Environmental Pollution Effect on Pakistan

HOW ENVIRONMENTAL POLLUTION EFFECT ON PAKISTAN ECONOMY A DISSERTATION SUBMITTED TO THE DEPARTMENT OF ECONOMICS IN PARTIAL FULLFILLMENT OF REQUIREMENT FOR THE DEGREE OF MASTER IN ECONOMICS

BY ZILL-E-HUMA Roll No. 06 SESSION 2013-2015

SUPERVISED BY Dr. Abdul Ghafoor Awan

DEPARTMENT OF ECONOMICS

INSTITUTE OF SOUTHERN PUNJAB, MULTAN i

Abstract

Like most developing countries, Pakistan faces serious environmental problems. Rapid population growth (averaged about 3 percent a year since the early 1970s) and impressive GDP growth (of about 6 percent a year) have put enormous pressure on the country’s natural resource base and have significantly increased levels of pollution. Rapid expansion in industrial production and urbanisation have led to increased levels of waste water pollution, solid waste, and vehicle emissions that have resulted in serious health problems in many areas of the country. Soil erosion and salinity have caused crop yields to decline in some areas on what were previously some of the most productive soils in Pakistan. Forests are being depleted, especially in the Northern areas, as land is cleared for livestock fodder and fuelwood. Rangelands are increasingly becoming degraded, some irreversibly, and the marine environment has been affected by industrial pollutants and increasing levels of salinity as a result of upstream irrigation. A recent study Brandon (1995) attempts to value environmental costs in Pakistan and puts the estimate of environmental damage at $1 billion to 2.1 billion per year, or 2.6 to 5.0 percent of GDP in 1992 values. Which explore empirically the link between the level of economic activity and air pollution at the macroeconomic level. A special emphasis is given to the application of recent tools developed in the nonparametric field as they allow for a better control of potential misspecifica- tion biases and they give more flexibility to the underlying relationships. Chapter 1 tests whether a sustainable link between per capita GDP levels and the environment exists for a variety of air pollutants’ emissions in a panel of 48

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Spanish provinces over the period 1990-2002. Chapter 2 investigates how cross-country gaps in per capita CO2 emissions evolved over the 1960-2002 period for a panel of 166 world areas as well as for several country sub-groupings (rich/poor countries, specific geographic regions, economically integrated areas). An analysis of the dynamic of the cross-sectional distributions is conducted with robust scale and shape measures and formal shape and multimodality tests are applied. The latter approach is contrasted with a stochastic convergence analysis a la Evans (1998). Chapter 3 makes use of a broad range of regression techniques to fit a reduced form function, where growth rates in per capita CO2 emissions are explained with past pollution levels, past per capita GDP levels and per capita GDP growth rates. Panel models are estimated with standard linear and nonlinear least squares and these specifications are tested against their nonparametric counterpart. This framework also allows exploring beta-convergence in per capita emissions conditional on GDP as well as beta-convergence in GDP conditional on pollution.

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Acknowledgement

All humble words of profoundest thanks are due to ALLAH Almighty the compassionate and the merciful who blessed me determination, potential and ability to complete this work successfully within the stipulated time. All praise is for the Holy prophet Hazrat Muhammad (SAW) who brought the message of peace and happiness to all creatures. I deem it may utmost pleasure to avail myself this opportunity in recording my deep feelings of regards and sense of gratitude to my great supervisor, Dr Abdul Ghafoor Awan department of Economics, who extended his sincere cooperation and invigorating encouragement during the course of present study. I wish to express my sincere thanks to my valued teachers for their invaluable educational guidance and devotion during course work and accommodating behavior during research work. I would also like to thanks my loving and sweet Friends and brother for their encouragement, immense horizons, mellifluous moral support, and patience, spiritual and intellectual inspirations who have always wish to see me glittering high on the skies of success and whose hands always rise in prayers for my success. I express love and appreciation to my family for their never ending love and extended cooperation.

Zill-e-Huma

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Dedication

This Assignment is dedicated to My Respected Teacher Who has supported me all the way With her knowledge, wisdom, And guidance, I would not have the goals I have to strive and Be the best to reach.

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Declaration I solemnly declare that this research work is written by me in partial fulfillment for the degree of MSc in Economics.

Zill-e-Huma ROLL NO. 06 DEPARTMENT OF ECONOMICS INSTITUTE OF SOUTHERN PUNJAB, (MULTAN CAMPUS)

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Approval Certificate

This thesis "How Environmental Pollution Effect on Pakistan Economy" by Zille-Huma is accepted in its present form by the Department of Economics as satisfying the thesis requirement for the degree of MSc in economics.

Supervisor

Chairman

External Examiner

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TABLE OF CONTENTS

CHAPTER – 1

INTRODUCTION

01

1.1

Pressure

05

1.2

Energy Demand Projections

05

1.3

Households

07

1.4

Transport

08

1.5

Industry

08

1.6

Fuel Substitution

09

1.7

Energy Inefficiency

10

1.8

Ozone Depleting Substances (ODSs)

11

1.9

Others

11

CHAPTER – 2

LITERATURE REVIEW

12

CHAPTER – 3

METHODOLOGY AND DATA

23

3.1

Model

23

3.2

Methodology

24

3.3

Descriptive analysis

26

3.4

Data

26

CHAPTER – 4

HISTORICAL TRENDS

27

4.1

Evolution of cross-section distributions

30

4.2

Pakistan: Some Energy Facts

32

CHAPTER – 5

RESULTS & FINDINGS

34

5.1

Unit root Test

34

5.2

The Long run

35

5.3

Diagnostic Tests

35

5.4

The Short Run

37

5.5

Environmental Kuznets Curve

37

CHAPTER – 6

CONCLUSIONS AND RECOMMENDATIONS

39

References

42

Annexure

52

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List of Figure Fig No. 1

Percentage shares of commercial energy delivered in 1992-93

06

Fig No. 2

Estimated environmental pollutants from industry

09

Fig No. 3

Time Trend in world CO2 emissions period 1960-2002

30

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List of Tables Table No. 1

Projected sector fuel consumption

07

Table No. 2

Fossil fuel reserves as on June 30, 1996

09

x

Chapter – 1

Introduction

There is no doubt in it that Pakistan would have to be environmental standards compliant if it wants to have a healthy nation and desires to do business with the rest of the world. It would have to look more vigorously into all environment related issues ranging from fast increasing level air pollution to water pollution and noise pollution. Today, when the whole scenario has been changed and world has become a global village, environmental issues could easily demolish the economy of any country because all major countries including European Union have set very strict environmental standards and are reluctant to import goods, specially food, from countries facing the environmental problems like Pakistan. Pakistan is already facing many crisis including energy, law and order and political instability etc, therefore, it can’t afford more crisis. Government and masses have to pay attention towards the issue of pollution; otherwise it would not only play a major role to derail Pakistan’s economy but also adverse effects on our future generations. A study report of World Health Organization (WHO) says “in the developing world, pollution is guilty for 5% of total demises”. As far as Lahore is concerned, near 65,000 people die every year while 1300 people lose their lives because of diseases produced by the polluted environment. A medical expert has also revealed that 22% Lahoris are suffering from Asthma, a disease that born and grow in only polluted environment. Pakistani people are facing three types of pollution, air, water and land. Major reason of air pollution is rapidly increasing vehicles, thanks to car leasing schemes of banks. Approximately, daily 12 lakh vehicles travel on the roads of Lahore city and number of vehicle escalating with the ratio of 15 to 20 percent annually. Though, high petroleum

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prices have encouraged the people to use CNG as fuel in their vehicles, still the usage of petrol is on the higher side and creating serious environmental issues. Noise is also a kind of pollution. Vehicles at roads and machines in factories make noise throughout the day and disturb the whole atmosphere while illiterate and senseless drivers are also responsible to poison the environment as they are use to use horns without any need and hesitation. They do not care whether they are making it in the front of a hospital, school mosque or residential area. Noise pollution is putting heavy mental strain on the people of Lahore. Besides increasing blood pressure and stress levels, noise pollution can also put deadly effects on hearing. Water pollution infects the water and makes it unfit for drinking and other human use. It is also a major cause of most of the water-borne diseases like Hepatitis and dysentery and skin problems. In response to environmental concerns, the government of Pakistan prepared its National Conservation Strategy (NCS) in March 1992. The NCS has been useful, especially in raising awareness of environmental problems among government institutions. Following the release of the report several institutional improvements were made, among them the establishment of an NCS implementation unit in the Environment and Urban Affairs Division (EUAD) and the creation of an Environmental Section, mandated to integrate environmental concerns in economic development planning, in the Planning Commission. The Sustainable Development Policy Institute (SDPI) was set up on the basis of NCS recommendations to provide economic and policy analysis for sustainable economic development, and most of the provinces have created environmental cells in their Planning and Development (P&D) Departments in order to screen investment projects for their effects on the environment.

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Following early successes in implementing the NCS, however, progress now appears to be faltering because of several major factors. First, not enough attention has been given to government policies that provide incentives for individuals to pollute the environment and exploit natural resources in an unsustainable manner. Second, institutions set up for managing the environment, such as the EPAs, appear to be weak and incapable of implementing an appropriate environmental strategy or coordinating the actions of donors to help protect the environment. Third, the goals set by the NCS may have been overambitious given technical, economic, and institutional constraints Pakistan faces. Fourth, the role of the private and nongovernmental (NGO) sectors has not been defined. Finally, many attributed slow progress to a lack of political commitment to sustainable environmental improvement. Broadly speaking, there are two ways of protecting (improving) the environment—policies and regulations. Policies can be general (economy-wide) with impacts on the environment, or specific, directed policies to aimed at environmental protection. This paper assesses how economic policies (and in some cases the absence of economic policies) have affected the environment in Pakistan. This should help in assessing what policies or areas warrant special attention to improve environmental protection. The paper focuses on both brown and green issues, examining problems affecting water pollution (domestic and human waste water, industrial waste water discharge); air pollution (vehicle emissions, urban air pollution, industrial emissions); and marine and coastal zones; irrigated and rainfed agriculture; forests; and rangeland. Economic policies that ensure efficient allocation of resources is a necessary, but not a sufficient condition for creating appropriate environmental incentives. Environment-

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specific policies are also needed to correct market failures leading to environment problems. Two types of policies can be used to deal with environmental problems— command and control policies and incentive- or market-based policies. Command and control policies involve government mandating of environmental quality standards on emissions, technology type, or input use. Incentive- or market-based policies use prices to try to affect pollution and resource use. Despite the advantages of marketbased approaches, Pakistan, like many other countries, mostly followed control policies. But these policies have often failed to achieve results because regulating institutions lack the financial and technical resources to implement these policies effectively. Pakistan’s brown environmental problems include industrial waste water pollution, domestic waste water pollution, motor vehicle emissions, urban and industrial air pollution, and marine and coastal zone pollution. Economic policy failures are contributing significantly to many of these problems. Green environmental problems affect irrigated agriculture, rainfed agriculture, forests, and rangelands. In irrigated agriculture, economic policies, such as subsidies on irrigation water, have provided incentives for farmers to over use water in their production practices, thereby exacerbating the problem of waterlogging and salinity. Deforestation and rangeland degradation have resulted, in part, due to lack of property rights in communal forests and lack of incentive for local communities to participate in forest management decisions. The world wide environmental concerns due to adverse climate change impacts over planet earth have tended world economies towards the usage of green energy along with considerable reduction in CO emission.

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According to the recent studies the large part of carbon emission will be coming from the developing economies due to rapid economic growth. The globalization, where it advantages the developing economies to nurture their economies through reduced investment and trade barriers and opening of technology transfer and mobilized capital and labor, it also transfers the burden of increasing share of pollution due to increase in energy consumption. Now the situation arrived where neither the environmental degradation nor the economic growth can be compromised. However, turning the simple economic growth to environmental friendly growth is a way forward. In this regard Environmental Kuznets Curve (EKC) is a hypothesized relationship between several indicators of environmental degradation and growth. Initially the EKC was an inverted U shaped relationship between income and income inequality proposed by Simon Kuznets in 1955. The environmental Kuznets curve is adopted in environmental economic literature since 1990's, the prominant researchers such as, Grossman and Krueger (1991, 1995), Shafik and Bandyopadhay (1992), Lucas et al (1992), Panayotou (1993, 1997), Selden and Song (1994), Vincent (1996) found an inverted U-shape relationship between income and pollution for various pollutants. For two decades the EKC has been hypothesized in empirical studies and various statistical tests have been used on panel and time series data techniques related to group of countries, as a single country and cross country data as well. However, it is claimed that in order to acquire the better findings and implications single country analysis is more suitable option as countries differ in size, location and other economic characteristics. It is also suggested that EKC shows pollution and other variables of environmental degradation relationship with time so the EKC is a long run

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phenomenon (Lindmark 2002). As a result the time series data technique is advantageous over other techniques (Akbostanci 2008). In this study we have hypothesize the EKC for Energy consumption, Trade, Economic Growth and Population Growth. This type of empirical study is incorporated first time with four major environmental degradation variables. The economy of pakistan has shown enormous growth during 2001 to 2007 consequently the energy consumption especially in industrial sector has added pollution to the environment. Most of the CO􀬶 emission is generated by natural Gas which is almost the half of the total emission. Country's better economic condition transfers the fruit to masses in shape of rise in income level which further deteriorate the environmental pollution. The transportation sector grow rapidly with simultaneous increase the number of individual and commercial vehicles. As per the statistics the per capita energy use has increased (40%) from 2001 to 2007, where the total energy used by industrial and manufacturing sector has increased (43%) during the (2008-2009). Unfortunately during this entire scenario the inefficient and under developed technology divided the environmental pollution in shape of green house gas emission. Higher demand and lack of technology fuelled up the environmental degradation. Though environment is a global issue so, the participation of every single country is valuable. Being a developing country, Government of Pakistan has taken remedial action towards the sustainable development, as a part Pakistan is also one of those countries which announce the National environmental policy NEP in 2005. The basic purpose of this initiative is to safeguard the natural environment and ensure healthy atmosphere to the citizens. The growing economy in all sectors especially industrial, increase in energy consumption. This study

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is undertaken in order to comprehensively test the hypothesis of Environmental Kuznets Curve for both long run and short run in presence of energy consumption, trade openness, economic growth and more especially population growth rate. It is now an established fact that the most important environmental problem of our era is global warming2. The rising quantity of worldwide carbon dioxide (CO2) emissions seems to be escalating this problem. As the emissions generally result from consumption of fossil fuels, dropping energy spending seems to be the direct way of handling the emissions problem. However, because of the possible negative impacts on economic growth, cutting the energy utilization is likely to be the “less preferred road”. Moreover, if the Environmental Kuznets Curve (EKC) hypothesis applies to the emissions and income link, economic growth by itself may become a solution to the problem of environmental degradation (Rothman and de Bruyn, 1998). Coondoo and Dinda (2002), however, argue that both developing and developed economies must sacrifice economic growth. Still, countries may opt different policies to fight global environmental problems, mainly depending on the type of relationship between CO2 emissions, income, and energy consumption over the long run (Soytas and Sari, 2006). Hence, the emissions– energy–income nexus needs to be studied carefully and in detail for every economy, but more so for the developing countries. In this paper, we investigate the relationship between energy consumption, CO2 emissions and the economy in Pakistan from a long run perspective, in a multivariate framework controlling for gross fixed capital, labor and exports by employing ARDL bounds testing approach.

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Pakistan can be a good case study for the analysis because it needs to adjust her infrastructure, economy, and government policies (including environmental, energy, and growth. Generically, environmental pollution is caused by fuel combustion in various sectors: domestic use, power generation, transport, and industry. The problem is aggravated by meteorological conditions and a combination of population density and urbanization. Environmental pollution results in several problems, such as health hazards, especially for women and children, adverse effects on agriculture, livestock, building material and structures, cultural and archaeological monuments. While environmental pollution is generally considered to be an urban phenomenon, it is becoming a rural problem with the penetration of transport and expansion of industry and the growth of brick kilns. Environmental pollution monitoring and control efforts are both inadequate and tend to be urban-centered. The state of environmental pollution has been described, discussed and assessed employing the Pressure, State, Impact and Response (P-S-I-R) framework. This framework links pressures on environmental quality as a result of human activities with changes in the state (quality) of environmental. In this connection different pressures and their impacts on environmental quality are discussed. The response of the government to mitigate the pressure or to improve the environmental quality by instituting environmental and economical programs and policies has also been examined. Having described and discussed the key factors contributing to environmental pollution in the first section, the ambient environmental quality of a few metropolitan cities and the

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resulting health impacts, especially on children, are described and discussed in sections 2 and 3. To combat environmental pollution, the government has formulated acts and policies, including the National Environmental Action Plan (NEAP). An account of the activities and programs implemented to mitigate environmental pollution is given in section 4. Besides identifying the areas and measures for control of environmental pollution, the paper also emphasizes the need and importance of energy conservation, efficient energy use and development of alternate energy sources, in a power-deficient country like Pakistan. 1.1

Pressure

Rapidly growing energy demand, high-energy intensity (a measure of energy inefficiency), and an expected change in fuel composition based on recoverable reserves are the key factors contributing to environmental pollution in Pakistan. 1.2

Energy Demand Projections

The energy system in Pakistan is traditionally demand based. The level, structure, and evolution of this demand is derived from the level of economic activity and the intensity of energy use at the sector level (Hagler Bailly, 1997a). The distribution of energy sources is presented in Fig.1: Figure 1: Percentage shares of commercial energy delivered in 1992-93

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Source: Ashfaque Mahmood, National Conservation Strategy, Energy, National Conservation Strategy Secretariat, Islamabad, 1998

Energy sector studies predict that if energy demands continue to grow at the currently estimated rate of about 7% per annum, Pakistan will need annual energy investments of about 6%-8% of GDP during the decade, as opposed to the historical average of about 4% of GDP. Load forecasting studies undertaken by the Water and Power Development Authority (WAPDA) indicate that over a span of 24 years, power demand will increase to between 4.8 to 5.8 times its present level (Hagler Bailly, 1997b). Table 1 shows the projected energy demand by sector. Fuel consumption is expected to increase six fold by 2050. The domestic sector is the major energy-consuming sector (55%), followed by industry (22%), transport (18%), and agriculture (2%).

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Table 1:

Projected sector fuel consumption 1996

2000

Power Sector

8,925

Industry

2005

2010

2035

2050

11,400 16,500 24,300 47,400

53,000

58,000

7,729

9,400

12,500 15,500 23,500

35,000

50,000

Agriculture Sector 1,800

3,700

4,700

6,200

10,700

12,000

15,000

Domestic Sector

3,365

3,300

4,500

6,000

11,700

20,000

30,000

Transport Sector 7,494

7,600

10,500 13,500 22,500

30,000

40,000

Commercial

888

950

1,200

5,000

7,500

Sector Total

30,201 36,350 49,900 67,000 117,950 155,000 200,500

1,500

2020

2,150

Source: Khan and Iqbal, 2001 1.3

Households

The household sector is the largest single energy-consuming sector in Pakistan. Biomass fuel, fuel-wood, crop residue and dung account for 95% of energy consumed by households in rural areas, with the share dropping to 56% in urban areas. However, this figure conceals its disproportionately high concentration in the low-income settlements. Biomass combustion is a major source of indoor environmental pollution and primarily affects the health of women and children. In this context, the expected switch to conventional fuels as a result of depleting biomass stocks will be an encouraging development.

1.4

Transport

Increasing prosperity and population growth in developing countries have resulted in accelerated growth in vehicle numbers and kilometers traveled. Pakistan is no exception.

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The total road length measured at 94,000 kilometers in 1981 has increased to 232,000 kilometers in 1998, an overall increase of 147%. The number of vehicles has increased almost precipitously, from 0.8 million to about four million within 20 years, an overall increase of more than 400%. Road transport consumes 47.2% of the total petroleum products produced and imported. Lead compounds are added to petrol to increase the efficiency of car engines and to reduce engine knock. The high lead content in petrol is released into the environment. On average, it measures about 0.35 gram/liter, which is relatively very high as compared to the United States and many European standards (0.00-0.15 gram/liter). Two other factors contributing to high emissions are the predominant use of diesel (in about 72% of the vehicles driven) and fuel use inefficiency, as explained under section 1.3 (Khan and Iqbal 2001). 1.5

Industry

Industrial activity without adequate environmental emission treatment or control is one of the major causes of the ambient environmental quality’s deterioration. It has not been possible to assess the magnitude of industrial environmental pollution, as there is little information available. However, figure 2 illustrates the problem reasonably well.

Figure 2: Estimated environmental pollutants from industry

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Industrial discharges have increased significantly over the past 20 years. Emission of carbon dioxide and sulfur dioxide has increased by a factor of four and five hundred, respectively. 1.6

Fuel Substitution

Inter-fuel substitution trends indicate a move towards high emitting fuels. This is likely to continue, based on diminishing reserves of the less polluting energy sources, such as natural gas and hydroelectricity. The status and prognosis with regard to fossil fuel reserves is shown in table 1.2. Table 2: Fuel

Fossil fuel reserves as on June 30, 1996

(Million gigajoules)

Original

Cumulative

Balance

Recoverable

Production

Recoverable

Reserves

Production Depletion Per Year

Reserves

Time (Year)

Crude Oil

3309

2005

1309

125

10.5

Natural Gas

25116

9383

15736

622

26

Coal

10527

80

Source: Energy Wing, GOP, 1998

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The nation’s original gas reserves had already fallen from 25.12 billion gigajoules in 1996 to 15.74 billion gigajoules in 2000. At a yearly production rate of 62 billion gigajoules, the reserves are likely to be exhausted by 2026 (Beg, 2000), while oil reserves are forecast to last for the next ten years. These were not very significant to start with and Pakistan has been meeting the bulk of its oil requirements through imports, which it is likely to continue doing. In fact, imports notwithstanding, electricity production is moving rapidly towards thermal energy and in this category towards oil as a fuel source. Another potential energy supply source is coal. The country has recoverable reserves of about 184 billion tons as of June 1996; 95% of which consist of the recently discovered coal in the Thar region. The coal is of a low- grade quality with very high sulfur content and, therefore, its emission potential is high. A rising trend is likely to be seen in coal use, both for thermal power generation and household consumption. The future tendency will be to tap the extant large coal reserves, as cleaner fuel sources diminish. 1.7

Energy Inefficiency

Pakistan is an energy deficient country but uses the commercial energy at its disposal in a highly inefficient manner. Some of the factors contributing to high-energy intensity are (a) transmission and distribution losses in power generation (an average 25% over the past 20 years), (b) tariff concessions on imported, second-hand machinery, (c) fuel price subsidies on diesel, (d) an ageing vehicle fleet (50% over 10 years old), which is primarily diesel powered (75%), (e) relative inefficiency of vehicle production, and (f) rapid penetration of new appliances (environmental conditioners, refrigerators, heaters) in private homes (Energy Wing, GOP, 1998). Energy inefficiency in developing countries is a source of growing concern.

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1.8

Ozone Depleting Substances (ODSs)

In Pakistan, the ODSs are used mainly in deep freezers, refrigerators, car environmentalconditioners, foam, fire extinguishers and solvents. The country does not produce any ODS and imports all of its requirements. Total consumption of ODS in 1995 was 0.0319 lakh tons. This translates into an annual per capita consumption of roughly 0.018 kg. Twelve ODS were identified in use in 1999. The most common among them in descending order were CFC-12, HCFC-22, CFC-11 and carbon tetrachloride (MELGRD, 2001). 1.9

Others

For a developing country like Pakistan, urbanization has been one of the significant factors contributing to environmental degradation and environmental pollution in particular. The number of cities with a population of over one million increased from three in 1981 to seven in 1998. Environmental pollution in urban centers, particularly in the larger cities, has assumed alarming proportions. Open burning of solid wastes, including plastic bags, is a common practice in the urban and rural areas, and is compounded by biodegradation of such wastes. Methane produced by the decomposition of municipal and industrial waste is estimated to contribute 338,000 tonnes/year of methane (Hagler Bailly, 1997c). Another significant source of environmental pollution, especially around suburbs of major cities, is brick manufacturing. Large quantities of coal, leather and rubber wastes are used as fuel in brick kilns, which emit quantities of ash and other pollutants in the environmental. Emission levels worked out on the basis of energy consumption and emission factors indicate that emissions of carbon monoxide and sulfur dioxide from brick manufacturing exceed those from most industries (TTSID, 1995).

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Chapter – 2

Literature Review

There are quite a few theoretical studies that formally model a direct link between the environment and growth, energy and growth, and energy and environment. The empirical literature appears to be richer. Initially we underpin some the theoretical concerns. Then, we introduce the empirical surveys that relate the transmission mechanisms within the energy– environment–economy (E-E-E) nexus. The theoretical work on economic growth mostly relies on the Solow growth model. More recently growth models depend heavily on the endogenous growth theory. There are a significant number of studies that model the relationship between the natural resource management, environment and economic growth (for review see Xepapadeas, 2005). Whereas Jorgenson and Wilcoxen (1993) selectively cover the theoretical work that models intertemporal general equilibrium framework to develop the interrelationships between energy, the environment, and economic growth. As claimed by Xepapadeas (2005) early works on the growth failed to take into account of the environmental issues of growth. Reviewing the recent literature, he argues that, “ (there is a)…necessity for growth theory to delve deeply into the analysis of the interrelationships between environmental pollution, capital accumulations and the growth of variables which are of central importance in growth theory.” p. 1221). Kolstad and Krautkraemer (1993) point out that the resource use (particularly energy) cede instant economic benefits, its negative blow on the environment may be observed in the long run. Since, most of the theoretical work is dynamic; the empirical studies are mostly static in nature, entailing the need for dynamic empirical analysis. Jorgenson and Wilcoxen (1993) find out that the common feature of the models is relying on the effect

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of policies on capital accumulation in modeling the relationships between the economy, energy and environment. Theoretically, there may be several transmission mechanisms through which environmental policy and economic growth may relate; partly due to some models considering pollution as an input to production; and partly, as a negative byproduct (Ricci, 2007). Generally, environmental policies are considered to have negative impact on growth, due to their role as additional constraints in the models. Certainly, Dudek et al. (2003) show that the additional benefits from reduction of emissions will exceed the average cost. Hence, the methodology for empirical analysis should base on the dynamic effects in the energy– environment–economy nexus. Theoretical studies mainly believe that any effective policy should take the dynamic nature of the relationships and sight for a long run perspective. The mismatch between theoretical work and empirical studies about the relationship between growth, energy and environment is pointed by Brock and Taylor (2005) and argue that the key is the so-called Environment-Kuznets-Curve (EKC) literature. Brock and Taylor (2005) find a tighter connection between theory and data. The focuses of many empirical studies has been on the relationship between the environment and economic growth (see Dinda, 2004; Stern, 2004 to review). The EKC studies that analyze linear (Shafik and Bandyopadhyay, 1992; de Bruyn et al., 1998), plus quadratic and cubic (Canas et al., 2003; de Bruyn et al., 1998; Heil and Selden, 1999; Roberts and Grimes, 1997) connection between GDP per capita and CO2 emissions, could not explore agreedupon findings. Dinda (2004) find a dynamic link between CO2 emissions and income and CO2 emissions may lead economic growth from a production perspective. It may still be

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possible to observe the emissions to lead energy use if the energy production process of a county is responsible for a major portion of emissions. In another line of empirical research, there are a sizeable number of studies that examine the bond between energy use and economic growth. Since Kraft and Kraft (1978), the literature has tested the Granger (non) causality between energy and income with miscellaneous results (Akarca and Long, 1980; Yu and Hwang, 1984; Erol and Yu, 1987; Hwang and Gum, 1992; Glasure and Lee, 1997). Most of these studies faced a numeral of methodological setbacks; particularly the omitted variables bias. In this regard the first significant study is Stern (1993) who supports using a multivariate analysis. Following Stern (1993), many studies employed recent and powerful time series techniques, (see for example, Stern, 2000; Asafu-Adjaye, 2000; Yang, 2000; Sari and Soytas, 2004; Ghali and El-Sakka, 2004; Lee, 2006). Nevertheless, this line of research also failed to accomplish common results. For instance, Soytas et al. (2007) study the long run Granger causality between emissions, energy use, and growth for US economy, with additional considerations for labor and capital. Though they do not find any evidence of causality between carbon emissions and income; and energy consumption and income, but verify that energy use is the foremost source of emissions. In both directions of literature, and particularly in the EKC literature, the large size of the work is on developed economies. There is still very limited literature that studies the link between energy use, economic development and environmental degradation in Pakistan, yet alone the dynamic link between CO2 emissions and income. Siddiqui (2004) in this regards is one of the pioneer studies that analyze the link between energy and economic growth. According to the results of her model, energy is a major source of economic

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growth and indicates the possibility of inter-fuel substitution which may be result of changes in price structure. The work on environmental Kuznets curve started in 1990’s when the world started to realize that earth average temperature increased dramatically and in the same period the Earth summit in Reo-de-Janeiro, Brazil was held to discuss the Global issue of climate change . In the same period researchers of environmental economics hypothesized Environmental Kuznets curve which got alarming attention rapidly. At the time it is found that the industrialization has cause economies to emit greenhouse gases especially CO2, therefore the first relationship was made between economic growth and CO2 emission. The work was first started by the Grossman and Krueger 1991 to study the effect on NAFTA but, EKC got more attention and importance when Shafik and Bandyopadhyay’s (1992) contribute in the background study for the 1992 World Development Report stating the environmental quality improvement is essential for the sustainable development. Further this study was followed by Shukla and Parikh (1992); Grossman and Krueger (1995); Shafik (1994); Selden and Song (1995); Jaeger et al. (1995); Tucker (1995); Jha (1996); Horvath (1997); Barbier (1997); Matyas et al. (1998); Ansuategi et al. (1998); Heil and Selden (1999); List and Gallet (1999); Brandoford et al. (2000); Stern and Common (2001); Roca (2003); Friedl and Getzner (2003), Dinda and Coondoo (2006); Managi and Jena (2008); Coondoo and Dinda (2008), jalil and Mahmud (2009) and Akbostanci et al. (2009). The economic growth engaged several industries and firms and it tends to increase the demand for energy consumption, subsequently energy consumption was also included in the hypothesis of environmental Kuznets curve. The energy consumption contribute

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highest to the environmental degradation, therefore the energy-economic growth nexus is included as the important determinant of carbon emission. Some of the studies in this regard include Hwang and Gum (1991); Stern (1993; 2000); Masih and Masih (1996); Yang (2000); Glasure (2002); Hondroyiannis et al. (2002); Ghali and El- Sakka (2004); Wolde-Rufael (2006, 2009); Narayan and Singh (2007); Narayan et al. (2008), and Jalil and Mahmud (2009). After determining the energy and economic growth the trade openness is considered to be the next critical contributor to the environmental degradation. The relationship between environment and international trade has been empirically investigated but this effect depends mainly on the policies implemented within the economy. On trade determinant of environmental degradation there are two types of studies, one is for and other is against. The studies which shown trade openness influence negatively include; Suri and Chapman (1998); Schmalensee et al (1998); Beghin et al (1999); Abler et al (1999); Lopez (1994); Cole et al (2000) and Antweiler et al., (2001); Copeland and Taylor (2001); Chaudhuri and Pfaff, (2002); Ozturk and Acaravci (2010) Nasir and Rehman (2011) but it is also believed that trade openness also help to counter the negative effect in helping the economy seek technology to attain the efficiency and after certain level of growth the environmental degradation is also decline and trade play vital role. Therefore, the mix results are found in literature regarding the role of international trade. The studies whose results favor environment because of trade openness are Lucas et al. (1992); Shafik and Bandyopadhyay (1992); Birdsall and Wheeler (1993); Runge (1994); Helpman (1998); Ferrantino (1997), and Grether et al. (2007.

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The other variables which are also considered beside economic growth, energy consumption and trade openness are technological movement and population growth and density. The studies which include population as the determinant of environmental degradation is Dina (2004), Shahbaz (2011) and prior to this Panayotou (1997) indicated population is one the factor contributing to the environmental degradation. The role of the economic growth rate and population density is also an essential factor. Booming economic growth and increasing population do increase moderately the environmental price. Therefore in our study we have included the population with three other major factors of environmental degradation in order to sketch the comprehensive view of economy. Copeland and Taylor (2003) point out, in the absence of change in the structure and technology of the economy, increasing economic activity would result in an equiproportionate growth in pollution or other environmental impacts. This ‘scale’ effect suggests a monotonically increasing relationship between real GDP and pollution and makes economic growth and sustainable development two conflicting goals. However, economic growth generates technological progress; polluting inputs are used more efficiently in the production process or through abatment technologies. If the ‘technical’ effect is strong enough to offset the scale effect, economic growth is compatible with less pollution and the link may become locally decreasing. Three other mechanisms also lead to changes in the output composition of countries: unbalanced growth processes of production factors; biased technological progress between industries or variations in relative world prices. These specialisation patterns between unequally pollution-intensive sectors are usually referred to as ‘composition’ effects. The sources-of-growth explanation of the

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EKC shape relies on that particular argument. If economic growth is first induced by accumulation of a production factor (capital) used relatively more intensively in a polluting sector but then shifts toward accumulation of a factor (labor or human capital) more intensively used in a less or non polluting sector, a straightforward application of Rybczinsky’s theorem leads pollution to follow the same path as the production of the polluting good, an U-inverted pattern. A similar argument can be used to explain why capital abundant economies (rich countries) are expected to pollute more than laborabundant ones (poor countries). All these supply side arguments have two major implications. Firstly, economic growth may not require any environmental policy measure to be compatible with a more efficient use of polluting inputs or natural resources. Secondly, as Copeland and Taylor (2003, Ch. 3.1) indicate, we can have a stable relationship between pollution and technology and primary factors, and between income and these same variables, without having a simple and stable relationship between pollution and income. In plain words, the same level of income may be linked to different levels of pollution, depending on the factor which generated this income level. From a social point of view, the willingness to tolerate the inconveniences of pollution in order to increase income plays a major role in determining the strength of policy responses to environmental damages. Consequently a pure scale effect generated by neutral growth could be overcome by environmental policy measures if, at some level of income, the relative willingness to pay for pollution reduction exceeds the relative growth in income5. The income-pollution relationship is also sensitive to the way pollution is measured (i.e. in levels, per capita or intensity terms), as well as to the level of spatial aggregation of the data. In this paper, we focus on per capita levels of pollution as it

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represents the most common specification of the dependent variable in the IER literature on air pollutants. Given the variety of theoretical foundations, no single functional form can be advocated a priori to link indicators of environmental degradation with measures of economic activity. As the income-pollution relationship is a reduced form function, all the underlying forces which determine its shape for a particular geographical area are subsumed, i.e. they remain unexplained. The early empirical IER literature has addressed the functional uncertainty by retaining three main parametric flexible specifications: quadratic and cubic functions which capture nonlinearities and spline linear functions which gauge thresholds effects. More recently, researchers have turned to nonparametric and semiparametric regressions which leave the functional form unspecified and avoid the risk of choosing an inadequate parametric function. Moreover, the lack of long time series on pollutants at the country level has made authors favour crosscountry/region panel data. The absence of a range of explanatory variables which consistently capture the differences between countries may lead to biased estimates. This heterogeneity issue has been neglected in most of the parametric and nonparametric analysis of IER panels. Moreover, when it has been investigated, the F-tests used were not robust to functional misspecification. Consequently, the estimated IER appears to be highly sensitive to the pollutant or environmental damage considered, to changes in the sample composition (size or/and time periods considered) and to differences in econometric specifications. The case of air pollutants is suggestive, particularly the one for CO2 emissions. Many authors make use of different versions of the database from the Carbon Dioxide Informa-

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tion Analysis Center (CDIAC) to test the EKC hypothesis with a panel of world countries. Holtz-Eakin and Selden (1995) (HES95), Heil and Selden (2001) (HS01) and Schmalensee et al. (1998) (SSJ98) use similar countries’ panel data sets including over 120 countries and covering roughly 40 years6; they estimate time- and country-fixed effects quadratic functions (HES95 and HE01) and a spline-regression model with the same fixed effects (SSJ98). HES95 and HE01 find U-inverted shapes with very different turning points, ranging from US$35,000 to several millions depending on whether per capita income and emissions are measured in levels or in logarithms. SSJ98 get a within sample maximum of US$10,000 with a 10-segment regression. A nonparametric pooled regression is used by Taskin and Zaim (2000) to investigate the link between a CO2 environmental efficiency index and GDP per capita for 52 countries over 1975-1990. Their results point towards a third order polynomial specification. A semiparametric version of the time- and country- fixed effects models used by HES95, HS01, and SSJ98 is estimated by Bertinelli and Strobl (2005) for a panel7 of 122 countries over the 19501990 period. They find that the pooled regression are monotonically increasing. Recently, Dijkgraaf and Vollebergh (2005) and Azomahou et al. (2006) tackle the fundamental assumption of poolability for CO2-IER panels in parametric or nonparametric frameworks respectively. Focusing on the sample of 24 OECD countries mainly responsible for the U-inverted shape found in HES95, HS01 and SSJ98, Dijkgraaf and Vollebergh (2005) compare directly different versions of fixed-effects models to country-specific time- series regressions (with and without trends) and conclude that less than half (11) of the OECD countries display the U-inverted shape depicted by the pooled fixed-effects estimates. Azomahou et al. (2006) check the structural stability of the per capita IER

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with a nonparametric poolability test for a panel of 100 countries over the 1960-1996 period. They conclude that there is a stable cross-sectional relationship through time which allows the pooling of the data. The pooled country-fixed effects nonparametric regression displays a monotonically increasing pattern. In addition, nonparametric estimates are shown to be preferred to parametric ones. Some authors have carried IER estimates with panels at low level of spatial aggregation. List and Gallet (1999) use state levels of SO2 and NOx emissions for the US spanning from 1929 to 1994. They estimate IERs with per capita data and a linear trend. The statefixed effects models produce global EKCs for all states; quadratic and cubic state-specific regressions also yield a majority of respectively 79% and 98% hump-shaped functions for SO2 emissions and a rough 80% EKCs for NOx with both specifications. However, the vast majority of the state-specific turning points fall outside the confidence interval for the peak produced by the fixed-effects models. With the same data, Millimet et al. (2003) compare pooled time- and individual-fixed effects cubic models and spline regressions with time- and state-fixed effects semiparametric specifications8. They show that while the EKC obtained for per capita NOx emissions is robust to the estimation strategy, the functional forms for SO2 vary substantially. However, the null hypothesis of equality between the spline or cubic models and the partial linear models is rejected for both pollutants. These authors also compute specific semiparametric estimates for selected US states9 and they conclude that the EKC shape remains robust at the state level for NOx, but the results for SO2 are mixed. De Groot et al. (2004) utilise a panel dataset on Chinese provinces covering the period 1982-1997. They investigate the IER for wastewater, waste gas (aggregate emissions of CO2, NOx and SO2) and solid waste from

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the industrial sector with the pooled region-fixed effects model. They contrast the results obtained when expressing the dependent variable in levels, per capita and intensity terms. The relationship is shown as being monotonically decreasing for wastewater regardless of the dependent variable, increasing (respectively decreasing) for waste gas with the explained variable in levels or per capita (respectively intensity) terms and very versatile for solid waste depending on the dependent variable used. More recently, Aldy (2005) tests the EKC hypothesis for production as well as consumption-based per capita CO2 emissions in the US at the state level. The author globally validates the EKC shape with the state- and year-fixed effects quadratic models as well as with the spline regressions. He provides evidence of significant different peaks for both CO2 series. When statespecific quadratic models are fitted, the equality of the estimated functions and EKC peaks between states is rejected despite the fact that the vast majority of the states does depict EKC-type relationships. Since the data span over a long time period, Aldy (2005) also controls for common stochastic trends in the time-series and concludes that only about 20% of the state-specific relationships were cointegrated10.

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Chapter – 3

3.1

Methodology and Data

Data and Type

Our empirical findings are based on the data from different sources. We obtained GDP, exports and CO2 emission data from World Bank (WB); Employed Labor Force from Pakistan Economic Survey (PES) and SBP website; Gross Fixed Capital Formation (GFCF) from IFS; and energy from PES and WB. The data are used in log form. The currency unit of the GDP, exports and GFCF is Rupee in Million. Since we are using aggregate data of energy, these are converted into single unit by applying scientific formulae of the measurement of energy. The tons of fuel consumption, the million cubic feet of natural gas, and metric tons coal consumption are used to convert each energy source into Giga-watt Hour (Gwh) of energy it could produce. The electricity consumption is already measured on Gwh. There could be many other units of energy, e.g., joules, calories, etc., but the measureable values of these units would have increased enormously, that could be difficult to use. This calculation method and data can be obtained from the authors upon request.

Selected Variables These are the selected variable in this model Y = log of real GDP in Rs. Million, E = log of energy consumption, converted to Giga-watt-hours. CO2 = the log of emission of carbon dioxide per capita measured in tons, K = the log of gross fixed capital formation, in Rs. Million, L = log of employed labor force in Million persons and

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X = the log of exports in Rs Millions.

3.2

Estimation Techniques

For this model we develop a methodology based on the Pesaran et.al. (2001), that provide a bounds test approach to find out the short and long run relationships among the variables of interest. It would also base on the results of the unit root test. A priori, we can assume different order of integration of the variables of the model. This is made clear in section 5.1. The Pesaran el.al (2001) methodology is based on the Autoregressive Distributed lag model. The ARDL approach involves two steps for estimating the long-run relationship. The first step is to examine the existence of a long-run relationship among all variables in the equation under examination. Conditional upon the confirmation of cointegration, in the second stage, the long-run coefficients and the short-run coefficients are estimated using the associated ARDL and ECMs. To test for cointegration in model (2) by the bounds test, the following conditional Unrestricted Error Correction Model (UECM), is constructed as Notice that this is almost akin to traditional Error-Correction Model. The alphabets i,j,k,m,n and s in the subscript of each variables define the lag structure of that variable. If the optimal lag length is found one using Schwarz criterion, then this lag length is used for each variable. To investigate the presence of long-run relationships among the Y, E, CO2, K, L and X, under the bounds test approach formulized by Pesaran, et al. (2001), after regression of Equation (3), the Wald test is applied. The Wald test can be conducted by imposing restrictions on the estimated long-run coefficients of Y, E, CO2, K, L and X, The null hypothesis is H0: θ0 = θ1 = θ2 = θ3 = θ4 = θ5 = 0 where there

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is no cointegration among the variables. The F-stat is computed and compared with the critical value (upper and lower bound) given by Pesaran, et. al.(2001). If the F-computed exceeds the upper critical bound, then the hypothesis of no cointegration will be rejected. However, if the F-computed is less than the lower critical bound, then H0 cannot be rejected, concluding that there is no cointegration among the variables. If the F-computed falls between the lower and upper bounds, then the result is inconclusive. The empirical evidence for the existence of an EKC has been found in various studies. These studies share some common characteristics with respect to the data and methods employed. Most of the data used in these studies are cross-sectional/panel data. The following reduced form model is used to test the various possible relationships between pollution level/environmental pressure and income: Here all the variables are self-explaining. Model (4) provides us to test several shapes of environment–economy development relationships where energy demand is used an intervening variable. It follows;

I. β1 = β2 = β3 = 0. A flat pattern or no relationship.

II. β1 >0 and β2 = β3 = 0. A monotonically increasing relationship or a linear relationship between environment and economy III. β1 < 0 and β2 = β3 = 0. i.e., monotonically decreasing relationship between both. IV. β1>0, β2< 0 and β3= 0. An inverted-U-shaped EKC. V. β1 0 and β3= 0. A U-shaped curve. VI. β1 0. A cubic or N-shaped curve.

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VII. β1 0 and β3< 0. Opposite to N-shaped curve. Proposed Model In this paper we investigate the dynamic relationship between energy use, CO2 emissions and GDP (as suggested by Xepapadeas, 2005; Kolstand and Krautkraemer, 1993; and Jorgenson and Wilcoxen, 1993) in an emerging Asian economy, accounting for possible affects of labor and fixed capital formation3. The paper attempts to make a contribution to the existing empirical literature by combining the two lines of empirical research in a developing, using relatively new time series techniques that cater some of the methodological issues of the past studies. Besides, the choice of the variables is not random or arbitrary but relies on theory,4 which may be missing from many empirical findings. We hope the empirical results of this study may be helpful in guiding policy makers to devise long run sustainable policies. Y = f(E, CO2, K, L, X) (1) Where Y is log of real GDP in Rs. Million, E is log of energy consumption, converted to Giga-watt-hours. CO2 is the log of emission of carbon dioxide per capita measured in tons, K is the log of gross fixed capital formation, in Rs. Million, L is log of employed labor force in Million persons and X is the log of exports in Rs Millions. The econometric specification of the model will be;

Where expected signs of the parameters are: α1 > 0, α2 < 0, α3 > 0, α4 > 0, α5 > 0.

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3.3

Descriptive analysis

It first introduces the distributional tests and the robust scale and shape measures and then applies these approaches to cross-sectional PCE distributions for the world as well as for group-specific panels formed according to income, geographic, initial PCE levels and RIA membership criteria. Our empirical findings are based on the data from different sources. We obtained GDP, exports and CO2 emission data from World Bank (WB); Employed Labor Force from Pakistan Economic Survey (PES) and SBP website; Gross Fixed Capital Formation (GFCF) from IFS; and energy from PES and WB. The data are used in log form. The currency unit of the GDP, exports and GFCF is Rupee in Million. Since we are using aggregate data of energy, these are converted into single unit by applying scientific formulae of the measurement of energy. The tons of fuel consumption, the million cubic feet of natural gas, and metric tons coal consumption are used to convert each energy source into Giga-watt Hour (Gwh) of energy it could produce. The electricity consumption is already measured on Gwh. There could be many other units of energy, e.g., joules, calories, etc., but the measureable values of these units would have increased enormously, that could be difficult to use. This calculation method and data can be obtained from the authors upon request.

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Chapter – 4

Historical Trends

In order to get an overview of the main trends for the CO2 series in levels as well as in per capita terms, Table 2.1 provides some descriptive statistics for the whole 1960-2002 period. Columns 2 to 4 concern total emissions and report for each country/area initial and final total emissions, with the corresponding emissions’ share for each country/aggregate group relative to the reference sample CDIAC166 in brackets. The emission’s average growth rate over the 43 investigated years is provided in column 4. Summary statistics for carbon emissions in per capita terms are presented in columns 5 to 12. They include initial and final per capita levels with their related rank in the CDIAC166 individual sample, as well as location and variance statistics related to the time dimension of the series over the 43 years. Figure 2.1 displays graphically time patterns of total versus per capita CO2 emissions for the world (CDIAC World, CDIAC166 and CDIAC 130) samples as well as for the main country groupings (essentially income and geographical groups). In lines 1 to 3 in Table 2.1, we can see that the CDIAC166 sample we built for the empirical investigation accounts for almost 97% (95%) of the total emissions of the CDIAC World in 1960 (2002), while the world sample without outliers CDIAC130 represents a fair 95% (90%) share of total emissions. This means that the most erratic series are not large carbon emitters. The three world samples display very similar trends. CDIAC World emissions went up from 2577 to 6973 thousands millions of metric tons during the 43 years, which represents an average growth rate of 2.4% per year. In per

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capita terms, the increase over the same period is significantly lower, 0.6% per year for the three world samples. The initial per capita emissions for CDIAC World is 0.85 metric tons of per capita emissions (mtPCE henceforth), which ranks CDIAC World at the 36th position among the CDIAC166 individual countries. By 2002, the per capita level and rank were 1.12 and 70th respectively. If we focus on selected individual countries from lines 4 to 14, we can see that the USA are by far the largest individual total carbon emitter in 1960 and 2002, with respectively 32% and 24% of CDIAC166 world’s emissions. In 1960, Germany and China were at the second and third position, with respective shares of 8.9% and 8.1%. By 2002, while Germany managed to stabilize total emissions at its 1960 level, emerging economies such as China and India strongly increased their total emissions (by 3.6% and 5.5% per year respectively) and emissions’ share (+6% and +3.7% ). The largest increases in total emissions since 1960 took place in oil-producing countries (such as United Arab Emirates, Qatar, etc). It is important to highlight that in per capita terms, Chinese and Indian emissions are far below the ones from more advanced economies. Luxembourg, UK, Germany and USA were the only rich and diversified economies producing over 3 mtPCE in 1960. These levels decreased over the period for the three European countries while US emissions have kept increasing on average by 0.5%. By 2002, Luxembourg, USA, Australia and Canada were the only diversified advanced economies producing more than 4 mtPCE, i.e. four times the world average level in 2002. The aggregate series for the country groupings are shown in lines 14 to 28 of Table 2.1. In the Income groups, we notice that economies which have become the richest by 2002 (HI group) have increased their emissions by 1.8% per year on average. Despite the fact

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that the emissions’ share of the latter group has declined in favor of the low and middle income economies, the wealthiest countries in 2002 are still the most intensive polluters in total as well as in per capita terms, and the emissions gap between the richest and the poorest has widened. The latter comment also holds for most intensive polluters in 1960 with respect to middle or less intensive ones. From the geographical point of view, East & Pacific Asia and South Asia has become the largest carbon emitter, followed by North America and Europe & Central Asia. In per capita terms, North America is by far the major polluter since 1960 and the pollution gap with respect to all other world regions has deepened. Regarding the time trends in Figure 2.1, it is very clear on the left graphs that total emissions for all samples (world, income, regions, etc) have been strongly raising before the oil shocks, and kept increasing afterward at a lower pace. The aggregate emissions for the CDIAC world raised by roughly 2750 millions (+106%) of metric tons from 1960 until 1980 and 1340 millions metric tons (+25%) during the 1980-2002 period. The growth rates for the CDIAC166 and CDIAC 130 samples are fairly close. During the latter two sub-periods, total emissions have increased by +75% and +20% in the High Income as well as the OECD countries, by +140% and +16% in the Middle Income and +243% and +148% in the Low Income category. Regarding the geographic groups, the European & Central Asia group is the only one that has reduced its total emissions, by -25%, after the second oil shock. However, the most striking empirical fact comes from the series of per capita carbon emissions: most of them clearly stabilize either after the first or the second oil shock. In that respect, our data are in line with McKitrick and Strazicich (2005). In the presence of structural breaks in the individual and aggregate

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series, we may expect to find the same feature in the distributional dynamics of per capita emissions, i.e. nonstationary cross-sectional distributions that stabilize at some point. This is the question we address in the next section.

4.1

Evolution of cross-section distributions

Distributional dynamics has been extensively used in the income convergence literature, as well as in the empirics of PCE convergence with discrete and continuous approaches. As noted in section 2.2.2, this approach points towards ‘relative persistence’ worldwide, i.e. countries tend to keep their position relative to the world average but convergence is

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detected for a group of industrial economies. However, these conclusions have several methodological shortcomings65. For example, lack of consistency of the estimates when few observations are available and sensitivity of the results to initial conditions as well as to arbitrary scaling of the data. Our approach intends to address in a simple way these last two issues by proposing estimates which require neither assumptions on initial conditions nor scaling of the data. Robust scale and shape statistics and distributional tests Instead of adopting the Markovian process framework, we propose a simple exploratory distributional analysis. Firstly, we compute annual cross-sectional univariate kernel densities for unscaled PCE levels and report the results in 3D plots by stacking the annual estimates. The density estimates at time t are given by

where nt is the sample size in year t, ht is the smoothing parameter (bandwidth) for ˆ ft and K(.) represents a kernel function. We use Silverman’s rule of thumb to determine ht and a gaussian kernel for all years. Histograms with unequal bins are also presented to provide empirical frequency estimates for the first, middle and last years of the panel. Secondly, we use robust scale and shape distributional measures to characterize how cross-sectional distribution evolve over time. Indeed, a more peaked density over time would suggest absolute convergence while a flattening shape with heavier tails provide hints for divergence. In between lies conditional convergence, which is compatible with a variety of distributional dynamics, i.e. tendencies to bi-polarity or multimodality that may stabilize once the balanced growth path is achieved for the ‘converging’ economies.

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Many authors emphasize that scale and shape distributional measures based on statistical moments display excessive sensitivity to outliers. Moreover, it is now clear that the standard Kurtosis coefficient is heavily influenced by the tail weights. In that context, measures distinguishing peakedness from asymmetry and/or tail weight are to be preferred. The presence of highly volatile series also calls for a robust approach, which avoids arbitrary deletion of countries.

4.2

Pakistan: Some Energy Facts

Over the years the demand of energy has increased manifold, particularly for transportation and industrial sectors. The consumption share of natural gas for house hold and industrial use increased during last twenty years, while the share of power sector decreased during this period. The cement sector has substituted the energy use from gas to coal. The fertilizer sector has also shown a decrease in its percentage share of the use of gas. Transportation has increased its share in use of natural gas, but during last two years, it shows a downward trend in its growth. Transportation and power sectors are the major consumers of the oil products throughout the period of analysis, and their share has increased over time. The household, agricultural and industrial consumption of oil products has decreased during last twenty years. Households are the largest consumer of electricity. The share of industrial sector decreased about 10% over the last two decades. This reflects that the resource substitution by the industrial sector from relatively costly (electricity) to cheaper (gas) one. Coal being the least technically applied source has never been preferred by the industrial sector before 1990s. The major use (about 90%) has been brick kilns. The cement sector started using coal in 2001 and now its share of

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total use has increased to 61% which reflect resource substitution. Households and power sector decrease use of coal. Power sector peaked its use of coal in late 1990s. Figure A5 shows the growth rate of the energy demand. The demand for coal has shown huge oscillations whereas gas consumption has been steady. The use of coal and electricity have show similar trend, while the use of gas and oil products has shown the same pattern. The consumption of electricity has also been volatile during past forty years.

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Chapter – 5

5.1

Findings & Results

Unit root Test

Table 2 presents the results of the unit root test using ADF test. The series of CO2 emissions, GDP growth rate, exports and population are significant at level, i.e., they do not exhibit stochastic trend. Both GDP and GDP growth have significant intercept, despite different order of integration. In all this the result of exports (ex) has shown interesting feature: Pakistan’s export are stationary at level; but with significant trend and intercept. This is deterministic trend, not the stochastic one. The distinction between a deterministic and stochastic trend has important implications for the long-term behavior of a process: (i) time series data with deterministic trend always revert to the trend in the long run (the effects of shocks are eventually eliminated) the intervals for forecasting comprise constant width; (ii) time series with a stochastic trend never recover from shocks to the system (the effects of shocks are permanent). Forecast intervals grow over time. Thus the stochastic trend in GDP, energy demand, energy supply and imports show that these series are difference stationary. The maximum order in integration is one in this model while minimum is 0, so we can not apply general cointegration technique (e.g., Johansen and Juselius (1990)) on this model. We confirmed that none of the variables in the model is I(2) using KPSS test. So the mixture of order of integration confirms the use of so-called autoregressive distributed lags (ARDL) model and long run causality test. We apply ARDL model. used to estimate the unrestricted ECM for the bounds test.

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5.2

The Long run

The long run analysis of our E-E-E model6 is based on the UECM used in econometric literature. The evidence of such modeling procedure is Narayan (2004), Altinay (2007) and Sultan (2010). Most of the individual coefficients are statistically significant at 5 and 10% level of significance. Here our objective is to compute F stat using Wald test of joint significance of this unrestricted model to test the hypothesis of long run relationships among the variables. To compute F-state for bounds testing, we applied Wald test of joint significance of coefficients θ0, θ1, θ2, θ3, θ4 and θ5. In table 3 we have gone through the estimation Autoregressive distributed lag Model of equation 3. 5.3

Diagnostic Tests

In dynamic time series analysis the selection of variables for a model is critical. Almost all individual time series which show trend, are supposed to have serial correlation and specification problem. But to use a time series model we have to perform some diagnostic tests on the model of unconstrained/unrestricted error correction model. Table 4 shows that our specified UECM passes all the diagnostic tests, i.e., (i) The residuals are normally distributed, because we fail to reject the null of Normality in JB test; (ii) the Fstat in Ramsey RESET test shows that model is correctly specified; (iii) For serially independent residuals, we used BG LM test and failed to reject the null hypothesis of no auto correlation.; and (iv) and the variance of residuals is persistent, as pointed by ARCH LM test for the estimated model. For the dynamic stability of the UECM model, the inverse root before and after differencing (Figure A6 and A7 in the appendix) are confirmed. The before differencing inverse root exhibit the instability, thus differencing is required. After differencing none

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of the roots lay on the X-axis, it's clear that we have three complex pairs of roots. Accordingly, the short-run dynamics associated with the model are quite complicated. For the bounds test, the F-stat 4.48 is compared with lower bound at 5% level of significance from the Pesaran et.al (2001) table in case III to test the relationships at level, with drift but no intercept at k = 5 The statistics in table 4, shows that the computed F-stat is greater than the upper bound, indicating the existence of long run relationships between variables of the model. Thus cointegration exists and the estimated coefficient of equation 3 can be used to calculate the long run elasticities of the model. The long run elasticities can be computed as: ξy, E = - (θ1/ θ0 ) = 1.70 ξy, co2 = -( θ2 / θ0 ) = -1.20 ξy, K = -( θ3 / θ0 ) = 0.35 ξy, L = -( θ4 / θ0 ) = 0.447 ξy, X = -( θ5 / θ0 ) = 0.133 In the long run one percentage increase in energy use leads to 1.7 % increase in GDP. The energy is positively linked with aggregate demand. On contrary, the effect of carbon emissions on economy is negative. These absolute values of the two elasticities are greater than unity. Thus reflecting, the negative externality produced from the use of energy (particularly use of fossil resources) in the shape of CO2 emission can retard economic growth. Nevertheless, the net effect of energy is positive (i.e., 1.70-1.20 = 0.5) and less than unity. This implies that for Pakistan still we can use the energy resources with positive output effect.

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Similarly the positive elasticities of capital and labor reflect that both have standard theoretical interpretation, but interestingly, in the presence of externalities, these results imply decreasing return to scale production function. The exports elasticity of demand is very low and statistically insignificant. This result contradicts theory, yet, due to the presence of the factors that directly affect the economy, so the ambiguous effect of exports remained insignificant. 5.4

The Short Run

For short run we estimated the error correction model of equation 4, by estimating the logged model at levels then used error term as an explanatory variable in the error correction model. The Results are presented in table 5. We can notice that the coefficient of error term Zt is negative and statistically significant, which also confirms the existence of cointegration between the variables of the model. The magnitude of the coefficient implies that about 16% of the disequilibrium is corrected in one period of time. 5.5

Environmental Model

Our model so far has shown important implications for the Pakistan economy. This E-E-E model is now being used under the methodology discussed; we developed seven hypotheses to be tested for equation 4.7. (Note: all the coefficients are statistically significant at 1% level of significance.) According to our setting, we find that hypothesis (iv) which reflects EKC does not hold in case of Pakistan’s data. Rather it is a cubic and opposite to an N-shaped curve as assumed in hypotheses (iv). Hypotheses (i) is rejected through Wald test.8 In nutshell, we can say that the EKC is not in place in Pakistan, given the energy use data. This cubic function also elaborates that at the early stages of economic growth, Pakistan has been an

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agrarian economy, with less use of fossil fuels and had not any environmentally negative impact. But with the wave of industrialization, over the long run the emission grow and after some point in time when a certain level of GDP per capita is achieved, the environmental degradation increases. Thus this curve which is monotonically decreasing at early stages of growth, become increasing at higher income levels; and after some turning point, it will look like an EKC. Since, EKC have been used as an argument that economic growth and increased environmental quality go hand in hand, this may not be true for the case of developing countries (Richmond and Kaufman, 2006).

43

Chapter – 6

Conclusions and Recommendations

It is a matter of satisfaction that PML-N Government has initiated a campaign to create awareness among the masses, regarding deadly and unaffordable effects of pollution. This awareness would be enhanced through newspapers, television, workshops and seminars gradually. Awareness in masses could be very helpful to curtail pollution problems hurriedly. Indian government has done a lot to curtail the pollution and yield very positive results by initiating awareness campaign. A large number of Indian drivers turn their vehicle engines off at traffic signal, not only to save some fuel but also to control pollution generated by fuel. Forests not only help to reduce the environmental problems but also control temperature. In Pakistan a large number of trees are being “murdered” without any adequate efforts to plant new plants. This leads to deforestation, which has caused a rise in the pollution levels and distortion of natural order. Government should not only take measures to protect the existing forests but also start growing non-natural jungles like Changa Manga. I do want to share with you that why I chose the environmental issue at this point in time when the whole world is facing financial constraints. Actually the global recession has provided us an opportunity to increase our exports to the desired levels but to my understanding the environmental issues are coming in the way quite prominently. To my understanding, the private sector in Pakistan alone cannot handle the environment related issues and neither the government can do it. Both the sides would have to join hands to tackle the situation. Like neighbouring India who sensing the gravity of pollution situation Delhi, converted all its public transport on CNG, we would have to take some solid and sector specific measures to deal with the monster.

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We should start “war against pollution” to save not the economy alone but the whole nation as well otherwise our future generation will not forgive us. In this study we have used an ARDL approach to find the long run nexus between E-E-E and found some robust results after estimating the long run elasticities. The demand elasticity of energy is positive and greater than unity, but the negative externality produced due to the use of energy, may reduce this effect. The elasticities of capital and Labor show that due to negative by-products of energy use, the production function exhibits decreasing return to scale (still, this hypothesis need further investigation). We also estimated the model to test the EKC in the presence of energy demand and find no such evidence. The energy substitution behavior is found as claimed by Siddiqui (2004), particularly industrial/ cement sector has switched from the use of other resources to the coal. In summary we can forward following implications of our analysis Implications: It is found that the energy use has positive impact on economy it is a dire need of time to explore more sources of energy which can be helpful in mitigating the increasing demand of energy. The fuel substitution from costly to cheaper one should be monitored by the government and carbon tax should be imposed on the industries that produce more pollutants. The green technologies can reduce these pollutants. The EKC is used to support do nothing policy, which unfortunately cannot happen for the case of Pakistan. This also may not be useful due to turning points that loudly need to think of the factors that explain it, before making assessments about the necessary components of environmental policy. After an initial stage of economic development we

45

have to take serious measure to tackle the issues of environmental degradation (as a result of energy use). This analysis limits in many ways. For future research the EKC can be tested for the turning points. This would be interesting to find out the income per capita that limits the relationship between E-E-E. In order to avoid functional specification bias, we follow many authors who turned to nonparametric estimation techniques to model this reduced form function. Most of them made the implicit assumption that every region or country included in the panel shares the same pollution-income relationship, up to some specific fixed temporal and/or individual effects. Our findings show that the temporal poolability assumption holds in the Spanish provinces for three (CH4, CO, CO2) out of four air pollutants when poolability tests robust to functional misspecification are employed. The pooled nonparametric regressions give rise to U-inverted income-pollution relations. However, these hump-shaped functions only reflect relatively short-run cross-sectional regressions for different periods. Our parametric and nonparametric tests reject overwhelmingly the null hypothesis of spatial homogeneity as well as the goodness-of-fit of the time- or individual-fixed effects semiparametric models. Investigating the reduced form function of the pollution-income relationship for per capita air pollutants emissions with fixed-effects panel data models, be it parametric or semiparametric, failed to account for differences in functional shapes between regions. It is likely that heterogeneity would be even greater when applied to cross-country rather than cross-regional panels.

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Having established that spatial heterogeneity matters in panels and blurs the EKC picture, one may consider three avenues of future research when assessing the income pollution reduced form function. From an econometric point of view, the evidence points toward the use of estimation methods which better account for differences between countries/ regions such as non- or parametric quantile regressions, parametric random coefficients estimators, varying smooth coefficients models or country/region specific regressions when large individual series are available. From an economic point of view, the persistent heterogeneity in patterns between regions/countries support the extreme sensitivity of the income-pollution relation to differences in regional/country-specific factors, such as factors endowment, sources of growth, differences in production and abetment technologies or local sensitivity to environmental damages. At the same time, structural stability through time points toward the existence of some stable structural determinants which shape the income-pollution relation. The identification of these determinants certainly would deserve more research effort.

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Annexure

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