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AN ECONOMETRIC ANALYSIS OF AIR CONTAMINATION IN UZBEKISTAN
Khalimjonov Nurbek
Tashkent State University of Economics
ORCID: 0009-0005-0180-9447
nurbekkhalimjonov070797@gmail.com
Abstract.
Pollution and the economy have been closely interconnected throughout the
entirety of human history. The understanding of the relationship between environmental
degradation and economic development is incomplete as a result of disciplinary prejudices. The
aim of this research is to comprehend the dynamic correlation between air pollution and the
economy of Uzbekistan. Additionally, it seeks to examine the presence of the Environmental
Kuznets Curve (EKC) to identify the most effective policy options for reducing emissions while
sustaining economic growth. This study utilized the Bayer-Hanck Cointegration test and Granger
Causality tests to accomplish its objectives. The presence of Bayer-Hanck cointegration suggests a
durable connection between air pollution and economic growth. Furthermore, the Granger
causality test demonstrates that there is a causal relationship between economic growth and the
three air pollutants, with a significance level of 0.05. This study seeks to address a gap in the
current literature by examining the relationship between air pollutants and economic growth. It
attempts to investigate the EKC hypothesis and the effects of air pollution on economic growth
specifically in Uzbekistan.
Keywords:
Bayer-Hanck, Carbon emissions, Economic growth, EKC.
O’ZBEKISTONDA HAVO IFLOSLANISHINIG EKONOMETRIK TAHLILI
Xalimjonov Nurbek
Toshkent davlat iqtisodiyot universiteti
Annotatsiya.
Atrof muhitning ifloslanishi va iqtisodiyot butun insoniyat tarixi davomida
bir-biri bilan chambarchas bog'langan. Ushbu tadqiqotning maqsadi havoning ifloslanishi va
O'zbekiston iqtisodiyoti o'rtasidagi dinamik bog'liqlikni aniqlashdir. Bundan tashqari, u iqtisodiy
o'sishni ta'minlashda emissiyalarni kamaytirish uchun eng samarali siyosat variantlarini
aniqlash uchun Atrof-muhit Kuznets egri chizig'ining (EKC) mavjudligini o'rganishga intiladi.
Ushbu tadqiqot o'z maqsadlariga erishish uchun Bayer-Hanck Cointegration testi va Granger
Causality testlaridan foydalanadi. Bayer-Hanck kointegratsiyasining mavjudligi havoning
ifloslanishi va iqtisodiy o'sish o'rtasidagi mustahkam bog'liqlikni ko'rsatadi. Bundan tashqari,
Granjer sabab-oqibatlilik testi iqtisodiy o'sish va havoni ifloslantiruvchi uchta omil o'rtasida
sababiy bog'liqlik mavjudligini ko'rsatadi, muhimlik darajasi 0,05. Ushbu tadqiqot havoni
ifloslantiruvchi moddalar va iqtisodiy o'sish o'rtasidagi munosabatlarni o'rganish orqali joriy
adabiyotdagi bo'shliqni bartaraf etishga intiladi. U EKC gipotezasini va havo ifloslanishining
O'zbekistonning iqtisodiy o'sishiga ta'sirini o'rganishga harakat qiladi.
Kalit so’zlar:
Bayer-Hanck, Karbonad angidrid emissiyasi, Iqtisodiy o'sish, EKC.
UO‘K: 338.242
V SON - MAY, 2024
133-142
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134
ЭКОНОМЕТРИЧЕСКИЙ АНАЛИЗ ЗАГРЯЗНЕНИЯ ВОЗДУХА В УЗБЕКИСТАНЕ
Халимжонов Нурбек
Ташкентский государственный экономический университет
Аннотация.
Загрязнение окружающей среды и экономика были тесно
взаимосвязаны на протяжении всей истории человечества. Понимание взаимосвязи
между деградацией окружающей среды и экономическим развитием является неполным
из-за дисциплинарных предрассудков. Целью данного исследования является понимание
динамической взаимосвязи между загрязнением воздуха и экономикой Узбекистана.
Кроме того, он стремится изучить наличие экологической кривой Кузнеца (ЭКК) для
определения наиболее эффективных вариантов политики по сокращению выбросов при
сохранении экономического роста. В этом исследовании для достижения своих целей
использовались тест коинтеграции Байера-Ханка и тесты причинности Грейнджера.
Наличие коинтеграции Байера-Ханка предполагает прочную связь между загрязнением
воздуха и экономическим ростом. Кроме того, тест причинности Грейнджера
показывает, что существует причинная связь между экономическим ростом и тремя
загрязнителями воздуха с уровнем значимости 0,05. Это исследование направлено на
устранение пробела в современной литературе путем изучения взаимосвязи между
загрязнителями воздуха и экономическим ростом. В нем предпринята попытка
исследовать гипотезу EKC и влияние загрязнения воздуха на экономический рост,
особенно в Узбекистане.
Ключевые слова:
Bayer-Hanck, выбросы углерода, экономический рост, EKC.
Introduction.
Air pollution encompasses a diverse array of contaminants that are generated by one or
more sources. According to a survey conducted by the European Commission, almost 82% of
Europeans are subjected to air pollution (Gehrsitz, 2017). Poor air quality is a significant
environmental issue that affects individuals owing to the presence of air pollutants such ozone,
nitrogen dioxide, and carbon dioxide (Collivignarelli et al., 2020). Urban regions experience
higher levels of air pollution as a result of elevated traffic and population density (Amin et al.,
2020).
The correlation between pollution and economic growth has garnered significant scrutiny
in the realms of both scientific research and social sciences (Qiu et al., 2019). The
Environmental Kuznets curve (Ozokcu and Ozdemir, 2017) has made it difficult to see some of
the less apparent links between economic growth and environmental impacts.
The ecological system's carrying capacity is a crucial issue that must be considered.
Furthermore, a significant portion of research is conducted inside limited contexts, which
prevents us from evaluating an integrated framework. In less developed nations, where
pollution levels are rapidly increasing, there are sometimes contradictory accounts regarding
economic advancement and human development. Air pollution can have negative effects on
trees and agricultural yields in multiple ways (Rupakheti, 2015). Ground-level ozone is likely
to cause a decline in agricultural and forest yields, hinder tree growth, and make plants more
susceptible to diseases, pests, and other challenges (Jebli et al., 2016).
Due to the limited availability of empirical data in Uzbekistan, the objective of this study
is to assess the relationship between air pollution and economic growth in Uzbekistan, both in
the short and long term, using the Environmental Kuznets Curve (EKC) hypothesis. This work
is expected to make a significant contribution towards the development of environmentally-
friendly economic policies for future environmental policy design. If stricter regulations are not
implemented, the levels of air pollution emissions and concentrations are projected to increase
fast, which will pose a significant risk to both human health and the environment. Air pollution
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135
is anticipated to have detrimental health consequences, leading to substantial economic
expenses. These expenses are likely to manifest as considerable yearly global welfare costs at
both the regional and sectoral levels (Jalil and Feridun, 2011).
Literature review.
Kuznets (1955) proposed the theory that there is a curvilinear relationship between
income inequality and economic development, characterized by an inverted U shape. The
concept was rearticulated in the 1990s as the Environmental Kuznets Curve, which establishes
a correlation between economic growth/income and environmental conditions (EKC). During
the initial phases of growth, there is a positive correlation between GDP per capita and CO
2
emissions per capita. Following the tipping point, there is a gradual reduction in CO
2
emissions
per person as a certain income threshold is reached. This is because countries and individuals
become more sensitive to environmental concerns, leading to a decrease in environmental
degradation through the implementation of preventive measures. Figure 1 illustrates a clear
relationship between income and environmental deterioration, which follows an inverted U-
shaped pattern. In their 1991 publication, Grossman and Krueger presented an essay that
aimed to prove a correlation between air quality and economic growth, thus popularizing the
Environmental Kuznets Curve (EKC) theory. Early empirical research of the Environmental
Kuznets Curve (EKC) have focused on two variables: CO
2
emissions and GDP per capita. These
two variables, CO
2
emissions and GDP per capita, have been used in these investigations.
Figure 1. Environmental Kuznets curve
Several previous research have investigated the correlation between CO2 emissions and
economic growth in relation to environmental degradation. The study examined the long-term
relationship between air pollution and economic growth by analyzing the idea known as the
environmental Kuznets Curve (EKC). The EKC theory was founded on the concept of an
Inverted-U relationship between the level of carbon emissions and the level of income within a
specific country. Grossman and Krueger (1995) were the pioneering researchers who
examined the correlation between per capita income and environmental conditions. They were
the first to propose the Environmental Kuznets Curve (EKC) hypothesis as a means to explore
this relationship. Nevertheless, their empirical analysis encountered issues in relation to the
EKC hypothesis as it yielded divergent outcomes. The EKC theory has received support from
several investigations, each yielding distinct conclusions. Several scholars have presented
reasons in favor of the Environmental Kuznets Curve (EKC) hypothesis, as shown by Rauf et al.
(2018), Pata (2018), Cosmas et al. (2019), Rana and Sharma (2019), Bekun et al. (2020), and
Wasti and Zaidi (2020). However, several research challenge the Environmental Kuznets Curve
(EKC) theory, as evidenced by the works of Sarkodie (2018), Koc and Bulus (2020), Dogan and
Inglesi-Lotz (2020), and Leal and Marques (2020).
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Gökmenoğlu and Taspinar (2016) conducted a study in Turkey to examine the correlation
among foreign direct investment (FDI), economic growth, energy consumption, and CO
2
emissions from 1974 to 2010. The researchers employed the Toda Yamamoto causality
technique and their results revealed a reciprocal causation between CO
2
emissions and FDI
inflows, as well as between CO
2
emissions and energy. Moreover, a unidirectional relationship
was found between FDI inflows and real growth, as well as between energy use and real growth.
In their study, Saboori and Sulaiman (2013) conducted an analysis of the cointegration
and causative link between energy consumption, CO
2
emissions, and economic growth for each
individual ASEAN member country. The researchers found that the economies of all five nations
showed cointegration between economic growth, CO
2
emissions, and energy consumption.
They also observed a positive correlation between CO
2
emissions and energy consumption in
both the short and long run. The correlation between CO
2
emissions decrease and economic
growth was observed exclusively in Singapore and Thailand, while the opposite trend was
observed in Indonesia and the Philippines.
Hanif (2018) employed a generalized linear model (GMM) to assess the correlations
among economic growth, fossil fuel consumption, renewable energy usage, CO
2
emissions,
urban expansion, and solid fuel consumption in the economies of Sub-Saharan Africa. The study
encompassed the years 1995 to 2015. The study revealed that the utilization of fossil and solid
fuels had a beneficial effect on CO
2
emissions, whereas the utilization of renewable energy had
an adverse impact on CO
2
emissions.
Heidari et al. (2015) investigated the relationship between EC (environmental
degradation), CO
2
emissions, and GDP (economic output) in the ASEAN-5 countries. They
employed the Panel Smooth Transition Regression approach, which considers variations and
changes over time. Nonlinearity was detected by employing two threshold settings.
Environmental degradation increased in parallel with economic growth in regime one, but this
correlation did not exist in regime two. Granger causality analysis revealed that energy use is
responsible for CO
2
emissions in both systems.
Zhou and Liu (2016) examined the impact of income, demographic variables, and carbon
dioxide emissions. The study employed the STIRPAT approach paradigm, covering the time
frame from 1990 to 2012. Income has emerged as the primary catalyst for the escalation of CO
2
emissions in China. The study's findings indicate that demographic characteristics do not have
any impact on the increase of CO
2
emissions. Except for western China, urbanization has led to
a rise in energy consumption and CO
2
emissions. To address the environmental impact in China,
it is very advisable for the government to implement measures that control the pace of
urbanization and encourage energy efficiency.
Khoshnevis and Beygi (2018) employed PMG and Granger Causality methodologies to
investigate the correlation between CO
2
emissions, trade openness, financial development,
renewable energy, and economic growth in a sample of 25 African countries. The findings
indicated a one-way relationship where renewable energy had a causal effect on CO
2
emissions,
and real production also had a causal effect on CO
2
emissions. In addition, Munir et al. (2020)
investigated the correlation between CO
2
emissions and economic and energy factors from
1980 to 2016. The researcher discovered a loop causation in Malaysia, Singapore, Thailand, and
the Philippines, which connects economic expansion to the emissions of carbon dioxide (CO
2
).
During the study conducted in Indonesia, Malaysia, and Thailand, it was observed that there is
a unidirectional relationship between economic growth and energy consumption. Similarly, in
Singapore, there are evidence of a one-way causal relationship from economic growth to energy
usage. After doing a literature study, a research gap on research efforts on air pollution in
Uzbekistan was observed. Therefore, this research was conducted to address this gap.
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Methodology.
The data utilized for this research is derived from secondary sources, specifically World
Bank publications, spanning the years 2000 to 2020. The model is trained to forecast
Uzbekistan's economic growth (GDP Per Capita) using air pollution variables including nitrogen
dioxide, ozone, and carbon dioxide. The functional regression model is represented by the
following equation:
GDP per Capita
= 𝛽
0
+ 𝛽
1
CO
2
+ 𝛽
2
NO
2
+ 𝛽
3
O
3
+ ⋯ 𝛽
n
Xn + 𝜀
t
(1)
Where
𝛽
1
to
𝛽
3
are the coefficient estimates of the air pollution variables. Table 1 shows
the variables used in this analysis.
Table 1.
Variables description and data source
Variable
Definition
Abb.
Period
Source
Economic
growth
GDP "per capita"
GDP
2000
− 2020
WDI
Carbon dioxide
CO
2
"emitted from fossil consumption in
kilotons"
CO
2
2000
− 2020
WDI
Nitrogen
Dioxide
NO
2
"emitted from fossil consumption in
kilotons"
NO
3
2000
− 2020
WDI
Ozone pollution
O
3
"emitted from fossil consumption in
kilotons"
O
3
2000
− 2020
WDI
WDI: World Bank Development Indicators
In order to handle ARMA (g,r) models with unknown orders, Augmented Dickey-Fuller
(1984) improved upon the basic autoregressive unit root test. The ADF test is the name given
to this particular test. The test is sometimes referred to as the augmented Dickey-Fuller (ADF)
test. The model states that the lag time in auto-regression grows according to the sample size,
T, at a controlled pace of T
(1/3)
. The fundamental equation of the model is as follows:
𝑁
𝑡
= 𝛽
0
𝐷
𝑡
+∝ 𝑁
𝑡−1
+ 𝜋
𝑡
(2)
∝ (𝑉)𝑈
𝑡
= 𝜃(𝑉)𝜀𝑡, 𝜀𝑡 ∼ 𝑀𝑍(0, 𝜎2) (3)
The null hypothesis of the Augmented Dickey-Fuller (ADF) test states that a time series y
t
is integrated of order 1 (I(1)) if the alternative hypothesis is that it is integrated of order 0
(I(0)). The null hypothesis of the Augmented Dickey-Fuller (ADF) t-test is:
𝐻
0
: 𝛽 = 0
𝐻
1
: 𝛽 < 0
This is predicated on the idea that the intricacies of the data exhibit an ARMA
configuration. The ADF test is conducted to ascertain the presence of a regression in the test.
𝑁
𝑡
= 𝛾
′
𝑆
𝑡
+∝ 𝑁
𝑡−1
+ ∑
𝑝
𝑗=1
𝛿
𝑗
Δ𝑁
𝑡−𝑗
+ 𝜌
𝑡
𝑢
𝑡
(4)
The deterministic component is represented by the symbol D
t
, while the expression Δy
t-j
indicate the presence of serial correlation.
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The t-statistic for the Augmented Dickey-Fuller (ADF) test and the normalized bias
statistic are as follows:
𝐴𝐷𝐹
𝑡
= 𝑙
∝=1
=
∝ˆ−1
𝑆𝐸(∝)
(5)
ADF
t
demonstrates asymptotic
𝑡
𝜙
= 1
distributions when white noise errors are
present, provided that p is appropriately chosen.
Time series in econometric analysis are considered integrated when several series are
cointegrated individually, however there exists a linear combination of these series that has a
lower level of cointegration. Furthermore, it has been shown that these cointegration
procedures have different theoretical foundations and yield contradictory results. It has also
been observed that the effectiveness of cointegration approaches is influenced by the choice of
error estimators (Pesavento, 2004). In order to strengthen the effectiveness of the
cointegration test, the Bayer-Hanck test, developed by Bayer and Hanck in 2013, combines
various tests such as Engle and Granger, Phillips and Outliers, Johansen, Boswijk, and Banerjee.
This joint test-measurement is particularly useful for detecting the absence of cointegration.
Given its ability to combine many individual cointegration test results to yield a more
definitive finding, this innovative approach is also employed in this analysis to ascertain the
presence of a cointegrating relationship between Economic growth and its determinant in
Uzbekistan. Bayer and Hanck (2013) proposed a method to integrate the calculated significance
level (p-values) of each cointegration test using Fisher's formulas:
𝐸 − 𝐽 = −2[𝐿𝑁(𝑃𝐻
𝐸
) + 𝐿𝑁(𝑃𝐻
𝐽
)]
(6)
𝐸 − 𝐽 − 𝐵 − 𝐵𝑀 == −2[𝐿𝑁(𝑃𝐻
𝐸
) + 𝐿𝑁(𝑃𝐻
𝐽
) + 𝐿𝑁(𝑃𝐻
𝐵
) + 𝐿𝑁(𝑃𝐻
𝐵𝑀
) (7)
The p-values of various independent cointegration tests, including those performed by
Engle and Granger (1987), Johansen (1988), Boswijik (1994), and Banerjee et al. (1998), are
denoted as PHE, PHJ, PHB, and PHBM, respectively. If the calculated Fisher statistics exceed the
critical values provided by Bayer and Hanck (2013), it is possible to reject the null hypothesis
that there is no cointegration.
Results.
Given that time series data were utilized in this investigation, it was crucial to assess if the
data exhibited stationarity at their original levels or if they needed to be transformed by
differencing to achieve stationarity. The validity of the results produced from the data analysis
was ensured. This study used the initial generation unit root tests, which disregard structural
breakdowns but were frequently used in the economic growth literature. Specifically, it utilizes
the Augmented Dicky-Fuller, 1987 (ADF) test. All unit root tests, without any exceptions,
assume non-stationarity as the null hypothesis. The data series was tested for stationarity using
the Augmented Dickey-Fuller (ADF) methods.
Table 2.
Augmented Dickey fuller unit root tests
Variables
Level t stats
p-value
𝟏
st
Diff. t stats
p-value
CO
2
-2.874
0.1923
−4.5482
∗
0.000
NO
2
-0.894
0.0013
−8.2419
∗
0.000
O
3
-0.192
0.0000
−7.0845
∗
0.000
GDP
-2.998
0.0341
−4.6494
∗
0.000
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Table 2 displays the outcomes of the unit root analysis conducted on the factor
components for Carbon Dioxide, Nitrogen Dioxide, Ozone Pollution, and GDP Per capita.
All of the series exhibit a unit root, indicating that they are non-stationary at their levels.
However, the series are stationary when considering their beginning differences, as
demonstrated in Table 2. The results of the ADF unit root test suggest that CO2, NO2, O3, and
GDP are all integrated at the same order, specifically I(1). The cointegration relationship among
series can be analyzed using the Bayer-Hanck (2013) cointegration approach, which accounts
for different structural breaks.
Table 3.
Bayer and Hanck combine cointegration analysis
Estimated models
EG-JH-BA-BO
Cointegration
GDP = f(CO
2
, NO
2
, O
3
)
41.124 ∗
Yes
Significance level
Critical value
1%
level
33.197
5%
level
22.738
10%
level
16.868
The results from the long-run nexus between the variables utilizing the Bayer and Hanck
test are reported in Table 3 below. Given that the unit root test indicates that all variables have
an integration order of I(1), we employed the combined cointegration test. The outcome
displays the Fisher-statistics for the combined tests of EG-JH-BO-BA, namely the tests
developed by Johansen (1995), Boswijk (1995), and Bannerjee et al. (1998). The Fisher-
statistics value for GDP exceeds 10% of the critical values. The levels of CO
2
and NO
2
surpassed
the threshold values of 5%. The Fisher-statistics value for O3 exceeds 10% of the critical values.
These statistical tests enable us to reject the null hypothesis that there is no long-term link and
confirm the presence of cointegration between GDP and the explanatory factors.
Table 4.
Granger causality wald test
Equation
Prob
>
Chi-2
GDP
cause ALL
p < 0.05
NO
2
cause ALL
p < 0.05
CO
2
cause ALL
p < 0.05
CO
2
cause ALL
p < 0.05
The following presents the results of a Granger causality test, indicating that GDP has a
substantial causal effect on CO
2
, NO
2
, and O
3
at a 5% significance level. This indicates that there
is a causal relationship between GDP and the three air contaminants examined in this study. In
addition, both CO
2
and NO
2
have a substantial impact at a 0.05 level of significance, as indicated
in Table 4.
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Conclusion.
This study aims to investigate the relationship between air pollution in Uzbekistan from
2000 to 2020 using the Environmental Kuznets Curve. This study used three distinct pollution
metrics to evaluate the air quality. The three gases are carbon dioxide (CO
2
), nitrogen dioxide
(NO
2
), and ozone (O
3
).
In Uzbekistan, there are standard factors used to regulate air quality.
Initially, the stationarity of variables was assessed using the Dickey-Fuller unit root test.
Subsequently, the data was analyzed using the cointegration and Granger causality method
proposed by Bayer-Hanck (2013). The results of the Bayer-Hanck cointegration method
indicate the presence of a stable and long-term equilibrium relationship between the variables.
The Granger causality analysis demonstrates that there is a causal relationship between GDP
per capita and air pollution. However, these actions alone are inadequate to mitigate
environmental pollution without compromising Uzbekistan's economic growth.
Consequently, the following supplementary measures for attaining these objectives may
be proposed. These measures encompass the modification of regulations pertaining to the
reduction of greenhouse gas emissions from industry, transportation, and heating. Additionally,
they involve the promotion of bio-diesel fuel as a substitute for fossil fuels, the enhancement of
alternative energy sources, specifically solar and wind energy projects, the elevation of public
consciousness, and the provision of tax incentives to support environmentally friendly
investments.
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