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THE IMPACT OF PUBLIC INVESTMENT IN ENVIRONMENTAL
INFRASTRUCTURE ON NIGERIA’S ECONOMIC GROWTH
HASSAN, Anthony Emmanuel
Reg. No. 09591020
A THESIS SUBMITTED TO THE POST-GRADUATE SCHOOL,
UNIVERSITY OF ABUJA, IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE AWARD OF THE DEGREE, DOCTOR OF
PHILOSOPHY (PHD) IN ECONOMICS
JULY, 2016
i
Declaration
I hereby declare that this thesis for the award of doctoral degree in economics has
been written by me, HASSAN, Anthony Emmanuel. It had not been previously
presented elsewhere for the award of any degree in any institution.
All materials used therein have been appropriately acknowledged.
………………………………… ………………….HASSAN, Anthony Emmanuel Date
The above declaration is certified by:
…………………….. ……………………Prof. OLANIYI, Oyinlola Date(Main Supervisor)
i
Certification
This Thesis Titled, “The Impact of Public Investment in Environmental
Infrastructure on Nigeria’s Economic Growth” by HASSAN, Anthony
Emmanuel (09591020) has been read and approved as meeting the requirements
for the award of the degree, Doctor of Philosophy (Ph.D) in Economics, Faculty
of Social Science, University of Abuja, Abuja, Nigeria.
………….……….……. ……….……….
Prof. OLANIYI, Oyinlola DateInternal Examiner I
……….……………. ………………..
Prof. SIYAN, Peter DateCo-Supervisor
……………………… ……………….Dr. Ayuba Bello DateInternal Examiner II
……………………… ……………….
Prof. ANYANWU, Sarah. O DateHead of Department
………………………. …..……………
Prof. MUNDI, Rhoda DateDean, Faculty of Social Science
……………………… ………………..
Prof. Micheal Enwere Dike DateExternal Examiner
……………………… ………..……….
Prof. E. J. C. NWANA DateDean, Post Graduate School
ii
Dedication
This thesis is dedicated to my family for the sacrifices made in all respects and
more importantly, to my Lord, for grace and health.
iii
List of Tables
Page
Table 4.2.1 Summary of Stationarity Test on Variables in the Models 42
Table 4.3.1: Causality Test 46
Table Ai - A22: Unit Root Tests 97-104
Table A23 – A24: Causality Test 104
Table A25: Stability Diagnostics on Environmental Infrastructure
Model 105
Table B1: Test for Equality of Means between Series 107
Table B2: Correlation Matrix 108
Table C1: Vector Error Correction Estimates for Environmental
Infrastructure Model 109
Table C2: Johansen Co-Integration for Environmental Infrastructure
Model 112
Table C3: Impulse Response 113
iv
Table C4: Vector Error Correction Estimates for Environmental
Quality GNI Relationship 114
Table C5: Co Integration Test for CO2, FORLAND and GNI 115
Table C6: Impulse Response 116
v
List of FiguresPage
Figure 4.1: Kuznets Curve for Nigeria 1990 – 2013 with CO2 as
a Measure of Environmental Quality. 48
Figure 4.2: Kuznets Curve for Nigeria 1990 – 2013 with FLD as
a Measure of Environmental Quality. 48
Figure A1: CUSUM Graph 106
Figure C1: Impulse Response Function for Environmental Infrastructure
Economic Growth Link 114
Figure C2: Impulse Response Function for Environmental Quality Economic
Growth Link 117
Figure C3: Impulse Response Function for Environmental Quality Environmental
Infrastructure Link 125
vi
Acronyms
AFDB African Development Bank
EIA Environmental Impact Assessment
CBN Central Bank of Nigeria
CO2 Carbon Emission
CUSUM Cumulative Sum of Square
DFID Department for International Development
EPPD Environmental Planning and Protection Division
EKC Environmental Kuznets Curve
FLD Ratio of Forest to Total Land Area
FEPA Federal Environmental Protection Agency
GDP Gross Domestic Product
GNI Gross National Income
LPG Liquefied Petroleum Gas
MDGS Millennium Development Goals
vii
NBS National Bureau of Statistics
NGOS Non-Governmental Organisations
OECD Organisation for Economic Co-Operation and Development
UNDP United Nations Development Programme
UNICEF United Nations Children Education Fund
US United States
USD United States Dollar
VAR Vector Auto-Regression
VEC Vector Error Correction
VECM Vector Error Correction Methodology
WHO World Health Oganisation
viii
Acknowledgements
I humbly wish to express my profound gratitude to Professor Oyinlola Olaniyi my
thesis supervisor for the invaluable contributions towards the successful
completion of this thesis.
I am also grateful to Professor Siyan Peter who co – supervised the thesis,
Professor Uka Ezenwe; Professor Odama, J. S; Dr. Ayuba Bello and senior
colleagues in Economic Department, University of Abuja for their useful
comments towards fine tuning the thesis.
I would not forget to appreciate the Federal Ministry of Environment, National
Bureau of Statistics (NBS) and Central Bank of Nigeria for information and data
obtained from them whether directly or through the web.
ix
Table of Contents
Contents
Tittle Page................................................................................................................I
Declaration...............................................................................................................I
Certification............................................................................................................II
Dedication.............................................................................................................III
List of Tables.........................................................................................................IV
List of Figures.......................................................................................................VI
Acronyms.............................................................................................................VII
Acknowledgements...............................................................................................IX
Table of Contents...................................................................................................X
Abstract................................................................................................................XV
Chapter One:Introduction........................................................................................1
1.1 Backgroundto the Study....................................................................................1
x
1.2 Statement of Problem........................................................................................7
1.3 Research Questions...........................................................................................8
1.4 Objectives of the Study.....................................................................................8
1.5 Statement of Hypotheses:..................................................................................9
1.6 Justification for the Study..................................................................................9
1.7 Scope of the Study...........................................................................................11
1.8 Study Outline...................................................................................................12
Chapter Two: Literature Review...........................................................................13
2.1 Conceptual Review..........................................................................................13
2.1.1 The Environment..........................................................................................13
2.1.2 Environmental Infrastructure.......................................................................14
2.1.3 Economic Growth.........................................................................................19
2.1.4 Infrastructure, Economic Growth and Environment Nexus.........................22
2.1.5 Government’s Efforts at Addressing Environmental Issues in Nigeria.......23
xi
2.2 Theoretical Review..........................................................................................25
2.2.1 Sustainability Theory...................................................................................27
2.2.2 Environmental Quality and Economic Growth............................................27
2.2.3 Environmental Kuznets Hypothesis.............................................................30
2.2.4 The Neo-Classical Theory............................................................................31
2.2.5 Endogenous GrowthTheory.........................................................................33
2.2.6 The Brett Frischmann’s Theory...................................................................34
2.3 Empirical Review............................................................................................35
2.4 Theoretical Framework...................................................................................48
2.5 Gap in the Literature........................................................................................50
Chapter Three: Research Methodology.................................................................51
3.1 Analytical Approach........................................................................................51
3.2 Model Specification........................................................................................54
3.3 Discussion of Variables...................................................................................59
xii
3.4 Data.................................................................................................................62
Chapter Four: Data Presentation and Analysis......................................................66
4.1 Data Presentation.............................................................................................66
4.2 Empirical Analysis..........................................................................................66
4.3 Presentation and Discussion of Model Estimates............................................67
4.3.1 Estimates for Environmental Infrastructure and Growth Nexus..................67
4.3.2 Estimates on Environmental Quality and Economic Growth Model...........73
4.4 Test of Hypothesis...........................................................................................78
4.5 Major Findings................................................................................................80
4.6 Policy Implications..........................................................................................81
Chapter Five: Summary, Conclusions and Recommendations.............................84
5.1 Summary of Major Findings...........................................................................84
5.2 Conclusions.....................................................................................................86
5.3 Recommendations...........................................................................................87
xiii
5.4 Contribution to Knowledge.............................................................................90
5.5 Study Limitations and Suggestion for Further Research.................................91
References.............................................................................................................93
Appendix A: Unit Root, Causality and Stability Tests.........................................99
Appendix B: Preliminary Analysis......................................................................109
Appendix C: Regression Outputs........................................................................111
Appendix D: Time Series Used for Analysis......................................................126
xiv
Abstract
The study analyses the impact of public investment in Environmental
Infrastructure on Economic Growth in Nigeria as the main objective. The Vector
Error Correction Methodology was employed for the study using quarterly data
over the period 1980 to 2013. The results indicate that Environmental
Infrastructure variables namely, transportation, education, electricity and
petroleum infrastructure had positive significant impact on Nigeria’s economic
growth while water infrastructure had negative significant impact. The result in
respect of economic growth and environmental quality in Nigeria is mixed. In this
respect, per capita gross national income (GNI) had positive though insignificant
impact on carbon emission while per capita (GNI) had negative significant
impact on the size of the forest as a percentage of total land area in the country.
In addition, the result indicated that feedback mechanism does exist between
environmental infrastructure and economic growth in the economy. It is hence
recommended that policies be directed at harnessing the relationships between
these environmental infrastructures and economic growth in Nigeria, such that
economic growth is enhanced in an environmentally friendly manner. This will
minimise the attendant adverse effects of economic growth on the environment for
the sake of not only the present generation and its developmental efforts, but also
the future generation.
xv
CHAPTER ONE
INTRODUCTION
1.1 Background to the StudyAccording to the World Bank Countries income groups, Nigeria has been
classified as a lower middle income economy with an abundant supply of natural
resources (World Bank, 2011). The country is also the 12th largest producer and the
8th largest exporter of petroleum in the world and has the 10th largest proven oil
reserves. Petroleum plays a big role in the Nigerian economy, accounting for 40
percent of GDP and 80 percent of government earnings on the average over the
period 2000 and 2009 (US Department of Energy, 2005). Decades of economic
stagnation and declining living standards have seen Nigeria enlisted among the
poorest countries in the world. The economy has neither been able to leverage on
the country’s enormous wealth in natural resources to overcome poverty that
affects about 57 percent of its population nor use the derived wealth to boost her
economy considerably. The co-existence of vast wealth in natural resources and
extreme poverty in developing countries like Nigeria is referred to as the “resource
curse” or 'Dutch disease' (Auty, 1993).
However, the Nigerian economy is said to have witnessed some progress in macro-
economic performance in recent times, with an average economic growth of 7.0
percent over 2007 and 2011 driven by the non-oil sector (NBS, 2012). The non-oil
sector has been the main driver of growth over this period, with services 1
contributing about 57 percent, while manufacturing and agriculture contributed
about 9 percent and 21 percent respectively. This indicate that the productive base
of the economy is being broadened as the services sectors, particularly retail and
wholesale trade, real estate, information and communication are increasingly
contributing to growth. The agricultural sector features prominently as an
important contributor to economic growth, yet infrastructure that could have
enabled it perform even better, such as good road network, power, water resources
among others are seriously inadequate. According to the National Bureau of
Statistics (NBS) over the last decade, Nigeria’s infrastructure spending contributed
only 1.9 percent (approximately $4 billion) per annum to GDP.
Thus, the country continues to face rising incidence of poverty, social deprivation
and preventable diseases (UNDP, 2011). Unemployment rate rose from 19.7
percent in 2009 to 21.1 percent in 2010 and 23.9 percent in 2011; income
distribution continues to be skewed, with a GINI coefficient of 0.44 in 2011. About
63 percent of Nigerians live below the poverty line of USD 1 per day; 42 percent
do not have access to safe drinking water and 69 percent do not have access to
basic sanitation (African Economic Outlook, 2012). Social and economic
indicators show huge regional disparities in the country. In a nutshell, the overall
economic improvements have not translated into improvements in the welfare of
the average Nigerian (African Economic Outlook, 2012).
2
The 2012 economic outlook for the country showed that infrastructure deficit in
Nigeria is a major bottleneck for the structural transformation of the Nigerian
economy and that this is particularly so in the electric power industry,
environmental protection, road and rail transport to mention just a few (African
Economic Outlook, 2012). Increased investment in infrastructure is said to be
critically needful (Sanusi, 2012). For instance, inadequate power, inefficient modes
of transportation and decayed education infrastructure have imposed economic
limitation within the country and these call for actions that can reverse the trend.
It is a common feature in most developing countries, to identify the mass of the
people with slums, poverty and certain ailments that have gone extinct in the
advanced economies. This is not because these phenomena are synonymous with
the masses, but rather because institutions are feeble and investment in
environmental infrastructures have fallen out of pace with rising demand for them.
The activities in the services sector in addition to the oil and gas sector which are
the main contributors to economic growth in the country, have not been without
some level of trade off for environmental quality (Den Butter & Verbruggen,
1994). The country is experiencing the dearth of investment in environmentally
friendly infrastructure capable of mitigating the negative effects of economic
growth. The environment itself as a natural infrastructure and a critical factor of
3
production need to be taken into consideration as economic growth is being
pursued (Panayotou, 1993).
The desire for greater economic growth has been linked to fall in the quality of the
environment (Synder, Miler, & Stavins, 2003 & Shi, Zhao, &Zhang, 2011). Rise in
the scale of production, urban growth and slum manifestation due to urban
unemployment and high cost of house rent as well as increased vehicular traffic
have been sources of falling quality of the environment. Refuse dumps have posed
danger to health in our major cities in the country. These dumps have been sources
of contamination of water and the soil as well as breeding ground for several
ailment causing vectors. The quality of the environment can also be measured by
the degree of particulate matters in the air. The effect of particulate matter on
human health has been noted by several scholars. For instance, (Abam &
Unachukwu, 2009 & Efe, 2005) assert that high rates of respiratory diseases
occasioned by increased pm10 (particulate matter up to 10 micrometers in
size) concentrations were experienced by residents of most urban areas.
The Environmental Kuznets Hypothesis (EKH) has featured more frequently in the
literature in terms of the relationship between the quality of the environment and
income growth in a country or region. The Kuznets hypothesis posits that an
inverted - u shaped relationship exist between a country’s per capita income and its
level of environmental quality: increased per capita income is associated with an
4
increased pollution in poor countries, but a decline in pollution in rich countries.
The hypothesis is bringing to the front burner, the idea that as people and countries
get to appreciate the need for better environment, they will demand for it and the
authorities will also get to invest in technologies that will address the negative
fallout of economic growth on the environment (Grossman & Krueger, 1993).
Economic analysts are of the opinion that infrastructure has an important role to
play in driving growth and for optimal utilization of the potentials in most
developing economies (Perkins, Fedderke, & Luiz, 2005).
Generally, there are costs to deficit of infrastructure. These include rising
overheads to firms and industries, leading to high prices of goods and services, low
competitiveness of industries, low capacity utilization and low employment.
Juxtaposing this with the low income status of the economy and the incidence of
poverty leaves less to be desired. Also, policies and investments that can forestall
air, water and land pollution are yet inadequate or that the policies are not being
implemented. An example is the impact of oil exploration on the environmental
pollution in the Niger delta region.
Furthermore, the appearance of slums in the urban areas and the deplorable
sanitation and hygiene condition is a product of infrastructure deficit (Morakinyo,
Ogunrayewa, Koleesho, & Adenubi, 2012). Most of the cities in the country are
spotted with refuse dumps and dirty neighborhoods. 5
In addition, the level of awareness and education of most Nigerians on the need for
good environmental practice is low. In this regard, environmental education or
awareness itself is an environmental infrastructure (Falk, 2005). Investment in
education has been severally canvassed as being low in Nigeria. Education is an
important factor for raising quality of human capital in the country. It is said that
the people perish for lack of knowledge.
The country has incurred colossal losses as a result of deficit infrastructure. Some
common ailments have gulped a sizable part of the national resources (human and
financial) which could have been used for other developmental projects. Firms
have produced below capacity and are still doing so while those who could not
cope have folded up or relocated to better economies.
Between one third and one half of infrastructure services are used as final
consumption by households (Prud'homme, 2005). Besides, basic services such as
water and electricity, cooking fuel often occupy a significant fraction of poor
households’ budgets. Households in developing countries spend a significant
fraction of their income on water and electricity in addition to time loss in the
process of sourcing for water (Foster & Yapes, 2005).
Obviously, environmental infrastructure has very important role to play not only in
terms of promoting economic growth, but also in terms of guaranteeing qualitative
environment for good health. However, the present situation in the country does 6
not seem to key into this fact. The government in particular must explore every
opportunity it has to address this problem.
Earlier studies in the area of infrastructure and economic growth as well as
economic growth and environmental quality have led to debates that have
contributed to economic theory. This study seeks to provide evidence, in line with
the emerging findings across the globe, for Nigeria’s economy.
1.2 Statement of ProblemThe Nigerian economy, though adjudged to be performing above Africa’s average,
cannot be said to be at its best given the abundant resources at its disposal and the
pace at which its contemporaries are progressing. There has been deficit in
environmental infrastructure translating into fall in environmental conditions
coupled with non-inclusive growth, as the growth recorded was possibly not in the
area that could have benefited the mass of the people, or that it does not
commensurate with the country’s potential, hence breeding inequality in the
country.
Investment in environmental infrastructure in Nigeria appears to have fallen out of
pace with the demand for them, given their role in environmental sustainability and
wealth creation. In other words, the stock of infrastructure generally, has placed
limitation on sustainable economic growth potentials in the Nigerian economy. It
also appears that the level of awareness and education among Nigerians on
7
environmental best practices lives much to be desired. In addition, the desire for
economic growth, in the face of inadequate environmentally friendly infrastructure
such as, efficient power mix, (electricity generation) and mode of transportation,
for instance, is likely to have at least, increased the level of carbon emissions. This
has the potential of endangering the environment and its sustainability and
eventually, the gains from growth might be jeopardized.
1.3 Research QuestionsIn the light of the above, the following research questions were raised:
i) How relevant is investment in environmental infrastructure to Nigeria’s
economic growth?
ii) What impact has environmental infrastructure on environmental quality in
Nigeria?
iii) To what extent has economic growth impacted the quality of the
environment in Nigeria?
iv) What is the direction of causality between environmental infrastructure and
economic growth in Nigeria?
1.4 Objectives of the StudyThe main objective of this study is to analyse the impact of public investment in
environmental infrastructure on economic growth in Nigeria.
8
Specifically, this study sought to:
i) Evaluate the relevance of investment in “environmental infrastructure” on
economic growth in Nigeria.
ii) Inquire into the impact of environmental infrastructure on environmental
quality in Nigeria
iii) Investigate the impact of economic growth on environmental quality in
Nigeria.
iv) Examine the direction of causality between environmental infrastructure and
economic growth in Nigeria.
1.5 Statement of Hypotheses:H01: Environmental Infrastructure has no significant impact on economic growth
in Nigeria.
H02: Environmental infrastructure has no significant impact on environmental
quality in Nigeria
H03: Economic growth does not impact on environmental quality in Nigeria.
H04: There is unidirectional causality which runs from environmental
infrastructure to economic growth in Nigeria
1.6 Justification for the StudyThis study is of relevance in two distinct areas. First, it extends the study in the
area of environment and economic growth using recent statistics to see if findings
9
in the area of environment quality and economic growth nexus have been
sustained. Secondly, this thesis is focusing on environmental infrastructure and
economic growth nexus. Environmental infrastructure here is a subset of
infrastructure. Doubtless, earlier studies such as those of (Aschauer, 1989; Canning
& Pedroni, 1999; Jacoby, 2000 & Estache, 2003) have established the link between
infrastructure and growth. A number of studies have been done in the area of
environment and health outcome. For instance, (Turley, Saith, Bhan, Rehfuess, &
Carter, 2013) examine the effectiveness of slum upgrading strategies involving
physical environment and infrastructure interventions for improving the socio-
economic wellbeing of slum dwellers in low and middle income countries.
Employing qualitative method, they found limited but consistent evidence to
suggest that slum upgrading may reduce diarrhea in slum dwellers and their water-
related expenses. Indeed, environmental degradation is more noticeable in slum
areas where housing and living conditions are exceptionally poor. The activities by
dwellers in slums have environmental and health consequences and this should
warrant for adequate investment in infrastructure which can mitigate the attendant
challenges.
Unlike the reviewed studies, this study is in respect of harnessing infrastructure to
have abating effect on the environment and also promoting economic growth. In
this work, attempts have been made to fill this gap by modeling a relationship
10
between transport, water, electricity and crude petroleum/natural gas infrastructure
on one hand and aggregate output on the other. Findings are extended and
replicated in line with studies outside Nigeria that are close to the focus of this
study. The outcome of this study is beneficial not only to the government for
policy but also all stake holders on environmental matters by raising awareness on
the transmission mechanism between “environmental infrastructure” and economic
growth as well as stimulate actions in the right direction. It is also of great benefit
to ordinary Nigerian as policy suggestions would affect not only their health but
also their ability to create wealth.
1.7 Scope of the StudyThis thesis is only concerned with environmental infrastructure emphasising on
how they can serve to mitigate negative fallouts of economic growth through
production and consumption. We modeled the relationship between selected
environmental infrastructure output and aggregate output (GDP) in the Nigerian
economy. In addition, quarterly series from 1980 first quarter to 2013 fourth
quarter are employed. The reason for considering this period being that the series is
adjudged to be more stable over the range as they reflect the post-civil war
experience of the Nigerian economy. Moreover, the country has witnessed series of
policy efforts at promoting growth as well as reducing negative impacts of
production and consumption on the environment, over the period considered. It is
11
therefore hoped that the series, over this period, will provide us with useful
information in relation to this study.
1.8 Study OutlineThis thesis is organised into five chapters. The preceding section is the chapter one
and it covers: background to the study; statement of problem; research question;
objective of the study; hypotheses; justification for the study and scope of the
study. The next chapter is the literature review made up of conceptual review;
theoretical review and empirical review. This is closely followed by chapter three,
titled research methodology and it covers theoretical framework; method of
estimation; specification of model and discussion of variables. Chapter four is on
data presentation and analysis and it covers: data presentation; analysis of
empirical data; model estimates; discussion of result and policy implications. The
last chapter dwells on summary, conclusions recommendations, study limitation
and suggestions for further research.
12
CHAPTER TWO
LITERATURE REVIEW
2.1 Conceptual ReviewThe key concepts covered by this study include: environment, infrastructure and
economic growth.
2.1.1 The environmentThe concept, environment has been regarded from diverse viewpoints and defined
in several ways (Singh, 2003). The diversity of definitions for environment can be
traced to the fact that environment is multi-disciplinary, and its definitions are
coined to interest. In this respect, (Bain, 1973) defines environment as all the
external and non-personal conditions and influences that determine the welfare of a
people in a given area. Nigeria’s Federal Environmental Protection Agency
(FEPA, 1989) considered the environment as including water, air, land, plants,
animals, and human beings living therein, and the inter-relationships that exist
among them.
The medical perspective on the environment suggests that it includes the
surroundings, conditions or influences that affect an organism (Davis, 1989).
Along these lines, (Last, 2001), defines the environment for the international
epidemiological association as: "all that which is external to the human host. It can
be divided into physical, biological, social, cultural, etc., any or all of which can
influence health status of populations ...” According to this definition, the 13
environment would include anything that is not genetic, although it could be
argued that even genes are influenced by the environment in the short or long-term.
Environment has also been defined as all the physical, natural and social factors
external to a person, and all the related behaviours (Smith, Covertlan, & Kjellstron,
1996).
It follows that man and other living organisms derive a measure of sustenance from
the environment. The activities of man in particular can impact positively and or
negatively on the environment. The environment as a natural infrastructure,
supports life on earth. The natural environment plays a similar functional role to
traditional infrastructure.
Accordingly, and for the purpose of this study, we viewed environment as all
physical factors which are within the purview of man to influence by way of his
economic and social activities and which can as well affect the wealth, health and
social status of man
2.1.2 Environmental InfrastructureInfrastructure is the basic physical and organizational structures needed for the
operation of a society or the services and facilities necessary for an economy to
function. It can be defined as "the physical components of interrelated systems
providing commodities and services essential to enable, sustain, or enhance
societal living conditions" (Fulmer, 2009). It is the enterprise or the products,
14
services and facilities necessary for an economy to function (Sullivan & Sheffrin,
2003).
When we think of "traditional" infrastructure, we typically think of large scale,
physical resources or facilities made by humans for public consumption - for
example, roads systems and telephone networks. These resources play incredibly
important roles in society and generate substantial social value by serving as
shared means to many ends: infrastructure resources, enable, frame and support a
wide range of human activities and generally are accessible to all members of a
community who wish to use the resources on non-discriminatory terms, though
such use is not necessarily free. It can be generally defined as the set of
interconnected structural elements that provide framework supporting an entire
structure of development. It is an important term for evaluating a country’s or
region’s development.
The term typically refers to the technical structures that support a society, such as
roads, bridges, water supply, sewers, electrical grids, telecommunications, etc. For
instance, in Keynesian economics, the word infrastructure was exclusively used to
describe public assets that facilitate production, but not private assets for the same
purpose. However, in the last decade, the concept infrastructure has grown in
popularity. It has been applied with increasing generalisation to suggest the
15
internal framework discernible in any technological system or business
organization (Martini & Lee, 1996).
Udjo, Simelane, & Booysen (2000) also identify infrastructure as having both
direct and indirect impact on the growth of an economy. Infrastructure is said to
add to economic growth and development by raising efficiency and providing
facilities which enhance the quality of life. Infrastructure is defined by
(Akinyosoye, 2010) as the “unpaid factor of production” that raises productivity of
other factors while serving as intermediate inputs to production. The services
engendered as a result of an adequate infrastructure base will translate to an
increase in aggregate output.
Two types of infrastructure can be identified; “hard and soft" infrastructure. Hard
infrastructure refers to the large physical networks necessary for the functioning of
a modern industrial nation. "Soft infrastructure” includes institutions which are
required to maintain the economic, health, cultural and social standards of a
country. Examples of these are the: financial, educational, health, governance,
judiciary and security systems (Kumar, 2005).
Five major activities were regarded as infrastructure projects by (Grimsey &
Lewis, 2002). These include, energy (power generation and supply), transport (toll
roads, light rail systems, bridges and tunnels), water (sewerage, waste water
treatment and water supply), and telecommunications, social infrastructure
16
(hospitals, education institutes, and government buildings). Among these activities,
a number can be regarded as infrastructure projects that can promote good
environmental practice and hence can be referred to as environmental
infrastructure.
These environmental infrastructures are vital for supporting economic growth and
improving the quality of life. Some infrastructure investments are directly linked to
the millennium development goals (MDGs). Provision of clean water, for instance,
is an integral part of target 10 (in the environmental goal) and is critical to
achieving target 5 (reduction of child mortality).
Salvador (2012) defines environmental infrastructure as those projects “that will
prevent, control or reduce environmental pollutants or contaminants, improve the
drinking water supply, or protect flora and fauna so as to improve human health,
promote sustainable development, or contribute to a higher quality of life”. This
definition by Salvador reflects our view of environmental infrastructure for this
study. For the purpose of this study, water/sanitation; power (electricity), transport,
education and crude petroleum/natural gas are considered as environmental
infrastructure because they have been severally and collectively adjudged relevant
for driving economic growth in the Nigerian economy. In addition, the scale of
their exploitation for production can have implication for the quality of the
environment.
17
This leads to the issue of who should fund infrastructure generally and why? The
status of infrastructure as being public or private good has been severally contested
in the literature. Physical infrastructure development was the focus for overseas
development in the 1980s. It was later realised that this approach failed to meet the
needs of the poor as it neglected the ‘software’ of development, such as social,
environmental, health, education and gender issues, so the approach changed, to
realise the value of developing human capital and institutions for poverty reduction
and growth (Dervis, 2005 & DFID, 2001).
Given that the initial assertion of market-led development in the 1980s was to
effectively replace public provision wherever possible, by the mid-1990s a more
balanced view of the role of state and market had emerged. First, it was understood
that infrastructure development was crucial for economic growth and poverty
reduction. The effect on poverty might operate directly (by improving living
conditions and access to services) or indirectly (through economic growth and
reduction of gender imbalances). Second, public provision was (and remains) the
majority source of funding for infrastructure. It was estimated that the public sector
accounts for 70 percent the private sector 20 percent and aid 10 percent of funding
(Estache, 2006). Third, however, public investment in infrastructure in developing
countries was grossly insufficient to meet human need; and public provision of
infrastructure was often inadequate, inefficient, and incapable of meeting the needs
18
of the poor (DFID, 2002). Fourth, aid could not adequately make up for the gap in
financing. Fifth, private sector participation – through a combination of ownership,
investment funding and management – had the potential to make up the difference.
The basic rationale that infrastructure is essential for development (growth and
poverty reduction) remains predominant in all levels of the literature. The view that
private investment is a crucial ingredient in infrastructure development is
prominent in the donor literature but contested elsewhere, though it is also quite
widely accepted in the academic literature. Recent world bank estimates now
consider that the average infrastructure share of GDP for developing countries
needs to double, from 3-4 percent to 7 percent; for low income countries, the
financing requirement may be even higher, at 8 percent to 9 percent. Approaches to
public private initiative, however, have evolved. There is now an increasing
recognition of opportunities for synergy between private and public investment
that may reduce poverty and help achieve the new global goals of the MDGs. The
old dichotomies (especially of private versus public provision and growth versus
pro-poor targeting) are being rejected in favour of more nuanced approaches to
‘what works’ (Meridian Institute, 2005 & UNDP, 2006).
2.1.3 Economic GrowthEconomic growth has been perceived to involve the provision of inputs that lead to
greater outputs and improvements in the quality of life of a people (Herrick &
Kindleberger, 1983). It was also defined as a quantitative and sustained increase in 19
a country’s per capita output or income accompanied by expansion in its labour
force, consumption, capital and volume of trade and welfare (Jhingal, 1985 &
Thirlwall, 1972). Determining the growth of any country’s economy, require
careful consideration of certain indicators. These indicators include: the nation’s
gross domestic product (GDP); the nation’s per capita income; the welfare of the
citizens; and the availability of social services and accessibility of the people to
these services (World Bank, 1997). Gross domestic product refers to the total
output of final goods and services produced in a country and exchanged in the
market during any given period of time by residence of a country irrespective of
their nationality. While economic growth hinges on production, the transmission of
such growth to better welfare for the majority of the populace is critical. The issues
of efficiency and conservation of effort in achieving macroeconomic goals in
addition to prudence in the management of resources is very important for growth
and is situated within the purview of the economy itself (Todaro, 1997).
Adam Smith views the growth process as strictly endogenous (Eltis, 1984), placing
special emphasis on the impact of capital accumulation on labour productivity. His
work on the inquiry into the wealth of nations stated that “income per capita must
in every nation be regulated by two different circumstances; first, by the skill,
dexterity, and judgment with which its labour is generally applied; and secondly,
by the proportion between the number of those who are employed in useful labour,
20
and that of those who are not so employed” smith was of the opinion that there is
no limit to the productivity of labour. Accordingly, he maintained that an
investigation of the growth of income per capita is first and foremost an inquiry
into 'the causes of this improvement, in the productive powers of labour, and the
order, according to which its produce is naturally distributed among the different
ranks and conditions of men in the society. Smith was also convinced that the
productivity of labour is dependent on capital accumulation.
(Kuz & Salvadori, 1995), see the accumulation of capital as being able to propel
productivity and growth process by opening up new markets, enlarging existing
ones and increasing effectual demand. (Romer, 1986) suggests that new
technologies are important factors for growth in an economy. Indeed, new
technologies would be more efficient, leading to cost reduction and greater output.
Though the opportunity cost of this might be some quantum of jobs that will be
lost.
A given economy is the result of a set of processes that involves its culture, values,
education, technological evolution, history, social organization, political structure
and legal systems, as well as its geography, natural resource endowment, and
ecology, as main factors. These factors give context, content, and set the conditions
and parameters in which an economy functions. Some cultures create more
productive economies and function better than others, creating higher value of
21
gross domestic product (Benedict, 2008). If the strength of an economy is hinged
on its resources, it behooves the economic managers to grow them. The
productivity of an economy would depend on the quality of its resource and not
just the size.
2.1.4 Infrastructure, Economic Growth and Environment NexusOne prominent macroeconomic goal for every country is sustainable economic
growth. A requirement for achieving this objective is increased domestic
productivity. However, for this to be, there has to be sufficient domestic physical
capital to stimulate such desired economic growth (Owolabi-Merus, 2015). The
extent to which infrastructure may enter the growth equation as capital, would
determine its contribution to growth in an economy. Moreover, the effect of the
scale of production (a factor in growth) will also have implications for
environmental quality. This position is canvassed in studies by (Grossman &
Krueger, 1995 & Copeland & Taylor, 2003). The tendency is that countries would
pursue growth at the expense of the environment if deliberate policies are not
fashioned out to take environmental concern in to account as growth is being
pursued. Such policies will have to look into, among other things, the development
of infrastructure that can reduce the impact of production and consumption
activities, growing population and emergence of new settlements. Education, by
way of sensitization of the teaming populace on the need for best practices in
environmental management is an area that also requires attention. Rising level of
22
education and national income has been found to be positively correlated with the
quality of the environment at least in the developed economies (Grafton &
Knowles, 2004).
2.1.5 Government’s Efforts at Addressing Environmental Issues in NigeriaConcerted effort by the Nigerian government to address environmental problems
dates back to 1988 following the unfortunate incident of the dumping of toxic
hazardous wastes at Koko in the then Bendel state. Prior to this time,
environmental matters were handled by the environmental planning and protection
division (EPPD) within the ministry of works and housing. Apart from the
establishment of the Federal Environmental Protection Agency (FEPA) as a result
of the incident, the federal government, through FEPA, formulated a national
policy on the environment with the overall goal of achieving sustainable
development in the country.
The first attempt at fashioning a national policy on the environment was in 1989.
Since then, efforts have been geared towards implementation arrangements,
including the establishment of guidelines, standards and regulations for efficient
quality control. A key issue in the implementation strategy is the promulgation of
the Environmental Impact Assessment Decree in 1992. The decree stipulates that
all major development projects should be subjected to Environmental Impact
Assessment (EIA) before the commencement of such projects. Intensive
awareness campaigns were also mounted through workshops, seminars, and 23
symposia and through the encouragement of activities of conservation clubs and
non-governmental organizations (NGOs) especially in the area of information
dissemination and training.
In recognition of the gaps which existed in the 1989 policy on the environment, as
it relates to the activities of some sectors in the economy, it became necessary to
review it. This led to the review of the policy in 1999. The revised policy
document on the environment stressed the commitment of the government to
sustainable development based on proper management of the environment. The
document cited the need for positive and realistic planning that balances human
needs against the carrying capacity of the environment by way of policies,
strategies and management approaches that; integrate environmental concerns into
major economic decision-making processes, builds environmental remediation
costs into major development projects and applies environmentally friendly
technologies among others. The implementation strategy, for 1999 environmental
policy frame centered around strengthening legal, institutional, regulatory,
research, monitoring, evaluation, public information and other relevant
mechanisms for ensuring the attainment of the specific goals and targets of the
policy.
The strategies was expected to bring about: (1) the establishment of adequate
environmental standards as well as the monitoring and evaluation of changes in
24
the environment and the adoption of appropriate restorative measures; (2) the
acquisition and publication of up-to-date environmental data and the dissemination
of relevant environmental information; (3) prior environmental assessment of
proposed activities which may impact the environment or the use of a natural
resource; (4) improvement in the quality of life of the people.
It is now over fifteen (15) years since 1999 review. It is debatable whether the
goals of the revised policy is being achieved and at what rate. The issues related to
the environment are dynamic and would normally be taken as they come.
Currently, efforts are being galvanized to further review the national policy on the
environment. In a two-day workshop held in Abuja, recently with the support of the
United Nations Development Programme (UNDP), stakeholders agreed that the
review and validation meeting of the national policy on environment is a crucial
step in capturing diverse views and perspectives of all stakeholders, and
establishing the multi-sectoral partnership needed for successful implementation.
“The new national policy on environment is expected to reflect key emerging
issues from the post-2015 development dialogues/agenda and the linkages with
climate change and disaster risks management in the country” (Muyiwa, 2014).
2.2 Theoretical ReviewThe conjectural basis for examining the nexus between environmental
infrastructure and economic growth can be positioned within the context of the
25
neoclassical paradigm to the extent that infrastructure is considered as capital and
endogenous to growth process.
This study considered the theoretical stance in respect of this nexus from two
perspectives. First, some school of thought aligned with the argument that
infrastructure generally promotes growth as a cost reducing input. We should
therefore have no problem accepting that what we regard as environmental
infrastructure would have the same effect. Second, other schools of thought are of
the opinion that there is an opportunity cost to economic growth. Indeed, this is
evident across economies just like the outcome of the other school of thought.
Accordingly, economic theory identifies channels through which infrastructure can
impact on economic growth and, eventually, the environment, positively or
negatively. Infrastructure may simply be regarded as a direct input into the
production process and, hence, serve as a factor of production. Infrastructure may
be regarded as a complement to other inputs into the production process, in the
sense that its improvements may lower the cost of production or its deficiency may
create a number of costs for firms. Infrastructure may stimulate factor
accumulation through, for example, providing facilities for human capital
development. An educated, well trained and healthy populace can be a catalyst for
the regeneration of a deteriorating environment. Infrastructure investment can also
boost aggregate demand through increased expenditure during construction, and
26
possibly during maintenance operations. Government might attempt to activate this
channel by investing in specific infrastructure projects with the intention of guiding
private-sector investment decisions (Fedderke & Garlick, 1995).
2.2.1 Sustainability TheoryGiven that economic growth is desirable but then it comes at a cost which can
surpass the benefit from growth, there is therefore need to deliberately invest in
projects that can reduce the environmental impact of drive for economic growth.
This notion has placed the discussion on sustainable economic growth and
development in the front burner in recent times. Sustainable development means
the ability of the present generation to pursue her growth and developmental
desires without denying the future generations to so do. (Hartwick, 1977 & Solow,
1986). Future generation should be at least as well off as current generation, by
keeping the society’s capital stock constant. It stresses the creation of unhindered
improvement in the quality of life of all people in present and upcoming generation
through increase in per capita income, education, health and wellbeing as well as
improvement in the natural environment. Hence, the proponents of sustainability of
economic growth and development have come to recognise the environment as an
important factor of production that must be given its reward as the others.
2.2.2 Environmental Quality and Economic GrowthWhy might economic growth benefit the environment? There are a number of
theoretical explanations that suggest the vulnerable side of the environment will be
27
less impacted as incomes rise. First, environmental quality is often cited as a
normal good, if not even a luxury good. In other words, the income elasticity of
demand for environmental quality is greater than zero, possibly even greater than
one. This implies that as income grows environmental concern rises as well,
perhaps even more than proportionally so (Beckeman, 1992). In addition, rich
countries may be better able to meet the higher demands for environmental
protection through their institutional capacity (Neumayer, 2003b).
However, it is contested whether rich people care more about the environment than
the poor (Mainez-Alier, 1995) and the available evidence is far from conclusive.
Second, it is likely that economic growth increases the possibility that more
modern and less pollution intensive man-made capital and technology are
introduced (Grossman & Krueger, 1995). While pollution per unit of output might
go down, absolute pollution levels might very well go up as economic growth
increases. Therefore the effect of technological change on pollution is in principle
ambiguous (Lopez, 1994).
Third, as economic development progresses and income grows, the share of
industry will go down as share of services goes up, and thus, sectoral changes may
favor less-polluting sectors (Janicke, Binda, & Monch, 1997). Yet if starting from
low income levels, structural changes in the economy will most likely have a
detrimental effect on the environment. Pollution will increase as the share of
28
agriculture goes down and industry goes up. Also, there may be limitations in the
scope of these changing patterns of output, given that people’s revealed
preferences indicate that pollution-intensive material goods are still highly valued
(Neumayer, 2003). It is also suspected that high-income countries have become
cleaner because they have exported their pollution-intensive industry to less
developed countries. This is known as the “pollution haven hypothesis”. By
importing goods that the manufacturing processes are resource or pollution
intensive, developed countries’ environmental track records appear cleaner than
they actually are. Despite some recent evidence for such claims, the empirical
record for this argument remains somewhat inconclusive (Neumayer, 2001).
Fourth, rising income brings population growth rates down, therefore population
pressure on the environment decreases. Although not all agree that population
growth is detrimental to the environment (Simon, 1996) the evidence is clear,
larger populations generate more emissions and waste (UNDP, 1999). However,
with considerable variance in the data, it is clear that population growth is
determined by factors other than a country’s income level as well (Neumayer,
2003). Factors such as cultural and or religious orientation, education, government
policy and health can be very important in this case.
Therefore, it can be inferred from the above line of argument that economic growth
is not logically equivalent to rising output in material terms but to rising output in
29
value terms (Pezzey, 1992). By making reference to value, it becomes necessary to
do everything possible to mitigate the other side of economic growth, population
increase and income disparity through investment that can confer value on quality
of life. For instance, expenditure that reduces gas flaring (given it effect on the
environment) and convert it to useful economic product can be regarded as an
investment in environmental infrastructure. Similarly, all expenditures at cleaning
oil spillages and more importantly, ensuring that it does not occur in the first
instance, can also be factored in as investment in environmental infrastructure. The
economic value of such actions can best be imagined if we consider the number of
jobs in farming and fishing that have been lost in communities where oil
exploration and production take place. This has further worsened the poverty
situation in the country.
2.2.3 Environmental Kuznets HypothesisThe relationship between environmental quality and economic growth is explained
in terms of environmental Kuznets hypothesis. Interest in this link arose from the
pioneering work by (Grossman & Krueger, 1993) which subsequently led to a
growing literature on what has come to be known as the environmental Kuznets
curve (EKC). The environmental Kuznets curve is an inverted U–shaped
relationship between a country’s per capita income and its level of environmental
quality. Increased incomes are associated with an increase in environmental
concerns like pollution in poor countries, but a decline in pollution in rich
30
countries. This hypothesis is cristalising the notion that if demand for better
environment is a normal good, increases in income brought about by economic
growth will both increase the demand for environmental quality and increase the
ability of governments to afford costly investments in environmental infrastructure
(abatement technology). Though there have been a number of criticisms of this
hypothesis, it has provided quite convincing evidence that there is an income effect
that raises environmental quality. Moreover, there are strong indications that this
income effect works because increases in the stringency of environmental
regulation accompany higher per capita incomes. Therefore an analysis of the
effects of economic growth on the environment cannot proceed without taking into
account endogenous policy responses (Brain & Taylor, 2004).
Lopez (1994) derives a theoretical model showing that under certain conditions the
inverted u-shaped relationship between pollution and income holds. (Munasinghe,
1999) presents both a theoretical model and results from empirical case studies. He
focused on the marginal benefits and marginal costs of pollution reduction. He
concluded that in the early stages of development, the perceived marginal benefits
of environmental protection are simply too small for decision makers to forgo the
benefits of added economic development.
2.2.4 The Neo-Classical TheoryThis theory was developed by (Solow, 1956) and posits that growth comes from
adding more capital and labour inputs and also from ideas and new technology. 31
The Solow model suggests that a sustained rise in capital investment increases the
growth rate only temporarily. This is said to be due to rising ratio of capital to
labour. However, the marginal product of additional units of capital may decline
(as there are diminishing returns) and thus an economy moves back to a long-term
growth path, with real GDP growing at the same rate as the growth of the
workforce plus a factor to reflect improving productivity. A ‘steady-state growth
path’ is reached when output, capital and labour are all growing at the same rate, so
output per worker and capital per worker are constant.
Neo-classical economists believe that to raise the trend rate of growth requires an
increase in the labour supply and also a higher level of productivity of labour and
capital. Differences in the rate of technological change between countries are said
to explain much of the variation in growth rates that we see.
The neo-classical model treats productivity improvements as an ‘exogenous’
variable – they are assumed to be independent of the amount of capital investment.
The Solow model features the idea of catch-up growth when a poorer country is
catching up with a richer country – often because of a higher marginal rate of
return on invested capital in faster-growing countries. The Solow model predicts
some convergence of living standards (measured by per capita incomes) but the
extent of catch up in living standards is questioned given the existence of the
32
middle-income trap when growing economies find it hard to sustain growth and
raise per capita incomes beyond certain level.
2.2.5 Endogenous Growth TheoryThe endogenous growth theory hypothesised that improvements in productivity
can be linked directly to a faster pace of innovation in addition to investment in
human capital. It stresses the need for government and private sector institutions to
nurture innovation, and provide incentives for individuals and businesses to be
inventive. The theory is anchored on the principle that government policies can
raise a country’s growth rate if they lead to more intense competition in markets
and help to stimulate product and process innovation. In addition, it assumes
increasing returns to scale from capital investment. Investment in research &
development is a key source of technical progress. Protection of property rights
and patents are essential in providing incentives for businesses and entrepreneurs
to engage in research and development, while investment in human capital is a key
ingredient of growth.
Accordingly, environmental concerns need to be factored into the process of
innovation at least through government policies and persuasive outreach to firms
and entrepreneurs (as their objective function is anchored on profit maximization)
bearing in mind the principle of sustainable development. The second component
of this growth theory stresses the importance of human capital. Human capital
development has the capacity to create positive externalities on the environment as 33
well educated citizens, who are on high income, would demand for more
qualitative environment. This is in-line with the environmental Kuznets hypothesis.
2.2.6 The Brett Frischmann’s TheoryThis is one of the theories that explain the importance of public accessibility to
infrastructure. It posits that public access to infrastructure would generate value for
the society (Frischmann, 2007). A major thrust of the theory is the fact that open
access to infrastructure, in this instance, environmental infrastructure, would
generate significantly positive results for a society.
In line with this theory, the state is generally responsible for the provision of
environmental infrastructure through diverse revenue sources. Though private
provision of environmental infrastructure is not unheard of, the state is often in
charge of welfare services delivery to the preponderance of the people (Adejiumo,
2004). The classical social contract theory maintains that the state must guarantee
law and order, human dignity and social welfare. Similarly, the issue of social
welfare features in both liberal and Marxist theories of the state. The liberal theory
stipulates that “the state is required to perform some functions such as maintaining
law and order, administration of justice and erection and maintenance of public
works which may be beneficial to the society but which the private sector finds
uneconomical to produce” (Marx & Engel, 1965).
34
This assertion was further extended focusing on the division of state functions
across different level of government (Kelleher & Wolak, 2007). They argue that
citizens’ confidence in state institution depends on political process, the nature of
representation, economic and policy performance of the government. The various
tier of government can respond to the environmental infrastructure need of the
people through their functions as enshrined in the constitution. This is particularly
very important in the face of rising environmental challenges confronting our
world today, to the extent that all living creatures and man’s developmental
achievements are threatened.
2.3 Empirical ReviewThe literature is not in want of studies that have been carried out to investigate the
role of infrastructure in promoting economic growth as well as the impact of
economic growth on the quality of the environment both in the advanced and
developing economies.
Esrey (1996) used eight demographic and health surveys to identify the effects of
sanitation on diarrhea. The study establishes a reduction of diarrhea of 13-44
percent for flush toilets and a reduction of diarrhea of 8.5 percent for latrines. The
study also finds complementarities between water and sanitation. He shows that
improved water supply has no effect on health if improved sanitation is not present
and even if sanitation is present the health benefits of water are reported to be
lower than the health benefits of improved sanitation.35
A lower bound of $5 for each $1 investment in water and sanitation infrastructure
was estimated for the United States (Hutton et al, 2007). These estimates are,
however, based on the assumption that investments in water and sanitation lead to
high time savings that can then be used for economic activity.
The important historical contribution of water and sanitation infrastructure to the
secular decline in mortality in Europe and the America at the turn of the 19th
century appears well documented. Studies which discussed the critical role of
water and sanitation in the historical decline in infant mortality in the late 19th
century in England and Wales include (Deaton, 2006; Aiello, Larson, & Sedlak,
2008; Woods, Waterson, & Woodward, 1988; Szreter, 1988). The surge in
sanitation investments in Germany as a response to the devastating cholera
epidemic of the 19th century was also instructive (Brown, 1989). It was observed
that water and sanitation improvements account for 50 percent of total, and 75
percent of child mortality reductions experienced in major US cities throughout the
20th century (Cutler & Miller, 2005). Elsewhere, it was found that sanitation
investment in native Indian reservations was the key driver for the convergence in
child health between native Indian and the surrounding populations in the US
(Watson, 2006).
Study revealed that a 17 percent reduction in diarrhea induced by improved water
supply and a 22 percent reduction induced by improved sanitation infrastructure
36
(Esrey, 1996). Another study showes a reduction in illness of 25 percent for water
and 32 percent for sanitation infrastructure (Fewtrell & Kaufmann, 2005). The
results were, however, insignificant for water interventions if only diarrhea is
considered as the dependent variable. Another study on the effect of water and
sanitation reports no significant impact on diarrhea morbidity for water supply and
a 37 percent relative reduction in diarrhea incidence for sanitation (Waddington,
Snilstveit, White, & Fewtrell, 2009).
A well-managed system of piped water, sanitation, and drainage and garbage
removal would greatly diminish the health hazards to which people are currently
exposed and reduce their poverty even without increasing their income
(Oostervveer & Spaargaren, 2010).
These lists of studies with respect to water and sanitation clearly show the
dimensions of the social impact of the infrastructure ranging from poverty,
diseases and low economic opportunities for the majority of the people in
developing countries. Infrastructure was responsible for a net contribution of
around one percentage point to Nigeria’s improved per capita growth performance
in recent years, in spite of the fact that unreliable power supplies held growth back.
They suggested that raising the country's infrastructure endowment to that of the
region's middle-income countries could boost annual growth by around four (4)
37
percentage points. The impact which infrastructural development has on the
environment via economic growth remains unresolved (Vivien & Nataliya, 2011).
Another study was conducted by (Canning & Pedroni, 1999) to test causality
between investments in three types of infrastructure; that is, kilometers of paved
road, kilowatts of electricity generating capacity, and number of telephones based
on data from a panel of 67 countries between 1960 and 1990. Strong evidence in
favour of causality running in both directions between each of the three
infrastructure variables and GDP among a significant number of the countries
investigated. Accordingly, they suggested more investment in these infrastructures
since they have the potential to influence economic growth. Moreover, they
submitted that countries need to package sound macroeconomic policies that target
higher economic growth. The finding in relation to electricity GDP feedback effect
portrays great hope for the environment, particularly for economies whose
industries are being run on fossil fuel.
A study on infrastructure investment and economic growth in South Africa by
(Wolassa, 2012) from 1960 to 2009 used bi-variate vector auto regression (VAR)
model with and without a structural break. The result indicates that there is a strong
causality between economic infrastructure investment and GDP growth that runs in
both directions implying that economic infrastructure investment drives the long
term economic growth in South Africa while improved growth feeds back into
38
more public infrastructure investments. This finding further confirms the
submission of (Canning & Pedroni, 1999) with respect to the need to expand
infrastructure access and promotion of productivity in an economy.
Another study explores the relationship between infrastructure and economic
growth by including the data of expenditure in infrastructure as a share of GDP in
traditional growth cross-country regressions (Sanchez-Robles, 1998). The results
were, however, inconclusive but elaborated some new indicators of investment in
infrastructure employing physical units of infrastructure. He found positive and
significant correlation with growth in two different samples of countries. The
policy suggested was for countries to pay closer attention to physical infrastructure
provided as against the quantum of budgetary expenditure on infrastructure as this
may be misleading.
The Fisher’s tests of Pesaran, Shin and Smith was used to conduct a study in South
Africa. The study finds causation between infrastructure and economic growth
(Perkins et al, 2005). They identified long-run relationships from public-sector
economic infrastructure investment and fixed capital stock to gross domestic
product (GDP), from roads to GDP and from GDP to a range of other types of
infrastructure. They also established that the relationship between economic
infrastructure and economic growth run in both directions and conclude that
39
policies aimed at attracting investment in infrastructure and promotion of
economic growth can greatly improve the welfare of the people.
Aside from the effects of infrastructure development on aggregate income growth,
another strand of recent literature has examined its effects on income inequality.
The underlying idea is that, under appropriate conditions, infrastructure
development can have a positive impact on the income and welfare of the poor
over and above its impact on average income. This hypothesis is confirmed
empirically in the study by (Lopez, 2004).
There are good reasons why environmental infrastructure development may have a
disproportionate positive impact on the income and welfare of the poor. Taking an
aggregate perspective, (Ferreira, 1995) presents a model of public-private capital
complementariness in which expanding public investment reduces inequality.
Conceptually, infrastructure helps poorer individuals and underdeveloped areas to
get connected to core economic activities, thus allowing them to access additional
productive opportunities (Estache, 2003). Likewise, infrastructural development in
poorer regions is expected to reduce production and transaction costs. In this vein,
it was found that enhanced access to roads and sanitation has been a key
determinant of income convergence for the poorest regions in Argentina and Brazil
(Estache & Fay, 1995). Along the same line, infrastructure access can raise the
value of the assets of the poor. For example, recent research links the asset value of
40
poor farm areas - as proxy by the net present value of the profits generated by their
crops -to the distance to agricultural markets. Improvements in communication and
road services imply capital gains for these poor farmers (Jacoby, 2000).
Infrastructure development can also have a disproportionate impact on the human
capital of the poor, and hence on their job opportunities and income prospects. This
refers not only to education, but also to health. A number of recent papers has
focused specifically on the impact of expanding infrastructure services on child
(and maternal) mortality, and educational attainment. This literature shows that
policy changes that enhance the availability and quality of infrastructure services
for the poor in developing countries have a significant positive impact on their
health and or education and, hence, on their income and welfare as well.
Recent evidence on these impacts with regards to education suggests that, a better
transportation system and a safer road network help raise school attendance
(Brenneman & Kerf, 2002). Electricity also allows more time for study and the use
of computers (Leipziger, Fay, & Yapes, 2003). Regarding health, access to water
and sanitation plays a key role. Several studies have identified instances in which
access to clean water has helped significantly to reduce child mortality. In
Argentina, for example, a recent study by (Galiani, Gertler, & Schargrodssky,
2002) finds that expanded access to water and sanitation reduces child mortality by
8 percent, with most of the reduction taking place in low-income areas where the
41
expansion in the water network was the largest. More generally, it was found that a
quarter of the difference in infant mortality and 37 percent of the difference in
child mortality between the rich and the poor is explained by their respective
access to water services (Leipziger et al, 2002). Allowing the poor to access safe
water at the same rate as the rich would reduce the difference in child mortality
between the two groups by over 25 percent.
Indeed, for infrastructure expansion to reduce income inequality, it must result in
improved access and/or enhanced quality particularly for low-income households.
Hence the key issue is how the development of infrastructure impacts access by the
poor (Estache, Gomez-Lobo, & Leipziger, 2000).The literature has also linked
economic growth to environmental degradation. Studies in this respect dates back
to the 1970s. These include: Forster (1973), Solow (1973), Stiglitz (1974) and
Brock (1977). These culminated into the more recent work investigating the
environmental Kuznets hypothesis such as stokey (1998), Aghion & Howitt (1998)
and Jones & Manuelli (2001).
Grossman and Krueger, (1991) were the first to model the relationship between
environmental quality and economic growth. They analyzed the EKC relationship
in the context of restriction policy. This was particularly with respect to trade
contentions between countries with low and high environmental standards. It was
42
believed that pollution intensive factories would seek refuge in economies with the
lowest environmental standards.
Grossman and Krueger propose that rising incomes from trade would lead to
stricter environmental control. In other words, free trade would protect the
environment. They found that ambient levels of both sulfur dioxide and dark
matter (smoke) suspended in the air increase with per capita GDP (gross domestic
product) at low levels of national income but decrease with per capita GDP at
higher levels of income. These findings provided statistical evidence for the
existence of an EKC relationship for these two indicators of environmental quality.
The turning point came when per capita GDP was in the range of $4,000 to $5,000
measured in 1985 U.S. dollars (or about $6,200 to $8,200 in 2001 U.S. dollars).
Unlike the relationship found for sulfur dioxide and smoke, no turning point was
found for the mass of suspended particulate matter in a given volume of air. In this
case, the relationship between pollution and GDP was monotonically increasing
Selden, Forest, & Lockhart (1999) analyse scale, composition and technique
effects at the sector level to decompose changes in various US emissions. Scale is
indicated through the growth of emissions when the ratio of emissions to GDP
remains constant. Composition effects are changes in emissions due to differential
growth rates among sectors within an economy, and technique effects are all other
changes in emissions per unit of output at the sectoral level, including energy
43
efficiency, energy mix, and other technique effects. They found that increased
economic growth will trigger a composition shift of economic activity away from
heavy manufacturing to services, and that economic growth may also generate
environmental benefits through the development and adoption of new technology,
that is, cleaner production and improved energy efficiency. Therefore the policy
prescription for alleviating at least some environmental problems may equal more
economic growth, but their finding that emissions abatement technology played a
significant role in bringing about improvement in environmental quality points
towards a policy-induced response. They also submit that global energy prices may
signal emissions downturns because price incentives most likely provide incentives
for increased energy efficiency. Therefore, the question remains whether emissions
will rise again as international energy prices fall from their peaks, if policy is not
introduced.
Hilton and Levinson (1998) examine the link between lead emissions and income
per capita using a panel of 48 countries over the period of 1972-1992. They found
strong evidence of an inverted u-shaped relationship between lead emissions and
per capita income and then factors the changes in pollution into two different
components. The first is a technique effect that produces an almost monotonic
relationship between lead content per gallon of gasoline and income per capita.
The second is a scale effect linking greater gasoline use to greater income. This
44
study is the first to provide direct evidence on two distinct processes (scale and
technique effects) that together result in an EKC.
Shafik and Sushenjit (1992) study the relationship between economic growth and
several key indicators of environmental quality reported in the World Bank’s
cross-country time-series data sets. They discovered a consistently significant
relationship between income and all indicators of environmental quality examined.
As income increases from low levels, quantities of sulfur dioxide, suspended
particulate matter and fecal coli form-increase initially and then decrease once the
economy reaches a certain level of income.
Hettige, Lucas, & Wheeler (1992) explored the EKC phenomenon further. They
develop a production toxic intensity index for 37 manufacturing sectors in 80
countries from 1960 to 1988. Their goal was to avoid focusing on individual
measures of environmental quality such as air quality, but rather to generalize the
environmental impact of manufacturing by determining if manufacturing became
more or less “toxic” in relation to income. The index, based on information from
the US environmental protection agency and the US census of manufacturers,
attempted to measure a country’s toxicity or pollution intensity. The researchers
could then identify the extent to which polluting production did or did not shift
from higher- to lower-income countries when incomes rose faster in one than the
other location. The results of the study indicate the existence of an EKC
45
relationship for toxic intensity per unit of GDP. No evidence, however, was found
for toxic intensity measured per unit of manufacturing output. The authors
observed that when the mix of manufacturing was held constant, manufacturing in
low income countries was not more toxic, nor manufacturing in high-income ones
less toxic. Manufacturing, which is just one part of GDP, did not become cleaner
or dirtier as income changed. Instead, manufacturing became smaller relative to
services and trade in expanding economies. This suggests that higher income leads
to a demand for a cleaner environment regardless of whether the environment has
been damaged by a toxic producing manufacturing sector. They concluded that the
GDP-based intensity result is due solely to a broad shift from industry toward
lower-polluting services as development proceeds.
Using the annual percentage change in forest area between 1972 and 1991 as an
indicator of environmental quality, (Bhatarrai, 2000) analyses the EKC relationship
for tropical deforestation across 66 countries in Latin America, Asia, and Africa.
The study quantifies the relationship between deforestation and income,
controlling for political and governing institutions, macroeconomic policy, and
demographic factors. The results from his empirical analysis suggest that
underlying political and civil liberties and governing institutional factors (the rule
of law, quality of the bureaucracy, level of corruption in government, enforcement
of property rights) are relatively more important in explaining the process of
46
tropical deforestation in the recent past than other frequently cited factors in the
literature—for example, population growth and shifting cultivation. The study
suggests that improvements in political institutions and governance, and
establishment of the rule of law significantly reduce deforestation. In a related
way, macroeconomic policies that lead to increased indebtedness and higher black
market premiums on foreign exchange (measures of trade and exchange rate
policies) will increase the process of deforestation.
The institutions whose functions bordered on enhancing growth and development
across the globe have offered their opinions on the need for countries and regions
to demonstrate greater commitment towards conserving the environment and
provision of socio-economic infrastructure for the people as these are vital to
improvement in welfare not only for the present but also for the future generations.
The African Development Bank (ADB) estimates that less than two-thirds of
Africa’s urban population has access to safe water and barely one half to
sanitation. Access in rural areas is much lower and other linkages are likely to be
strong but indirect. It added that good infrastructure is part of the enabling
conditions for sustained economic growth, which in turn, is a prerequisite to
reducing poverty.
The organisation for economic co-operation and development estimates that total
global expenditures on infrastructure in energy, transportation, and water from 47
2000 to 2030 will be about $57 trillion (in constant 2000 US $) in order to achieve
targeted economic growth rates. Nearly half of this expenditure will be in
developing countries, which have the greatest needs for additional infrastructure.
To support continued high growth, infrastructure investment need to average $700
billion a year in this decade, rising to over $1 trillion a year by the 2020s. The
world bank estimates that developing countries need to invest about six percent of
their gross domestic product (GDP) annually in infrastructure, rising to as high as
nine percent for the lower income countries. However, current investment levels in
Africa and Latin America and the Caribbean are well below this target level, which
has contributed to their relatively lower growth rates. Investment levels in Asia are
generally high, exceeding seven percent of GDP for infrastructure in rapidly
growing countries.
This lagging behind by Africa need to be immediately addressed through policy
and eventual resources deployment in other to key in the benefits of in investment
in general
2.4 Theoretical FrameworkThis thesis is anchored upon the neoclassical theory which is a theory of the stock
and spread of national product based on a society’s endowment of production
factors, technical conditions of production and consumer preferences (Cesaratto,
1999).The theory considers economic growth as endogenous in the sense that
growth depends on the society’s choice between savings and current consumption. 48
Capital, which comes from savings, plays very key role both in distribution theory
and growth theory. The neoclassical theory posits that aggregate economic growth
comes from positive rate of profit such that if the market forces are unfettered, it is
able to allocate resources most productively (Cesaratto, 1999). It follows that
labour would get its reward for contributing to output following the marginal
productivity theory and is not cheated of its surplus value. Likewise, capital would
be deployed for optimum productivity. The theory gives the assurance of
maximization of firms’ and individual consumers’ objectives and posits that this
will ultimately guarantee social welfare as the government concern itself with right
policies formulation.
The neoclassical theory suggests that the government should play less role in
shaping economic activities and places the bulk of the task of driving the economy
at the door step of the firms who are the owners of capital. In neoclassical theory,
economies become wealthy by capital formation, accumulation and investment.
In any case, that “little” role left to the government would not be abused if it is
played in such a way that firms are placed on better footings for greater
productivity. It can do this by working indirectly through policies and sometimes
directly through investment in infrastructure that will offset some cost for the firms
and also promote social welfare.
49
2.5 Gap in the LiteratureThe literature so far perused suggests that while considerable studies have been
devoted to finding out the link between general infrastructure and economic
growth as well as environmental quality and economic growth, not much has been
researched directly into how economic growth and infrastructures can be tailored
to improve the environment. In other words, for this study, we are beaming the
searchlight on a subset of infrastructure, that is, environmental infrastructure.
Water/sanitation and education infrastructure have implications for social
wellbeing and the environment, while power and transport infrastructure have
economic undertone and also have implications for the environment. In
addition, this study contributes to knowledge empirically by re-investigating this
area of study and extending the time period covered by related previous studies.
Moreover, few or no country specific studies in respect of the nexus between the
environment and economic growth exist for Nigeria. There is need to investigate
possible link between the two for informed policy options specifically for the
country, given the current trends in environmental issues across the globe.
50
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Analytical ApproachEarlier studies concerning the relationship between environment and economic
growth have employed various methods depending on the availability of data and
theory that explains the relationship explored. The reviews in the preceding chapter
revealed a number of studies that have employed survey method to study the effect
of investment in environmental infrastructure such as water and sanitation, at the
micro level, that is, households and community level, from the angle of health (See
Esrey, (1996) and Hutton et al, (2007)). These were primary researches for which
survey was most suitable. Other studies which were secondary in nature, examined
the impact of infrastructure in general on aggregate income and have employed
methods amenable to time series studies to address the issues canvassed (See
Vivien & Nataliya, (2011); Brenneman & Kerf (2002)). Some strand of the
literature which explored the link between drive for economic growth and the
consequent effects on the environment, have employed causality, error correction,
and panel data methodologies. (See Hettige et al. (1992); Jones & Manuelli,
(2001)).
This study employs Vector Error Correction Methodology (VECM), a restricted
vector auto regression (VAR) designed for use with non-stationary series that are
51
known to be co-integrated. For purposes of analyzing and forecasting
macroeconomic activity and tracing the effects of policy changes and external
stimuli on the economy, it has been found that simple, small-scale VARs without a
possibly flawed theoretical foundation have proved as good as or better than large-
scale structural equation systems (Bjornland, 2000). In addition to forecasting,
VARs have been used for two primary functions, testing Granger causality and
studying the effects of policy through impulse response characteristics. Sims
(1980) first introduced VAR models as an alternative to the large scale macro
econometric models. Since then the methodology has gained widespread use in
applied macroeconomic research.
The VECM has co-integrating relations built into its specification so that it restricts
the long-run behavior of the endogenous variables to converge to their co-
integrating relationships while allowing for short-run adjustment dynamics. The
co-integration term is known as the error correction term since the deviation from
long-run equilibrium is corrected gradually through a series of partial short-run
adjustments (Engle & Granger, 1987)
Time series models built to study relationship among variables of interest are
structured bearing in mind economic theory. However, economic theories are often
not adequate to provide a dynamic picture that recognises all of these interactions.
In addition, the fact that endogenous variables may appear on both sides of
52
equations, complicates estimation and inference. This has led to alternative, non-
structural approaches to modeling the relationship among several variables. The
VECM is one of such non-structural approaches to modeling macroeconomic
variables. (Engle & Granger, 1987). The idea is that a percentage of disequilibrium
in one period is corrected in the next period. For instance, change in price in a
given period may be dependent on the extent of excess demand in the previous
period. This type of pattern can be seen as an optimal behaviour with some sort of
adjustments cost.
Theoretical and empirical literature suggest the existence of positive relationships
between infrastructure investment and sustainable economic growth (Munnel,
1992). Labour intensive infrastructure construction programmes, particularly in
rural areas of developing economies, often lead to employment opportunities
thereby reducing rural poverty. Access to improved infrastructure capable of
abating environmental degradation (water and sanitation, electricity et cetera) in
hitherto unreached areas, will not only spur on private sector investment, leading to
further job creation and economic growth, but also guarantees the sustenance of
these gains.
Environmental infrastructure are capital in nature as they will serve to promote
further production in the economy by raising the quality of human capital,
lowering cost and enhancing efficiency of other factors of production (Vivien &
53
Nataliya, 2011). Therefore, it is on this basis that the literature has come to
acknowledge environmental infrastructure investment as a growth propeller. The
main academic approaches to modeling the relationship between capital and
economic growth are the Solow (1956) and Romer (2004) growth models.
The Solow model is derived from the Cob-Douglas production function given as:
Qt =f(A, Kt, Lt) (3.1.1)
where: Qt, Kt, Lt are aggregate real output, capital and labour, respectively, while A
is the coefficient of technical progress.
Economic growth has always been linked to accumulation of physical capital and
embodied technology which allows labour to be effective and efficient. One can,
therefore, re-affirm the endogeniety of capital and the strong positive correlation
between capital and growth of output in an economy.
Accordingly, a number of studies have been carried out which mixed endogenous
with exogenous factors to explain economic growth across economies using what
is termed “augmented neoclassical production function”. Some of these studies
include Romer, (1990); Chete & Adeoye, (2002).
3.2 Model SpecificationGiven a time series Yt which is integrated of order one, in other words,
∆Yt= Yt – Yt-1 (3.1.2)
54
is stationary. Assuming the time series is generated from a VAR (1) process
Yt = A1Yt-1 + ut (3.1.3)
The Vector Error Correction Model (VECM) specification will hence be:
∆Yt = θYt-1 + ut (3.1.4)
Where θ = A1 − Im is an m × m matrix of rank r with 0 ≤ r < m marking the number
of cointegration relations in the system. θ can be decomposed as θ = αb′, where b is
the r long-run cointegrating relations and α is a loading matrix of rank r. In this
setting, VEC determination reduces to selection of the correct cointegration rank
(Liang & Schienle, 2014).
Relying on the preceding analogy and adapting the specification in (Baun, 2013);
(Greene, 2002) we specified the following models to relate growth rate of gross
domestic product (GDP) to environmental infrastructure outputs in Nigeria. A
vector autoregressive (VAR) specification with p lag will be:
LGdpt = α0 + α1∆LWt +α2∆LTt + α3 ∆LEt +α4 ∆LCPt + α5∆LEDt+α6∆LLF +
λ1LGdpt-1+ … + λpLGdpt-p + et …….. (3.1.5)
LGdpmt = g0 + g1∆LWt +g2∆LTt + g3∆LEt +g4 ∆LCPt + g5∆LEDt+ g6∆LLF +
Δ1LGdpmt-1 + … + δpLGdpmt-p + et…….. (3.1.6)
55
Rewriting the VAR as a VECM:
∆LGdpt= b0 + b1∆LWt+b2∆LTt +b3∆LEt +b4∆LCPt + b5∆lLEDt + b6∆LLFt+
γ1∆LGdpt-1 +γi∑i=2
p−1
∆ LGdpt−i+d1∆LGdpmt-1 + di∑i=2
p−1
∆ LGdpmt−i+et
………… (3.1.7)
∆LGdpmt= β0 + β1∆LWt +β2∆LTt + β3 ∆LEt +β4 ∆LCPt + β5∆LEDt + β6∆LLFt +
a1∆LGdpmt-1+ ai∑i=2
p−1
∆ LGdpmt−i+θ1∆LGdpt-1 + θi∑i=2
p−1
∆ LGdpt−i+ et
………… (3.1.8)
Where:
γ = ∑j=1
j=p
λ j –ik and di = - ∑j=i+1
j=p
λ j
Similarly,
ai = ∑j=1
j=p
δ j – ik and θi = - ∑j=i+1
j=p
δ j
LGdpt = log of gross domestic product
LWt= log of water output, (strictly exogenous)
LTt = log of transport output (strictly exogenous)
56
LEt = log of Electricity output (strictly exogenous)
LCPt = log of crude petroleum and natural gas output (strictly exogenous)
LEDt = log of education (strictly exogenous)
LLFt= log of labour force (strictly exogenous)
∑i=1
k −1
∆ LGdpmt−i= vector of the lag differenced values for manufacturing GDP
∑i=1
k −1
∆ LGdpt−i = vector of the lag differenced value of GDP
The value of p (lag length) is determined based on AIC and SIC
It is assumed that: E(et/Xi) = 0, Var.( et/Xi) = σ2 , Cov(eiej/XiXj) = 0, et᷉ N(0,σ2)
As is the practice, unit root test would normally precede co-integration test. We
followed both the augmented dickey-fuller (ADF) and Phillips-Perron (PP) test
procedure.
Accordingly, and in addition to equations (3.1.7) and (3.1.8) we specify the
following equations to test the impact of economic growth on environmental
quality and environmental infrastructure on environmental quality in the Nigerian
economy.
∆CO2 = q0 + q1∆GNIt + s1∆CO2t-1 + si∑i=2
p−1
∆ CO 2t−i+ w1∆FLDt-1 +
57
wi∑i=2
p−1
∆ FLD t−i + et ….. (3.1.9)
∆FLDt= r0 + r1∆GNIt + z1∆FLDt-1 + zi∑i=2
p−1
∆ FLD t−i+ π1∆CO2t-1 +
πi∑i=2
p−1
∆ CO 2t−i + et ………… (3.1.10)
∆LCO2 = b0 + b1∆LTt + b2∆LEt + b3∆LCPt + b4∆LEDt + γ1∆LCO2t-1 +
γi∑i=2
p−1
∆ LCO2t−i + d1∆LFLDt-1 + di∑i=2
p−1
∆ LFLD t−i+ et (3.1.11)
b0 > 0, b1 ˂ 0, b2 ˂ 0, b3 ˂ 0, b4 ˂ 0
∆LFLDt= a0 + a1∆LTt + a2∆LEt + a3∆LCPt + a4∆LEDt + γ1∆LFLDt-1 +
γi∑i=2
p−1
∆ LFLD t−i + d1∆LCO2t-1 + di∑i=2
p−1
∆ LCO2t−i+ et (3.1.12)
a0 > 0, a1 ˂ 0, a2 > 0, a3 > 0, a4 > 0
Where: CO2 is carbon (measure of environmental quality)
GNI is gross national income (strictly exogenous)
FLD is percentage of forest to total land area (measure of environmental
quality)
58
q0 ˃ 0, q1 ˃ 0 and r0 ˃0, r1˂ 0
It is assumed that: E(et/GNI) = 0, Var(et/GNI) = σ2, Cov(eiej/GNI) = 0, et ᷉ N(0,σ2)
We follow the approach of disaggregating the growth rate of GDP so as to isolate
the impact of chosen environmental infrastructure on the growth of outputs in
manufacturing sector.
3.3 Discussion of VariablesThe time series employed for this study include; the total Gross Domestic Product
GDP), manufacturing Gross Domestic Product (GDPm). Our proxies for
environmental infrastructure are; electricity output, water output, transportation
output, primary education enrolment, as well as crude petroleum and natural gas
output. Better proxies could have been actual investments in these infrastructures,
but data were not available for Nigeria in these respects.
Electricity enters our model as an environmental infrastructure variable since its
efficient and effective supply will reduce carbon emission that would result due to
wide spread use of alternate source of power such as petrol and diesel fuels
generators. Its output is expected to impact positively on the environment as well
as promote growth through enhancement of economic activities for production
units that depend heavily on its constant supply. It will also guarantee reduced cost
compared to where generators are the alternative. Other remote impacts of
adequate and constant supply of electricity include comfort provided at homes
59
particularly arising from the fall out of tropical climate as well as preservation of
food items.
Water is one of the environmental resources that is showing sign of exhaustion due
to over exploitation and global warming. According to UNDP 2006, about half of
those in hospital beds in developing countries can be traced to diseases caused by
poor water, sanitation and hygiene. Increases investment in water and sanitation
infrastructure is expected to have positive impact on economic growth in an
economy. Moreover, water also enters our model as an environmental
infrastructure because an effective and efficient water infrastructure is that which
harness more of surface water as against underground water (boreholes) which can
have adverse effects (Datta, 2015).
Transportation as an auxiliary of production, plays the role of adding place value to
goods and services. It is expected that this variable will have positive impact on
economic growth in an economy. Efficiency and effectiveness of the various
modes of transportation can have far reaching effects on economic growth in a
country. An efficient and environment friendly mode of transportation would be
that which makes use of technologies that minimise carbon emission. It follows
that higher transportation output implies more mileages, hence, higher carbon
emission. Investment in electric trains that leads to less usage of fossil fuel
consuming vehicles can be of great advantage to the environment.
60
Crude petroleum and natural gas outputs have been the major revenue earner for
the Nigerian economy. It is expected that this variable will have positive impact on
economic growth in the country. In addition, oil and gas output entered our model
as environmental infrastructure in two ways. First, crude oil exploration and the
consumption of its various refined components have negative impact on the
environment through soil degradation, associated gas flaring and carbon emission.
Second, the promotion of greater use of liquefied petroleum gas (LPG) for home
and industrial purposes is expected to have positive impact on health and the
environment. This is because LPG is a cleaner source of energy and when its use is
encouraged across the country, tree felling will reduce and the encroachment of the
desert will be kept under control.
The level of education and awareness of the citizenry is expected not only to
confer quality and efficiency on the labour force, but also influence the attitude of
the people towards environmental best practices. We expect that education will
positively relate to the environment and by implication, impact positively on
economic growth.
Population is our proxy for labour force as an important factor of production in an
economy. The rising size of an economy’s workforce presents potential
opportunity to drive economic expansion and increase GDP. A relationship
certainly exists between the labour force and the population of a country. However,
61
the quality of a country’s population by way of investment in its human capital,
particularly the working age, is that which holds the potential to promote economic
growth. For example, World-watch Institute, 2013, observed that “industrialised
countries are home to only some 16 percent of the world’s potential labor force,
but they produce more than two-thirds of global GDP, meanwhile, nearly a
quarter of the world’s potential labor force lives in south Asia, yet that region’s
share of the global economy is a little over 3 percent.”
In respect of economic growth and environmental quality nexus, the literature
tends to project the notion that the quality of the environment will deteriorate in
low income economies because economic growth target is crucial. However, the
environment is expected to be improved with gain in growth for high income
economies to a limit (Brock & Taylor, 2004). Accordingly, per capita gross
national income (GNI) is expected to pick up a positive sign in relation to carbon
emission (CO2). Also, the study uses the size of the forest area as a measure of
environmental quality, based on the argument that climate change, tree felling and
other predatory activities of man have led to the shrinking of the forest (Bhatarrai,
2000). It is expected that trade off exist between GNI and forest area.
3.4 DataThe dataset is the quarterly series from first quarter1980 to fourth quarter 2013
covering a period of 34 years but representing 135 observations. Annual series
from 1990 to 2013 was used for the model on the relationship between GNI and 62
environmental quality as quarterly series were not available. In addition, the annual
series in the latter case were not available for 1980 to 1989. The proxy data for
measuring environmental quality are carbon emission and percentage of forest to
total land area. Given the fluid nature of the environment and the difficulty of
tracking down environmental damage to particular factors, the practice has been to
pick on one or more of those factors regarded as having effect on the environment.
Carbon emission and size of the forest are among the variables which have data for
the Nigerian economy on the World Development Index data base.
The macroeconomic data used for this study are the pre – rebased ones. This is to
avoid outliers as the rebased data set are much larger. We have no actual time
series on the frequency of data used in respect of labour utilization for Nigeria. The
practice is to use change in population as a proxy for labour. Accordingly,
population series was used as a proxy for labour in (3.1.1). Moreover, we
constructed the quarterly series of the population data by splitting the annual data
in to four since the growth rate of population is constant annually.
The quality of the data is assumed as they were all sourced from the Central Bank
of Nigeria, and World Development Index which we believed have engaged
qualified personnel to collect the data. The quality of the information decipherable
from the data largely depends on this assumption. Generally, the challenge of data
63
paucity is one that every researcher has to acknowledge particularly in country like
ours where culture of meticulous data keeping is not yet deepened.
As usual, most macroeconomic time series are integrated, that is, they have unit
root. Series xi with no deterministic components and which have stationary
invertible, ARMA representation after differencing d times is said to be integrated
of order d. This is written as Xt=I(d). If d = 0, it implies: (1) the variance of X t is
finite; (2) a shock has only a momentary effect on Xt; (3) the autocorrelation, Pk,
decreases steadily in magnitude for large enough k so that their sum is finite. This
is most desired for time series in economic modeling. However, this property is not
true of series that are not I(0), that is, d > 0. The variance of X t becomes infinite as
t tends to infinity; a shock has a permanent effect on the value of X t since Xt is the
sum of all previous changes; the theoretical autocorrelation, Pk , tends to 1 for all k
as t tends to infinity (Engle & Granger, 1987).
Accordingly, we investigated the integration property of the data and did accept the
hypotheses that all except crude petroleum and natural gas series are integrated of
order higher than 0 (See Tables A1 to A22 on appendix A). The implication of this
is that one cannot use the series at their levels but have to be transformed which we
did. We also carried out test of equality of mean to find out if variability between
the series’ means is the same as the variability within any particular series. The
results showed that there is strong evidence that the series differ as both the
64
standard Analysis of Variance (ANOVA) and the Welch adjusted ANOVA
statistics are in excess of 40, with probability values near zero (See Table B1 on
appendix B). The correlation matrix (See Table B2 on appendix B) indicates that
the series are correlated at various degrees.
65
CHAPTER FOUR
DATA PRESENTATION AND ANALYSIS
4.1 Data PresentationThe data employed for this study were obtained from the Central Bank of Nigeria
(CBN) Statistical Bulletin 2013 edition. We also obtain data on the proxy we used
for environmental quality from the World Development Index for the period 1990
to 2013. The data are as presented on appendix D
4.2 Empirical AnalysisThis thesis sets out to investigate the impact of public investment in environmental
infrastructure on economic growth in Nigeria using macroeconomic time series
over the period first quarter 1980 to third quarter 2013. The preceding chapter set
the stage for this chapter on analysis of data.
The results for the model were obtained using econometrics views (E-views)
software (version 8.0). The summary result of unit root test, using the Adjusted
Dickey Fuller and Phillips-Perron procedures, for variables in the models specified
are as presented in table 4.2.1 above.
66
Table4.2.1: Summary on Stationarity Test
Variables T-Statistics ˃ ADF Critical Value 1% Statistics
T-Statistics ˃ Phillips-Perron Critical Value 1% Staistics I(d)
GDP -11.31 -12.66 I(1)GDPm -10.75 -10.74 I(1)E -17.29 -23.34 I(1)W -17.74 -13.48 I(1)T -14.88 -14.92 I(1)CP -8.82 -9.38 I(0)ED -23.34 -17.29 I(1)CO2 -7.41 -8.53 I(2)GNI -4.78 -4.78 I(1)FLD -6.38 -6.38 I(2)LF -16.05 -36.37 I(1)Source: Author’s Analysis.
The result from the augmented Dickey-Fuller and Phillips-Perron set of unit root
test of the data series shown above indicates that the series are not stationary (see
tables A1 to A22 on appendix A). In other words, they are integrated. VECM can
then be conveniently used to tie the short run and long run behaviour of the
variables in the specified model.
4.3 Presentation and Discussion of Model Estimates
4.3.1 Estimates for Environmental Infrastructure Economic Growth Link
This sub section begins with stability diagnostics of a model built with data over
1980 to 2013. The cumulative sum (CUSUM) procedure was followed. The
CUSUM test is based on the cumulative sum of the recursive residuals. It follows
the plots of the cumulative sum together with the 5% critical lines (See figure A1
67
in appendix A). Parameter of a model is said to be unstable if the cumulative sum
goes outside the area between the two critical lines (Brown et al, 1975)
The test conducted therefore suggests parameters of the model are stable at 5
percent significance over the period being investigated. This suggests that relying
on a model built with data from 1980 to 2013 would not be misleading.
Accordingly, the VEC estimation was carried out with sampled quarterly data from
1980 to 2013. We found that co-integrating vector (the long run relationship) has a
significant coefficient at 1 percent and that about 42 percent of a disequilibrium is
corrected within a quarter. The error correction term (ECT), which is the short run
relationship, took on expected sign and is statistically significant at 1 percent also.
This confirms that shot run equilibrating relationship exist in the VEC system and
that error correction mechanism does exist in the system. (See table C1 in appendix
C). Furthermore, the Johansen co-integration test confirmed two co-integrating
equation by the trace as well as the maximum Eigen value statistics at 5 percent.
This confirms that long run relationship does exist between selected environmental
infrastructure and level of output in the Nigerian economy (See table C2 in
appendix C).
The impulse response function indicates that the response of GDP to innovation
from itself is positive both in the short and long runs though insignificant. The
response of manufacturing GDP to shock from GDP for both short and long runs 68
was largely negative. In addition, the response of manufacturing GDP to
innovation from itself was positive but insignificant over short to long runs while
the response of GDP to manufacturing GDP was also positive but insignificant
over short to long runs. (See figure C1 in appendix C). This indicates that policy
did not trigger any significant feedback response between these variables.
Judging from the estimates on table C1 in appendix C, water infrastructure
dampened manufacturing sector growth significantly at 1 percent but the whole
economy’s growth insignificantly. In other words less and less public water
infrastructure was available for economic growth and this affected the
manufacturing sector most. This supports the available statistics that water
coverage is generally low in the country and most production units and even homes
have to bear additional cost of drilling boreholes to get water.
Transport infrastructure had positive significant impact at 1 percent level of
significance on both the manufacturing sector and the economy at large. A
percentage increase in transport infrastructure contributed about 0.4 percent to
manufacturing output growth and about 0.2 percent to the whole economy’s
growth. This might be a contribution too small, however, the relationship and its
significance point to the potential of the sector for economic growth in the country.
The whole gamut of the sector is yet to be fully harnessed since only the air and
road modes are being utilized as at now. This contribution however could not have
69
been without damage to the environment through carbon emission and or distortion
of the biodiversity in the process of roads and bridges constructions. Investing in
efficient transportation system will be a panacea for the adverse effect of carbon
emission on the environment.
Electricity infrastructure had positive significant impact at 5 percent on the
manufacturing sector but positive insignificant impact on the whole economy,
contributing about 1 percent to manufacturing output growth over the period
investigated. This is indeed very poor and accounted for the shutdown of many
firms which cannot afford the cost of running diesel engine to provide alternative
source of power which has the potential to take a negative toll on the environment
as it aggravates carbon emission. In any case, the positive sign taken on by
electricity infrastructure alluded to the potential it has to drive economic growth in
the country, as electricity (power) has been severally adjudged as a vital input for
quantum leap for any economy in terms of productivity gains by way of jobs and
wealth creation.
Education infrastructure was found to have had positive significant impact on the
growth of not only the manufacturing sector, but the economy at large at 1 percent
level of significance. However, its contribution to economic growth was very
small. This only affirms the opinion severally canvassed, that educational
infrastructure has decayed and that the sector has been underfunded in the country.
70
However, the positive relationship it has with economic growth in the country
portends hope if proper reforms and investment are put in place.
Labour force was found to have positive significant impact, at 1 percent, on both
the manufacturing sector and the economy at large, contributing about 34 and 9
percent respectively. This is revealing and suggests that the productivity of the
Nigerian work force, particularly those engaged in the real sector, is on the
increase.
Crude petroleum had negative significant impact on manufacturing sector growth
at 5 percent but positive significant impact on the economy’s growth though the
contribution was very small over the period investigated. This suggests two things.
First, the resource curse hypothesis and second, that the sector has the potential to
drive economic growth in the country. The required reforms and legal framework
which will position the industry to contribute to economic growth in line with the
potentials it has need to be urgently put in place. Moreover, the natural gas
resources of the country need to be harnessed to the benefit of the environment as
it holds the solution to tree felling for firewood which can exacerbate deforestation.
The wide spread use of liquefied petroleum gas (LPG) is all that need to be
encouraged.
In addition, the lag variables of manufacturing output severally had negative
significant impact on the growth of domestic output. This suggests that inventory 71
buildup in the manufacturing industry affected subsequent capacity utilization
which then slowed down economic growth. This possible inventory buildup might
be explained by low effective demand and or influx of imported goods in the
economy. This underscores the need for efficient exchange rate policy
management in the country.
The coefficient of determination R2 suggests that about 88 and 97 percent variation
in the economy’s and manufacturing outputs respectively can be attributed to the
environmental infrastructure variables. The Fisher’s (F) statistic indicates good fit
at 1 percent (See table C1 in appendix C).
Table 4.3.1: Summary of Causality Test Result
72
Variables Direction of causalityGdp↔Water FeedbackGdp↔Electricity FeedbackGdp↔Crude Petroleum FeedbackGdp↔Education FeedbackGdp↔Labour FeedbackGdp →Transport UnidirectionalGNI – CO2 IndependenceGNI↔FLD FeedbackCO2→Transport UnidirectionalCO2 − Education IndependenceCO2 − Crude Petroleum IndependenceCO2 − Electricity IndependenceFLD ← Transport UnidirectionalFLD − Education IndependenceFLD ← Crude UnidirectionalFLD − Electricity Independence
Source: Author’s Computation
The summary of the granger causality test results on tables A23 and A24 in
appendix A are presented here on table 4.3.1. This indicates that feedback causality
exist between GDP and each of the environmental infrastructure variables except
for transport infrastructure where causality runs from GDP to transport. The result
indeed corroborates the relationships obtained in the VEC output. Furthermore, in
respect of environmental quality and economic growth, causality test result shows
that feedback exist between the ratio of forest to total land area (FLD) and per
capita gross national income (GNI) while GNI and CO2 were independent.
This result is in consonance with the findings of (Perkins et al, 2005); (Vivien &
Nataliya, 2011) that infrastructure generally, is a key determinant of economic
growth in any economy and that growth itself raises the need to enlarge
infrastructural capacity in a country. In particular, the nature of causality observed,
suggest that the selected environmental infrastructure for this study have the
potential to propel economic growth in the Nigerian economy and the desire for
more growth would mean more investment in these infrastructure.
73
4.3.2 Estimates on Environmental Quality Economic Growth Link
The VEC estimation was carried out with the sampled annual data over 1990 to
2013. We found that co-integrating vector (the long run relationship) has a
significant coefficient at 1 percent. The error correction term (ECT) which is the
short run relationship, took on expected sign and is statistically significant. This
affirms that correction mechanism exists in the system and that the mechanism was
equilibrating (See table C5 on appendix C).
The parameter of per capita gross national income (GNI) was found to have
positive insignificant impact on the quality of the environment as measured by
CO2 but negative significant impact at 1 percent on forest as a ratio of total land
area. A percent increase in per capita GNI led to about 0.04 percent increase in
CO2 emission and 0.05 percent decline in ratio of forest to total land area. The lag
component of the VECM showed that the lags coefficient of CO2 ware
insignificant while that of FLD were severally statistically significant. This was
reinforced by a better fit for the FLD equation (See table C5 on appendix C)
Furthermore, the Johansen co-integration test confirmes two co-integrating
equation by the trace as well as the maximum Eigen value statistics at 1 percent.
This suggests that co integrating relationships do exist between environmental
quality and per capita GNI in the Nigerian economy (See table C6 on appendix C).
74
The Impulse Response Function cannot give a clear picture about the responses of
the endogenous variables to innovations since the AR roots polynomial indicates
that at least one moduli lie outside the unit circle.
This result shows that economic growth has had mild adverse impact on the
environment in Nigeria. The figures above indicate that CO2 and per capita GNI
relationship for Nigeria is fairly given by an inverted u shape while the relationship
between forest as a percentage of total land area and per capita GNI is taking an L
shape. This is in agreement with the finding by Panayotou (1993), suggesting that
economic growth has had adverse impact on the environment as the size of the
forest in the country is declining with increase in per capita GNI. On the other
hand, CO2 emission initially rose with increase in GNI but subsequently declined
on the average with rising per capita income for most of the period investigated,
meaning that CO2 emission has had no significant adverse effect on the
environment over the period investigated as supported by the VEC result. The
result in respect of environmental quality economic growth nexus for Nigeria is
therefore mixed for the proxies used. The confirmation of EKC for an economy
actually depends on the proxy employed to measure environmental quality and the
role of such proxy in the growth of the economy (Shafik, 1994).
75
Figure 4.1: CO2 and GNI Relationship in Nigeria 1990 – 2013 with CO2 as a Measure of
Environmental Quality.
Source: Author’s Analysis
Figure 4.2: Forest and GNI Relationship in Nigeria 1990 – 2013 with FLD as a Measure
of Environmental Quality.
Source: Author’s Analysis
4.3.3 Estimates for Environmental quality on Environmental Infrastructure
The VEC estimation was carried out with sampled annual data over 1990 to 2013.
We found that co-integrating vector (the long-run relationship) has a significant
76
coefficient at 1 percent. The error correction term (ECT) which is the short-run
relationship, took on expected sign and is statistically significant. This affirms that
correction mechanism exists in the system and that the mechanism was
equilibrating (See table C7 on appendix C).
The coefficient of transport infrastructure took on positive sign for both carbon
emission (CO2) and percentage of forest out of total land area (FLD), it was
however only statistically significant with respect to FLD. The a priori was met
for CO2 but not for FLD.
The coefficient of electricity infrastructure took on negative sign for CO2 but
positive for FLD meeting a priori in both cases, it was statistically significant at 1
percent for CO2 but not for FLD. The coefficient of crude petroleum and natural
gas (CP), took on positive sign with respect to CO2 but negative sign with respect
to FLD and was statistically insignificant in both cases. In addition, education took
on negative sign and was statistically insignificant.
The coefficient of determination R2 suggests that about 62 and 95 percent variation
in CO2 and FLD respectively can be attributed to the environmental infrastructure
variables. The Fisher’s (F) statistic indicates good fit at 1 percent for FLD equation
while the CO2 equation had a poor fit. It appears FLD is a better proxy for the
environment than CO2.
77
Furthermore, the Johansen co-integration test confirmed one co-integrating
equation by the trace as well as the maximum Eigen value statistics at 1 percent.
This suggests that co integrating relationships do exist between environmental
quality and the included environmental infrastructure in the Nigerian economy
(See table C8 on appendix C).
The Impulse Response Function cannot give a clear picture about the responses of
the endogenous variables to innovations since the AR roots polynomial indicates
that at least one moduli lie outside the unit circle.
The result indicates that environmental infrastructure has bearing on environmental
quality in one way or the other. For instance, the sign taken on by transport
infrastructure suggests that it exacerbated carbon emission though not significantly
and also had a drag on forest size in the country. In addition, the sign taken on by
electricity infrastructure and its significance is instructive. If the country can attain
required power generation, it will greatly dampen carbon emission in the country.
As it is, crude petroleum and natural gas infrastructure had the tendency to worsen
the state of carbon emission, though it has not done that significantly. There is the
need for the country to pursue an optimal mix of energy sources. The non-full
utilization of the natural gas endowment by the country and particularly for
domestic cooking as well as the effect of crude oil exploration on the environment, 78
might be a reason why the forest size is seen to be declining with rising investment
in crude petroleum and natural gas infrastructure in the country. Though not
significantly, the notion that as people get to know the importance of clean and
safe environment, they will demand for it, is confirmed by this study with respect
to carbon emission but not for forest size.
4.4 Test of HypothesesThe hypotheses for the study are:
H01: Environmental infrastructure has no significant impact on economic growth in
Nigeria.
H02: Environmental infrastructure” has no significant impact on environmental
quality in Nigeria.
H03: Economic growth does not impact on environmental quality in Nigeria.
H04: There is unidirectional causality which runs from environmental
infrastructure to economic growth in Nigeria.
Starting with the first hypothesis, given 120 observations and theoretical t-statistic
of 2.576 and 1.96 for 1 and 5 percent significance level respectively, we concluded
based on the result from the study (table C1 on appendix C) that environmental
infrastructure had significant impact on economic growth in Nigeria.
79
On the test of the second hypothesis, (table C7 appendix C) given 18 observations
and theoretical t-statistic of 2.878 and 2.101 for 1 and 5 percent significance level
respectively, it is concluded that environmental infrastructure, particularly
transport and electricity infrastructure had significant impact at 1 percent and 5
percent respectively on environmental quality in Nigeria.
Regarding the test of the third hypothesis, (table C5 appendix C) given 18
observations and theoretical t-statistic of 2.878 and 2.101 for 1 and 5 percent
significance level respectively, it is concluded that economic growth had to a
certain degree, impacted the quality of the environment in Nigeria.
On the test of the fourth hypothesis (table A23 and A24), judging from low
probability values for the fisher’s statistic, we concluded that feedback causality
existed between economic growth and each of the environmental infrastructure
variables except for transport infrastructure where causality ran from growth of
GDP to transport.
4.5 Major FindingsThe major findings from this study are as follows:
i) Water infrastructure has negative significant impact on manufacturing
sector growth at 1 percent but insignificant impact on the whole
economy’s growth.
80
ii) Transport infrastructure has positive significant impact at 1 percent level
of significance on both the manufacturing sector and the economy at
large.
iii) Electricity infrastructure has positive significant impact at 5 percent on
the manufacturing sector but positive insignificant impact on the whole
economy.
iv) Education infrastructure has positive significant impact on the growth of
not only the manufacturing sector, but the economy at large at 1 percent
level of significance.
v) The labour force has positive significant impact, at 1 percent, on both the
manufacturing sector and the economy at large
vi) Crude petroleum has negative significant impact on manufacturing sector
growth at 10 percent but positive significant impact on the economy’s
growth at 1 percent level of significance
vii) Per capita gross national income (GNI) has positive insignificant impact
on the quality of the environment as measured by CO2.
viii) Per capita gross national income (GNI) has negative significant impact at
1 percent level of significance on forest as a ratio of total land area.
ix) Transport and electricity infrastructure has significant impact at 1 percent
and 5 percent respectively on environmental quality in Nigeria.
81
x) Feedback causality existed between economic growth and each of the
environmental infrastructure variables except for transport infrastructure
where causality ran from growth of GDP to transport.
4.6 Policy ImplicationsThe results obtained from this study indeed provide some suggestions for policy
formulation in the Nigerian economy. First, it is reaffirmed here that environmental
infrastructure investment is essential for checkmating the adverse effect of
economic growth on the environment.
The seeming evidence in support of the existence of EKC in Nigeria calls for
urgent actions through policies and advocacies so that the country do not find itself
in a precarious situation at least as it relates to the quality of air in Nigeria as a
result of CO2 emission and shrinking forest reserves. A symbiotic relationship
exists between the forest and CO2 and it is on this basis that tree planting is
globally encouraged. However, the poor and those on low income depend largely
on the trees for fuel; this is a possible explanation for dwindling forest reserve of
the country. Policy will have to be fashioned to explore the vast potential the
country has in gas production and to encourage more use of liquefied natural gas as
source of fuel for the majority of Nigerian homes. Particularly, given the current
state of the selected environmental infrastructure and the link they have with
environmental issues, it becomes very important to fashion policies that would
explore the linkages for a better environment for all. 82
Furthermore, policy option exists for the government via promotion of investment
in infrastructure that can lead to a qualitative and safe environment for all. This
area includes holistic and efficient transport and energy policies in line with their
effect on the environment.
Second, the long run equilibrium relationship between the selected environmental
infrastructure and economic growth in the economy implies that policies can be
fashioned to gradually, through cohesive planning, move from the present state of
deficit of these environmental infrastructures to the desired state.
Third, the causations between the various environmental infrastructures are also
instructive. Policy makers can fashion out policies in a sector, for instance, with
the view of using such a sector as bait for productivity gain in other sectors of the
economy.
83
CHAPTER FIVE
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
5.1 Summary of Major FindingsThis study was embarked upon to investigate the nexus between environmental
infrastructure and economic growth in Nigeria. The main thrust of the problem
statement was inadequacy of certain infrastructure that can mitigate environmental
challenges that are and or would be due to the pursuit of economic growth in a
country or region. A number of research issues were raised and hypotheses were
formulated based on such issues.
The literature was perused to feel the pulse of earlier studies related to ours and to
have deep understanding of the area being researched. Accordingly, and relying on
the neoclassical theoretical framework in respect of the production function, which
adduces key roles to capital and labour as factors of production, we specified
84
model for the Nigerian economy within the scope of this study using quarterly time
series for the economy.
The summaries of findings for this study are as follows:
i. Environmental infrastructure has impact on economic growth in Nigeria.
Those environmental infrastructure which have economic undertone, like
transport infrastructure and which can have adverse effect on the
environment if the modes are not in optimally efficient mix, has
significant positive impact on the economy. Crude petroleum and natural
gas infrastructure which has economic undertone has negative significant
impact on manufacturing sector growth but positive significant impact on
the economy as a whole, though its contribution was very low over the
period covered. Electricity (power), also an economic environmental
infrastructure has positive significant impact on manufacturing output
growth but did not significantly impact the economy as a whole.
ii. Those environmental infrastructures which have social undertone and
which also can have implication for the environment, such as water has
significant negative impact on manufacturing sector growth but
insignificant negative impact on the economy as a whole. Education
infrastructure has positive significant impact on the economic growth
over the period investigated.
85
iii. The study also confirms mixed evidence in support of adverse impact of
economic growth on the quality of the environment in Nigeria. Per capita
gross national income had positive though insignificant impact on carbon
emission confirming the position in the EKC literature for developing
countries. Per capita gross national income however has significant
negative impact on the ratio of forest to total land area.
iv. The study also found evidence for link between environmental
infrastructure and quality of the environmental in Nigeria. Transport and
electricity infrastructure had significant impact at 1 percent and 5 percent
respectively on environmental quality as measured by CO2 and FLD in
Nigeria.
v. The study found feedback causality between GDP and each of the
environmental infrastructure variables except for transport infrastructure
where causality runs from GDP to transport. However, causality could
not be established between CO2 emission and per capital GNI but there
exist two way causality between percentage of forest out of total land
area and per capital GNI in the country.
vi. The study found that error correcting mechanism and co-integration do
exist between environmental infrastructure investment and economic
growth in Nigeria. This implies that the short run and the long run
86
relationship between environmental infrastructure investment and
economic growth in Nigeria are tied-up
5.2 ConclusionsThe following conclusions were reached based on the findings above.
i. Environmental infrastructure has the potential to promote economic
growth but such potential has not been adequately harnessed to guarantee
better environment for all.
ii. There is a fair evidence to suggest that environmental quality has been
adversely affected by the pursuit of economic growth in Nigeria.
iii. Environmental infrastructure, particularly transport and electricity had
has impacted the quality of the environment in the country. While
transport infrastructure drags up forest size, electricity infrastructure
dampened carbon emission.
iv. Feedback mechanism does exist between environmental infrastructure
and output in the Nigerian economy.
v. It is also concluded that the size of the forest has decline in relation to
rising per capita gross national income in the country but our finding
does not suggest that carbon emission has had significant adverse impact
on the environment in Nigeria.
87
vi. Short run disequilibrium between investment in environmental
infrastructure and economic growth in Nigeria, can be corrected
gradually on the long run.
5.3 RecommendationsThe following are our recommendations based on the conclusions above:
i. It is recommended that the current reform and investment in the transport
sector as it relates to the aviation industry be broadened to extend to other
modes of transportation. Particularly, the rail subsector needs to be
revamped so as to optimize the potential and gains from this sector of the
economy. Given the relationship between vehicular movements and
carbon emission, environmentally friendly transport technologies should
be greatly encouraged. Moreover, it is important to have an appropriate
mix of the various possible mode of transport in the country with the
view of minimising carbon emission.
ii. The finding from this study concerning water and its impact on economic
growth suggests that much still needed to be done in terms of water
infrastructure in the country. The government should partner with
relevant international agencies and non-governmental organizations to
work towards meeting the need for water in Nigeria. Access to water in
terms of quality and quantity sure has implication for health condition
anywhere in the world. 88
iii. Power (electricity) has and will always play important role in any
economy desiring growth. Nigeria cannot be an exception if it is to attain
the feat of becoming one of the top-most twenty economies by the year
2020. There is need for focused investment in the power sector and raise
electricity generation from the current capacity to the level that can raise
productivity in the economy and attract right kinds of investments in to
the country. This will certainly promote job opportunities and wealth
creation in the economy. The instability of power (electricity) sure has
implication for the environment as the situation in which industries and
homes rely on generators for power has posed great danger for the
environment and eventually on plants and animals as well as the
sustainability of man’s development efforts.
iv. Crude petroleum and natural gas is one nature endowment that ought to
be a blessing to this country, but it has not, in real terms, lived up to this
bidden. Proper legal framework and domiciliation of crude refining in the
country would be a wise option in order for the sector to contribute to
economic growth optimally in the country. The natural gas aspect of the
petroleum sector needs to be harnessed and directed to mitigate
desertification as a result of tree felling for cooking fuel. The use of
liquefied petroleum gas (LPG) need to be encouraged as it is cleaner (for
the environment), healthier and cheaper.
89
v. The government should look into the feedback mechanism existing
between GDP and the selected environmental infrastructure in the
economy and apply policies accordingly so as to use them to drive
growth in the entire economy. For instance, the feedback between crude
petroleum/natural gas and gross domestic output can be exploited for
greater productivity in the economy. Same thing applies to electricity,
water and education infrastructure.
vi. Furthermore, since it is not economically and technically feasible to
attain the desired state of environmental infrastructure within the short
run, the relevant government agencies need to take planning more
seriously, optimize the available resources now and identify paths to
follow at getting to the desired state of environmental infrastructure in
the economy.
vii. Government agencies responsible for monitoring environmental
standards and compliance for firms and other production units need to be
on the watch. Though the data employed showed that rising per capita
GNI has not significantly dragged carbon emission along, it has however
led to the shrinking of the forest area in the country. The environmental
standard regulation agencies should take steps at enforcing existing
legislations and champion new ones, for a better environment, capable of
sustaining development in the country.
90
viii. Moreover, government should allocate more resources to the
environment since environmental quality is largely a public good.
Investment in environmental infrastructure would generate positive
externalities on public health, environmental quality and economic
growth potential.
5.4 Contribution to KnowledgeThis thesis investigated the nexus between environmental infrastructure and
economic growth in Nigeria. We sought to know from the strength of the data
available to us whether environmental infrastructure have promoted economic
growth in Nigeria over the period considered and also whether this economic
growth has had impact on the quality of the environment.
This research contributed to knowledge in the area of empirics by updating
research and the literature in this specialty.
The study also re-echoes the intrinsic implications of the selected infrastructure for
the Nigerian environment in particular and the world in general.
In addition, the research findings of this thesis corroborated earlier findings with
respect to the role of infrastructure in promoting economic growth (Canning &
Pedroni, 1999; Wolassa, 2012) as well as the implications of economic growth on
the quality of the environment.
91
5.5 Study Limitations and Suggestion for Further ResearchObviously, this study suffers from a number of limitations. One of which is the
proxy used for environmental infrastructure investment, that is, outputs from those
sectors perceived to have capacity for impacting on the environment. For instance,
previous studies on infrastructure and economic growth have used proxy like
kilometers of paved roads to measure transports infrastructure, it has been argued
that they do not represent actual investment in those infrastructure. The same thing
applies to our environmental infrastructures proxies in this study. They can only
give a faint picture of what is happening in real situation and therefore may not
give the true picture of environmental infrastructures having abating effect on the
environment and promoting growth in the economy. In addition, the challenge of
data paucity is one that every researcher has to acknowledge particularly in a
country like ours where culture of meticulous data keeping is not yet deepened.
This could have inflicted some elements of biases on the findings from our study;
hence they have to be taken along with some other considerations.
There is the need for funded microeconomic studies in the area of how production,
consumption, settlement and demographic patterns have affected the physical
environment and consequently, humans, animals and plants. This will shed light on
the whole gamut of economic and social dimensions of the nexus between
economic growth and the environment. In addition, there is need for environmental
92
agencies to put in place strategies at collecting environmental statistics so as to
further enhance quality research in this field.
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AppendixA: Unit Root, Causality and Stability Tests
Table A1: Phillips Perron (PP) Unit Root Test on GdpNull hypothesis: d(GDP) has a unit rootExogenous: constant, linear trendBandwidth: 7 (newsy-west automatic) using Bartlett kernel
Adj. T-stat prob.*
Phillips-perron test statistic -12.65622 0.0000Test critical values: 1% level -4.028496
5% level -3.44396110% level -3.146755
*Mackinnon (1996) one-sided p-values.Source: Author’s Analysis
Table A2: ADF Unit Root Test on Gdp
Null hypothesis: d(GDP) has a unit rootExogenous: constant, linear trendLag length: 0 (automatic - based on sic, maxlag=0)
T-statistic prob.*
Augmented dickey-fuller test statistic -11.30708 0.0000Test critical values: 1% level -4.028496
5% level -3.44396110% level -3.146755
*Mackinnon (1996) one-sided p-values.Source: Author’s Analysis
Table A3: PP Unit Root Test on GdpmNull hypothesis: d(gdpm) has a unit rootExogenous: constant, linear trendBandwidth: 5 (newey-west automatic) using Bartlett kernel
Adj. T-stat prob.*
Phillips-perron test statistic -10.74689 0.0000Test critical values: 1% level -4.028496
5% level -3.44396110% level -3.146755
Source: Author’s Analysis
100
Table A4: ADF Unit Root Test on GdpmNull hypothesis: d(gdpm) has a unit rootExogenous: constant, linear trendLag length: 0 (automatic - based on sic, maxlag=0)
T-statistic prob.*
Augmented dickey-fuller test statistic -10.75311 0.0000Test critical values: 1% level -4.028496
5% level -3.44396110% level -3.146755
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
Table A5: ADF Unit Root Test on ElectricityNull hypothesis: d(e) has a unit rootExogenous: constant, linear trendLag length: 0 (automatic - based on sic, maxlag=0)
T-statistic prob.*
Augmented dickey-fuller test statistic -17.29633 0.0000Test critical values: 1% level -4.028496
5% level -3.44396110% level -3.146755
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
Table A6: PP Unit Root Test on ElectricityNull hypothesis: d(e) has a unit rootExogenous: constant, linear trendBandwidth: 7 (newey-west automatic) using bartlett kernel
Adj. T-stat prob.*
Phillips-perron test statistic -23.33746 0.0000Test critical values: 1% level -4.028496
5% level -3.44396110% level -3.146755
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
101
Table A7: PP Unit Root Test on WaterNull hypothesis: d(w) has a unit rootExogenous: constant, linear trendBandwidth: 12 (newey-west automatic) using bartlett kernel
Adj. T-stat prob.*
Phillips-perron test statistic -17.74830 0.0000Test critical values: 1% level -4.028496
5% level -3.44396110% level -3.146755
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
Table A8: ADF Unit Root Test on WaterNull hypothesis: d(w) has a unit rootExogenous: constant, linear trendLag length: 0 (automatic - based on sic, maxlag=0)
T-statistic prob.*
Augmented dickey-fuller test statistic -13.48293 0.0000Test critical values: 1% level -4.028496
5% level -3.44396110% level -3.146755
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
Table A9: ADF Unit Root Test on TransportNull hypothesis: d(t) has a unit rootExogenous: constant, linear trendLag length: 0 (automatic - based on sic, maxlag=0)
T-statistic prob.*
Augmented dickey-fuller test statistic -14.87704 0.0000Test critical values: 1% level -4.028496
5% level -3.44396110% level -3.146755
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
102
Table A10: PP Unit Root Test on TransportNull hypothesis: d(t) has a unit rootExogenous: constant, linear trendBandwidth: 5 (newey-west automatic) using bartlett kernel
Adj. T-stat prob.*
Phillips-perron test statistic -14.92693 0.0000Test critical values: 1% level -4.028496
5% level -3.44396110% level -3.146755
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
Table A11: ADF Unit Root Test on Crude Petroleum and Natural GasNull hypothesis: cp has a unit rootExogenous: constant, linear trendLag length: 0 (automatic - based on sic, maxlag=0)
T-statistic prob.*
Augmented dickey-fuller test statistic -8.823556 0.0000Test critical values: 1% level -4.027959
5% level -3.44370410% level -3.146604
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
Table A12: PP Unit Root Test on Crude Petroleum and Natural GasNull hypothesis: cp has a unit rootExogenous: constant, linear trendBandwidth: 5 (newey-west automatic) using bartlett kernel
Adj. T-stat prob.*
Phillips-perron test statistic -9.384987 0.0000Test critical values: 1% level -4.027959
5% level -3.44370410% level -3.146604
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
103
Table A13: PP Unit Root Test on EducationNull hypothesis: d(ed) has a unit rootExogenous: constant, linear trendBandwidth: 7 (newey-west automatic) using bartlett kernel
Adj. T-stat prob.*
Phillips-perron test statistic -23.33746 0.0000Test critical values: 1% level -4.028496
5% level -3.44396110% level -3.146755
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
Table A14: ADF Unit Root Test on EducationNull hypothesis: d(ed) has a unit rootExogenous: constant, linear trendLag length: 0 (automatic - based on sic, maxlag=0)
T-statistic prob.*
Augmented dickey-fuller test statistic -17.29633 0.0000Test critical values: 1% level -4.028496
5% level -3.44396110% level -3.146755
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
Table A15: ADF Unit Root Test on CO2Null hypothesis: d(co2,2) has a unit rootExogenous: constant, linear trendLag length: 0 (automatic - based on sic, maxlag=0)
T-statistic prob.*
Augmented dickey-fuller test statistic -7.408900 0.0000Test critical values: 1% level -4.467895
5% level -3.64496310% level -3.261452
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
104
Table A16: PP Unit Root Test on CO2Null hypothesis: d(co2,2) has a unit rootExogenous: constant, linear trendBandwidth: 5 (newey-west automatic) using bartlett kernel
Adj. T-stat prob.*
Phillips-perron test statistic -8.531508 0.0000Test critical values: 1% level -4.467895
5% level -3.64496310% level -3.261452
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
Table A17: ADF Unit Root Test on GNINull hypothesis: d(gni) has a unit rootExogenous: constant, linear trendLag length: 0 (automatic - based on sic, maxlag=0)
T-statistic prob.*
Augmented dickey-fuller test statistic -4.786555 0.0049Test critical values: 1% level -4.440739
5% level -3.63289610% level -3.254671
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
Table A18: PP Unit Root Test on GNINull hypothesis: d(gni) has a unit rootExogenous: constant, linear trendBandwidth: 2 (newey-west automatic) using bartlett kernel
Adj. T-stat prob.*
Phillips-perron test statistic -4.785914 0.0049Test critical values: 1% level -4.440739
5% level -3.63289610% level -3.254671
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
105
Table A19: ADF Unit Root Test on FLDNull hypothesis: d(forland,2) has a unit rootExogenous: constant, linear trendLag length: 0 (automatic - based on sic, maxlag=0)
T-statistic prob.*
Augmented dickey-fuller test statistic -6.379697 0.0002Test critical values: 1% level -4.467895
5% level -3.64496310% level -3.261452
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
Table A20: PP Unit Root Test on FLD
Null hypothesis: d(forland,2) has a unit rootExogenous: constant, linear trendBandwidth: 0 (newey-west automatic) using bartlett kernel
Adj. T-stat prob.*
Phillips-perron test statistic -6.379697 0.0002Test critical values: 1% level -4.467895
5% level -3.64496310% level -3.261452
*mackinnon (1996) one-sided p-values.Source: Author’s Analysis
Table A21: ADF Unit Root Test on Labour ForceNull hypothesis: d(lb) has a unit root
Exogenous: constant, linear trendLag length: 0 (automatic - based on sic, maxlag=0)
T-statistic prob.*
Augmented dickey-fuller test statistic -16.05049 0.0000Test critical values: 1% level -4.028496
5% level -3.44396110% level -3.146755
*mackinnon (1996) one-sided p-values.Source: Author’s
Analysis
106
Table A22: PP Unit Root Test on Labour Force
Null hypothesis: d(pop) has a unit rootExogenous: constant, linear trendBandwidth: 14 (newey-west automatic) using bartlett kernel
Adj. T-stat prob.*
Phillips-perron test statistic -36.37191 0.0001Test critical values: 1% level -4.028496
5% level -3.44396110% level -3.146755
*mackinnon (1996) one-sided p-values.
Source: Author’s Analysis
Table A23: Pairwise Granger Causality Test On GNI, Co2 And FLDDate: 06/27/15 time: 14:18Sample: 1990 2013Lags: 2
null hypothesis: Obs F-statistic Prob.
gni does not granger cause co2 22 0.30603 0.7403 co2 does not granger cause gni 0.32971 0.7236
forland does not granger cause co2 22 1.60079 0.2307 co2 does not granger cause forland 0.27854 0.7603
forland does not granger cause gni 22 9.69400 0.0016 gni does not granger cause forland 26.3569 6.e-06
Source: Author’s Analysis
107
Table A24: Pairwise Granger Causality Tests on Environmental Infrastructure Variables and GDP.
Date: 09/07/15 time: 02:51Sample: 1980q1 2013q3Lags: 2
null hypothesis: Obs F-statistic Prob.
LF does not granger cause gdp 133 4.08278 0.0191 gdp does not granger cause LF 7.00111 0.0013
e does not granger cause gdp 133 11.7720 2.e-05 gdp does not granger cause e 22.0436 6.e-09
cp does not granger cause gdp 133 6.72860 0.0017 gdp does not granger cause cp 56.9632 2.e-18
edu does not granger cause gdp 133 4.33850 0.0150 gdp does not granger cause edu 13.2949 6.e-06
t does not granger cause gdp 133 1.80882 0.1680 gdp does not granger cause t 3.16472 0.0455
w does not granger cause gdp 133 54.0066 1.e-17 gdp does not granger cause w 68.0034 8.e-21
Source: author’s analysis
Table A25: Stability Diagnostics on Environmental Infrastructure Model
Dependent variable: d(gdp)
Method: least squares
Date: 09/06/15 time: 05:48
Sample (adjusted): 1980q2 2013q3
Included observations: 134 after adjustments
Variable Coefficient Std. Error T-statistic Prob.
C 117642.4 28899.85 4.070691 0.0001
D(w) 1726.022 405.0123 4.261654 0.0000
D(t) 5.545041 1.582591 3.503773 0.0006
Cp 0.009165 0.011347 0.807681 0.4208
108
D(edu) 3.630012 1.062043 3.417952 0.0008
D(pop) -0.362691 0.082330 -4.405315 0.0000
D(e) -14.84226 22.82566 -0.650244 0.5167
R-squared 0.288789 mean dependent var 84492.28
Adjusted r-squared 0.255188 s.d. dependent var 310953.8
S.e. of regression 268360.8 akaike info criterion 27.88888
Sum squared resid 9.15e+12 schwarz criterion 28.04026
Log likelihood -1861.555 hannan-quinncriter. 27.95039
F-statistic 8.594770 durbin-watson stat 2.030131
Prob(f-statistic) 0.000000
Source: Author’s Analysis
Figure A1: CUSUM Graph Source: Author’s Analysis
109
Appendix B: Preliminary Analysis
Table B1: Test for Equality of Means between Series
Date: 10/20/15 Time: 21:28Sample: 1980q1 2013q3Included observations: 135
Method Df Value Probability
Anova f-test (7, 1072) 1421.864 0.0000Welch f-test* (7, 402.191) 342.6346 0.0000
*test allows for unequal cell variances
Analysis of variance
Source of variation Df Sum of sq. Mean sq.
Between 7 9.54e+16 1.36e+16Within 1072 1.03e+16 9.59e+12
Total 1079 1.06e+17 9.80e+13
Category statistics
Std. Err.Variable Count Mean Std. Dev. Of mean
Cp 135 1059372. 2231029. 192016.4E 135 4610.083 6968.436 599.7475
Edu 135 33662.22 91253.56 7853.856T 135 36962.77 47813.11 4115.097W 135 276.9941 280.5160 24.14298
Gdp 135 2220393. 3191894. 274714.5Gdpm 135 1093049. 3009099. 258982.0Pop 135 28968809 7243569. 623427.1All 1080 4177142. 9897672. 301176.6
Source: Author’s Analysis
110
Table B2: Correlation Matrix
CP E EDU T W GDP GDPM LFCP 1.0000 0.7680 0.7256 0.4773 0.7159 0.7669 0.7039 0.655362E 0.7680 1.0000 0.8211 0.6562 0.9557 0.9872 0.8130 0.881621
EDU 0.7256 0.8211 1.0000 0.2497 0.8206 0.8332 0.9830 0.631381T 0.4773 0.6562 0.2497 1.0000 0.5903 0.6770 0.2806 0.756742W 0.7159 0.9557 0.8206 0.5903 1.0000 0.9651 0.8198 0.912508
GDP 0.7669 0.9872 0.8332 0.6770 0.9651 1.0000 0.8354 0.894838GDPM 0.7039 0.8130 0.9830 0.2806 0.8198 0.8354 1.0000 0.626853
LF 0.6553 0.8816 0.6313 0.7567 0.9125 0.8948 0.6268 1.000000Source: Author’s Analysis
111
Appendix C: Vector Error Correction Estimate
Table C1: Vector Error Correction Estimates for Environmental Infrastructure Economic Growth link
vector error correction estimates date: 10/27/15 time: 13:30 sample (adjusted): 1984q1 2013q4 included observations: 120 after adjustments standard errors in ( ) & t-statistics in [ ]
Cointegratingeq: Cointeq1
Gdp(-1) 1.000000
Gdpm(-1) -0.415262 (0.15129)
[-2.74475]***
C -3.877811
Error correction: D(gdp) D(gdpm)
Cointeq1 -0.242827 0.196176 (0.05218) (0.11669)
[-4.65353]*** [ 1.68114]*
D(gdp(-1)) -0.053041 0.079468 (0.08950) (0.20014)[-0.59266] [ 0.39706]
D(gdp(-2)) -0.064076 0.039834 (0.09011) (0.20151)[-0.71109] [ 0.19768]
D(gdp(-3)) 0.037524 0.438914 (0.09036) (0.20207)[ 0.41528] [ 2.17211]**
D(gdp(-4)) 0.073508 -0.424937 (0.09905) (0.22151)[ 0.74211] [-1.91837]*
D(gdp(-5)) 0.028957 -0.011498 (0.09330) (0.20864)[ 0.31037] [-0.05511]
D(gdp(-6)) 0.018963 0.141723 (0.09355) (0.20921)[ 0.20270] [ 0.67741]
D(gdp(-7)) 0.063155 0.259244 (0.09418) (0.21060)[ 0.67060] [ 1.23096]
D(gdp(-8)) -0.044297 -0.601320 (0.10347) (0.23138)[-0.42813] [-2.59887]**
112
D(gdp(-9)) 0.027881 -0.161758 (0.09325) (0.20853)[ 0.29900] [-0.77571]
D(gdp(-10)) 0.078580 0.294832 (0.09383) (0.20982)[ 0.83752] [ 1.40517]
D(gdp(-11)) 0.050555 0.243168 (0.09451) (0.21135)[ 0.53491] [ 1.15052]
D(gdp(-12)) 0.114752 -0.348094 (0.09582) (0.21427)[ 1.19761] [-1.62453]
D(gdp(-13)) 0.030269 -0.054342 (0.09149) (0.20461)[ 0.33083] [-0.26560]
D(gdp(-14)) 0.053647 0.346143 (0.09109) (0.20370)[ 0.58894] [ 1.69926]*
D(gdp(-15)) -0.009487 0.106675 (0.09102) (0.20354)[-0.10423] [ 0.52409]
D(gdpm(-1)) -0.118537 -0.161186 (0.03252) (0.07272)
[-3.64512]*** [-2.21646]**
D(gdpm(-2)) -0.096058 0.064548 (0.02940) (0.06574)
[-3.26756]*** [ 0.98186]
D(gdpm(-3)) -0.094441 -0.014773 (0.02913) (0.06515)
[-3.24170]*** [-0.22675]
D(gdpm(-4)) -0.113772 0.119734 (0.02779) (0.06214)
[-4.09428]*** [ 1.92679]*
D(gdpm(-5)) -0.077466 0.009242 (0.02860) (0.06396)
[-2.70829]** [ 0.14449]
D(gdpm(-6)) -0.154244 -0.127895 (0.03661) (0.08186)
[-4.21362]*** [-1.56233]
D(gdpm(-7)) -0.141134 -0.384084 (0.03175) (0.07099)
[-4.44581]*** [-5.41026]***
D(gdpm(-8)) 0.163404 1.165322 (0.06268) (0.14017)
113
[ 2.60692]** [ 8.31354]***
D(gdpm(-9)) -0.070985 0.110104 (0.03004) (0.06717)
[-2.36325]** [ 1.63916]*
D(gdpm(-10)) -0.100666 -0.124953 (0.02851) (0.06376)
[-3.53049]*** [-1.95962]**
D(gdpm(-11)) -0.145471 -0.216503 (0.03756) (0.08399)
[-3.87323]*** [-2.57771]**
D(gdpm(-12)) -0.157754 0.254203 (0.02985) (0.06676)
[-5.28442]*** [ 3.80778]***
D(gdpm(-13)) -0.091584 0.025840 (0.02915) (0.06518)
[-3.14234]*** [ 0.39647]
D(gdpm(-14)) -0.118271 -0.257826 (0.02865) (0.06406)
[-4.12858]*** [-4.02458]***
D(gdpm(-15)) -0.028761 0.249214 (0.03097) (0.06926)[-0.92870] [ 3.59839]***
C -0.758800 0.377838 (0.16143) (0.36101)
[-4.70041]*** [ 1.04662]
D(w) -3.73e-07 -3.52e-05 (2.0e-06) (4.5e-06)[-0.18743] [-7.90211]***
Dlog(t) 0.159350 0.400123 (0.03352) (0.07495)
[ 4.75450]*** [ 5.33851]***
Dlog(e) 0.004333 0.096030 (0.01591) (0.03559)[ 0.27225] [ 2.69833]**
D(edu) 5.64e-07 9.73e-06 (2.1e-07) (4.6e-07)
[ 2.74099]** [ 21.1465]***
Dlog(lf) 8.818518 33.89681 (2.01975) (4.51673)
[ 4.36614]*** [ 7.50473]***
Log(cp) 0.061906 -0.052478 (0.01366) (0.03056)
[ 4.53054]*** [-1.71740]*
r-squared 0.883096 0.977081
114
adj. R-squared 0.830346 0.966739 sum sq. Resids 0.121847 0.609349 s.e. equation 0.038548 0.086204 f-statistic 16.74128 94.48124 log likelihood 243.2764 146.6988 akaikeaic -3.421273 -1.811646 schwarzsc -2.538568 -0.928940 mean dependent 0.031621 0.029419 s.d. dependent 0.093587 0.472673
determinantresid covariance (dof adj.) 1.09e-05 determinant resid covariance 5.08e-06 log likelihood 390.8848 akaike information criterion -5.214746 schwarz criterion -3.402876
Source: Author’s Analysis
Table C2: Johansen Co-Integration for Environmental Infrastructure Economic Growth Link
Date: 10/27/15 time: 13:38Sample (adjusted): 1984q1 2013q4Included observations: 120 after adjustmentsTrend assumption: linear deterministic trendSeries: gdpgdpmExogenous series: d(w) dlog(t) dlog(e) d(edu) dlog(lf) log(cp)Warning: critical values assume no exogenous seriesLags interval (in first differences): 1 to 15
Unrestricted cointegration rank test (trace)
Hypothesized Trace 0.05No. Of ce(s) Eigenvalue Statistic Critical value Prob.**
None * 0.246351 47.21945 15.49471 0.0000At most 1 * 0.104763 13.27998 3.841466 0.0003
trace test indicates 2 cointegratingeqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **mackinnon-haug-michelis (1999) p-values
Unrestricted cointegration rank test (maximum eigenvalue)
Hypothesized Max-eigen 0.05No. Of ce(s) Eigenvalue Statistic Critical value Prob.**
None * 0.246351 33.93947 14.26460 0.0000At most 1 * 0.104763 13.27998 3.841466 0.0003
max-eigenvalue test indicates 2 cointegratingeqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **mackinnon-haug-michelis (1999) p-values
unrestricted cointegrating coefficients (normalized by b'*s11*b=i):
Gdp Gdpm-14.82880 6.157832
115
17.69329 -25.42994
unrestricted adjustment coefficients (alpha):
D(gdp) 0.016375 0.004520D(gdpm) -0.013229 0.021815
1 cointegrating equation(s): Log likelihood 390.8848
Normalized cointegrating coefficients (standard error in parentheses)Gdp Gdpm
1.000000 -0.415262 (0.15129)
Adjustment coefficients (standard error in parentheses)D(gdp) -0.242827
(0.05218)D(gdpm) 0.196176
(0.11669)
Source: Author’s Analysis
Table C3: Impulse ResponseResponse of gdp:
Period gdp gdpm1 0.038548 0.0000002 0.026956 -0.0015143 0.018463 -0.0002904 0.016659 0.0002045 0.015278 -0.0010776 0.012365 0.0012827 0.008739 -0.0039868 0.007595 -0.0048029 0.006146 0.02017510 0.006626 0.009516
Response of gdpm:
Period gdp gdpm1 0.010574 0.0855532 0.018633 0.0647933 0.022401 0.0679664 0.039737 0.0592945 0.016414 0.0660806 0.024533 0.0581897 0.029190 0.0446358 0.033868 0.0092529 0.009842 0.12157910 0.019780 0.079630
cholesky ordering: gdp, gdpmSource: Author’s Analysis
116
Figure C1: Impulse Response Function Source: Author’s Analysis
Table C4: Vector Error Correction Estimates for Environmental Quality GNI Relationship vector error correction estimates date: 10/21/15 time: 12:19 sample (adjusted): 1996 2013 included observations: 18 after adjustments standard errors in ( ) & t-statistics in [ ]
Cointegratingeq: Cointeq1
Log(CO2(-1)) 1.000000Log(FLD(-1)) -360.8478
(91.8087)[-3.93043]***
C 892.1205
Error correction: D(log(CO2)) D(log(FLD))
Cointeq1 -0.051192 0.036947 (0.27341) (0.00728)[-0.18723] [ 5.07727]***
D(log(CO2(-1))) -0.002900 -0.051331 (0.51927) (0.01382)[-0.00558] [-3.71420]***
117
D(log(CO2(-2))) 0.052847 -0.044177 (0.51511) (0.01371)[ 0.10259] [-3.22232]***
D(log(CO2(-3))) 0.133732 0.007259 (0.44593) (0.01187)[ 0.29989] [ 0.61163]
D(log(CO2(-4))) 0.550968 -0.019911 (0.45948) (0.01223)[ 1.19911] [-1.62816]
D(log(CO2(-5))) -0.617124 0.030017 (0.36363) (0.00968)[-1.69711]* [ 3.10157]***
D(log(FLD(-1))) -25.70958 12.25414 (93.4820) (2.48801)[-0.27502] [ 4.92527]***
D(log(FLD(-2))) -19619.73 -2280.750 (24219.3) (644.594)[-0.81009] [-3.53827]***
D(log(FLD(-3))) -2100.404 3258.120 (57945.2) (1542.20)[-0.03625] [ 2.11264]**
D(log(FLD(-4))) 81741.92 -5146.956 (65322.8) (1738.56)[ 1.25135] [-2.96047]***
D(log(FLD(-5))) -61462.26 5044.661 (42422.8) (1129.08)[-1.44880] [ 4.46795]***
C -31.58517 22.13913 (160.763) (4.27870)[-0.19647] [ 5.17426]***
Dlog(GNI) 0.039843 -0.051997 (0.55788) (0.01485)[ 0.07142] [-3.50198]***
r-squared 0.587080 0.973567 adj. R-squared -0.403927 0.910127 sum sq. Resids 0.177731 0.000126 s.e. equation 0.188537 0.005018 f-statistic 0.592408 15.34637 log likelihood 16.01979 81.29291 akaikeaic -0.335532 -7.588101 schwarzsc 0.307514 -6.945054 mean dependent 0.047054 -0.031414 s.d. dependent 0.159120 0.016738
determinantresid covariance (dof adj.) 8.53e-07 determinant resid covariance 6.58e-08 log likelihood 97.74262 akaike information criterion -7.749180 schwarz criterion -6.364157
Source: Author’s Analysis
118
Table C5: Co Integration Test for CO2, FLD and GNIDate: 10/21/15 time: 12:20Sample (adjusted): 1996 2013Included observations: 18 after adjustmentsTrend assumption: linear deterministic trendSeries: log(CO2) log(FLD)Exogenous series: dlog(GNI)Warning: critical values assume no exogenous seriesLags interval (in first differences): 1 to 5Unrestricted cointegration rank test (trace)
Hypothesized Trace 0.05No. Of ce(s) Eigenvalue Statistic Critical value Prob.**
None * 0.842002 50.45729 15.49471 0.0000At most 1 * 0.616344 17.24414 3.841466 0.0000
trace test indicates 2 cointegratingeqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **mackinnon-haug-michelis (1999) p-valuesUnrestricted cointegration rank test (maximum eigenvalue)
Hypothesized Max-eigen 0.05No. Of ce(s) Eigenvalue Statistic Critical value Prob.**
None * 0.842002 33.21315 14.26460 0.0000At most 1 * 0.616344 17.24414 3.841466 0.0000
max-eigenvalue test indicates 2 cointegratingeqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **mackinnon-haug-michelis (1999) p-values unrestricted cointegrating coefficients (normalized by b'*s11*b=i):
Log(CO2) Log(FLD)-6.152618 2220.159-16.92670 -2427.340
unrestricted adjustment coefficients (alpha):
D(log(CO2)) 0.008320 0.077960D(log(FLD)) -0.006005 -0.000375
1 cointegrating equation(s): Log likelihood 97.74262
Normalized cointegrating coefficients (standard error in parentheses)Log(CO2) Log(FLD) 1.000000 -360.8478
(91.8087)Adjustment coefficients (standard error in parentheses)
D(log(CO2)) -0.051192 (0.27341)
D(log(FLD)) 0.036947 (0.00728)
Source: Author’s Analysis
119
Table C6: Impulse ResponseResponse of log(CO2):
Period log(CO2) log(FLD)1 0.188537 0.0000002 0.186182 -0.0354573 21.44074 -96.031054 34.82714 83.784145 -48769.42 220377.46 47774.77 -768299.77 1.12e+08 -5.04e+088 -4.02e+08 3.08e+099 -2.56e+11 1.15e+1210 1.59e+12 -1.01e+13
Response of log(FLD):Period log(CO2) log(FLD)1 -0.001084 0.0048992 -0.002627 -0.0003823 2.483928 -11.233964 -0.469025 30.271055 -5701.410 25711.976 15992.23 -136761.77 13047579 -586723088 -70848954 4.67e+089 -2.98e+10 1.33e+1110 2.41e+11 -1.42e+12
Choleskyordering: log(CO2) log(FLD)Source: Author’s Analysis
120
Figure C2: Impulse Response Function Source: Author’s Analysis
Table C7: Vector Error Correction Estimates for Environmental Quality and Environmental Infrastructure Link
Vector Error Correction Estimates Date: 12/16/15 Time: 10:14 Sample (adjusted): 1994 2013 Included observations: 20 after adjustments Standard errors in ( ) & t-statistics in [ ]
Cointegrating Eq: CointEq1
LOG(CO2(-1)) 1.000000
LOG(FLD(-1)) -288.5877 (59.2074)[-4.87418]
C 729.9620
Error Correction: D(LOG(CO2)) D(LOG(FLD))
CointEq1 0.189503 0.016152 (0.16305) (0.00439)[ 1.16227] [ 3.68002]
D(LOG(CO2(-1))) 0.218974 -0.013381 (0.36272) (0.00976)[ 0.60371] [-1.37035]
121
D(LOG(CO2(-2))) 0.637019 0.003826 (0.61816) (0.01664)[ 1.03050] [ 0.22992]
D(LOG(CO2(-3))) 0.100295 0.002704 (0.31223) (0.00841)[ 0.32123] [ 0.32173]
D(LOG(FLD(-1))) -8441.599 -1247.969 (22529.1) (606.483)[-0.37470] [-2.05772]
D(LOG(FLD(-2))) -5243.312 530.7963 (50991.4) (1372.69)[-0.10283] [ 0.38668]
D(LOG(FLD(-3))) 17163.46 1021.991 (32496.0) (874.789)[ 0.52817] [ 1.16827]
C 84.32207 7.523698 (75.0421) (2.02013)[ 1.12366] [ 3.72437]
LOG(T) 0.104165 0.009946 (0.09288) (0.00250)[ 1.12150] [ 3.97772]
LOG(E) -0.229498 0.000897 (0.09158) (0.00247)[-2.50588] [ 0.36366]
LOG(CP) 0.384459 -0.003906 (0.30141) (0.00811)[ 1.27551] [-0.48140]
LOG(PRED) -0.529778 -0.019845 (1.09088) (0.02937)[-0.48564] [-0.67578]
R-squared 0.615775 0.954259 Adj. R-squared 0.087465 0.891364 Sum sq. resids 0.241805 0.000175 S.E. equation 0.173855 0.004680 F-statistic 1.165557 15.17240 Log likelihood 15.77477 88.07255 Akaike AIC -0.377477 -7.607255 Schwarz SC 0.219962 -7.009816 Mean dependent 0.022547 -0.032268 S.D. dependent 0.181997 0.014200
Determinant resid covariance (dof adj.) 6.62E-07 Determinant resid covariance 1.06E-07 Log likelihood 103.8479 Akaike information criterion -7.784795 Schwarz criterion -6.490343
Source: Author’s Analysis
122
Table C8: Co Integration Test for Environmental Quality and Environmental Infrastructure Link
Date: 12/16/15 Time: 10:16Sample (adjusted): 1994 2013Included observations: 20 after adjustmentsTrend assumption: Linear deterministic trendSeries: LOG(CO2) LOG(FLD)Exogenous series: LOG(T) LOG(E) LOG(CP) LOG(PRED)Warning: Critical values assume no exogenous seriesLags interval (in first differences): 1 to 3Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.651601 24.70401 15.49471 0.0016At most 1 0.165393 3.615888 3.841466 0.0572
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-valuesUnrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.651601 21.08812 14.26460 0.0036At most 1 0.165393 3.615888 3.841466 0.0572
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):
LOG(CO2) LOG(FLD)-4.194064 1210.355 20.03554 -1094.017
Unrestricted Adjustment Coefficients (alpha):
D(LOG(CO2)) -0.045184 -0.042651D(LOG(FLD)) -0.003851 0.000371
1 Cointegrating Equation(s): Log likelihood 103.8479
Normalized cointegrating coefficients (standard error in parentheses)LOG(CO2) LOG(FLD) 1.000000 -288.5877
(59.2074)Adjustment coefficients (standard error in parentheses)
D(LOG(CO2)) 0.189503 (0.16305)
D(LOG(FLD)) 0.016152 (0.00439)
Source: Author’s Analysis
123
Table C9: Impulse Response Function
Response of LOG(CO2):
Period LOG(CO2) LOG(FLD)
1 0.173855 0.000000 2 0.560002 -39.76283 3 -397.7781 49727.33 4 498911.9 -62309996 5 -6.25E+08 7.81E+10 6 7.83E+11 -9.78E+13 7 -9.82E+14 1.23E+17 8 1.23E+18 -1.54E+20 9 -1.54E+21 1.92E+23
10 1.93E+24 -2.41E+26
Response ofLOG(FLD):
Period LOG(CO2) LOG(FLD)
1 -3.71E-05 0.004680 2 0.046905 -5.857660 3 -58.76954 7339.840 4 73640.06 -9197047. 5 -92273269 1.15E+10 6 1.16E+11 -1.44E+13 7 -1.45E+14 1.81E+16 8 1.82E+17 -2.27E+19 9 -2.27E+20 2.84E+22
10 2.85E+23 -3.56E+25
Cholesky Ordering:
LOG(CO2) LOG(FLD)
124
Figure C3: Impulse Response Function Source: Author’s Analysis
Table C10: Causality Test
Pairwise Granger Causality TestsDate: 12/18/15 Time: 21:57Sample: 1990 2013Lags: 2
Null Hypothesis: Obs F-Statistic Prob.
CP does not Granger Cause CO2 22 0.19987 0.8207 CO2 does not Granger Cause CP 0.34730 0.7115
FLD does not Granger Cause CO2 22 2.25972 0.1348 CO2 does not Granger Cause FLD 0.03368 0.9669
PRED does not Granger Cause CO2 22 0.77775 0.4751 CO2 does not Granger Cause PRED 2.08291 0.1552
T does not Granger Cause CO2 22 0.19262 0.8266 CO2 does not Granger Cause T 3.59965 0.0497
E does not Granger Cause CO2 22 1.16917 0.3344 CO2 does not Granger Cause E 0.47394 0.6305
FLD does not Granger Cause CP 22 1.89107 0.1813
125
CP does not Granger Cause FLD 29.9768 3.E-06
PRED does not Granger Cause CP 22 0.73930 0.4922 CP does not Granger Cause PRED 0.30221 0.7431
T does not Granger Cause CP 22 16.8417 9.E-05 CP does not Granger Cause T 12.2860 0.0005
E does not Granger Cause CP 22 5.32575 0.0160 CP does not Granger Cause E 35.3291 9.E-07
PRED does not Granger Cause FLD 22 0.61700 0.5512 FLD does not Granger Cause PRED 1.39222 0.2755
T does not Granger Cause FLD 22 184.105 3.E-12 FLD does not Granger Cause T 2.57451 0.1055
E does not Granger Cause FLD 22 2.12763 0.1497 FLD does not Granger Cause E 1.01809 0.3823
T does not Granger Cause PRED 22 0.58029 0.5704 PRED does not Granger Cause T 3.01150 0.0759
E does not Granger Cause PRED 22 0.08100 0.9225 PRED does not Granger Cause E 0.98488 0.3938
E does not Granger Cause T 22 1.37393 0.2798 T does not Granger Cause E 127.404 6.E-11
Source: Author’s Analysis
126
Appendix D: Time Series Used for Analysis
YEAR GDP GDPM W T E CP EDU POP
1980q1 12105 284.3 0 437.5 55.1 4079.3 01842452
5
1980q2 12377.5 1254 0 431.8 62.8 3506.5 01842452
5
1980q3 11908.9 1214.9 0 383.3 69.2 3245.6 01842452
5
1980q4 13241 2409 0 383.1 57.5 3305.9 01842452
5
1981q1 11241.9 303.3 40.4 737.8 68.1 2948.9 621893239
4
1981q2 11958.3 1148.3 41.9 764.9 77.5 2534.9 59.71893239
4
1981q3 11743.6 1105.7 37.6 682.2 85.4 2346.3 52.31893239
4
1981q4 12675.9 2142.6 32.8 701.7 71 2389.8 56.61893239
4
1982q1 11420.9 334.8 42.6 650.2 72.7 2456.4 78.61943245
1
1982q2 12344.8 1234.9 44.2 685.6 82.7 2111.5 75.81943245
1
1982q3 12176.5 1187.4 39.7 613.4 91.2 1954.4 66.31943245
1
1982q4 13127.1 2290.5 34.6 636.1 75.8 1990.7 71.81943245
1
1983q1 12226.5 327.6 62.4 646.7 69.5 2132 82.61993232
8
1983q2 13361.6 1349.7 64.7 680.8 79.1 1832.7 79.61993232
8
1983q3 13226.1 1304.3 58.1 611.5 87.1 1696.3 69.61993232
8
1983q4 14293.2 2561.3 50.6 634.1 72.5 1727.8 75.41993232
8
1984q1 13859 283.1 56.3 699.7 68.1 2607.7 86.9 2044380
127
4
1984q2 15023.6 1179.6 58.4 730.9 77.5 2241.6 83.72044380
4
1984q3 14989.6 1140.7 52.4 656 85.4 2074.8 73.32044380
4
1984q4 15750.4 2244.1 45.7 678.5 71 2113.3 79.32044380
4
1985q1 15647 431.6 48.9 955.1 75.3 3282.3 91.32097539
3
1985q2 17115.3 1572.3 50.8 986.8 85.7 2821.4 882097539
3
1985q3 17097 1510.9 45.6 882.4 94.5 2611.5 772097539
3
1985q4 18049.3 2907.9 39.7 906.1 78.6 2660 83.32097539
3
1986q1 15874.4 462.7 57.2 995.8 56.7 2758.2 95.72152951
2
1986q2 17460.1 1617.7 59.3 1028.7 64.5 2370.9 92.32152951
2
1986q3 17418.8 1550.4 53.3 918.7 71.1 2194.5 80.72152951
2
1986q4 18393.6 2960.4 46.4 943.7 59.1 2235.3 87.42152951
2
1987q1 24995.7 508.5 61.7 1068 59.3 7710.8 100.12210323
0
1987q2 26452 1828.7 64 1093.3 67.4 6628.2 96.42210323
0
1987q3 26416.8 1756.8 57.5 981.1 74.3 6135 84.42210323
0
1987q4 27358.3 3374.5 50.1 1003.6 61.8 6248.9 91.42210323
0
1988q1 32232.7 736 74.7 1152.2 59.7 8615.8 122.82269340
4
1988q2 34961.2 2695.8 77.5 1175.7 68 7406.1 118.32269340
4
1988q3 35329.8 2591.8 69.6 1056.8 74.9 6855.1 103.5 22693404
128
1988q4 36561.7 4994.1 60.6 1079.3 62.3 6982.3 112.12269340
4
1989q1 53255.4 911.4 81.4 1227 171.2 22082.5 136.72329494
0
1989q2 54337.8 3060.8 84.4 1244.5 194.9 18982.1 131.72329494
0
1989q3 53793.9 2933.8 75.8 1119.5 214.8 17569.8 115.22329494
0
1989q4 55410.5 5569.5 66 1140.4 178.6 17895.9 124.82329494
0
1990q1 65929.7 990.8 92.6 1413.1 186.7 28919.1 151.22390433
8
1990q2 67103.8 3594.2 96 1430 212.4 24858.7 145.72390433
8
1990q3 66260.6 3458.3 86.3 1286.6 234.1 23009.2 127.52390433
8
1990q4 68255.9 6659.1 75.1 1309.1 194.7 23436.3 1382390433
8
1991q1 76449.4 1291.2 102.5 1599.5 205.1 33623.1 180.52452134
3
1991q2 78243.7 4729.1 106.3 1617.1 233.5 28902.3 173.92452134
3
1991q3 77318.5 4553 95.5 1454.4 257.3 26751.9 152.22452134
3
1991q4 80128.2 8782.7 83.2 1479.3 214 27248.5 164.72452134
3
1992q1 133927.1 1810.8 120.6 2360.3 214.1 71221.3 464.72514806
1
1992q2 133260.6 6592.2 125 2367.9 243.6 61221.5 447.92514806
1
1992q3 130710.2 6351.4 112.3 2128.2 268.5 56666.7 391.82514806
1
1992q4 134716 12249.6 97.8 2154.9 223.3 57718.5 424.22514806
1
1993q1 166745.6 2526.4 161 3938.8 223.7 69859.8 676.6 25786187
129
1993q2 171233.1 9513.4 166.9 3944.5 254.6 60051.3 6522578618
7
1993q3 170640 9171.3 149.9 3541.6 280.6 55583.4 570.52578618
7
1993q4 175251.1 17776.1 130.6 3583.5 233.4 56615.2 617.62578618
7
1994q1 211794.3 3869.7 172.2 8457 257.9 63223.2 783.82643819
9
1994q2 225286.2 15328.3 178.6 8414.1 293.6 54346.4 755.42643819
9
1994q3 227716.4 14798.6 160.4 7539.1 323.6 50303 660.92643819
9
1994q4 235066.4 28901 139.7 7614.3 269.1 51236.7 715.52643819
9
1995q1 475135.4 6571.8 191.3 13279 268.7 221175.9 905.72710620
7
1995q2 482976.8 25635.3 198.4 13212 305.9 190122 872.82710620
7
1995q3 481117.3 24769.4 178.2 11837.6 337.1 175976.8 763.62710620
7
1995q4 493982 48313.2 155.3 11986.4 280.4 179243.3 826.72710620
7
1996q1 670619.8 8353.4 202.7 17286 279.6 334111 932.72779155
3
1996q2 675141.6 32344 210.2 17201.8 318.2 287200.5 898.92779155
3
1996q3 670697.4 31262.2 188.8 15413.9 350.7 265832.7 786.42779155
3
1996q4 686260.4 60937.5 164.5 15629.7 291.7 270767.1 851.42779155
3
1997q1 686351 8961.4 211.9 19946 278.8 308449.7 1010.12849487
0
1997q2 700532.3 35077.8 219.7 19857.5 317.3 265142.2 973.52849487
0
1997q3 699923.5 33901.3 197.4 17803.6 349.7 245415.6 851.7 28494870
130
1997q4 715165.8 66166.5 172 18071 290.8 249971 922.12849487
0
1998q1 647960.4 8620.9 220.6 25745.9 267.6 212599.5 1655.92921684
3
1998q2 678288.7 34444.7 228.7 25623.7 304.5 182749.7 1595.82921684
3
1998q3 685015.5 33290.6 205.5 22963.9 335.6 169153.1 1396.22921684
3
1998q4 697166.2 65140.2 179 23318.7 279.2 172292.9 1511.62921684
3
1999q1 777023.9 9382.2 233.2 30654.8 276.9 295605.3 1899.82995797
2
1999q2 799246.2 36741.4 241.8 30540 315.1 254101.2 1830.92995797
2
1999q3 801411.1 35510.3 217.2 27400.1 347.3 235196 1601.92995797
2
1999q4 816333.7 69312.6 189.2 27906.8 288.8 239561.7 1734.32995797
2
2000q1 1165093 10880.4 246.2 33901.1 286.2 630959 3756.83071918
2
2000q2 1144268 40889 255.3 33809.7 325.8 542370 3620.53071918
2
2000q3 1124630 39522.2 229.3 30371.7 359 502017.6 3167.63071918
2
2000q4 1148136 76745.4 199.8 31009.6 298.6 511335.9 3429.43071918
2
2001q1 1164239 15113.6 255.9 37964.2 3479.1 481584 4745.73150124
8
2001q2 1182576 48409.2 265.3 37871.6 3959.8 413967.8 4573.63150124
8
2001q3 1181000 46780.7 238.4 34031.4 4364.1 383168.5 4001.53150124
8
2001q4 1197271 88775.9 207.6 34767.8 3629.8 390280.8 4332.23150124
8
2002q1 1625546 17257.9 279.2 46927.6 4005.9 519043.7 4983.7 32306160
131
2002q2 1735603 57589.5 289.5 46809 4559.3 446168.1 4802.93230616
0
2002q3 1792349 55663.8 260.1 42069.4 5024.9 412973 4202.13230616
0
2002q4 1758883 106314.2 226.5 42977.7 4179.3 420638.6 4549.43230616
0
2003q1 2039516 21103.3 307.6 59215.4 4787.1 791065.1 5482.63313753
7
2003q2 2127693 69965.2 318.9 58959.7 5448.5 679996.6 5283.73313753
7
2003q3 2171579 67627.9 286.5 52882.8 6004.8 629404.7 4622.83313753
7
2003q4 2148243 129043 249.6 53823.3 4994.4 641087.6 5004.83313753
7
2004q1 2631256 23278.3 324.6 93254.3 4536 1156329 5537.73399981
3
2004q2 2592273 75465.3 328.8 89052.9 5953.6 968706.5 5537.73399981
3
2004q3 2985542 79747.9 332.2 90932.7 7796.3 1023857 5537.73399981
3
2004q4 3201996 170824.8 328.9 92490.7 7229.3 1098824 6229.93399981
3
2005q1 3169613 28106.6 365.8 92530.1 4949 1348242 6313.33489647
3
2005q2 3399352 89584.9 370.6 93385.5 6517.7 1382232 6313.33489647
3
2005q3 3924775 95057.4 374.4 97962.5 8537.3 1450210 6313.33489647
3
2005q4 4078499 199957.6 370.7101603.
5 7902 1484199 7102.53489647
3
2006q1 3986280 33664.7 376.4 97291 9511.4 1752717 7197.53582872
7
2006q2 4426084 100673.8 434.5111207.
5 10416.9 1627024 7197.53582872
7
2006q3 4986489 122728.8 458 109140.2
12349.2 1745734 7197.5 35828727
132
2006q4 5165742 221456.9 371.8124183.
5 8696.6 1857461 8097.23582872
7
2007q1 4740807 37657.1 452105049.
7 10587.5 2050673 8086.53679683
8
2007q2 4853839 104158.9 514.9118310.
8 10280 1717937 8086.53679683
8
2007q3 5524364 140367.7 543120148.
3 12251.7 1815742 8086.53679683
8
2007q4 5538295 238699.2 447.8129936.
1 10700.8 1948691 9097.33679683
8
2008q1 5535964 40816.5 543.1105471.
9 11708.7 2456393 9848.53780202
0
2008q2 5720250 121177.8 595119427.
9 11472.9 2092483 9848.53780202
0
2008q3 6461895 160053.3 629 121639 12918.3 2183460 9848.53780202
0
2008q4 6578221 263525.5 485.6132587.
9 14318 2365415 9848.53780202
0
2009q1 5404850 59317.6 628.4107256.
6 17670.5 9097751 11631.13884525
5
2009q2 5880233 123589.8 666.4128966.
7 12597.2 1893705 118973884525
5
2009q3 6682026 162339.7 696.9128134.
9 13758 1728429 11650.83884525
5
2009q4 6745561 267367 543.3142362.
7 15589.4 1765819 11916.73884525
5
2010q1 7426524 7426524 720109450.
1 16725.8 2030196205686.
73992694
5
2010q2 8043198 8043198 749.2134848.
6 15079.8 7418149212773.
73992694
5
2010q3 9055633 9055633 785.2 135311 16730.9 3344984203304.
83992694
5
2010q4 9459399 9459399 606.1149376.
7 18896.6 3424850204906.
53992694
5
2011q1 8553988 8553988 807.9 112204 19408.7 3611524 242293.1
41048231
133
2011q2 9444841 9444841 919.9155700.
2 18114.9 4124401231668.
64104823
1
2011q3 9856176 9856176 882.9142486.
7 18631.61450575
9275525.
94104823
1
2011q4 9554855 9554855 675.4155401.
2 21288.9 3991385361233.
54104823
1
2012q1 9303445 9142859 889.8 4945.72 20077.7 3919559275013.
24220844
4
2012q21000731
4 9840227 1132.1 8150.84 21614.7 3693406262147.
34220844
4
2012q31111773
81096727
3 1067.1 9823.63 21223.2 3680654311196.
84220844
4
2012q41075312
11059374
2 82710201.5
5 24473.71528500
4404364.
44220844
4
2013q1 9657561 9493779 1000.3 5262.69 22804.3 4002862330269.
54340383
6
2013q21037073
71020483
8 1357 9294.41 25269.8 3602964313322.
44340383
6
2013q31133407
01116602
6 1231.511081.0
5 23806.1 4088640406309.
84340383
6
2013q48904361
5 868543090037.3
7 1197436531954.
6 9616490500032.
34340383
6
Source: central bank of Nigeria statistical bulletin 2013
Data on Environmental Quality Economic Growth Link
134
135
Year Co2 Gni Forland
199045375.4
6 29018.9224
5
199145247.1
1 27018.4726
1
1992 64883.9 27018.0227
7
199360061.7
9 19017.5729
3
199446658.9
1 17017.1230
9
199534917.1
7 17016.6732
5
199640421.3
4 23016.2234
2
199740190.3
2 28015.7735
8
199840182.9
9 27015.3237
4
199944788.7
4 299 14.8739
200079181.5
3 27014.4240
6
200183350.9
1 31013.9743
3
200298125.2
5 350 13.5246
200393138.1
3 41013.0748
7
200497047.1
6 61012.6251
4
2005104696.
5 66012.1754
1
200698513.9
6 84011.7256
8
200795209.9
9 97011.2759
5
200892621.0
9 116010.8262
2
200971719.1
9 116010.3764
9
Source: world development index 2015
Data on Environmental Quality Environmental Infrastructure Link
years CO2 FLD T E CP PRED1990 45375.46 18.92245 5438.8 827.9 100223.3 86.261731991 45247.11 18.47261 6150.3 909.9 116525.8 85.421641992 64883.9 18.02277 9011.3 949.5 246828 89.464831993 60061.79 17.57293 15008.4 992.3 242109.7 93.564741994 46658.91 17.12309 32024.5 1144.2 219109.3 93.352691995 34917.17 16.67325 50315 1192.1 766518 89.06061996 40421.34 16.22342 65531.4 1240.2 1157911 78.457441997 40190.32 15.77358 75678.1 1236.6 1068979 82.412521998 40182.99 15.32374 97652.2 1186.9 736795.2 86.987631999 44788.74 14.8739 116501.7 1228.1 1024464 93.809852000 79181.53 14.42406 129092.1 1269.6 2186683 98.357192001 83350.91 13.97433 144635 15432.8 1669001 96.044582002 98125.25 13.5246 178783.7 17769.4 1798823 97.648342003 93138.13 13.07487 224881.2 21234.8 2741554 98.862352004 97047.16 12.62514 365730.6 25515.2 4247716 100.25662005 104696.5 12.17541 385481.6 27906 5664883 100.92612006 98513.96 11.72568 441822.2 40974.1 6982936 101.66732007 95209.99 11.27595 473444.9 43820 7533042 92.890262008 92621.09 10.82622 479126.7 50417.9 9097751 83.766652009 71719.19 10.37649 506720.9 59615.1 14485704 85.035942010 78910.17 9.926765 528986.4 67433.1 16218179 84.804882011 87613.97 9.477036 565792.1 77444.1 26233070 84.116282012 89362.38 9.027307 33121.74 87389.3 26578623 88.254382013 94285.03 9.216542 1223074 603834.8 21310956 96.94236
Source: world development index 2015 and CBN Statistical Bulletin2014
136
137
