “share of total industrial production”
SHARt = 
 total industrial output in all China  t
t = time
ut = stochastic error term
The resulting estimates for the coefficients βi showed the partial effects of the different variables
on environmental quality. The coefficients were tested for statistical significance according to
their t-statistics, and the regressions were evaluated according to their R2 and adjusted R2
statistics. The overall significance of the multiple regression was evaluated by the F-test.
Shanghai and Xiamen Parallel Regressions
The parallel regressions for Shanghai and Xiamen incorporated only two experimental
variables, creating a model in which environmental quality (Y) is a function of the output value
of primary and secondary industry (PSI). The data were detrended by including time as an
independent variable. The variables were formulated and tested exactly as in the first Shanghai
model, yielding the following log-linear equations:
Shanghai:
ln YSt = β0 + β1 ln PSIt + β2 ln t + ut
Xiamen:
ln YMt = α0 + α1 ln PSIt + α2 ln t + ut
Where:
  SO 2  TSP
industrial watewater volume
Yt = 50

  max{SO 2  TSP} max{indutr ial wastewate r volume}

 i.e. “index of environmental
 t
quality” for either Shanghai (YS) or Xiamen (YM)
 tertiary output 
PSIt = 1 
 i.e. “primary and secondary industry”
GDP

t
t = time
ut = stochastic error term
The resulting estimates for the coefficients βi and αi provided a basis for comparison between
Shanghai and Xiamen. As in the first Shanghai regression, the coefficients were then tested for
statistical significance according to their t-statistics, and the regressions evaluated according to
their R2 and adjusted R2 statistics.
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Our second statistical approach was a shift-share analysis of the composition of industries
in Shanghai and Xiamen, compared with China as a whole. The use of shift-share analysis shed
light upon the overall trends in industrial composition in these two locations from 1978 to 1999,
and placed these regional trends in a broader national context. Shift-share analysis breaks down
industrial growth into national growth, industry mix, and regional shifts (Smith 2002). The
national growth component describes movements in output that can be credited to national
trends. Industry mix compares growth in a given local industry to output performance of the
same industry at the national level. The regional shift component attributes gains or losses in
industrial output to regional growth rates that exceed or lag behind national levels for the same
industry. (See Appendix A for formulas.) Shift-share analysis typically uses employment
figures to quantify the share of each sector in a regional economy, but because statistics on
employment in China generally exclude the large “floating population” of workers in urban
areas, we used output values to avoid overlooking substantial parts of regional activity.
Interviews
To more fully ascertain the situations in Shanghai and Xiamen, we interviewed experts in
the fields of economic development and environmental protection. Their diverse backgrounds
allowed us to collect a number of different perspectives on the issues. The interviewees were
college professors, government officials, environmental consultants, heads of business
organizations, and industrial firm managers. An open-ended interview format made discussing
potentially sensitive issues more comfortable, but it also made it more difficult to compare
answers, since the themes varied from one interview to the next and the same question was rarely
posed in the same way. Nevertheless, the interviews indicated similarities, illuminated
contradictions, and were useful in compiling anecdotal facts.
Findings and Results
Regressions
Shanghai In-Depth
The findings for the in-depth Shanghai equation are reproduced in Appendix B and are
summarized in Table 1. The Model Summary section describes the success of the overall model
in predicting levels of pollution for Shanghai, and from it we can see that the in-depth Shanghai
model was a reasonable approximation of our data. Both the R2 and adjusted R2 statistics were
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extremely close to their maximum value of 1. The fact that the Durbin-Watson statistic was
markedly higher than either R2 value suggests that the results were in fact valid, and not the
product of spurious regression. The F-test statistic was extremely high, signifying that the
overall multiple regression was significant at nearly a 99% confidence level. As a whole, the
Table 1: Shanghai In-Depth
R2
R2
.998
Adjusted
.996
β0 (constant)
ln PSI
ln HEV
ln SOE
ln SHAR
ln TIME
Coefficient
1.935
-.587
.832
1.547
-1.362
.126
Model Summary
Durbin-Watson
2.587
Coefficients
Standard Error t-statistic
.467
4.143
.362
-1.623
.209
3.975
.185
8.383
.193
-7.047
.026
4.890
F
440.275
p-value
.014
.180
.016
.001
.002
.008
regression equation appears to describe the relationship between Shanghai’s pollution and its
industrial organization with both reasonable accuracy and statistical significance.
The Coefficients section of Table 1 portrays the individual components of the regression.
PSI (primary and secondary industry) is the only variable not to show results at a statistically
significant level. The p-value of 0.18 for PSI means that there is an 18% chance of incorrectly
rejecting the null hypothesis that there is no relationship between the output of primary and
secondary industries as a share of GDP to pollution, so we did not reject the null hypothesis. In
the context of the other variables, there was no clear effect of PSI on the pollution index Y. This
was not a surprising result, since PSI was a broad category that included both polluting and nonpolluting industries.
The coefficients for HEV (heavy industry) and SOE (state-owned enterprises) both
returned positive values that were significant at the standard 95% confidence level. An increase
in the share of either heavy industry or state-owned industry in Shanghai’s total industrial
pollution was then linked with increases in pollution levels. In other words, if Shanghai were to
reduce its share of heavy industry or state-owned industry, we would expect a reduction in
pollution. As expected, the SOE variable produced the largest coefficient. This confirmed the
assertions of a number of our interviewees, who told us that SOEs are the most problematic
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polluters. It also suggested that in their efforts to control pollution, Shanghai authorities would
receive the greatest return on their effort if they were to concentrate on reducing the share of
state-owned industry. Reducing heavy industry would also have a noticeable effect, but to a
lesser degree than reducing SOE production.
The most perplexing result was the negative coefficient for the SHAR variable (the city’s
share of total industrial production) with a high t-statistic, denoting a statistically significant
effect on pollution. The development strategy of the city of Shanghai at least partially aims to
move industrial production from the city center out into the hinterlands – motivated, in part, by a
desire to reduce pollution and improve the quality of life for city residents. We would have
expected that as China’s industrial “center of gravity” moves farther away from Shanghai, there
would be a corresponding reduction in pollution levels. Yet the negative sign for the SHAR
coefficient suggested that, at least in the context of our multiple regression, a reduction in
Shanghai’s share of China’s total industrial output was actually linked to an increase in pollution
levels.
There are two related explanations for this seemingly anomalous result. First, it may be
that Shanghai’s efforts to move industry away from the city center follow the path of least
resistance. In other words, the industries that are easiest to regulate are also easiest to move out
– while the very same vested interests that allow SOEs to pollute with impunity might also
enable them to stay put in contravention of the city’s wishes. Meanwhile, those industries that
do move away may receive less regulatory scrutiny in the hinterland, contributing to the problem
of upstream pollution. Second, in the context of a multiple regression, the SHAR coefficient may
be important to the regression but meaningless in itself, as an analogy with the time variable may
illustrate. Time is not meaningful in a causal sense, but its inclusion as an independent variable is
a way to detrend the data – allowing the other variables to be analyzed outside the context of any
general drift over time. It may be that SHAR served a similar purpose. Shanghai has not
developed in isolation, but rather against the backdrop of broad national trends toward industrial
decentralization. Including SHAR in the regression may be a way to “control” for those trends,
while examining the interaction between the other experimental variables and our pollution
index. In any case, the results should motivate other researchers to examine the topic in more
detail.
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Overall, the in-depth regression model for Shanghai provided a reasonably accurate
picture of how different aspects of the city’s industrial organization affect its pollution levels. As
expected, heavy industry and SOEs were directly linked with pollution, but shifting industrial
production away from Shanghai may have been a misguided as a strategy for controlling
pollution.
Shanghai and Xiamen Parallel Regressions
The results for the Shanghai and Xiamen parallel regressions are also reproduced in
Appendix B and are summarized in Table 2 for Shanghai and Table 3 for Xiamen. The results
Table 2: Shanghai Parallel:
R2
.966
Adjusted R2
.957
β0 (constant)
ln PSIS
ln TIME
Coefficient
5.624
2.602
7.150 E-02
Model Summary
Durbin-Watson
1.386
Coefficients
Standard Error t-statistic
.130
43.125
.382
6.804
.056
1.284
F
112.162
p-value
.000
.000
.235
Table 3: Xiamen Parallel:
R2
.585
Adjusted R2
.447
α0 (constant)
ln PSIM
ln TIME
Coefficient
1.249
-1.699
.147
Model Summary
Durbin-Watson
2.613
Coefficients
Standard Error t-statistic
.453
2.759
.587
-2.896
.070
2.092
F
4.236
p-value
.033
.027
.081
provide a less clear picture of the relationship between sectoral composition and pollution. For
Shanghai, the single-variable regression comparing primary and secondary industrial output to
pollution levels showed a clear relationship. The high R2 and adjusted R2 statistics implied a
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very good fit, and the Durbin-Watson statistic of 1.386 was both reasonable and did not lead us
to suspect spurious regression. The F-test for overall significance was very high as well.
Therefore, we concluded that the model as a whole showed a significant relationship between
PSI and pollution for Shanghai.
The results for Xiamen were not so clear. The R2 and adjusted R2 statistics were fairly
low at 0.585 and 0.447, suggesting that the linear regression did not provide a very good model
for the data. The Durbin-Watson statistic was satisfactory, but the F-test for significance
returned a value of only 4.236. The corresponding level of significance for the F-statistic was
0.071, meaning that we cannot say at the standard 95% confidence level that there is a significant
relationship between PSI and pollution levels for Xiamen. However, we can posit such a
relationship at the 90% confidence level. From the regression results for Xiamen, we concluded
that there may have been a weak relationship between PSI and pollution, but the results are
unclear.
Looking more closely at the two regressions, we can compare the estimated coefficients
for PSI between the two cities. For Shanghai, the coefficient was both positive and highly
significant, with a t-statistic of 6.804 and an extremely small p-value. Yet for Xiamen, the
estimated coefficient for PSI was negative. The Xiamen value was also significant, with a high
absolute value of the t-statistic at –2.896, and a corresponding p-value suggesting significance at
over the 95% confidence level. The negative value suggested that as Xiamen increases the share
of primary and secondary industry in its GDP, pollution levels would decrease. Compared to
Shanghai’s positive result, the negative coefficient for Xiamen presented a difficult puzzle.
One explanation may simply be that the relationship between industrial composition and
pollution does not hold in Xiamen, as it does in Shanghai. As suggested in our interviews, this
may be due to the fact that Shanghai and Xiamen have had very different development
experiences. Shanghai became one of China’s primary industrial centers early on, and its present
strategy of reducing industrial production and promoting its function as a tertiary center is
connected with a desire to make Shanghai more livable. In contrast, Xiamen has no legacy of
heavily polluting, Mao-era industrialization, but instead a relatively recent experience of
carefully regulated industrialization. The city government of Xiamen takes great care to preserve
the city’s “green” image, and the industries that are allowed to develop within the city are
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generally not big polluters. As result, an increase in the share of primary and secondary
industries would not necessarily be linked with an increase in pollution.
The low significance of the regression for Xiamen may also be related to the fact that a
significant amount of its pollution originates outside of the city. One of the common threads in
our Xiamen interviews was the contention that Xiamen does a good job of controlling pollution
within its city limits, but that upstream industries in the hinterland continue to be a problem.
This contention was supported by the rather weak results of our Xiamen regression. If sectoral
composition is not a clear source of Xiamen’s pollution, it could very well be that the bulk of the
problem lies outside the city.
In summary, our regressions showed a strong relationship between industrial structure
and pollution levels for Shanghai, but a much less definite relationship for Xiamen. In Shanghai,
there was a clear relationship between industrial structure and pollution. In particular, SOEs and
heavy industry seemed to be the biggest factors in raising pollution levels. Also, there was some
indication that moving industrial production outside of the city may not have been the best
strategy for controlling pollution. In Xiamen, there was no clear relationship between industrial
structure and pollution. This may have been because much of Xiamen’s pollution problem comes
from upstream polluters, and because the city has been able to regulate the environmental impact
of its recent industrialization. Considered as a whole, these regressions suggested that while
industrial structure may have been a significant cause of pollution, localized efforts at controlling
pollution may only have shifted the burden elsewhere.
Shift-Share Analysis
Table 4 summarizes the findings of the broad-ranging shift-share analyses for Shanghai
and Xiamen.3 The table is divided into three sections; each represents a period of analysis for
both cities. Section A reports results for the entire two-decade period, whereas, Sections B and
C show results for the two component periods representing, respectively, the 1980s and the
1990s. The table is complicated by its incorporation of two industrial taxonomies4. The first
considers manufacturing only, and breaks manufacturing down into light industry and heavy
3
All numbers are calculated in units of 100 million yuan at 2000 prices.
One further discrepancy in the data used for the shift-share analysis should be addressed: the total
industrial composition is greater in value than total sectoral distribution. This anomaly has been found
throughout all yearbooks that we reference. We have attributed it to methodologies that appear to be
uniform across all Chinese Statistical Bureau publications. To avoid errors resulting from faulty data, we
have decided to use percentages in our models, as they express the proportion of growth over specific time
4
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industry. The second delineates all of industry into Primary (extractive), Secondary
(manufactures) and Tertiary (services). In the Chinese statistics, like those of any nation, there is
often a blurring of classifications, particularly between light manufacturing and services.
Accordingly, the “total” of manufacturing in the manufacturing breakdown, does not, as
Table 4 Shift Share Results ***
Shanghai*
4A
Xiamen**
Regional Shift
1978-1999
Regional Shift
1980-1999
Percent
Net
Percent
Net
Light Manufacturing
-2390.86
-6360.16
-610.935
-125.534
Heavy Manufacturing
-1232.12
-3055.54
154.7471
14.31085
Total Manufacturing
-1831.81
-9415.7
-373.285
-111.223
-693.78
-76.3158
-584.111
-25.5326
-1398.13
-2950.74
101.4345
11.86241
799.949
406.0541
1804.119
79.98458
-960.744
-2621.01
303.1866
61.31434
Primary Industry
Secondary Industry
Tertiary Industry
Total Industry
Table 4 continued
Shanghai*
4B
Xiamen**
Regional Shift
1978-1990
Regional Shift
1980-1990
Percent
Net
Percent
Net
Light Manufacturing
-328.675
-874.342
-193.973
-39.8572
Heavy Manufacturing
-181.294
-449.59
1087.739
100.593
periods and not net growth in absolute values. Furthermore, although data for industrial composition and
sectoral distribution in the yearbooks are not comparable, they are internally consistent.
14
Total Manufacturing
-257.569
-1323.93
203.841
60.73584
Primary Industry
-196.272
-21.5899
-112.808
-4.93105
Secondary Industry
-213.503
-450.598
55.49134
6.490157
Tertiary Industry
-200.477
-101.762
513.1958
21.32996
Total Industry
-210.385
-573.951
113.1817
22.88907
4C
Shanghai*
Xiamen**
Regional Shift
1990-1999
Regional Shift
1990-1999
Percent
Net
Percent
Net
Light Manufacturing
-208.762
-1767.44
139.6365
83.82684
Heavy Manufacturing
-85.0967
-677.472
-365.372
-517.766
Total Manufacturing
-148.831
-2444.92
-215.097
-433.94
Primary Industry
-43.0604
-14.0377
-100.958
-11.3086
Secondary Industry
-120.718
-582.683
-46.6684
-22.2457
Tertiary Industry
364.6122
879.3352
-52.2693
-24.2178
Total Industry
37.36071
282.6151
-54.9156
-57.7721
*Numbers Calculated found in the 2001 Shanghai Statistical Yearbook and the 2001 National
Economic Yearbook
**Numbers calculated from data found in the 1996 and 2001 Statistical Yearbook and the 2001
National Economic Yearbook
***In 2000 prices and calculated in 100 million yuan
sectoral distribution, while the industrial composition fell behind the national pace by –329%.
one would expect it to, aggregate to equal the magnitude of the “Secondary” category in the
breakdown of all industries. Nevertheless, the differentials are consistent and do not alter the
broad implications of the analysis. The variable presented in the tables is regional shift, reported
both as an absolute number (in 100 million yuan of output) and as a percentage. Regional shift
shows changes in a specific industry and in a specific city after the national growth component
15
(of the entire national economy) and the industry-specific growth component (of the specific
industry on a nation-wide basis) have been netted out of the total growth for the local industry.
Therefore, the measure of regional shift represents the change in industry output that lies beyond
the “expected” growth, “expected” in light of the standards of the national macro-economy and
the national performance of a specific industrial component. Because all measures in the table
are relative to national standards, changes in the Section B and Section C components do not
sum to the magnitudes reported in Section A.
Looking at the Shanghai sections of Table 4, we first note that all categories of the
manufacturing breakdown were negative over all three periods of the analysis. It is interesting to
observe the counter-intuitive result that, relative to national growth patterns, Shanghai’s light
manufacturing fared more poorly than its heavy manufacturing across both the total period and
its two component decades. On the other hand, the breakdown into broad categories of industry
showed 99% growth in the Tertiary (services) component across the two-decade period of
Section A of Table 4. As indicated in Sections B and C, the source of that growth occurred in
the 1990-99 period, the decade in which Shanghai’s economy was being steered toward
becoming a location for the financial and service industries.
Across the full two decades Xiamen data shows a 154.7% growth in heavy manufacturing
relative to what occurred on the national level. As noted in Sections B and C, all of that change
took place in the 1980-90 decade, a period of time when cross-straits political stability allowed
for a shift of the function of Xiamen from a strategic military fortress to a regional economic
center. However, in the decade of the 1990s most growth took place in the light manufacturing
category, indicating a relative shift of heavy manufacturing into the hinterlands. The Xiamen
columns of Table 4 also indicate a marked shift away from primary industries, toward secondary
and tertiary activities, particularly into tertiary, with its 1804% increase over the two decades.
Again, the greatest changes occurred in the 1980s, the decade that augured in the shift of Xiamen
to a regional economic center.
In summary, the shift-share analysis indicates that the growth of Shangai’s economy
slowed in almost all measures from 1978 to 1999, relative to the rest of the Chinese economy.
However its growth in the tertiary sector greatly exceeded that of the nation. This resulted from
policies that Shanghai implemented to develop four distinct activities: Trade, finance, commerce,
and services. These municipal policies also contributed to negative growth rates in primary and
16
secondary industry. These policies, which include industrial relocation and urban greenification,
were intended to attract more services, boosting tertiary industry and turning Shanghai into
China’s financial center.
As Xiamen was unable to begin industrialization until relations with Taiwan had
stabilized, there was a significant industrial growth in heavy manufacturing between 1980 and
1990. From 1990 to 1999 the shift share analysis indicates a negative growth rate for heavy
industry. Conversely, light industry in Xiamen from 1990 to 1999 showed a positive growth
rate. We attribute the negative growth rates of heavy manufacturing in the 1990s to policy
changes enacted in the mid 1980’s that discouraged heavy industry and favored light industry.
These regulatory shifts were part of a broader mandate to make the city greener and more
livable, in order to attract more foreign investment.
Institutional Parameters and Political Realities
In the course of our on-site study we were repeatedly reminded of the differences
between the normative content of environmental laws and actual levels of adherence. The
combination of the literature of environmental standards and our on-site interviews provided the
materials by which we could examine the hierarchy of laws, the content of those laws, how they
apply to varying ownership models, and how Shanghai and Xiamen’s laws and levels of
compliance compare. This section will discuss these topics in that order.
Hierarchy and Content of Laws
The environmental laws in China are constructed in a hierarchical fashion, with the
central government setting broad policies that guide environmental standards at lower levels.
Cities are allowed to create and enforce more stringent standards; however, they can be fined if
municipal ordinances conflict with national edicts. Many interviewees described national and
local policies as being similar in their strictness, but noted many larger cities have drafted more
restrictive regulations. Both Shanghai and Xiamen have their own environmental laws that
generally exceed nationally set minimums. We found that regulations, programs and laws in
these two cities have been designed to gradually raise the environmental quality of each area,
rather than all at once. Still, many of the experts we spoke with felt that Chinese environmental
laws were too rigorous and, therefore, unenforceable. This was attributed to the reason that the
laws did not give consideration to financially weak enterprises for whom the threat of layoffs and
economic loss is imminent.
17
Despite these criticisms, it appeared that environmental policies in Shanghai and Xiamen
have been quite successful. The basis for this success has been their multi-faceted approach to
controlling pollution. The municipal Environmental Protection Bureaus (EPBs) are at the helm
of addressing environmental supervision issues. The operations of the EPBs include overseeing
the initial permitting process, random and scheduled inspections, as well as the capacity to
penalize companies with fines based on the amount and type of pollution released. In order for
any new construction to be approved, companies must submit Environmental Impact
Assessments (EIAs) to the local EPB. EIAs ensure that companies have carefully considered the
logistics and minimized the environmental effects of their projects before building starts. In
more and more areas, the EPB also tracks emissions through twenty-four hour online monitoring.
Company management is responsible for paying fines when infractions occur; revenues from
fines go to the environmental bureaus, much like in the United States. Ongoing violations can
lead to forced shutdowns of factories.
Shanghai and Xiamen also share the strategy of concentrating the location of major
pollution sources into designated areas. Because most factories emit wastewater, it is difficult to
oversee all of them thoroughly if they are spread out. Relocation of factories to more remote
industrial parks is another key component of effective monitoring. Within these industrial parks,
air emissions and wastewater can be collectively measured and treated. In order to obtain
economies of scale, municipal EPBs have encouraged and, in some cases, actively participated in
planning the construction of centralized treatment plants for wastewater. A program known as
the “Green Space Policy” is meant to complement these relocation efforts. Under this program,
after factories are moved outside of city limits, the former industrial land is reclaimed and filled
with green parks. More recently, the EPBs have begun to focus on curbing automobile pollution
with emissions requirements and substandard vehicle “retirement.”
Enforcement and Compliance
Environmental policies can only be as effective as their implementation. Levels of
compliance and enforcement seem to be linked to ownership among state-owned enterprises
(SOEs), joint owned, and wholly foreign owned firms. Of these ownership models, state-owned
factories were most often cited as illegally emitting pollutants. As long as pollution in excess of
standards remains moderate, the EPB and the municipal government are unlikely to close down
SOEs because they provide so many jobs; high unemployment would likely cause social unrest.
18
Furthermore, with the cooperation of the EPB, the municipal government will occasionally offer
interest-free loans to SOEs to help them meet payroll expenses. These loans are rarely repaid.
Propping up failing SOEs through loans and lax inspections also means that government does not
have to admit to shortcomings in the state sector and “lose face.” One colorful Taiwanese
businessman commented on this situation, saying SOEs received these sorts of favors because
the state is “both the player and the referee.” Fortunately, media coverage and public awareness
are growing; this has put pressure on government agencies to reduce preferential treatment for
SOEs. Increasingly, foreign firms are looking to acquire SOEs that are on the verge of
bankruptcy. These firms assume the facilities and operations of the indebted SOEs with the
understanding that factories and equipment will need to be updated to meet environmental
standards.
As for nationality of ownership, in both Shanghai and Xiamen we found that municipal
EPBs often gave special treatment to Taiwanese factories. Many interviewees noted that cultural
similarities made it easier for Taiwanese businessmen to negotiate around environmental
regulations. When asked about this situation, one Taiwanese businessmen remarked, “with
relations (guanxi) there are no problems.” For example, Taiwanese managers will sometimes
take EPB officials out to dinner or give them “gifts.” In exchange, they may be forewarned
about the “random” EPB inspections or be held to more lenient regulations than other foreign
companies. Similarly, Taiwanese managers may use gifts to placate local residents when
protests arise over pollution or labor issues. A Taiwanese businessman in Shanghai felt this sort
of behavior is a result of Taiwanese companies needing to remain competitive in the face of
being held to standards more stringent than domestic companies. He also noted that a more
common method of dealing with regulatory discrepancies was simply to switch to natural gas or
update water treatment equipment.
Overall, foreign enterprises are in compliance with environmental regulations on a much
more regular basis than are SOEs. In fact, almost every multi-national corporation in China
adheres to ISO 14001 or ISO 9000 standards. Foreign companies, especially larger ones,
maintain this exemplary record for three main reasons: stricter EPB inspections, fear of future
liabilities and resource efficiency. Here it should be briefly noted that the legal code does not
formally allow for differences in regulations because of ownership. In fact, the EPB does hold
American and European companies to a much higher standard than other companies, much like
19
the Taiwanese companies are held to a higher standard than domestic companies. The rationale
for this unequal treatment is that American companies have more resources and experience with
environmental protection measures and, therefore, no reason not to comply with the laws.
Japanese and Taiwanese firms are less familiar with strict environmental regulations and so they
cannot be expected to attain the same level of compliance. American and European firms also
carry with them the history of legal suits brought against them long after the harmful effects of
industrial pollution became evident. Most Asian firms have yet to face financial liabilities for
lingering consequences of unregulated production. Frequently, the key incentive is purely
economic. Using cleaner processes often saves on energy, water and other input costs.
Often illegal pollution is less an issue of lax enforcement than of smaller companies
lacking the capital or willingness to invest in treatment equipment. For some factories, the
technology is not available; other factories are simply hesitant to use it. We consistently were
told that one of the most prominent characteristics associated with high pollution levels is small
factory size. To avoid regulations and fines, these companies devise methods to avoid attracting
the attention of the EPB. For example, companies may emit untreated water at night when some
EPB monitors are turned off and no employees are manning the treatment plants. One consultant
described finding a plant with a pipe built under the water treatment plant, dumping directly into
the river. Other companies will only turn on their pollution treatment equipment when the EPB
comes to inspect and then immediately shut it off to avoid operating costs. In many cases, the
equipment is of insufficient capacity to control the amount of pollution that the factory emits.
Firms can avoid being shut down by showing the EPB consultant’s reports that lead inspectors to
believe the factories are in the process of installing environmental control equipment, when in
reality the firms have no intention of doing so. Most of these problem factories are smaller,
older plants in less developed areas, where citizens are less environmentally aware.
A Comparison of Shanghai and Xiamen’s Policies
Given their mutual focus on environmental issues, it is not surprising that Shanghai and
Xiamen share a number of EPB and municipal level policies, including energy use guidelines,
efforts to concentrate manufacturing firms, factory inspections and automobile restrictions. In
both cities, enforcing regulations such as bans on high sulfur coal, requirements for companies to
use only high quality coal or natural gas, and the “Green Space Policy” are major parts of the
EPB agenda. Again, environmental impact assessments (EIAs) or feasibility reports must be
20
submitted to and approved by the EPB for any new construction. The Shanghai and Xiamen
EPBs use these reports to control the location of factories; both EPBs have mandates to
encourage companies to establish new factories outside the city centers. For Xiamen, factories
are rarely allowed to be built on Xiamen Island. Similarly, Shanghai has reserved land for
industrial parks in the suburbs of the city. With the support of EPBs, an increasing number of
plants in Shanghai and Xiamen are seeking to be certified under the ISO 14001 and 9000
standards. A couple of interviewees did, however, note that the validity of this certification is
sometimes dubious, as local authorities are often responsible for inspections. In addition,
Shanghai and Xiamen have both implemented plans routinely to inspect cars and buses to ensure
that they meet emissions specifications.
Both Shanghai and Xiamen’s municipal governments are pursuing development
strategies that are, although environmentally friendly, not completely focused on tertiary
industry. In the case of Shanghai, numerous interviewees told us that officials hoped to make
Shanghai into the financial capital of China, and yet they were unwilling to push the steel
industry out of the city. This is because the steel industry, although pollution intensive, has been
a hallmark of Shanghai since the beginning of the Communist period. Similarly, Xiamen’s
government has concentrated more on attracting light manufacturing than tertiary industry;
however, they have done this for a different reason. Until tensions cooled with Taiwan in the
1980’s, Xiamen had little or no civilian industry; from 1949 on, the island had been nothing
more than a military base. Following the legalization of Taiwanese investment in the mainland,
Xiamen’s leadership decided that attracting secondary industry should be the island’s priority.
Of course, with growth in commerce came the need for hotels, restaurants and entertainment, so
tertiary industry also grew.
On the other hand, many policies differ across Shanghai and Xiamen. For the most part,
these inconsistencies are centered around differences in methods, not in intention. For example,
in the past the Shanghai government had regularly given tax breaks to companies just for setting
up in the area. Now, these sorts of incentives are rarely given to companies. Xiamen, on the
other hand, still allows companies tax breaks as a relocation incentive. Other incentives used by
the Xiamen municipal government include land, grants, tariff free inputs and longer leases.
Policies targeting industrial emissions also differ. In Xiamen, the local government pays for
wastewater treatment plants, while in Shanghai emission fees from local companies may be used
21
to subsidize the building of treatment plants; more often, each company pays for its own
treatment facilities. Because these treatment facilities are not provided by the government and
do not afford economies of scale, some companies in Shanghai opt to hire other companies to
treat their wastewater. Tradable emissions permits do not appear to be in use at all in Xiamen.
Shanghai is currently experimenting with permits for chemical oxygen demand emissions
(CODs) in the upper Huangpu River area to determine their likely local efficacy. Another
example of a discrepancy is a Xiamen regulation, ordering no new motorcycles to be driven;
Shanghai has no such regulation, although both cities aim to curb traffic based pollution.
General Conclusions
In light of our regression modeling, shift share analysis, and interview findings, we can
evaluate our initial hypotheses. Our first hypothesis is that sectoral shifts have caused
environmental degradation to migrate from within cities out to the hinterland. Our shift share
analysis confirms the fact that heavy industry is not growing in either city. However, it seems
that the strategy of moving heavy industries away from the cities is only partially effective as a
means for pollution control, as evidenced both in our regressions and interviews. Our second
hypothesis is that close ties to the government make SOEs less likely to be in compliance with
environmental regulations and are, therefore, greater contributors to pollution. Both our
regressions and interviews confirmed that SOEs are particularly problematic in terms of
pollution. However, many interviewees stressed that size, not ownership, is the primary factor in
determining a firm’s environmental behavior. Finally, our third hypothesis stated that there
would be significant but explainable differences in compliance and emissions levels in Shanghai
and Xiamen. While we did find significant differences in emissions levels (see Figure 1 for the
environmental indexes of each city), in many ways Shangai and Xiamen are not comparable.
Their different size, location and economic development experiences make it very difficult to
draw simple conclusions about pollution levels.
22
100
Index
80
60
40
20
0
1990
1992
1994
Shanghai index
1996
1998
2000
Xiamen index
Figure 1: Shanghai and Xiamen pollution index levels, 1990-2000
Source: 2001 Shanghai Statistical Yearbook, Shanghai Environmental Protection Bureau, Yearbook of Xiamen
Special Economic Zone 1992-2000, Xiamen Environmental Protection Bureau
Policy Implications
On the whole, local regulation in Shanghai and Xiamen has effectively improved urban
industrial pollution, even in the face of rapid industrial growth. However, our findings indicate
that more guidance and enforcement from the national level is needed. Consider, for instance,
the extreme water contamination in Xiamen, despite environmentally sound development
strategies. The same was noted for Shanghai in the case of upstream polluters of its two rivers.
As noted above, many interviewees pointed to mainland pollution streams as the main culprits.
In cases like this, central intervention is the only solution. In another example, the seeming lack
of correlation between the outward shift of heavy industry in Shanghai and environmental
quality suggests that industrial decentralization is an inadequate solution, and yet, for China’s
government, which has promised continued economic development, depressing heavy industry
would also be unacceptable.
In this light, it would seem most reasonable for the central government to address the
problem of SOE pollution. In fact, gradually reducing the share of the public sector in the
economy is already one of Beijing’s goals. Integrating their extant economic development
strategy and environmental regulations at the national level could, in the long run, produce the
23
added benefit of dispelling the myth that economic prosperity and environmental protection are
incompatible.
Global Environment
The significance of our findings and recommendations is more obvious when viewed in
the context of the global environment. As China grows and prospers, policymakers must think
towards the future and consider the potential effects that their choices will have on global energy
resources and pollution levels. For example, the U.S. currently uses 97.0 quadrillion Btus of
energy, considerably more than China’s 36.7. If China used as much energy per capita as U.S.,
they would use 453.6 quadrillion Btus. Similarly, if the Chinese had as many cars per person as
do Americans, there would be 1 billion cars in China. The most highly used source of energy in
the U.S. is oil; if China used as much oil as the U.S. per capita, they would use 92,127 1000bbl/
day. This is significantly more than the current world total of 76, 021 1000bbl/day. This amount
of energy use would put a phenomenal drain on the natural resources of China and the world.
Clearly, the planet could not sustain energy use of this magnitude by both the U.S. and China for
very long (Energy Information Administration 2002).
If we expand our hypothetical beyond oil to other natural resources, the picture becomes
even grimmer. China, already the world’s largest user of coal, continues to increase its coal
consumption. If China had the same intensity of energy use as the U.S., the resulting pollution
could seriously affect the global environment. The Chinese contribution to greenhouse gases
would surpass that of the U.S. (the current leader in carbon dioxide emissions).y Because coal is
used to generate most of China’s electricity, the emissions from these power plants would
devastate the ozone layer and significantly decrease air quality, leading to marked global climate
changes (Energy Information Administration). To avoid this situation, steps must be taken soon
not only in China, but in the U.S. and worldwide.
24
Appendix A - Shift Share Formulas
These formulas served as a tool for our own shift share analysis (Keil 1983):
National Growth
Nij = Eij (E*oo/Eoo - 1)
Industry Effect
Iij = Eij (E*io/Eio - E*oo/Eoo)
Competitive Shift
Cij = Eij (E*ij/ Eij - E*io/Eio)
Where:
Eoo is total national employment
Eij is local employment in the ith industry
Eio national employment in the ith industry
And an asterisk (*) designates the end year
25
Appendix B- Regression Results
Shanghai in-depth regression:
Model Summary
Model
1
R R Square Adjusted Std. Error of the Durbin-Watson
R Square
Estimate
.999
.998
.996
1.2736E-02
2.587
a Predictors: (Constant), LNTIME, LNHEV, LNSHAR, LNPSI, LNSOE
b Dependent Variable: LNPOL
ANOVA
Model
1
Sum of
Squares
Regression
.357
Residual 6.489E-04
Total
.358
df Mean Square
5
4
9
7.142E-02
1.622E-04
F
Sig.
440.275
.000
a Predictors: (Constant), LNTIME, LNHEV, LNSHAR, LNPSI, LNSOE
b Dependent Variable: LNPOL
Coefficients
Unstandardized Standardized t-statistic
Coefficients Coefficients
Model
1 (Constant)
LNPSI
LNHEV
LNSOE
LNSHAR
LNTIME
B
1.935
-.587
.832
1.547
-1.362
.126
Std.
Error
.467
.362
.209
.185
.193
.026
p-value
Beta
-.291
.235
2.956
-1.123
.465
4.143
-1.623
3.975
8.383
-7.047
4.890
.014
.180
.016
.001
.002
.008
a Dependent Variable: LNPOL
Regressions were computed, and tables generated, with SPSS 10.0.0 for Windows.
26
Shanghai parallel regression:
Model Summary
Model
1
R R Square Adjusted Std. Error of the Durbin-Watson
R Square
Estimate
.983
.966
.957
4.936E-02
1.386
a Predictors: (Constant), LNTIME, LNPSI.S
b Dependent Variable: LNPOL.S
ANOVA
Model
1
Sum of
Squares
Regression
.547
Residual 1.949E-02
Total
.566
df Mean Square
2
8
10
.273
2.436E-03
F
Sig.
112.162
.000
a Predictors: (Constant), LNTIME, LNPSI.S
b Dependent Variable: LNPOL.S
Coefficients
Unstandardized Standardized t-statistic p-value.
Coefficients Coefficients
Model
B
(Constant)
LNPSI.S
LNTIME
Std.
Error
5.624 .130
2.602 .382
7.150E-02 .056
Beta
1.186
.224
43.125
6.804
1.284
.000
.000
.235
a Dependent Variable: LNPOL.S
Regressions were computed, and tables generated, with SPSS 10.0.0 for Windows.
27
Xiamen parallel regression:
Model Summary
Model
1
R R Square Adjusted Std. Error of the Durbin-Watson
R Square
Estimate
.765
.585
.447
5.380E-02
2.613
a Predictors: (Constant), LNTIME, LNPSI.M
b Dependent Variable: LNPOL.M
ANOVA
Model
1
Sum of
Squares
Regression 2.453E-02
Residual 1.737E-02
Total 4.189E-02
df Mean Square
2
6
8
1.226E-02
2.895E-03
F
Sig.
4.236
.071
a Predictors: (Constant), LNTIME, LNPSI.M
b Dependent Variable: LNPOL.M
Coefficients
Unstandardized Standardized t-statistic
Coefficients Coefficients
Model
1 (Constant)
LNPSI.M
LNTIME
a Dependent Variable: LNPOL.M
B
Std.
Error
1.249 .453
-1.699 .587
.147 .070
p-value
Beta
-1.228
.887
2.759
-2.896
2.092
.033
.027
.081
Regressions were computed, and tables generated, with SPSS 10.0.0 for Windows.
28
Appendix C- Likert Correlation Analysis
Likert correlation analysis consists of computing the correlation coefficient between each
individual item and the summative scale, which measures the internal consistency of the index.
We feel that the computed correlation coefficients, displayed below, are sufficiently close
enough to the maximum value of 1 to merit their inclusion in the index (Carmines 1980, 22-28).
SO2 and index: .953
TSP and index: .989
Industrial wastewater emissions and index: .995
29
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