usaee presentation

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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)


** 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)


** 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)


** 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)


** 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)


** 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


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


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


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)


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


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)


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

the Environment’. Science 268, 520-521

25

Beckerman, W. (1992) ‘Economic Growth and the Environment: Whose Growth? Whose

Environment?’ World Development 20, 481-496

Cavlovic, T., K.H. Baker, R.P. Berrens, and K. Gawande (2000) ‘A Meta-Analysis of

Environmental Kuznets Curve Studies’. Agricultural and Resource Economics Review

29, 32-42

Cole M.A. (2004) ‘Trade, the Pollution Haven Hypothesis and the Environmental Kuznets curve:

Examining the linkages’. Ecological Economics 48, 71-81

Cole, M.A., A. J. Rayner, J.M. Bates (1997) ‘The environmental Kuznets curve: an empirical

analysis’. Environment and Development Economics 2, 401-416

Dasgupta, S., B. Laplante, H. Wang, and D. Wheeler (2002) ‘Confronting the Environmental

Kuznets curve. Journal of Economic Perspectives 16, 147-168

Day K., and R.Q. Grafton (2001) ‘Economic Growth and the Environment: A Canadian

perspective’. Working Paper 0101E. Ottawa: University of Ottawa, Department of

Economics

Devlin R.A., and R.Q. Grafton (1998) ‘Trends in Canada’s Natural Capital’. Paper prepared for

the conference on the State of Living Standards and the Quality of Life in Canada,

Ottawa, October 30-31, 1998

Environment Canada (2014) ‘National Inventory Report 1990-2012: Greenhouse gas sources and

sinks in Canada’.

Gale, L.R., and J.A. Mendez (1998) ‘The Empirical Relationship between Trade, Growth and the

Environment’. International Review of Economics and Finance 7, 53-61

Government of Canada 2012

Grossman, G,M., and A,B. Krueger (1995) ‘Economic Growth and the Environment’. Quarterly

Journal of Economics 110, 353-377

Grossman, G,M., and A,B. Krueger (1993) ‘Environmental Impacts of the North American Free

Trade Agreement. In: The U.S.-Mexico Free Trade Agreement, P. Garber, Massachusetts

Institute of Technology’. The MIT Press, Cambridge, MA., 13-56

Grossman, G,M., and A,B. Krueger (1991) ‘Environmental Impacts of the North American Free

Trade Agreement’. National Bureau of Economic Research Working Paper 3914, NBER,

Cambridge MA

26

Hayward, S., and L. Jones (1998) ‘Environmental Indicators of Canada and the United States’.

Critical Issues Bulletin. Vancouver: Fraser Institute

Kuznets, S. (1955) ‘Economic Growth and the Income Inequality’. American Economic Review

49, 1-28

List, J.A. and C.A. Gallet (1999) ‘The Environmental Kuznets Curve: does one size fit all?’

Ecological Economics 31, 409-424

Mani, M., and D. Wheeler (1993) ‘In Search of Pollution Havens? Dirty Industry in the World

Economy, 1960-1995’. Journal of Environment and Management 8, 103-108

OECD online Database

Panayotou, T. (1993) ‘Empirical Tests and Policy Analysis of Environmental Degradation at

Different Stages of Economic Development’. Working Paper WP238, Technology and

Employment Programme, International Labour Office, Geneva

Perman, R., and D.I. Stern (2003) ‘Evidence from panel unit and cointegration tests that the

environmental Kuznets curve does not exist’. Australian Journal of Agricultural and

Resource Economics, Vol.47

Selden, T.M., and D. Song (1994) ‘Environmental Quality and Development: Is there a Kuznets

curve for air pollution?’ Journal of Environmental Economics and Environmental

Management 27, 147-162

Shafik, N., and S. Bandyopadhyay (1992) ‘Economic Growth and Environmental Quality: Time

Series and Cross-Country Evidence’. Background Paper for the World Development

Report 1992, The World Bank, Washington DC

Stern D.I. (2003) ‘The Environmental Kuznets Curve’. International Society of Ecological

Economics

Stern D.I., M.S. Common, and E.B. Barbier (1996) ‘Economic Growth and Environmental

Degradation: the Environmental Kuznets Curve and Sustainable Development’. World

Development 24, 1151-1160

Suri, V., and D. Chapman (1998) ‘Economic Growth, Trade and Energy: implications for the

Environmental Kuznets Curve’. Ecological Economics 25, 195-208

World Bank online Database

World Commission on Environment and Development (1987) ‘Our Common Future’. Oxford

University Press, Oxford

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