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THE IMPACT OF ECONOMIC GROWTH AND TRADE ON THE ENVIRONMENT:
THE CANADIAN CASE
OLAIDE KAYODE EMMANUEL STUDENT #: 201398088
Memorial University Of Newfoundland, Canada
Environmental Economics (Econ6022) Project, April, 2015
ABSTRACT
There is often the presumption that economic growth and trade liberalization are good for the
environment. However, the risk is that policy reforms designed to promote growth and trade
liberalization may be encouraged with little consideration of the environmental consequences
(Arrow et al., 1995). All economic activity occurs in the natural, physical world. It requires
resources such as energy, materials and land. Also, economic activity invariably generates
material residuals, which enter the environment as waste or polluting emissions. The Earth, being
a finite planet, has a limited capability to supply resources and to absorb pollution. Hence, There
is growing recognition that gross domestic product (GDP) produced at the expense of the global
environment, and at the expense of scarce and finite physical resources, overstates the net
contribution of that economic growth to a country’s prosperity. Using mainly four environmental
indicators of air pollution (greenhouse gas, carbon dioxide, nitrogen oxide and Sulphur oxide),
this paper provides an empirical analysis of the verification of the EKC within the Canadian
context, and the causal relationship between economic growth and trade and environmental
degradation from the Canadian perspective. The study reveals that the traditional EKC does not
hold for Canada, for these indicators of environmental degradation. It also, shows that a long run
relationship and a bidirectional Granger-causality exist between economic growth and trade, and
these indicators.
I. INTRODUCTION
There is often the presumption that economic growth and trade liberalization are good for the
environment. However, the risk is that policy reforms designed to promote growth and trade
liberalization may be encouraged with little consideration of the environmental consequences
(Arrow et al., 1995). All economic activity occurs in the natural, physical world. It requires
resources such as energy, materials and land. Also, economic activity invariably generates
material residuals, which enter the environment as waste or polluting emissions. The Earth, being
a finite planet, has a limited capability to supply resources and to absorb pollution. Hence, There
is growing recognition that gross domestic product (GDP) produced at the expense of the global
environment, and at the expense of scarce and finite physical resources, overstates the net
contribution of that economic growth to a country’s prosperity.
The early stages of the environmental movement began with some scientists questioning how
natural resource availability could be compatible with sustained economic growth (Meadows et
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al., 1972). Neoclassical economists, however, fiercely defended that limits to growth due to
resource constraints were not a problem (e.g. Beckerman, 1974). This marks the beginning of the
debate between the so-called environmental pessimists and optimists. This debate which still
continues till date is centered on nonrenewable resource availability. However, in the 1980s large
issues such as ozone layer depletion, global warming and biodiversity loss began to change the
main focus of the debate to the impacts of environmental degradation on economic growth.
Hence, interest was shifting away from natural resource availability towards the environment as
a medium for assimilating wastes (i.e. from ‘source’ to ‘sink’) (Neumayer, 2003). Also,
following the Brundtland Report (WCED, 1987), the discourse of sustainable development
largely embraced the economic growth logic as a way out of poverty, social depravation and also
environmental degradation particularly for the developing world. As such, the relationship
between economic growth and trade liberalization, and the environment came under increased
scrutiny.
The empirical literature on the link between economic growth and environmental pollution
literally exploded in the 1990s (Stern, 2003; 2004). Much of this literature sought to test the
Environmental Kuznets Curve (EKC) hypothesis. The environmental Kuznets curve is a
hypothesized relationship between various indicators of environmental degradation and income
per capita (Stern, 2003). The EKC posits that in the early stages of economic development
environmental degradation and pollution will increase until a certain level of income is reached
(called the turning point) and then environmental improvement will occur. Several possible
reasons for this hypothesized relationship has been put forward: the income elasticity of demand
for environmental quality may exceed one, so that as income rises citizens support initiatives and
policy propositions to reduce environmental degradation; rising incomes may be associated with
shifts from resource-intensive to research-intensive output in the economy; and rising incomes
together with improvements in technology and human capital may help ‘decouple’ economic
growth and environmental degradation. The turning points vary for different indicators of
environmental degradation. This relationship between per capita income and pollution is often
shown as an inverted U-shaped curve. Hence, the curve is named after Simon Kuznets (1955)
who hypothesized that as per capita income increases, economic inequality increases over time
and then after some turning point starts declining. In the early 1990s the EKC was introduced
and popularized with the publication of Grossman and Krueger’s (1991) work on the potential
environmental impacts of a North American Free Trade Agreement (NAFTA), and the 1992
World Bank Report (Shafik and Bandyopadhyay, 1992; World Bank, 1992). These studies have,
however, being criticized for a variety of reasons (Stern, 2003, 2004). One of these reasons is
that, most of the empirical studies concentrated on few pollutants, and this may lead to the
incorrect interpretation that all other pollutants have the same relation to income. Also, the
relationship between the environment and income growth might vary with the source of income
growth, since different types of economic activities have different pollution intensities. One
implication of this is that the pollution consequences of economic growth are dependent on the
underlying source of growth (Antweiler et al. 2001). It was also demonstrated that
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methodological choices can significantly influence the results (Cavlovic et al. 2000). It has also
been argued by several researchers that the simplest form of the EKC does not account for trade
patterns (Suri and Chapman, 1998; Antweiler et al., 2001; Cole, 2004). Specifically, they
indicate that trade patterns may partially explain a reduction in pollution in high income
countries, but with the reverse in low income countries. Hence, trade as a driver of economic
growth has also been seen to impact greatly on the environment. In particular, two major
hypotheses have been used in the literature, to link trade with the environment; these are the
Pollution Haven Hypothesis (PHH) and the Factor Endowment Hypothesis (FEH). The PHH
argues that less stringent environmental regulations in developing countries will provide them
with a comparative advantage in the production of pollution-intensive goods over developed
countries (Cole, 2004). The FEH on the other hand, argues that factor abundance and
technology determine trade and specialization patterns, and that such countries relatively
abundant in factors used intensively in polluting industries will on average get dirtier as trade
liberalizes and vice versa (Mani and Wheeler, 1998).
Using mainly four environmental indicators of air pollution (greenhouse gas, carbon dioxide,
nitrogen oxide and Sulphur oxide), this paper provides an empirical analysis of the verification
of the EKC within the Canadian context, and the causal relationship between economic growth
and trade and environmental degradation from the Canadian perspective. The rest of this paper is
structured as follow: section II briefly discusses the Canadian environmental policies and
progress experience; section III looks at the review of literatures on the economic growth and
trade, and environment nexus; section IV focuses on the description of the data and the
methodology used; section V presents the empirical analysis of the results; section VI provides
conclusions about the study and its implications.
II. THE CANADIAN ENVIRONMENTAL POLICIES AND PROGRESS
Canada has over the past few years taken some actions, enact and implement some policies
aiming at improving the quality of the environment, not only within its geographical jurisdiction,
but also globally. Canada ratified the United Nations Framework Convention on Climate Change
(UNFCCC) in December 1992, under which it committed stabilizing, greenhouse gas (GHG)
emissions at 1990 levels by 2000; and the Convention came into force in March 1994. However,
in 2000, Canada’s absolute GHG emissions were 22% higher than they had been 10 years earlier
(Environment Canada, 2014). Canada went to ratify the Kyoto Protocol in 2002, pledging to
reduce GHG emissions to 6% below 1990 levels between 2008 and 2012. As of 2010, however,
absolute GHG emissions remained 17% above 1990 levels. At the 15th session of the
Conference of the Parties (COP15) to the UNFCCC in 2009, Canada signed the Copenhagen
Accord, under which Canada has committed to reducing its GHG emissions to 17% below the
2005 level by the year 2020. Canada’s National Inventory is prepared and submitted annually to
the UNFCCC by April 15 of each year, in accordance with the December 2005 version of the
Guidelines for the preparation of national communications by Parties included in Annex I to the
Convention, Part I: UNFCCC reporting guidelines for national inventories (Environment Canada
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2014). Canada has also ratified the United Nations Convention on Long-range Transboundary
Air Pollution (LRTAP Convention), which has three international protocols to reduce Sulphur
dioxide emissions. The first, the 1985 Sulphur Protocol, adopted a flat rate target to reduce
national annual Sulphur emissions by at least 30 percent between 1980 and 1993. The next two,
the 1994 Sulphur Protocol and the 1999 Gothenburg Protocol to abate Acidification,
Eutrophication and Ground-level Ozone, are based on effects. They aim to reduce Sulphur
emissions where environmental effects are more severe. The 1985 Sulphur Protocol called for
Canada to cap national Sulphur dioxide emissions permanently at 3.2 million tonnes by 1993.
Canada met this cap in 1992, with national emissions of 3.1 million tonnes. The 1994 Sulphur
Protocol allows emission reductions to be geographically targeted to achieve maximum
environmental benefit. Canada has met all its current protocol commitments, including capping
Sulphur dioxide emissions in the Sulphur oxide management area at 1.75 million tonnes by 2000
(Environment Canada). The 1988 Canadian Environmental Protection Act (revised in 1999 and
proclaimed in 2000) has played an important role in improving waste disposal, and the 1998
Environmental Harmonization Accord set national standards for important air and water
pollutants. A 1995 report by the Organization for Economic Cooperation and Development
(OECD) provides the first snapshot of Canadian environmental performance in terms of
ecosystems, water, waste, air and public policy (OECD 1995). Hayward and Jones (1998) and
Devlin and Grafton (1999) provide overviews or syntheses of environmental trends over the two
decades prior to 2001. Hayward and Jones used 20 separate measures of environmental
degradation in the categories of air quality, water quality, natural resources and solid waste and
conclude that overall environmental quality improved between 1980 and 1995. Devlin and
Grafton conclude that in a number of significant areas, particularly air quality, Canada has
improved its environmental quality but important challenges remain. The Conference Board of
Canada, a body with an overarching goal of measuring the quality of life for Canada and its
OECD’s peer countries, provides an overview of performance and relative ranking of the
environmental performance of Canada and its peer countries in 2014.The Board used fourteen
indicators to assess environmental performance across six dimensions. The indicators used
include, Greenhouse gas (GHG) emissions, Nitrogen oxides emissions, Sulphur oxides
emissions, VOC emissions, PM10 concentration, Municipal waste generation, Water Quality
Index, Water withdrawals, Threatened species, Forest cover change, Use of forest resources,
Marine Trophic Index, Low emitting electricity production, and Energy intensity; while the
dimensions used include, air quality, waste, water quality and quantity, biodiversity and
conservation natural resource management, climate change and energy efficiency. According to
the Board, Canada receives a “C” grade on environmental performance and ranks 15th out of 17
peer countries. Compared with the 17 country average, Canada’s performance is above average
on five indicators: use of forest resources low emitting electricity production, Water Quality
Index, threatened species, particulate matter concentration. Canada’s performance is below
average on nine indicators: forest cover change nitrogen oxides emissions Sulphur oxides
emissions Marine Trophic Index greenhouse gas (GHG) emissions, water withdrawals volatile
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organic compound (VOC) emissions Canada’s success in improving its environmental
performance has been mixed. It has improved air quality, reduced its energy intensity, and
increased the growth of forest resources relative to forest harvest, municipal waste generation
and energy intensity. Although Canada ranks below the best performing
Country on all of the environmental indicators, it does earn “A” grades for four indicators: Water
Quality Index, threatened species, use of forest resources, and low emitting electricity
production. It earns a ‘B’ for Sulphur oxide emissions, forest cover, and PM10 concentration;
and receives a ‘C’ grade on water withdrawals. Canada receives “D” grades on six indicators:
nitrogen oxides emissions, VOC emissions, municipal waste generation, Marine Trophic Index,
GHG emissions, and energy intensity But Canada must do more to lower greenhouse gas
emissions, to use its freshwater resources more wisely, and to reduce waste. Canada ranks 15th
out of 17 countries for greenhouse gas (GHG) emissions per capita. Canada’s per capita GHG
emissions decreased by nearly 5 per cent between 1990 and 2010, while total GHG emissions in
Canada grew 17 per cent. The largest contributor to Canada’s GHG emissions is the energy
sector, which includes power generation (heat and electricity), transportation, and fugitive
sources. Canada ranks 16th out of 17 peer countries. Canada’s per capita nitrogen oxides
emissions have been decreasing since 1990. Canada needs to do more to reduce emissions from
the transportation, electricity, and industrial sectors. Canada ranks 16th out of 17 countries.
Canada’s per capita Sulphur oxides emissions are nearly 17 times that of the best performer,
Switzerland. Canada’s Sulphur oxides emissions decreased between 1990 and 2005. The board
maintained that, to improve its overall performance, Canada must promote economic growth
without further degrading the environment, partly by encouraging more sustainable consumption.
III. LITERATURE REVIEW ON ECONOMIC GROWTH, TRADE AND
ENVIRONMENT NEXUS
Examination of the empirical relationship between national income and measures of
environmental quality began with Grossman and Krueger’s (1991) paper on the potential impacts
of NAFTA. There, they estimated reduced-form regression models relating three indicators of
urban air pollution to characteristics of the site and city where pollution was being monitored and
to the national income of the country in which the city was located. This was when the EKC
concept emerged. However, a 1992 World Bank Development Report (Shafik and
Bandyopadhyay, 1992; World Bank, 1992) made the notion of the EKC popular by suggesting
that environmental degradation can be slowed by policies that protect the environment and
promote economic development. Panayotou (1993), who actually coined the curve EKC,
suggested the addition of other explanatory variables, and estimated the EKC for 55 developed
and developing countries using a panel data regression. Selden and Song (1994) estimated the
EKCs for four emission series using longitudinal data from World Resources (WRI, 1991). The
data used are primarily for developed countries, and the study showed that the turning point for
emissions was likely to be higher than that for ambient concentrations. The reason for this being
that urban and industrial development tends to be more concentrated in a smaller number of
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cities, at the early stage of economic development, and these cities will also have rising central
population densities. The trend will be reversed in the later stages of development; hence, it is
possible for peak ambient pollution concentration to fall as income rises even if total national
emissions are rising (Stern et al., 1996). Grossman and Krueger (1995) identify three different
channels through which economic growth can affect the quality of the environment that shape
the EKC: the scale effect, the increase in pollution when the economy grows, the composition,
and the technique effect. The composition effect in this context refers to structural changes that
occur in the economy, leading to different environmental pressures in the long-term. Furthermore
it is assumed that the dominant role is played by public pressure towards more governmental
regulation and the use of cleaner production techniques by firms (technique effect). This is based
on the assumption that, as income grows income elasticity of the environmental quality
increases. Therefore, after a threshold level of income, wealthier countries tend to be more
willing and able to channel resources into environmental protection and higher environmental
standards. Cole et al. (1997) also confirm the existence of the EKC, using emissions data for the
OECD countries. List and Gallet (1999) estimated the EKC for the fifty U.S. states using
emissions data for the period 1929 to 1994. Neumayer (2003) posited that rich countries may be
better able to meet the higher demands for environmental protection through their institutional
environmental capacity. However, Martinez-Alier (1995), contested whether rich people care
more about the environment than the poor. Beckerman (1992) claimed that “there is clear
evidence that, although economic growth usually leads to environmental degradation in the early
stages of the process, in the end the best – and probably the only – way to attain a decent
environment in most countries is to become rich.” The notion of the EKC has however, been
challenged for some reasons: first, the EKC has never been shown to apply to all pollutants or
environmental impacts alike (Dasgupta et al., 2002; Perman and Stern, 2003); second is the
econometric critic of the reduced form model (Day and Grafton, 2001; Stern, 2003). Delvin and
Grafton (1999) showed that there is considerable evidence of environmental degradation in a
number of critical areas, such as species and habitat loss and depletion of natural resources in
Canada, despites its wealth. Day and Grafton (2001) used the cubic specification of the reduced
form model, and found out that the EKC does not hold for carbon dioxide emissions;
concentrations of carbon monoxide, nitrogen dioxide, ground level ozone, sulphur dioxide, total
particulate matter; concentrations of dioxin in herring gull eggs in the St. Lawrence river;
concentrations of fecal coliform in the Saskatchewan River; and concentrations of dissolved
oxygen in the Saskatchewan and St. John’s Rivers.
Arrow et al. (1995) and Stern et al. (1996) argued that if there was an EKC type relationship, it
might be partly or largely a result of the effects of trade on the distribution of polluting
industries. Suri and Chapman (1998) introduced trade explicitly into the EKC model, and
consequently, numerous studies have examined the relationship between trade and environment
in the last few years. However, the empirical results reported from these studies appear to be
mixed; the study by Antweiler et al. (2001) shows that trade liberalization reduces pollution, the
findings by Dasgupta et al. (2002) appear to be skeptical about the positive environmental effects
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of trade liberalization. Furthermore, some studies (Grossman and Krueger, 1993; Gale and
Mendez, 1998) find empirical support in favor of the factor endowment hypothesis (FEH) and
against a significant influence of environmental regulation on trade patterns, while some others
find evidence in support of the PHH (Suri and Chapman, 1998; Mani and Wheeler, 1998).
IV. METHODOLOGY
The reduced-form specification that is commonly employed in the empirical literature to
examine the relationship between environmental degradation and per capita income in the
context of the EKC is given as: 𝐸𝑖𝑡 = ∝0+ ∝1 𝑌𝑖𝑡 + ∝2 𝑌𝑖𝑡2 + ∝3 𝑌𝑖𝑡
3 + ∝4 𝑍𝑖𝑡 + 휀𝑖𝑡
(1)
Where, 𝐸𝑖𝑡 represents environmental degradation, i.e. the specific pollutant that is used for the
estimation, 𝑌𝑖𝑡 is income per capita, and 𝑍𝑖𝑡 are other covariates, for example population density,
population growth, or income inequality. Trade has occasionally been included as an additional
covariate in the EKC model.
The basic EKC models start from a simple reduced-form quadratic function, whereas most
recent studies include the cubic level. The inverted-U shaped curve derived from such a formula
requires to be positive ∝1 and to be ∝2 negative, and both statistically significant. However,
empirical results could display several other variants different from the EKC; if ∝1 is negative
and statistically significant but ∝2 and ∝3 are statistically insignificant, then we get pattern
downward sloping straight line. These are indicators that show an unambiguous improvement
with rising per capita income, such as access to clean water and adequate sanitation. If ∝1 is
positive and statistically significant, but ∝2 and ∝3 are statistically insignificant, then we get
pattern an upward sloping straight line. These are indicators that show an unambiguous
deterioration as incomes increase. If ∝1 and ∝2 are statistically significant, but ∝2 is positive and
∝3 is statistically insignificant, then a U shaped curve results. There is also the possibility of a
second turning point in which case, ∝3 is statistically significant, and multiple turning points
could also result, which may not fit the model. The model could also be estimated in its
logarithmic form; this imposes a non-negativity constraint on the values of the variables. As
noted by Stern (2003), economic activity inevitably implies the use of resources and by the laws
of thermodynamics, use of resources inevitably implies the production of waste. Regressions that
allow levels of indicators to become zero or negative are inappropriate except in the case of
deforestation where afforestation can occur. This paper estimates the model in a time series form
and makes use of both the quadratic and the cubic specifications; both are estimated in both the
level and logarithmic forms.
The paper estimates the co-integration relationship between economic growth, trade and the
environmental degradation indicators using the Autoregressive Distributed Lagged (ARDL)
model. In Pesaran et al (2001), the co-integration approach, also known as the bounds testing
method, is used to test the existence of a co-integrated relationship among variables. The
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procedure involves investigating the existence of a long-run relationship in the form of an
unrestricted error correction model for each variable as follows:
∆𝑌𝑡 = µ1 + ∑ 𝑛𝑠=0 𝛾 1,𝑠∆𝑌𝑡−𝑠+ ∑ 𝑛
𝑠=0 𝛾 2,𝑠∆𝐸𝑡−𝑠+ ∑ 𝑛𝑠=0 𝛾 3,𝑠∆𝑍𝑡−𝑠+𝛿1𝑌𝑡−1+ 𝛿2𝐸𝑡−1+ 𝛿3𝑍𝑡−1+𝜂1,𝑡
(2)
∆𝐸𝑡 = µ2 + ∑ 𝑛𝑠=0 𝜃 1,𝑠∆𝑌𝑡−𝑠+ ∑ 𝑛
𝑠=0 𝜃 2,𝑠∆𝐸𝑡−𝑠+ ∑ 𝑛𝑠=0 𝜃 3,𝑠∆𝑍𝑡−𝑠+ 𝜋1𝑌𝑡−1+𝜋2𝐸𝑡−1+𝜋3𝑍𝑡−1 +𝜂2,𝑡
(3)
∆𝑍𝑡 = µ3 + ∑ 𝑛𝑠=0 𝜑 1,𝑠∆𝑌𝑡−𝑠+ ∑ 𝑛
𝑠=0 𝜑 2,𝑠∆𝐸𝑡−𝑠+ ∑ 𝑛𝑠=0 𝜑 3,𝑠∆𝑍𝑡−𝑠
+𝜙1𝑌𝑡−1+𝜙2𝐸𝑡−1+𝜙3𝑍𝑡−1+𝜂3,𝑡 (4)
The F-tests are used to test the existence of long-run relationships. The F-test used for this
procedure, however, has a nonstandard distribution. Thus, the Pesaran et al (2001) approach
computes two sets of critical values for a given significance level. One set assumes that all
variables are I (0) and the other set assumes they are all I (1). If the computed F-statistic exceeds
the upper critical bounds value, then the null hypothesis (no co-integration) is rejected. If the F-
statistic falls within the bounds set, then the test becomes inconclusive. If the F-statistic falls
below the lower critical bound value, it implies no co-integration. When a long-run relationship
exists, the F-test indicates which variable should be normalized.
The causal relationship between the variables is determined using Vector Error Correction
(VEC) model. Following Granger (1988), and Engle and Granger (1987), the VEC
representation is as follow:
∆𝑌𝑡 = µ1 +∑ 𝑟𝑘=1 𝛼 1,𝑘𝜈𝑘,𝑡−𝑝 +∑ 𝑝
𝑠=1 𝛾 1,𝑠∆𝑌𝑡−𝑠+ ∑ 𝑝𝑠=1 𝛾 2,𝑠∆𝐸𝑡−𝑠+ ∑ 𝑝
𝑠=1 𝛾 3,𝑠∆𝑍𝑡−𝑠+𝜂1,𝑡
(5)
∆𝐸𝑡 = µ2 + ∑ 𝑟𝑘=1 𝛼 2,𝑘𝜈𝑘,𝑡−𝑝 + ∑ 𝑝
𝑠=1 𝜃 1,𝑠∆𝑌𝑡−𝑠+ ∑ 𝑝𝑠=1 𝜃 2,𝑠∆𝐸𝑡−𝑠+ ∑ 𝑝
𝑠=1 𝜃 3,𝑠∆𝑍𝑡−𝑠+𝜂2,𝑡
(6)
∆𝑍𝑡 = µ3 +∑ 𝑟𝑘=1 𝛼 3,𝑘𝜈𝑘,𝑡−𝑝+ ∑ 𝑝
𝑠=1 𝜑 1,𝑠∆𝑌𝑡−𝑠+ ∑ 𝑝𝑠=1 𝜑 2,𝑠∆𝐸𝑡−𝑠+ ∑ 𝑝
𝑠=1 𝜑 3,𝑠∆𝑍𝑡−𝑠+𝜂3,𝑡 (7)
Where 𝑌𝑡 is GDP per capita, 𝐸𝑡 is the environmental degradation indicator, 𝑍𝑡 is trade, p is lag
length and is decided according to information criterion and final prediction error. The
parameters 𝜈𝑘,𝑡−𝑝are the co-integrating vectors, derived from the long-run co-integrating
relationships regression, and their coefficients 𝛼 𝑖,𝑘are the adjustment coefficients. The
parameters μi, (i=1, 2, 3, 4, 5) are intercepts and the symbol Δ denotes the difference of the
variable following it. Using the model in Equations (5–7), Granger causality tests between the
variables can be investigated through the following three channels:
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(i).The statistical significance of the lagged error-correction terms (ECTs) by applying separate t-
tests on the adjustment coefficients. This shows the existence of a long-run relationship
(ii). A joint F-test or a Wald χ2-test applied to the coefficients of each explanatory variable in
one equation. For example, to test whether environmental indicator Granger-causes growth in
Eq. (5), we test the following null hypothesis:𝐻0: 𝛾 2,1=𝛾 2,2=…=𝛾 2,𝑝0. This is a measure of
short-run causality;
(iii). A joint F-test or a Wald χ2-test applied jointly to the terms in (i) and the terms in (ii)
V. EMPIRICAL RESULTS AND DISCUSSION
V. 1. Data and Variable definition
Four environmental degradation indicators are made use of in this empirical work, each of them
in per capita (kiloton per capital) and in total (kiloton) concentrations. The indicators include,
Nitrogen oxides emissions, sulphur oxides emissions, greenhouse gases emissions (excluding
land use and land use charge measure and forestry- LULUCF) emissions, and carbon dioxide
emissions; the carbon dioxide emissions was also treated separately after from having been
included in the GHG emissions in order to determine its separate impact, since it accounts for
over 70 percent of the GHG emissions in Canada for each year under consideration. The data for
the variables are taken from the OECD online data bank. The GDP per capita is measured in the
constant US$ with 2005 as the base year. The data for the trade is the sum of the export and
import as a percentage of the GDP. The data for the GDP per capital and the trade are taken from
the World Bank online data bank. All data comprise of data from 1990 to 2012. The variables’
notations and definitions are as follows.
GDPPK: Per capita real GDP
GHGPK: Per capita greenhouse gas emissions
NOPK: Per capita nitrogen oxides emissions
SOPK: Per capita sulphur oxides emissions
CO2PK: Per capita carbon dioxide emissions
TGHG: Total greenhouse gas emissions
TNO: Total nitrogen oxides emissions
TSO: Total sulphur oxides emissions
TCO2: Total carbon dioxide emissions
TRADE
V.2. Test results for unit roots
When working with time series data, the first question to ask is whether or not the series is
stationary. A stochastic process is said to be stationary if its mean and variance are constant over
time, and if the covariance exists between the two time periods and not the actual time at which
the covariance is computed. Since, the VEC specification in Equations (5)–(7) requires that some
or all the variables are integrated of order one, I herein investigate the stationarity status of the
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variables using both the augmented Dickey–Fuller (ADF) and the Dickey-Fuller Generalized
Least Square (DF-GLS) tests for unit roots. The null hypothesis tested is that the variable under
investigation has a unit root against the alternative that it does not (that is, it is stationary). In the
ADF, lag-length is chosen using the Akaike Information Criteria (AIC) after testing for first and
higher order serial correlation in the residuals while in the DF-GLS, the optimal lag is
determined using Ng-Perron seq t, Schwarz Criteria (SC) and AIC. Table 1 reports the results of
testing for unit roots in the level variables as well as in their first difference. The table shows the
estimated t- statistics. Based on the ADF test, only NOPK and LNNOPK are stationary in their
level; CO2PK, GDPPK, TCO2, LNCO2PK, LNGDPPK, and LNTCO2 are stationary in their
first difference, while others are stationary in their second difference. Based on the DFGLS,
GDPPK and LNGDPPK are stationary in their level; GHGPK, NOPK, TGHG, TNO,
LNGHGPK, LNNOPK, LNTGHG and LNTNO are stationary in their first difference, while
others are stationary in their second difference.
Table1 Results of unit roots test
VARIABLE ADF DFGLS VARIABLE ADF DFGLS VARIABLE ADF DFGLS
GHGPK -0.898 -1.767 DGHGPK -2.766 -3.455 D2GHGPK -6.425 -
NOPK 3.006 -2.378 DNOPK - -3.388 D2NOPK - -
SOPK 0.05 -1.942 DSOPK -2.793 -2.088 D2SOPK -4.599 -3.343
CO2PK -1.452 -1.829 DCO2PK -3.301 -2.382 D2CO2PK - -72.418
GDPPK -1.42 -3.75 DGDPPK -3.076 - D2GDPPK - -
TRADE -2.356 -2.198 DTRADE -1.899 -1.938 D2TRADE -4.618 -3.343
TGHG -2.43 -3.155 DTGHG -2.955 -3.579 D2TGHG -6.686 -
TNO 2.078 -2.103 DTNO -1.737 -3.712 D2TNO -6.421 -
TSO 0.346 -1.918 DTSO -2.541 -2.078 D2TSO -4.373 -3.833
TCO2 -1.734 -1.81 DTCO2 -3.295 -2.303 D2TCO2 - -13.375
LNGHGPK -0.859 -2.601 DLNGHGPK -2.716 -3.428 D2LNGHGPK -6.329 -
LNNOPK 3.802 -2.56 DLNNOPK 0 -3.962 D2LNNOPK - -
LNSOPK 0.587 -2.131 DLNSOPK -2.111 -2.226 D2LNSOPK -3.452 -3.804
LNCO2PK -1.461 -1.933 DLNCO2PK -3.337 -2.432 D2LNCO2PK - -9.238
LNGDPPK -1.735 -3.523 DLNGDPPK -3.045 - D2LNGDPPK - -
LNTRADE -2.669 -2.486 DLNTRADE -1.959 -2.838 D2LNTRADE -4.899 -3.262
LNTGHG -2.631 -3.012 DLNTGHG -2.763 -3.539 D2LNTGHG -6.4 -
LNTNO 2.479 -2.184 DLNTNO -1.407 -4.147 D2LNTNO -6.416 -
LNTSO 0.404 -2.129 DLNTSO -2.132 -2.249 D2LNTSO -3.467 -3.257
LNTCO2 -1.778 -1.864 DLNTCO2 -3.346 -2.33 D2LNTCO2 - -12.881
*The critical values of t-statistics for the ADF are -3.00 and -2.63 (and that of DFGLS, -3.19
and-2.89) at 5% and 10% level of significance respectively.
11
V.3. EKC ANALYSIS
Figure1 (a-f) and Table2 (a-f) shows the result for the EKC analysis. The graphical analysis
shows that none of the environmental degradation indicators considered follows the traditional
inverted U EKC curve pattern, both in their levels and their logarithmic form. In the quadratic
specification of the model, the coefficients on GDPPK and GDPPKSQ in the regressions with
GHGPK, NOPK, SOPK, TGHG, TNO, and TSO as the dependent variable, though have the
predicted EKC signs, are however, not statistically significant at the 5% level of significance.
The coefficients are neither statistically significant nor have the predicted EKC signs when
CO2PK and TCO2PK are the dependent variables. The coefficient on TRADE is only
statistically significant with GHGPK, TGHG and TCO2 as the dependent variable. Trade is
positively related to each of these indicators, suggesting that an increase in trade leads to an
increase in them. Also in the logarithmic form, the coefficients on LNGDPPK and
LNGDPPKSQ in the regressions with LNGHGPK, LNNOPK, LNSOPK, LNTGHG, LNTNO,
and LNTSO as the dependent variable, though have the predicted EKC signs, are however, not
statistically significant. The coefficients are neither statistically significant nor have the predicted
EKC signs when LNCO2PK and LNTCO2PK are the dependent variables. The coefficient on
TRADE is only statistically significant with LNGHGPK, LNTGHG and LNTCO2 as the
dependent variable. In the cubic specification, the coefficients on GDPPK and GDPPKSQ are
statistically significant with CO2PK, TCO2 and TNO as the dependent variables; the signs are
however, opposite to that predicted by the EKC analysis. Also, the sign on the GDPPKCB is
negative and statistically significant for these variables; this signifies the existence of more than
one turning point in the curve. This pattern implies that environmental degradation will first
decrease as GDP per capita rises, then increase The coefficients are neither statistically
coefficient nor with the predicted signs in the regressions with other indicators as the dependent
variables. The coefficient on TRADE is statistically significant with GHGPK, CO2PK, TGHG,
TCO2 and TNO as the dependent variables; the sign is positive, showing that an increase in trade
volume increases the emissions of greenhouse gases, nitrogen oxides and carbon dioxide at the
per capita level. The logarithmic form was dropped in the cubic specification due to multi-
collinearity.
12
FIGURE 1
(a)
(b)
20
21
22
23
24
GH
GP
K
25000 30000 35000 40000GDPPK
50
60
70
80
90
NO
PK
25000 30000 35000 40000GDPPK
40
60
80
100
120
SO
PK
25000 30000 35000 40000GDPPK
14
15
16
17
18
CO
2P
K
25000 30000 35000 40000GDPPK
13
(c)
(d)
550000
600000
650000
700000
750000
TG
HG
25000 30000 35000 40000GDPPK
1800
2000
2200
2400
2600
TN
O
25000 30000 35000 40000GDPPK
1000
1500
2000
2500
3000
TS
O
25000 30000 35000 40000GDPPK
450000
500000
550000
TC
O2
25000 30000 35000 40000GDPPK
14
(e)
(f)
3
3.0
53.1
3.1
5
LN
GH
GP
K
10.2 10.3 10.4 10.5LNGDPPK
44.1
4.2
4.3
4.4
4.5
LN
NO
PK
10.2 10.3 10.4 10.5LNGDPPK
3.5
44.5
5
LN
SO
PK
10.2 10.3 10.4 10.5LNGDPPK
2.7
2.7
52.8
2.8
5
LN
CO
2P
K
10.2 10.3 10.4 10.5LNGDPPK
15
(g)
(h)
13.3
13.4
13.5
LN
TG
HG
10.2 10.3 10.4 10.5LNGDPPK
7.5
7.6
7.7
7.8
7.9
LN
TN
O
10.2 10.3 10.4 10.5LNGDPPK
7.2
7.4
7.6
7.8
8
LN
TS
O
10.2 10.3 10.4 10.5LNGDPPK
13
13.0
513.1
13.1
513.2
13.2
5
LN
TC
O2
10.2 10.3 10.4 10.5LNGDPPK
16
Table2. Results of EKC Models
(1) (2) (3) (4)
GHGPK NOPK SOPK CO2PK
GDPPK 0.000918 0.0128 0.00716 -0.000905
(0.45) (0.94) (0.27) (-0.44)
GDPPKSQ -1.47e-08 -0.000000240 -0.000000187 1.38e-08
(-0.47) (-1.15) (-0.45) (0.44)
TRADE 0.0822* 0.258 0.124 0.0573
(2.54) (1.19) (0.29) (1.75)
_cons 2.380 -98.48 32.75 27.19
(0.08) (-0.48) (0.08) (0.88)
N 23 23 23 23 t statistics in parentheses * p < 0.05,
** p < 0.01,
*** p < 0.001
(a)
(1) (2) (3) (4)
TGHG TNO TSO TCO2
GDPPK 21.01 0.403 0.187 -37.88
(0.54) (1.24) (0.25) (-0.81)
GDPPKSQ -0.000161 -0.00000691 -0.00000468 0.000709
(-0.27) (-1.38) (-0.41) (0.98)
TRADE 2191.6**
8.100 6.784 1627.6*
(3.52) (1.56) (0.57) (2.17)
_cons 24719.4 -3830.3 725.0 871094.8
(0.04) (-0.78) (0.06) (1.23)
N 23 23 23 23 t statistics in parentheses * p < 0.05,
** p < 0.01,
*** p < 0.001
(b)
17
(1) (2) (3) (4)
GHGPK NOPK SOPK CO2PK
GDPPK -0.0349 -0.240 -0.379 -0.0492**
(-1.89) (-1.97) (-1.50) (-2.89)
GDPPKSQ 0.00000109 0.00000753 0.0000117 0.00000150*
(1.93) (2.02) (1.51) (2.87)
GDPPKCB -1.12e-11 -7.91e-11 -1.21e-10 -1.51e-11*
(-1.95) (-2.09) (-1.54) (-2.85)
TRADE 0.105**
0.416 0.365 0.0875**
(3.23) (1.95) (0.82) (2.93)
_cons 386.7 2612.0 4173.3 545.0**
(1.95) (1.99) (1.54) (2.97)
N 23 23 23 23 t statistics in parentheses * p < 0.05,
** p < 0.01,
*** p < 0.001
(c)
(1) (2) (3) (4)
TGHG TNO TSO TCO2
GDPPK -539.3 -6.255* -11.24 -1097.9
*
(-1.47) (-2.21) (-1.63) (-2.77)
GDPPKSQ 0.0171 0.000198* 0.000347 0.0333
*
(1.52) (2.28) (1.64) (2.75)
GDPPKCB -0.000000175 -2.09e-09* -3.58e-09 -0.000000332
*
(-1.54) (-2.36) (-1.67) (-2.69)
TRADE 2541.5***
12.26* 13.92 2289.6
**
(3.95) (2.47) (1.15) (3.30)
_cons 6034641.8 67578.2* 123291.1 12240671.8
*
(1.53) (2.21) (1.66) (2.87)
N 23 23 23 23 t statistics in parentheses * p < 0.05,
** p < 0.01,
*** p < 0.001
(d)
18
(1) (2) (3) (4)
LNGHGPK LNNOPK LNSOPK LNCO2PK
LNGDPPK 11.40 71.81 50.19 -25.70
(0.37) (1.06) (0.34) (-0.63)
LNGDPPK
SQ
-0.553 -3.519 -2.535 1.236
(-0.37) (-1.08) (-0.35) (0.62)
LNTRADE 0.253* 0.273 0.450 0.248
(2.52) (1.24) (0.93) (1.86)
_cons -56.74 -363.0 -245.4 135.3
(-0.36) (-1.03) (-0.32) (0.64)
N 23 23 23 23 t statistics in parentheses * p < 0.05,
** p < 0.01,
*** p < 0.001
(e)
(1) (2) (3) (4)
LNTGHG LNTNO LNTSO LNTCO2
LNGDPPK -0.299 60.18 38.57 -38.87
(-0.02) (1.20) (0.29) (-1.34)
LNGDPPK
SQ
0.0384 -2.931 -1.948 1.897
(0.05) (-1.22) (-0.31) (1.36)
LNTRADE 0.227***
0.247 0.424 0.229*
(4.07) (1.52) (0.99) (2.42)
_cons 11.44 -302.1 -184.5 211.2
(0.13) (-1.17) (-0.27) (1.40)
N 23 23 23 23 t statistics in parentheses * p < 0.05,
** p < 0.01,
*** p < 0.001
(f)
19
V.3. Test results for co-integration
An F deletion test was applied to equations (2) - (4) for each form of the environmental
degradation indicators (at per capita and total concentrations), per capital GDP and trade in order
to test the existence of a long-run relationship. The results of bounds testing show that in the
level form, there is a long-run relationship between the variables when per capita GDP is the
dependent variable and GHGPK, TGHG and TCO2 are the explanatory variables; when TRADE
is the dependent variable and GHGPK, TGHG, CO2PK and TCO2 are the explanatory variables,
because its F-statistic exceeds the upper bound critical value at a 5% level of significance. There
is also a co-integration when each of NOPK, SOPK, TGHG, TNO, TSO and TCO2 is the
dependent variable. The null hypothesis of no co-integration however, cannot be rejected when
each of GHGPK and CO2PK, is used as the dependent variable because F-statistics is below the
lower bound critical value at a 5% level of significance. In the logarithmic form, there is a co-
integration relationship when each of LNGDPPK and LNTRADE is the dependent variable, and
LNGHGPK, LNSOPK and LNTSO is the explanatory variable; and also when each of
LNNOPK, LNSOPK, LNTGHG, LNTNO, LNTSO and LNTCO2 is the dependent variable.
Also, there is no co-integration when each of LNGHGPK and LNCO2PK is the dependent
variable. The results of bounds testing are presented in table 3.
VARIABLE F-STATISTICS
VARIABLE F-STATISTICS
GDPPK/GHGPK 23.03 LNGDPPK/LNGHGPK 6.29
GHGPK/GDPPK 3.27 LNGHGPK/LNGDPPK 1.56
TRADE/GHGPK 14.06 LNTRADE/LNGHGPK 7.63
GHGPK/TRADE 3.27 LNGHGPK/LNTRADE 1.56
GDPPK/NOPK 0.51 LNGDPPK/LNNOPK 1.55
NOPK/GDPPK 4.59 LNNOPK/LNGDPPK 6.86
TRADE/NOPK 0.42 LNTRADE/LNNOPK 0.94
NOPK/TRADE 4.59 LNNOPK/LNTRADE 6.86
GDPPK/SOPK 2.68 LNGDPPK/LNSOPK 6.64
SOPK/GDPPK 52.8 LNSOPK/LNGDPPK 24.81
TRADE/SOPK 1.48 LNTRADE/LNSOPK 5.67
SOPK/TRADE 52.8 LNSOPK/LNTRADE 24.81
GDPPK/CO2PK 4.28 LNGDPPK/LNCO2PK 2.36
CO2PK/GDPPK 4.13 LNCO2PK/LNGDPPK 3.2
TRADE/CO2PK 15.35 LNTRADE/LNCO2PK 3.2
20
CO2PK/TRADE 4.13 LNCO2PK/LNTRADE 3.2
GDPPK/TGHG 4.73 LNGDPPK/LNTGHG 0.78
TGHG/GDPPK 8.73 LNTGHG/LNGDPPK 2.05
TRADE/TGHG 7.04 LNTRADE/LNTGHG 1.52
TGHG/TRADE 8.73 LNTGHG/LNTRADE 2.05
GDPPK/TNO 1.36 LNGDPPK/LNTNO 3.85
TNO/GDPPK 5.82 LNTNO/LNGDPPK 7.37
TRADE/TNO 0.56 LNTRADE/LNTNO 1.92
TNO/TRADE 5.82 LNTNO/LNTRADE 7.37
GDPPK/TSO 2.56 LNGDPPK/LNTSO 6.08
TSO/GDPPK 67.75 LNTSO/LNGDPPK 21.84
TRADE/TSO 1.45 LNTRADE/LNTSO 4.69
TSO/TRADE 67.75 LNTSO/LNTRADE 21.84
GDPPK/TCO2 4.86 LNGDPPK/LNTCO2 2.53
TCO2/GDPPK 6.76 LNTCO2/LNGDPPK 4.33
TRADE/TCO2 23.52 LNTRADE/LNTCO2 2.53
TCO2/TRADE 6.76 LNTCO2/LNTRADE 4.33
*The critical value ranges of F-statistics are 3.96-4.53 and 3.21-3.74 at 5% and 10% level of
significance respectively [Paresh Kumar Narayan (2005)].
IV.4. Test results for Vector Error Correction and Granger causality
The optimal lag length for the Vector Error Correction model was determined using the AIC and
the SBIC. It was found to be 2. This is shown in Table4 (a- d) below.
Table4. Lag length determination for VEC
GDPPK GHGPK NOPK SOPK CO2PK TRADE
Lag LL LR df p FPE AIC HQIC SBIC
0 -338.119 2.2e+08 36.2231 36.2736 36.5213
1 -230.917 214.41 36 0.000 150078 28.7281 29.0814 30.8158
2 -101.715 258.4 36 0.000 41.4054* 18.9173 19.5735 22.7945
3 2952.21 6107.8 36 0.000 . -298.759 -297.8 -293.092
4 3063.18 221.95* 36 0.000 . -310.44* -309.481* -304.774*
(a)
21
GDPPK TGHG TNO TSO TCO2 TRADE
Lag LL LR df p FPE AIC HQIC SBIC
0 -865.282 2.7e+32 91.7139 91.7644 92.0122
1 -757.118 216.33 36 0.000 1.7e+29 84.1177 84.471 86.2054
2 -639.841 234.55 36 0.000 1.7e+26* 75.5622 76.2184 79.4394
3 2457.62 6194.9 36 0.000 . -246.696 -245.737 -241.03
4 2578.45 241.66* 36 0.000 . -259.415* -258.456* -253.749*
(b)
LNGDPPK LNGHGPK LNNOPK LNSOPK LNCO2PK TRADE
Lag LL LR df p FPE AIC HQIC SBIC
0 126.153 1.3e-13 -12.6477 -12.5972 -12.3495
1 243.325 234.34 36 0.000 3.1e-17 -21.1921 -20.8388 -19.1044
2 371.928 257.2 36 0.000 9.2e-21* -30.9397 -30.2836 -27.0626
3 3270.05 5796.2 36 0.000 . -332.216 -331.257 -326.549
4 3634.05 728.01* 36 0.000 . -370.532* -369.573* -364.865*
(c)
LNGDPPK LNTGHG LNTNO LNTSO LNTCO2 TRADE
Lag LL LR df p FPE AIC HQIC SBIC
0 125.17 1.4e-13 -12.5442 -12.4937 -12.246
1 242.146 233.95 36 0.000 3.5e-17 -21.068 -20.7147 -18.9803
2 368.978 253.66 36 0.000 1.3e-20* -30.6293 -29.9731 -26.7521
3 3162.19 5586.4 36 0.000 . -320.862 -319.903 -315.195
4 3562.07 799.77* 36 0.000 . -362.955* -361.996* -357.289*
(d)
The results of short- and long-run Granger causality are determined within the VECM
framework. The short-run causal effects are demonstrated through the chi square-statistics of the
explanatory variables and long run causality is tested with the help of statistical significance and
sign of the error correction term. The short run granger causality results are present in Table5 (a-
d) below. The results show that there is a granger- causality from per capita GDP and trade to
each of the environmental degradation indicators(at both the per capita and total concentrations),
at the 5% significance level, both in the level and logarithmic forms, except the logarithmic form
of per capita and total greenhouse gas emissions. There is also a granger-causality from each of
the indicators to per capita GDP; also from each of the indicators to trade, except for per capita
nitrogen oxides and total nitrogen oxides emission in the level form, at 5% significance level.
22
Table5. Short run Granger-causality test
Equation Parms RMSE R-sq chi2 P>chi2
D_GHGPK 11 0.38853 0.7691 26.64339 0.0052
D_GDPPK 11 479.244 0.8421 42.66647 0.0000
D_TRADE 11 2.64138 0.7877 29.6858 0.0018
D_NOPK 11 1.2792 0.8863 62.37451 0.0000
D_GDPPK 11 453.783 0.8584 48.51146 0.0000
D_TRADE 11 3.11886 0.704 19.0301 0.0606
D_SOPK 11 1.01466 0.9731 289.5311 0.0000
D_GDPPK 11 526.925 0.8091 33.91179 0.0004
D_TRADE 11 3.03598 0.7196 20.52607 0.0386
D_CO2PK 11 0.444626 0.7691 26.64978 0.0052
D_GDPPK 11 374.367 0.9037 75.03081 0.0000
D_TRADE 11 1.78209 0.9034 74.79014 0.0000
(a)
Equation Parms RMSE R-sq chi2 P>chi2
D_TGHG 11 11877.1 0.7965 31.30422 0.0010
D_GDPPK 11 483.699 0.8392 41.73746 0.0000
D_TRADE 11 2.61557 0.7918 30.43314 0.0014
D_TNO 11 41.3064 0.832 39.62319 0.0000
D_GDPPK 11 421.763 0.8777 57.41785 0.0000
D_TRADE 11 3.1449 0.6991 18.58444 0.0690
D_TSO 11 29.7141 0.9707 265.4733 0.0000
D_GDPPK 11 525.308 0.8103 34.17016 0.0003
D_TRADE 11 3.02036 0.7224 20.8219 0.0353
D_TCO2 11 12331 0.8242 37.5035 0.0001
D_GDPPK 11 379.879 0.9008 72.63841 0.0000
D_TRADE 11 2.06076 0.8708 53.91367 0.0000
(b)
23
Equation Parms RMSE R-sq chi2 P>chi2
D_LNGHGPK 11 0.02161 0.6586 15.43533 0.1634
D_LNGDPPK 11 0.015802 0.8178 35.91034 0.0002
D_LNTRADE 11 0.042074 0.7556 24.72784 0.0100
D_LNNOPK 11 0.012966 0.9454 138.5122 0.0000
D_LNGDPPK 11 0.012177 0.8918 65.94735 0.0000
D_LNTRADE 11 0.04407 0.7318 21.83068 0.0257
D_LNSOPK 11 0.022972 0.9513 156.1909 0.0000
D_LNGDPPK 11 0.015621 0.822 36.93587 0.0001
D_LNTRADE 11 0.038658 0.7936 30.76848 0.0012
D_LNCO2PK 11 0.026767 0.7804 28.43338 0.0028
D_LNGDPPK 11 0.012366 0.8884 63.69894 0.0000
D_LNTRADE 11 0.02423 0.9189 90.68445 0.0000
(c)
Equation Parms RMSE R-sq chi2 P>chi2
D_LNTGHG 11 0.021205 0.6879 17.63648 0.0904
D_LNGDPPK 11 0.015815 0.8175 35.83636 0.0002
D_LNTRADE 11 0.042768 0.7474 23.67563 0.0142
D_LNTNO 11 0.013311 0.9125 83.47255 0.0000
D_LNGDPPK 11 0.011745 0.8994 71.48889 0.0000
D_LNTRADE 11 0.043918 0.7337 22.03792 0.0241
D_LNTSO 11 0.021387 0.9467 142.0978 0.0000
D_LNGDPPK 11 0.015807 0.8177 35.8808 0.0002
D_LNTRADE 11 0.039494 0.7846 29.14361 0.0022
D_LNTCO2 11 0.024881 0.8101 34.12781 0.0003
D_LNGDPPK 11 0.012003 0.8949 68.10246 0.0000
D_LNTRADE 11 0.025583 0.9096 80.52109 0.0000
(d)
VI. CONCLUSION AND POLICY IMPLICATION
The results in this paper derived from four indicators of air pollution in Canada show that the
reduced form models of the EKC do not provide an adequate representation of the growth-
environment relationship in Canada. It however, reveals a positive relationship between trade
and the emissions of greenhouse gases in general, carbon dioxide and nitrogen oxides. This
implies that an increase in the volume of the Canadian trade leads to the generation of more of
these pollutants. The results also show that there is a long run relationship between each of these
pollution indicators and economic growth and trade; and also a bi-directional granger-causality
between each of the indicators and economic growth and trade. Canada’s per capita GHG
24
decreased by nearly 5 percent between 1990 and 2010, while its total GHG emissions grew 18
percent (Environment Canada, 2014). The largest contributor to Canada’s GHG emissions is the
energy sector, which include power generation (heat and electricity), transportation, and fugitive
sources (Conference Board of Canada). The energy sector was responsible for 81 percent of
Canada’s total GHG emissions in 2010, out of which energy combustion accounts for 45 percent
(Conference Board of Canada). Carbon dioxide accounts for the largest proportion of the GHG
emissions; it contributed 79 percent of Canada’s total emissions in 2012 (Environment Canada,
2014). Canada is one of the largest emitters of GHG in the world, being one of the world’s
largest energy exporters. The main reason for the increase in Canada’s GHG has been growth in
exports of petroleum, natural gas and forest products. Air quality is affected by sulphur oxides
emitted from smelters, electricity generators, petroleum refineries, iron and steel mills, and pulp
and paper mills. Between 1990 and 2009, Canada decreased its per capita sulphur oxides
emissions by 34 percent. While the reduction was good, Canada’s progress was weaker than the
progress made by 14 of its 17 OECD’s peer countries (Conference Board of Canada). In 2012,
sulphur oxides emissions decreased by 0.3 percent from 2011 levels, and were 59 percent lower
than in 1990 (Environment Canada,2014). Nitrogen oxides contribute to smog and acid rain and
are hazardous to human health and the environment. Nitrogen oxides are released during the
combustion of fossil fuels, mainly by vehicles, electricity generation, and manufacturing process.
In 2012, nitrogen oxides emissions in Canada decrease by 5 percent from 2011 levels, and were
27 percent lower than in 1990 (Environment Canada, 2014). Canada needs to do more to reduce
emissions from the transportation, electricity, and industrial sectors (Conference Board of
Canada).
These results suggest that, Canada the main challenge for Canada is to further reduce urban and
regional air pollutants through more pollution control, technological progress, energy savings,
and sustainable transportations. There are already some moves in this direction. The Canadian
federal government recently set a new target of reducing total GHG emissions by 17 percent
from 2005 levels by 2020. To achieve this it has introduced three major initiatives: passenger
automobile and light truck GHG emissions regulation; heavy duties vehicles emissions
regulations; regulations on coal fire electricity regulations (Government of Canada, 2012).
Federal and provincial and United States agreements on capping sulphur oxides; and introduction
of cleaner technology and fuels for vehicles. However, Canada still needs to do more, in order to
maintain a cleaner environment.
REFERENCE
Antweiler, W., B.R. Copeland, and M.S. Taylor (2001) ‘Is free trade good for the environment?’
American Economic Review 91, 877-908
Arrow, K., B. Bolin, R. Constanza, P. Dasgupta, C. Folke, C.S. Holling, B.O. Jansson, S. Levin,
K.G. Maler, C. Perrings, D. Pimentel (1995) ‘Economic growth, Carrying capacity, and
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