The Impact of Economic Variables on Environmental Pollution with Emphasis on Financial Development Index: Application of Generalized Method of Moments

Introduction
Nowadays, environmental pollution is one of the main challenges in the world. Therefore, in addition to the
policies and measures within their borders, countries prefer international organizations in the field of
environmental issues. Previously it was thought that economic growth causes an increase in income and will lead
to improved quality of life. However, the high growth rate of the world economy in the last few decades with
reduced environmental quality puts the environmental pollution in the spotlight in the globe. In most studies in
the literature on the investigation of economic factors effects on environmental pollution, these factors have
been limited to economic growth and energy consumption. This study investigates the impact of
macroeconomic variables such as economic growth, energy consumption, environmental pollution and an index
of financial development on countries with different level of income (low, medium, high) during the period of
1980-2010. We apply a dynamic panel data approach with Generalized Method of Moment (GMM) estimate
methodology. Recent empirical studies show that the relationship between environmental degradation and
per capita income level is similar to the turn-down U (primary Kuznets curve). The message of Kuznets
hypothesis is that economic growth is the cause of infection and its treatment. In recent years we have witnessed
a backlash economy for changes in financial statement which emphasizes the important role of financial markets.
A variety of ways to finance the economy is moving toward the gates. But, there is a dichotomy in this case.
Degree of economic and financial development decreases the environmental degradation. The results of some
studies show that financial liberalization and the adoption of policies to financial openness and liberalization to
attract higher levels of R&D might reduce the environmental degradation. In this study, however, we are
interested in checking what the effect of financial development index is on the environmental pollution.
Material and Methodology
In general format EKC hypothesis can be specified as follow:
E= f (Y, Y2, Z) (1)
Where, E is environmental deterioration emission, Y is income indicator and Z is other variables affecting the
environment.
Following the empirical literature, the standard log-linear functional specification of long-run relationship
among per capita carbon emissions, per capita energy consumption, per capita real income, and the square of per
capita real income can be expressed as follows:
CO= 􀈕1 + 􀈕2 en + 􀈕3 yt + 􀈕4 yt2 + 􀈕5 fd + Ut (2)􀀃
Where, co is the carbon dioxide emission (measured in metric kilo grams per capita), en is the energy
consumption (measured in kg of oil equivalent per capita), y is per capita real GDP, y2 is the square of per capital
real GDP, fd is the financial development indicator (domestic credit to private sector as a percentage of GDP)
and Ut is error term.
Empirical results
The preliminary step in this analysis begins by investigating the unit root test of the variables using the Im ,
Pesaran and Shin (IPS) unit root test. Table 1 summarizes the outcome of the IPS unit root tests on the natural
logarithms of the levels of the variables.
Table 1. Unit Root Test Results
With Intercept
Countries with low income Countries with average income Countries with high income
coefficient probability coefficient probability coefficient probability
Variable
CO2 􀍲9.05􀀃 0.00􀀃 􀍲6.53􀀃 0.00 􀍲15.27􀀃 0.00
En 􀍲4.94􀀃 0.00􀀃 􀍲7.74􀀃 0.00 􀍲9.95􀀃 0.00
Fd 􀍲5.48􀀃 0.00􀀃 􀍲6.87􀀃 0.00 􀍲11.22􀀃 0.00
gr 􀍲5.66􀀃 0.00􀀃 􀍲4.60􀀃 0.00 􀍲4.9􀀃 0.00
Y2 􀍲5.41􀀃 0.00􀀃 􀍲6.86􀀃 0.00 􀍲7.19􀀃 0.00
Intercept and trend􀀃
Variable Countries with low income Countries with average income Countries with high income
coefficient probability coefficient probability coefficient probability
CO2 􀍲7.59􀀃 0.00 􀍲6.35􀀃 0.00 􀍲8.16􀀃 0.00
En 􀍲7.56􀀃 0.00 􀍲5.02􀀃 0.00 􀍲8.44􀀃 0.00
Fd 􀍲5.71􀀃 0.00 􀍲6.4􀀃 0.00 􀍲8.19􀀃 0.00
gr 􀍲5.71􀀃 0.00 􀍲4.7􀀃 0.00 􀍲4.53􀀃 0.00
Y2 􀍲8.56􀀃 0.00 􀍲4.62􀀃 0.00 􀍲8.43􀀃 0.00
The IPS unit root test results reveal that all the variables under investigation are stationary.
The empirical results and estimates for equation on per capita CO2 emission for three different income groups
of countries are presented in this section. First, we discuss the results for per capita CO2 emission, economic
growth and financial development along with energy consumption control variables. Then we discuss the EKC
or curvilinear relationship between economic growth and CO2 emission in three different income groups of
countries.
Table 2 presents estimation results of the model for a panel of three different income groups of countries
(low, medium, and high).
Table 2. Estimation results
variable Countries with low income Countries with average income Countries with high income
coefficient probability coefficient probability coefficient Probability
C(Constant) 3.51 0.04 -0.007 0.18 -0.85 0.01
CO2(-1) -0.344 0.00 -0.185 0.00 -0.01 0.61
En 0.41 0.11 0.277 0.00 0.86 0.00
Fd 0.10 0.08 0.036 0.14 -0.96 0.00
growth -2.35 0.00 -0.81 0.00 -6.31 0.00
Y2 11.6 0.00 4.6 0.00 -3.29 0.00
Wald test 45.33 138.9 201.46
Sargan test 115.5 138.2 124.3
The results for three different income groups of countries show that all signs of the estimated parameters are
consistent with the theory. The Sargan and Wald tests results confirm the validity of the interpretation of the
results. Energy consumption has positive effects on environmental pollution in all three income groups.
Financial development in low-income countries has a significant and positive effect on the level of air pollution,
while for the middle-income countries this relationship is not significant. The coefficient of financial
development in countries with high income has a negative and significant impact on environmental pollution.
Economic growth has decreased environmental pollution in all three income groups. However, environmental
Kuznets curve is only confirmed in the high-income countries.
Conclusion
This paper has investigated the impact of economic variables on environmental pollution with an emphasis on
financial development index. We have used panel data approach with GMM estimate method. Our results have
demonstrated that financial development in low-income countries increases environmental pollution. It can be
said that these countries represent the facilities granted to the private sector in production, regardless of the
environmental impact. In countries with high per capita income, this index has a negative impact on
environmental pollution. This shows that, the private sector uses of funds, with investments in environmental
protection measures and do their products. Moreover, the results show an inverse U relationship between 
economic growth and environmental pollution only for the countries with high per capita income. According to
these results, we suggest that the civilized world needs to move towards a new approach of the
economical environment: Take a holistic approach that need strengthening and support through
interdisciplinary collaboration and interaction, too much emphasis is placed on natural resources and
environmental multidisciplinary professionals and experts in economics and political elites. It is one of the most
necessary accessories to ensure the sustainable development.