Review of Business and Economics Studies, 2018, том 6, № 2

Review of  
Business and 
Economics  
Studies

EDITOR-IN-CHIEF
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Faculty, Financial University, Moscow, 
Russia
ailyinsky@fa.ru 

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Adam Smith Business School, 
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Dept.of Mathematical Sciences, Brunel 
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ISSN 2308-944X

Вестник 
исследований 
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и экономики

ГЛАВНЫЙ РЕДАКТОР
А.И. Ильинский, профессор, декан 
Международного финансо вого факультета Финансового университета 

ВЫПУСКАЮЩИЙ РЕДАКТОР
Збигнев Межва, д-р экон. наук

РЕДАКЦИОННЫЙ СОВЕТ

М.М. Алексанян, профессор Бизнесшколы им. Адама Смита, Университет 
Глазго (Великобритания)

К. Вонг, профессор, директор Института азиатско-тихоокеанского бизнеса 
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The Relationship between Growth  

and the Environment in Beijing,  

Using PM2.5 Concentrations

D. I. Jingyuan, L. I. Chong, Laura Marsiliani  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .5

Protection of Environment during Armed Conflicts

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Value Concepts and Value Creation Model  

in Integrated Reporting

Elvira Sheveleva  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .30

Mergers and Acquisitions of Enterprises  

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Comparative Analysis of Sovereign Credit Ratings. Statics

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Review of  
Business and  
Economics  
Studies

Volume 6, Number 2, 2018

Вестник 
исследований 
бизнеса  
и экономики

№ 2, 2018

Взаимосвязь между экономическим ростом  

и окружающей средой в Пекине  

на основе показателя PM2.5

D. I. Jingyuan, L. I. Chong, Laura Marsiliani  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 5

Защита окружающей среды  

во время вооруженных конфликтов

Tshibola Lubeshi Aimée Murphie   .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 19

Концепции и модель создания стоимости  

в интегрированной отчетности

Эльвира Шевелева   .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 30

Слияния и поглощения предприятий  

как инструмент повышения стоимости и конкурентоспособности:  

случай Масана и Синга

До Тхи Нгок Ань, Елена Мирошина   .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 44

Сравнительный анализ суверенных  

кредитных рейтингов. Статика

Алексей Ивкин  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 50

1. Introduction
The environmental Kuznets curve (EKC) hypothesis states that economic growth leads to 
degradation and pollution but, beyond some 
level of income per capita, it is conducive to an 
improvement in environmental quality (inverted U-shaped relationship). The EKC hypothesis 
has become a powerful tool in analysing the 
empirical relationship between growth and 
the environment. The literature on the EKC 
has relentlessly proliferated since the seminal 
contribution of Grossman and Krueger in 1991, 
to take into account different pollutants and 
control variables 1.

1 See for example, the country level studies by Grossman and 
Krueger (1995), Shafik and Bandyopadhyay (1992), Panayotou 
(1992, 1993 and 1995), Cropper and Griffiths (1994), Bhattarai 
and Hamming (2001), Markandya et al. (2006), Plassmann and 
Khanna (2006).

A recently published paper by Stern and Zha 
(2016) highlights two very recent developments 
in the extensive literature on EKC hypothesis, 
namely the importance of ambient pollution 
concentrations and the opportunity offered by 
newly recorded particular matter (PM2.5) at 
urban level.
The need to consider ambient pollution 
within the EKC framework is embedded in the 
concept of Urban EKC. The urban dimension is 
generally captured by including variables shedding light on the relationship between ambient 
pollution and economic growth, such as emissions from urban transport, suspended particular 
matter in urban areas, municipal solid waste, 
population density, characteristics of transport 
network etc. Examples of studies within the 
Urban EKC literature are Hilton and Levinson 
(1998) for 47 countries, Day and Grafton (2003) 

Review of Business and Economics Studies  
doi: 10.26794/2308-944X-2018-6-2-5-18
2018, Vol. 6, No. 2, 5-18
The Relationship between Growth and 
the Environment in Beijing, Using PM2.5 
Concentrations

d. i. Jingyuan 1, L. i. Chong 2, Laura Marsiliani 3

1 Ph.d. candidate, durham University Business School, durham University, United Kingdom; jingyuan.di@durham.ac.uk

2 Ph.d. candidate, School of Economics, Sichuan University, China; sculichong@foxmail.com

3 Ph.d. (corresponding author), durham University Business School, durham University, United Kingdom; Laura.
marsiliani@durham.ac.uk

Abstract
This study examines the relationship between income and the environment in Beijing from 2008 to 2017 using 
quarterly data. The indicator for environmental quality is concentrations of Particular Matter (PM) 2.5, from the 
Mission China Air Quality Monitoring Programme (MCAQMP), whose observation site is in the US embassy in 
Chaoyang district, Beijing. By adding cubic GdP and other variables consistent with the Urban Environmental 
Kuznets Curve Hypothesis, such as green space and the length of the road network, the result suggests an 
N-shaped pattern rather than the conventional inverted U shape. The per capita GdP for Beijing is currently 
slightly lower than the second turning point, suggesting that the degradation would become more severe as 
income grows, if no new development strategies are implemented in the city.
Keywords: Environmental Kuznets Curve; PM2.5; Beijing
JEL Classification: O18, Q53, Q56

Acknowledgements: We wish to thank Riccardo Scarpa, seminar participants at durham University, two 
anonymous referees and an associate editor for valuable comments that greatly improved this paper. The first 
author is also grateful to the China Scholarship Council for financial support while undertaking this research.

for Canada, Orubu and Omotor (2011) for African 
countries, Asahi and Yakita (2012) and Hossain 
and Miyata (2012) for the urban areas of Yokkaichi and Toyohashi, Japan, Kim et al., (2016) 
for South Korea and Sinha and Bhattacharya 
(2016) for India.
Nevertheless, to our knowledge, most of the 
research on the Urban EKC is based on China 
either at province or city level, as China is experiencing a remarkable urbanisation growth, 
coupled with consistently high energy consumption and pollution (Dhakal, 2009) 2. Since 
the early 2000s, studies on the urban EKC in 
China have been undertaken with regularity 
and include a wide range of environmental 
and urban indicators. Results also support a 
variety of estimated EKC, from the standard 
inverted U-shape to the more unusual U-shape 
and N-shape.
As data for PM2.5 concentrations have only 
recently become available with sufficient frequency 3, EKC studies using PM2.5 as an indicator 
of ambient pollution are scarce (Stern & Zha, 
2016; Hao & Liu, 2016). Yet PM2.5 concentrations have been proven to be extremely harmful 
to human health 4 by affecting respiratory and 
cardiovascular functions and causing cancer, 
and to ecological system.
In this paper, we examine the relationship 
between income and the environment in Beijing 
using PM2.5 concentrations as our chosen environmental indicator. In addition to being the 
national capital of China, Beijing is identified 
in the latest Chinese national plan 5 as one of 
35 major cities in terms of size and economic 
significance. These cities, with less than 20% 
of the national population, account for 40% of 
total energy consumption and are characterized 

2 The percentage of population living in urban areas has increased in China from 40% in 2005, to 57.3% in 2016, with 790 
million residents in urban areas (see https://data.worldbank.
org/indicator/SP.URB.TOTL.IN.ZS).

3 Only recently, concentrations of PM2.5 have been regulated 
and regularly recorded. Although some measurement of PM2.5 
concentration was undertaken in the US already in the late 
1990s, the US implemented daily standards in 2007, followed 
by Japan in 2009, Russia in 2010 and more recently by the EU 
and South Korea in 2015.

4 See Sørensen et al. (2003), Cohen et al. (2005), US EPA (2009), 
Janssen et al. (2011).

5 See http://www.mlr.gov.cn/tdsc/djxx/djjc/201004/
t20100401_143692.htm. Beijing has been listed as one of the 
main observation cities since 2008, by the Ministry of Housing 
and Urban-Rural Development.

by high pollution levels. As PM2.5 is considered 
an ambient pollutant, we include relevant local 
variables such as green space, and length of 
road network as controls. By using a recently 
available dataset for PM2.5 from the Mission 
China Air Quality Monitoring Programme 
(MCAQMP) which possesses high reliability 6, 
we are able to provide the first EKC analysis 
of a Chinese city for the medium run. Contrary 
to most of the existing literature, our analysis 
supports an N-shaped EKC relationship 7. The 
first turning point is about 60,000 CNY per year 
while the second turning point is about 132,000 
CNY per year. The income at the second turning 
point is just above the current average income 
of Beijing residents. The improved environment 
quality in the last several years can mainly be 
attributed to the implementation of stringent 
government environmental policy while the 
latest spur in pollution may be a consequence 
of the stimulus growth policies implemented 
since late 2014 8. These results suggest that in 
the next decades, it may be extremely challenging to achieve stable growth rates and high air 
quality in China.
The paper proceeds as follow: Section 2 surveys the existing literature on Urban EKC hypothesis for China; Section 3 describes the data 
used in this paper; Section 4 focuses on the empirical model and the econometric methodology; 
Section 5 presents the results from our empirical 
analysis; Section 6 includes some policy implications for Beijing and section 7 concludes and 
offers suggestions for future research on the 
Urban EKC.

2. A Survey of the Existing Literature 
on the Urban EKC in China
To our knowledge, the first study that addresses the existence of EKC in China is De Groot 
et al., (2004). They use data from 30 provinces 

6 Official data from China has been found not to be reliable as 
air quality measurements are related to the career progression 
opportunities of officials and therefore may be prone to manipulation (See Chen et al., 2012 and Ghanem & Zhang, 2014).

7 Other EKC studies that find an N-shaped EKC, although in 
a different setting, are Shafik and Bandayopadhyay (1992), 
Grossman and Krueger (1993), Selden and Song (1994), Panayotou (1997).

8 See Bloomberg. (2015). China Stimulus Kicks in to Help Keep 
2014 Growth Near Target. Available at https://www.bloomberg.
com/news/articles/2015–01–19/china-gdp-beats-estimatesleaving-2014-expansion-close-to-target.

The Relationship between Growth and the Environment in Beijing, Using PM2.5 Concentrations

from 1982 to 1997 and include waste water, 
water gas, and solid waste, as environmental 
indicators and a regional specific intercept. 
Their results support a typical EKC for water 
gas, an N-shaped relationship for solid waste, 
and a monotonically decreasing relationship 
for waste water.
Shen (2006) examines the EKC hypothesis 
for 31 Chinese provinces. It includes five pollutants, Sulphur Dioxide (SO2) and Fall Dust for air, 
Organic Pollutants, Arsenic, and Cadmium for 
water, with population density, industrial share 
and abatement expense as control variables. The 
results suggest an EKC relationship for water 
pollutants and for SO2, but no relationship for 
Fall Dust.
Liu et al., (2007) test the EKC hypothesis in 
Shenzhen, based on data from 1989 to 2003. 
They include a large number of pollutants for 
several environmental media, including major 
rivers and near-shore water. The results show 
that production induced pollution support the 
canonical EKC hypothesis, while consumption 
related pollutants do not.
Brajer et al. (2008) test the relationship between SO2 and per capita income based on city 
level data in China from 1990 to 2004, with 
population density as control. Using different 
econometric methods, both the inverted Ushaped and N-shaped EKC are supported.
Based on Chinese provincial data from 1985 
to 2005, Song et al. (2008) investigate the EKC 
hypothesis between GDP per capita and three 
environmental indicators: waste water, waste 
gas, and solid waste, without adding control 
variables. Their results assert that all these 
three environmental indicators follow an inverted U-shape EKC relationship and the turning point for waste gas is lower than the other 
two indicators.
Diao et al., (2009) analyse the relationship 
between GDP per capita and a number of industrial pollutants, with environmental policies, investment strategies, and contribution 
to GDP as control variables, for Jiaxing city. An 
inverted U-shape relationship is observed for 
industrial waste water, industrial waste gas, SO2, 
and industrial dust. The turning points for the 
pollutant are generally lower than previous studies in China and lower than the turning points 
in developed countries and can be explained 

by the early local government policies against 
industrial pollution.
Shaw et al. (2010) examine the EKC hypothesis for 99 cities in China from 1992 to 2004. Air 
pollution includes SO2, Nitrogen Oxide (NOx) 
and particle deposition, and control variables 
include population density, contribution of 
secondary industry to GDP, and a policy variable. The conclusion shows only SO2 supports 
an inverted U shape, while NOx increases as 
income grows.
He and Wang (2012) analysis the impact of 
economic structure, development strategy and 
environmental regulation on the shape of the 
EKC, using city level data from 1990 to 2001. 
The relationship between environmental indicators, SO2, NOx, total suspended particles 
(TSP) and GDP per capita are examined, with 
openness, regulation, population density, area, 
and capital/labour ratio as control variables. 
Openness, measured by FDI, always increases 
the level of the three pollutants, and capital 
abundance increase the concentration of TSP 
but decrease the concentration of NOx.
Luo et al., (2014) support a negative linear 
relationship between Gross Regional Product 
per capita and particulate matter 10 (PM10) 
concentrations in all province capitals for the 
last decade. However, only the PM10 concentration in the central parts of China is significantly 
related to GRP.
Sun and Yuan (2015) examine the relationship between GDP per capita and three indicators for environment, including industrial 
SO2, industrial soot, and industrial sewage discharged, based on data for 287 cities in China 
from 2003 to 2008. Besides, population density, 
area, variables standing for agglomeration were 
used as control variables. Their results show 
an N-shaped EKC for all three pollutants with 
industrial agglomeration having a significant 
influence on regional environmental quality.
Zhang et al. (2016) analyse the relationship 
between a comprehensive air quality index (API) 
and wealth based on data for 26 capital cities 
and 4 municipalities in China from 2002 to 
2010. As control they include population size, 
urbanization level, industrialization level, green 
coverage level, and pollution control investment. Economic level shows an inverted U shape 
EKC and the turning point is about 63,000 CNY.

The Relationship between Growth and the Environment in Beijing, Using PM2.5 Concentrations

Wang and Ye (2017) illustrate the monotonic increasing relationship between Carbon 
Dioxide (CO2) emission and GDP per capita 
using city-level data and employing a spatial 
lag model and a spatial error model. As a novelty from the previous literature, Wang and 
Ye include dummy variables for coastal and 
central cities.
Finally, the latest developments in the literature include the use of particulate matters 
2.5 data. Stern and Zha (2016) use PM10 and 
PM2.5 data from the years 2013 and 2014 for 
50 Chinese cities to regress pollution growth 
on GDP growth. They find U-shaped relationship which however results to be statistically 
insignificant. Similarly, based on data for 73 
Chinese cities in 2013, Hao and Liu (2016) examine the influence of GDP per capita, population density, transport, and industry on air 
quality. All estimation models, OLS, spatial 
lag model (SLM) and spatial error model (SEL) 
support a U-shaped EKC.
Given the fast-paced developments in connection to the Urban EKC hypothesis and the 
growing interest for China, as a key player in 
the global economy, we expect this literature to 
expand considerably in the next few years. Our 
paper intends to contribute to this literature by 
focusing on the case of Beijing.

3. Data
In this paper, the urban unit of reference is 
the city of Beijing and the pollutant used for 
the analysis is PM2.5 concentrations, whose 
source is local. Controls are also local level 
variables such as population, green space and 
length of road network. In the proceedings 
of this chapter we exactly define the urban 
area, the data and all issues surrounding their 
measurements.
The reason why the paper focuses on Beijing 
rather than other cities can be explained from 
several perspectives. Firstly, Beijing, as the 
capital of China, has all the hallmarks of an 
ideal unit of investigation for the EKC Hypothesis. Indeed, the city attracts large amounts of 
labour, capital, and intelligence resources which 
contribute to the rapid urban development, but 
also the degradation of the city’s air quality. 
Secondly, Beijing is due to undergo an ambitious urban restructuring plan as highlighted 

in the city development plan for the year 2035 9. 
Beijing will raise its profile as the political 
centre of China by focusing on developing its 
tertiary sector rather than industrial production and agriculture and restricting granting 
permanent residency rights to highly skilled 
workers. Heavy industries have already been 
relocated to neighbourhood provinces such 
as Hebei and Tianjin to decrease the effects 
of sulphur dioxide and particulate matter 10. 
Therefore, the findings of this paper may inform 
the city planners of the likely environmental 
impact of further development projects in the 
city. Thirdly, the availability of data for Beijing 
is higher than for other cities.

3.1. Definition of Urban Area
According to the China City Statistical Yearbook, 2016, Beijing metropolitan area includes 16 districts (see map in Figure A below). 
Dongcheng and Xicheng Districts are the core 
parts of Beijing, historically dating back to the 

9 See Beijing government. (2017). Beijing General Urban Plan 
2020–2035 . Available at http://zhengwu.beijing.gov.cn/gh/dt/
t1494703.htm.

10 One example is the Shougang Group, one of the largest steel 
companies located in Beijing that started moving to Hebei 
since 2005 in the preparation for the Olympic Games of 2008.

Figure A: Beijing Metropolitan Area.

Source: http://www.dsac.cn/file/attached/ima
ge/20150720/20150720164446_6118.jpg

The Relationship between Growth and the Environment in Beijing, Using PM2.5 Concentrations

Qing Dynasty 11. Together with the four surrounding districts of Haidian, Chaoyang, Fengtai and Shijingshan, they are referred to as the 
Urban Six District. In 1949, six more districts 
were added to the Beijing metropolitan administrative area: Shunyi, Changqing, Mentougou, 
Fangshan, Daxing, and Tongzhou. As the 2035 
city plan indicates, these districts are becoming increasingly important, with the city administrative offices being gradually moved to 

11 In 2010, the districts of Dongcheng (1 in the map in Figure A) and Chongwen (3 in the map) were merged into the 
Dongcheng district, and the districts of Xuanwu (2 in the map) 
and Xicheng (4 in the map) were merged into the Xicheng district.

this suburban area. In 2000, four more districts 
in the north of Beijing were included in the Beijing metropolitan area.
Recently, the newly-published city planning 
encourages residents to move away from the 
Urban Six Districts to other districts, in order to 
enjoy better living conditions and lower house 
prices. Therefore, nowadays, many workers still 
need to commute daily to the Urban Six Districts 
for work. As one of the major sources for local 
PM2.5 concentrations is transport (Zíková et al., 
2016), it makes sense to include all 16 districts 
of Beijing into our investigation area.
According to the statistic Yearbook of China, 
the acreage of Beijing does not change from April 

0

20

40

60

80

100

120

140

160

1
3
5
7
9
11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

seasonal effect of air quality: PM 2.5 

AIRQUALITY
ma.air

0

1000

2000

3000

4000

5000

6000

7000

8000

1
3
5
7
9
11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

seasonal effect of GDP 

GDP
ma.gdp

Figure B: Seasonal effects.

Source: own elaboration from relevant data.

The Relationship between Growth and the Environment in Beijing, Using PM2.5 Concentrations

2008 to June 2017, despite the implementation 
of a few changes affecting the districts borders 12. 
Therefore, acreage is not considered as a variable 
in this paper. Also, as acreage is fixed we only 
use population and not population density to 
capture the effects of urbanisation.

12 One is the consolidation of the four central districts into two, 
Dongcheng and Xicheng, in 2008; another is the establishment 
of a new district, Xiong’an, in 2017.

3.2. Data Description
Data for environmental quality PM PM2.5 concentrations are from the Mission China Air 
Quality Monitoring Programme (MCAQMP) 
available online at http://www.stateair.net/
web/historical/1/1.html, which started as a 
means to provide reliable information about 
air quality in China for US expats. The observation site is in the US Embassy, which is 
located in the Chaoyang District, one of the 

Table A
Data Description

Data
Source
Website
Frequency
Time span

PM2.5
US Embassy
http://www.stateair.net/web/
historical/1/1.html
hourly
Since April 
2008

Population
Beijing Macroeconomics 
database
http://www.bjhgk.gov.cn/
yearly
1949–2016

GdP
Beijing Bureau of Statistics
http://www.bjstats.gov.cn/tjsj/
yjdsj/GdP/2018/
quarterly
Since 
Q1 2005

Green 
Space
Beijing Macroeconomics 
database
http://www.bjhgk.gov.cn/
yearly
Since 1975

Length 
of road 
network

Beijing Macroeconomics 
database
http://www.bjhgk.gov.cn/
yearly
2003–2016

T
Time trend: T= year-2007

Quarter1
dummy Variable; =1 when the data is in the 1st quarter; =0 in 2,3,4 quarter

Quarter2
dummy Variable: =1 when the data is in the 2nd quarter; =0 in 1,3,4 quarter

Quarter3
dummy Variable::=1 when the data is in the 3rd quarter; =0 in 1,2,4 quarter

Table B
Descriptive Statistics

Variable
Mean
Std. Dev
Min
Max
No. of 
observation

Air quality
91.6
21.5
53.9
146.8
37

Population
2029.2
141.0
1732.6
2174.1
37

GdP
4564.6
1301.1
2511.9
7531.5
37

GdP per capita
2.2
0.5
1.4
3.5
37

Green space
15.3
0.9
13.2
16.4
37

Length of road 
network
14.0
0.6
13.3
15.2
37

The Relationship between Growth and the Environment in Beijing, Using PM2.5 Concentrations

busiest downtown areas in Beijing. Air quality 
recordings from the embassy site are less frequents than recordings from official national 
sites, nevertheless, they are the longest publicly available recorded data for PM2.5 in Beijing having started in April 2008. In addition, a 
new study (Zhang & Mu, 2017) finds that the 
data for PM2.5 from the Chinese Ministry of 
Environmental Protection are correlated with 
the data from the US Embassy, hence we expect 
our results not to be biased.
Our dataset contains data from April 2008 
to May 2017, typically with one observation per 
hour. Tables A and B below gives a brief summary 
of the data and their descriptive statistics. Our 
indicator for pollution is the quarterly average 
of PM 2.5 concentrations from the second quarter of 2008 to the second quarter of 2017 (the 
longest interval we have data for). There are 37 
observations in total.
The data for population and GDP are from the 
Statistic Yearbook of Beijing. Quarterly data for 
GDP are available, while for population defined 
as the number of residents in Beijing metropolitan area, observations are annual. By calculating the growth rate of population each 
year, interpolation is used for population. For 
other variables, including length of road network 
and green space, we use the same interpolation 
method to generate more data points for our 
regression analysis.
As both the data for PM2.5 and GDP present 
the problem of seasonality (see graphs below) 
we smooth the series by applying the moving 
average method.
Other variables of interest are green space 
(Zhang et al., 2016) and length of roads as those 
have been identified in the literature as having 
an impact on urban pollution. Green space, or 
parks, plays an essential role in ameliorating 
air quality in a city. Yin et al., (2011) estimate 
that vegetation in Shanghai contributes to 9.1% 
of TSP removal. Tallis et al., (2011) estimate 
that the removal of PM10 by urban trees in 
the Greater London Authority is between 0.7% 
and 1.4%. Longer road length is supposed to 
serve more vehicles. Vehicles and dust from 
the road are a major source of PM2.5 in urban areas (Cassady et al., 2004); furthermore, 
increasing highways capacity is found to be 
positively related to the vehicle mileages, sug
gesting a positive correlation with emissions 
as well (Noland, 2000).

4. Model and Methodology
The empirical model used in this paper is 
based on Grossman and Kruger (1995) and can 
be expressed as:

(
)

(
)

(
)

0
1

2
2

3

3
4

ma.airquality
.
/

.
/

.
/

ma gdp
population

ma gdp
population

ma gdp
population
Z

= β +β
+

+β
+

+β
+β
+ ε

where, ma.airquality  is measured by PM2.5 
concentrations; 
.
/
ma gdp
population  is Beijing per capita GDP. For completeness we 
both include the squared and cubic values of 

.
/
ma gdp
population . As control variables, we use: 
Greenspace (including public parks); Length of 
the road network (the length of road per capita, 
an indicator for transport activities); Year, a 
linear time trend; 3 dummy variables, one per 
quarter to capture the seasonal effects of pollution (with Q4 being our omitted dummy). We 
perform OLS estimations.
In the EKC literature, the most common 
shape for the relationship between income 
and pollution is an inverted u-shape pattern, 
that means 
3
β  should be insignificant, while 

1
2
,β β  should be both significant with 
1
0
β >
 
and 
2
0
β <
. For other patterns the coefficients 
take on the signs reported in the Table C below 
(Song et al., 2008).

5. Results
Table D below presents the results of 4 OLS regressions. Regressions 3 and 4 use logs of all 
variables, regressions 2 and 4 do not include 
green space and length of road as those are 
found to be highly correlated with GDP (see 
Table E below).
All four regressions (although the coefficients 
in regression 1 and 3 are not statistically significant) show an N-shaped relationship between 
air quality and income, with positive coefficient 
for per capita GDP and GDP cubic and negative 
for GDP square.
In regression 2, all coefficients are significant, 
and the goodness of fit is high (R 2 = 0.86), indicating that regression 2 is a good description of 
the EKC relationship in Beijing. The first turning 

The Relationship between Growth and the Environment in Beijing, Using PM2.5 Concentrations

point is reached at 15,272 CNY (2009 Q4) per 
quarter or 60,000 CNY per year and the second 
turning point will be reached at 33,500 CNY per 
quarter or 132,000 CNY per year. When income 
is in the interval of the first and second turning 
point, PM2.5 decreases as income grows. From 
the second turning point onwards, pollution 
starts increasing again as income increases. Per 
capita income in Beijing in 2017 Q2, the last 
quarter in our dataset was 29,280 CNY a little lower than the income associated with the 
second turning point. It suggests that Beijing 
will shortly reach the second turning point and 
it is possible that the environment will worsen 
as income grows, if tailored structural policies 
or stricter environmental policies are not implemented.
The negative coefficients of the seasonal variables Q1-Q3 suggests that air quality is worse 
(higher PM2.5 concentrations) in Q4, which may 
be explained by the start of the winter season in 
Beijing and therefore higher use of fossil fuels 
(including coal) for central heating. This effect 
is also well highlighted in the previous literature 
(see He et al., 2002; Duan et al., 2006; Zhao et 
al., 2009).
The time trend in all four regressions are 
positive, indicating that the pollution will rise 
as time goes. The reason for may be due to lowenergy efficiency of the Beijing economy (China 
energy development report, 2008) which calls 
for urgent energy efficiency reforms.
The coefficients for green space and length 
of road network, although insignificant, present 
the expected sign (see regressions 1 and 3). The 
concentration of PM 2.5 is positively related to 
length of road network, suggesting that longer 
roads lead to more vehicles and therefore high
er air pollution. The coefficient of green space 
is negative and suggests a small reduction in 
pollution by a unitary increase in green space. 
One potential explanation for these variables 
being insignificant is that they are highly correlated to GDP per capita, and GDP per capita is 
positively related to air pollution. As shown in 
Table E below, Variance Inflation Factors (VIF) 
of green space and length of the road network 
are greater than 30, suggesting multicollinearity. We therefore proceeded to eliminate those 
two variables from regressions 2 and 4 in which 
most of the coefficients are significant and of 
the correct sign.

6. Discussion and Policy  
Implications
In Panayotou (1997) some intuitions are given 
for the occurrence of the first turning point. 
When income reaches a relatively high level, 
consumers’ demand for environmental goods, 
such as energy efficient housing and cars, increases. Furthermore, more resources can be 
devoted by the government towards environmental protection further decreasing degradation 13.
The Beijing government has placed air pollution control as a priority since 1998, and a variety 
of measures has been significantly implemented 
ever since. These measures include clean energy promotion, on-road vehicle constraints, 
industrial construction upgrading, air quality 
monitoring and forecasting system, and education aiming at public awareness of air quality. 

13 Panayotou (1997) found that improvements in the quality of 
institutions (policies) by 10% will lead to a 15% reduction in 
SO2 emissions. Bhattarai and Hammig (2001) found that the 
quality of official policies is negatively related to deforestation.

Table C
Different EKC Patterns

Pattern
β1
β2
β3

N shape
>0
<0
>0

inverted N shaped
<0
>0
<0

inverted U shape
>0
<0
Insignificant

U shape
<0
>0
Insignificant

Monotonously increasing
>0
Insignificant
Insignificant

Monotonously decreasing
<0
Insignificant
Insignificant

The Relationship between Growth and the Environment in Beijing, Using PM2.5 Concentrations