UCD GEARY INSTITUTE FOR PUBLIC POLICY DISCUSSION PAPER SERIES Urbanization, trade openness, and air pollution: a provincial level analysis of China Wei Zheng School of Economics and Development, Wuhan University, China School of Politics and International Relations, University College Dublin, Dublin, Ireland Patrick Paul Walsh School of Politics and International Relations, University College Dublin, Dublin, Ireland Geary WP2018/18 July 27, 2018 UCD Geary Institute Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. Any opinions expressed here are those of the author(s) and not those of UCD Geary Institute. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions.
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UCD GEARY INSTITUTE FOR PUBLIC POLICY DISCUSSION PAPER SERIES
Urbanization, trade openness, and air pollution: a
provincial level analysis of China
Wei Zheng School of Economics and Development, Wuhan University, China
School of Politics and International Relations, University College Dublin, Dublin, Ireland
Patrick Paul Walsh School of Politics and International Relations, University College Dublin, Dublin, Ireland
Geary WP2018/18 July 27, 2018
UCD Geary Institute Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. Any opinions expressed here are those of the author(s) and not those of UCD Geary Institute. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions.
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Urbanization, trade openness, and air pollution: a provincial level analysis of China
Wei Zheng1 Patrick Paul Walsh2
Abstract As the largest developing country in the world, with fast-paced urbanization development,
China has achieved rapid economic growth since the “Reform and opening-up” policy
implemented in 1978. This growth, however, has resulted in persistent and severe
environmental problems. This paper evaluates urbanization, trade openness, energy
consumption and PM2.5 in the Chinese economy using Fixed effect (FE), fixed effect
instrumental (FE-IV), and system generalized method of moments (GMM-sys) estimation
methods from 29 provinces over the period 2001–2012. Results demonstrated that PM2.5 is
a continuous process that the previous period has positive effect on the current level of
PM2.5; Environmental Kuznets Curve (EKC) hypothesis was not supported by analyzing the
relationship between economic growth and PM2.5 in China; temperature is not a crucial
influencing factor in affecting the amount of PM2.5; urbanization is beneficial to the decrease
of PM2.5. PM2.5 from neighboring regions is an important factor increasing the local PM2.5,
and the influencing factors of international trade, heavy industry and private cars are
contributors to PM2.5 level as well.
Key words: PM2.5, Energy consumption, Urbanization, Average temperature level
Notes: Estimation is from a balanced panel of 29 provinces covering the period 2001-2012. The superscripts ***, ** and * denote significance at
the 1%, 5% and 10% levels respectively. Year dummies are included in each specification; standard errors in parentheses. Source: China Statistical Yearbooks (2002-2013), China Industry Economy Yearbooks (2002-2013), Energy Yearbooks (2002-2013) and
Provincial Yearbooks (2002-2013).
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Table 8 Results of dynamic model
(6) (7) (8)
Variables sys-GMM sys-GMM sys-GMM
L.PM2.5 0.328*** 0.419*** 0.322***
(0.116) (0.137) (0.116)
GDP 0.0192 -0.0739 -0.276
(0.560) (0.670) (0.685)
GDP2 -0.00690 -0.00124 -0.00275
(0.027) (0.032) (0.031)
Urbanization -0.542*** -0.414*** -0.555***
(0.157) (0.141) (0.170)
Tradeshare 0.215***
(0.057)
Tradepop 0.167***
(0.061)
Trade/GDPt-1 0.218***
(0.061)
Coal 0.130*** 0.113** 0.128***
(0.043) (0.045) (0.043)
Heavy industry 0.617*** 0.467** 0.639***
(0.181) (0.192) (0.186)
Private car 0.166** 0.141* 0.171**
(0.070) (0.075) (0.070)
Temperature -0.0496 -0.0228 -0.0494
(0.099) (0.104) (0.102)
Neighboring 0.381*** 0.327*** 0.383***
(0.083) (0.104) (0.083)
Constant 0 1.099 0
(0.000) (3.336) (0.000)
AR (1)
AR (2)
Hansen
0.001 0.789
0.792
0.001 0.912
0.381
0.001 0.810
0.762
Observations 319 319 319
Number of id 29 29 29
Notes: Estimation is from a balanced panel of 29 provinces covering the period 2001-2012. The superscripts ***, ** and * denote significance at
the 1%, 5% and 10% levels respectively. Year dummies are included in each specification; standard errors in parentheses. AR and Hansen tests are
the value of prob> z. In the GMM estimation, the predetermined variable is L.lpm, the instrumental variable is GDP and 𝑡𝑟𝑎𝑑𝑒𝑟𝑎𝑡𝑖𝑜;To L. lnpm,
using the lagged one as the instruments; to GDP and trade ratio, using the lagged one and two as the instruments. Source: China Statistical Yearbooks (2002-2013), China Industry Economy Yearbooks (2002-2013), Energy Yearbooks (2002-2013) and
Provincial Yearbooks (2002-2013).
Following the model specification and the introduction of the variables, Tables 7 and 8 report
a series of empirical results demonstrating the effect of driving forces on PM2.5 using FE, FE-
IV and sys-GMM estimation methods respectively.
According to the estimated results, economic growth has no significant effect on PM2.5 that
neither supports the existence of Environmental Kuznets Curve hypothesis (EKC). There is
no guarantee that economic growth improves the air quality. There are many other factors
that may affect the PM2.5 emission, such as the effectiveness of government regulation,
population levels and people’s awareness of environmental protection etc. Urbanization and
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PM2.5 show a negative relationship in most of the specifications, which proves urbanization
is one of the main contributors to the decrease of PM2.5 from 2001 to 2012. With the rapid
growth of urbanization, the implementation of new technology, the scientific and reasonable
urban planning, the popularity of electric and hybrid-energy cars, even the increasing of
people’s environmental protection awareness and the strict environmental protection laws
implemented by government can mean that the PM2.5 is gradually decreased, and air quality
will improve. The elasticity of urbanization ranges between 0.192 and 0.589.
Concerning effects of trade on PM2.5 are negative. On the negative point, the well-known
“race to the bottom” hypothesis is supported by the results. The hypothesis of “race to the
bottom” refers to countries trying to compete with other countries and adopting less
stringent environmental regulations and cutting tax rates due to the fear of adverse effects
on their international competitiveness (Frankel, 2009). Further, the comparative advantage
and industries division also contribute to this trend. Many developed economies have seen
a reduction in industry and growth in service sector, but they are still importing goods from
developing countries. In that sense, they are exporting environmental degradation. Pollution
may reduce in the UK and the US, but countries who export to these countries are seeing
higher levels of environmental degradation. Higher income countries tend to stop the
process of deforestation, but, at the same time, they still import meat and furniture from
countries who are creating farmland out of forests.
Temperature as an influencing factor has no significant effect on PM2.5, which demonstrate
that temperature as a kind of environmental influencing factor is not a main consideration
in curbing air pollution.
The factors of heavy industry and private car inventory also exert positive impacts on PM2.5,
though are not significant in some of the specifications. From 2001 to 2012, the share of
heavy industry had been accounted for around 70% and the number of private car increased
from 2,500 thousand in 1995 to 88,386 thousand in 2012, with an average annual growth
rate of about 23.33%. The positive sign of heavy industry indicates that the higher rate of
heavy industry is inclined to increase PM2.5. It is well known that coal accounts for 95% of
China’s fossil fuels endowments. It determines that the heavy industry is coal fuel powered,
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which is a main source of air pollution emissions. The elasticity of heavy industrial share on
PM2.5 ranges from 0.192 to 0.637; To private car inventory, the elasticity covers a range
between 0.049 and 0.171. Likewise, the share of coal in the energy consumption exerts
significant positive impact on PM2.5 that elasticity covers a range between 0.033 to 0.130.
The neighboring factor plays a significant positive role in increasing PM2.5 level. The
elasticity ranges from 0.327 to 0.904. That means that a region’s PM level that is correlated
with its neighbors might be caused by wind, rainfall or the other influencing factors. Air
pollution has no boarder, but that requires that environmental control must be a
comprehensive system project. The central government needs to make a blue map that
allows the locals work together to manage the environmental crisis.
The estimated coefficients on the lagged one period of PM2.5 variable show a positive and
statistically significant in all the specifications estimated by sys- GMM technique. The ranges
of these estimators are 0.328, 0.419 and 0.322, which implies that air pollution is a
continuous process and has a long-term effect on air quality.
5.3 Collinearity diagnostics
In this section, VIF test is used to check the multicollinearity between the variable. As a rule
of thumb, a variable whose VIF values are greater than 10 may merit further investigation.
Tolerance, defined as 1/VIF, is used by many researchers to check on the degree of
collinearity. A tolerance value lower than 0.1 is comparable to a VIF of 10. It means that the
variable could be considered as a linear combination of other independent variables.
By applying the VIF test, the results (Table 9) show that the values of VIF are all less than 10
and the values of Tolerance are more than 0.1, which means that multicollinearity is not a
problem.
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Table 9 Results of collinearity diagnostics
6 Conclusion and policy implications
Between 2001 and 2012, China’s economy experienced high-speed growth, with an average
annual growth rate of 10%. This rapid economic growth was achieved by huge energy
consumption (Zhao and Wang, 2015). At present, rapid energy consumption, increase of
private cars, and expansion of international trade have led to environmental deterioration.
In light of the severe environmental pollution and the higher growth rate of energy
consumption, the government policy makers should pay more attention to the linkage
among urbanization, energy consumption and air pollution. Although there has been
extensive literature exploring the linkage between urbanization, energy and air pollution in
the case of China, the most significant impact channel is not clear and very little is known
about whether temperature or the neighboring factor exert significant impacts on air
pollution. Based on the above empirical results under static (FE and FE-IV) and dynamic
(GMM-sys) estimation methods, it is found that neighboring factor plays an important role
in air pollution. Environmental management is a systematic comprehensive project that
needs the government co-ordinate all the regional activities of the nation.
Variable VIF SQRT Tolerance. R2
𝐺𝐷𝑃 6.78 2.60 0.1475 0.8525
𝐻𝑒𝑎𝑣𝑦 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 1.85 1.36 0.5401 0.4599
𝑇𝑟𝑎𝑑𝑒𝑠ℎ𝑎𝑟𝑒 4.57 2.14 0.2188 0.7812
𝑈𝑟𝑏𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛 5.59 2.36 0.1788 0.8212
𝑐𝑜𝑎𝑙 𝑟𝑎𝑡𝑒 1.01 1.01 0.9864 0.0136
𝑝𝑟𝑖𝑣𝑎𝑡𝑒 𝑐𝑎𝑟 1.91 1.38 0.5234 0.4766
𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑖𝑛𝑔 1.27 1.13 0.7870 0.2130
𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 2.11 1.45 0.4744 0.5256
Mean VIF 3.14
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The difficulty facing the government is how to realize the sustainable growth and curb
energy consumption and pollution while promoting urbanization. Fossil fuels (coal) as the
main energy consumption in China’s economic growth, the direct strategy of curbing fossil
fuels consumption might cause many social problems, such as unemployment and
bankruptcy of the firms (Zhao and Wang, 2015). So, the feasible ways can be summarized as
below:
Firstly, develop clean coal technology and improve energy efficiency. As is known, China is
rich in coal, which accounts for 95% of the country’s fossil fuels endowments. According to
the Chinese Ministry of Land and Resources, China’s proven coal reserves of 170 Mt
correspond to 19% of the global total, ranking second in the world after the United States
(Tu and Sabine, 2012). The lack of natural gas and the other clean energies mean that coal
will be the dominant fuel in next few decades in China. Therefore, developing clean coal
technology and improving energy efficiency becomes very urgent and necessary.
Secondly, upgrade and adjust the industrial structure. The main problem is the distortion of
the three industrial sectors (Agriculture, Industrial and Tertiary) of China. The main
direction of adjusting industrial structure is to reduce the proportion of the heavy industry
and promote the development of tertiary industry. Between 2001 and 2015, the tertiary
industry as a proportion of GDP increased from 41.2% to 50.2%, with an average annual
growth rate of 1.42%. The proportion of tertiary industry is lower by 10–20% point than
that of the developed countries’ level. The internal structural of industry also needs to be
adjusted. The heavy industry accounts for 70% in the past ten years of China’s economic
development, which led to the surge of energy consumption and environmental degradation,
and resulted in a relative surplus of industrial goods, hence industrial capacity is under-
utilized (Guo, 2001).
Thirdly, conduct scientific city planning based on the principle of sustainable development;
carry out urban construction rationally; strengthen city planning administration and
environmental protection. China’s urbanization has achieved a notable achievement since
1978 the reform and opening-up policy. Since 2012, more than half of the population has
lived in urban areas, which has led to energy scarcity and environmental degradation. At the
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same time, the government should improve the infrastructure of the big cities, promote the
development of small and medium-sized cities, emphasize the quality of urbanization and
take a path of energy-conserving and environment-friendly urbanization.
Fourthly, reasonable control over the number of private car power by the traditional energy
resources, and develop hybrid, electric cars.
Finally, strengthen exchange and cooperation among the regions to decrease the air
pollution. The estimation results show that air pollution is not just a local problem. A region’s
air quality is also affected by its neighboring regions. So, curbing the air pollution emission
requires an overall planning and design. Th central and provincial level governments should
work together to solve it.
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