June 7, 2010 15:15 WSPC/MEDJ S1793-8120 00018

Middle East Development Journal, Vol. 2, No. 1 (2010) 139–158c© Economic Research ForumDOI: 10.1142/S1793812010000186

LONG AND SHORT–RUN LINKAGES BETWEENECONOMIC GROWTH, ENERGY CONSUMPTION

AND CO2 EMISSIONS IN TUNISIA

HOUSSEM EDDINE CHEBBI

Faculte des Sciences Economiques et deGestion de Nabeul (FSEGN) and

Laboratoire d’Economie et de Gestion Industrielle (LEGI)University of 7 November at Carthage, Tunisia

chebbihe@planet.tn

Received 18 May 2009Revised 4 February 2010

This paper provides some insights into the linkages between energy consumption, carbonemissions and the sectoral components of output growth using Tunisian data over theperiod 1971 to 2005.

Results of the long–run analysis do not support the neutrality hypothesis betweenenergy consumption and sectoral output growth in Tunisia. Results from short–rundynamics indicate that linkages between energy consumption and economic growth,as well as economic growth and environmental pollution are not uniform across sec-tors (agriculture, industry and services). These outcomes suggest that prudent energyand environmental policies should distinguish the differences in the relationship betweenenergy consumption and output growth by sector.

Keywords: Economic growth; energy consumption; CO2 emissions; cointegration;generalized impulse response function; Tunisia.

1. Introduction

The relationship between energy consumption and economic growth, as well aseconomic growth and environmental pollution, has been one of the most widelyinvestigated questions in the economics literature during the last three decades.Whether energy consumption stimulates, delays or is neutral with respect to eco-nomic activities has motivated curiosity and interest among economists and policyanalysts to find out the direction of causation between energy consumption andeconomic variables.

The pioneering study by Kraft and Kraft (1978) found a unidirectional Grangercausality running from output to energy consumption for the United States usingdata for the period 1947–1974. Subsequent studies on this subject with differenttime periods, countries, econometric techniques, and proxy variables have reportedmixed results. Depending upon the direction of causality, the policy implications

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140 H. E. Chebbi

can vary considerably for energy conservation, emission reduction and economicperformance.

Earlier studies have used Vector Autoregression (VAR) models to test whetherenergy use causes economic growth or whether energy use is determined by the levelof output. Earlier empirical work on this topic has used Granger (1969) or Sims(1972) tests. Their empirical findings are generally inconclusive (see for example,Akarca and Long 1980; Yu and Hwang 1984; Yu and Choi 1985; Ebohon 1996;and Erol and Yu 1987). With advances in time series econometric techniques, morerecent studies have focused on Vector Error Correction Models (VECM) and thecointegration approach (see for example, Masih and Masih 1997; Asafu-Adjaye 2000;Stern 2000; Yang 2000; Soytas and Sari 2003; Oh and Lee 2004; Ghali and El-Sakka2004; Wolde-Rufael 2005; and Lise and Van Montfort 2007). However, existingoutcomes have varied considerably.

The relationship between output growth and pollution level has also been welldiscussed in the literature on the Environmental Kuznets Curve (EKC) where envi-ronmental degradation initially increases with the level of per capita income, reachesa turning point, and then declines with further increases in per capita income(Grossman and Krueger 1991).a Whether continued increase in national incomebrings more degradation to the environment is critical for the design of devel-opment strategies for an economy (Ang 2007). Hence, a number of studies haveattempted to assess the link and to test for linear, as well as quadratic and cubicrelationships between per capita income and CO2 emissions. These studies deal withenvironmental degradation measure(s) as the dependent variable(s) and income asthe independent variable and provide mixed results.b

The existing literature reveals that empirical outcomes are not conclusive andcannot support policy recommendations that can be applied across countries. Inaddition, few studies test the two nexus of economic growth–energy consumptionand economic growth–environmental pollution under the same multivariate empir-ical approach (Table 1 provides a succinct summary of some recent studies usingtime series techniques). The aim of this paper is to investigate long and short–run linkages between economic growth, energy consumption and carbon emissionsusing Tunisian data. These linkages were largely under-considered and unansweredfor policy makers in Tunisia and this research attempts to present some findings tobetter integrate environmental concerns into economic development decisions.

aAntweiler et al. (2001) and Coxhead (2003) postulate that this non-linear relationship betweenenvironmental pollution and income levels can be explained by three factors: scale, composition,and technique effects. The scale effect occurs as pollution increases with the size of the economy.The composition effect refers to the change in the production structure of an economy fromagriculture-based to industry and service-based that results in the reallocation of resources. Finally,the pollution–income relationship also depends on techniques of production. An improvement intechniques of production, i.e., the technique effect, may reduce the amount of pollutant emissionsper unit of production.bFor a review of the Environmental Kuznets Curve research see for example the works of Stagl(1999), Yandle et al. (2002), Dinda (2004) and Stern (2004).

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Economic Growth, Energy Consumption and CO2 Emissions in Tunisia 141

Table

1.

Over

vie

wofso

me

rece

nt

studie

son

the

link

bet

wee

nec

onom

icgro

wth

,en

ergy

consu

mpti

on

and

CO

2em

issi

ons.

Auth

ors

Countr

ies

and

data

per

iods

Vari

able

sM

ethods

Main

Res

ult

s

Ang

(2007)

Fra

nce

(1960–2000)

GD

PC

O2

emis

sion

Com

mer

cialen

ergy

use

Auto

regre

ssiv

edis

trib

ute

dla

gm

odel

(AR

DL)

Gra

nger

causa

lity

GD

Pca

use

sC

O2

emis

sions

and

ener

gy

consu

mpti

on

(long

run)

Unid

irec

tionalca

usa

lity

from

gro

wth

of

ener

gy

use

toG

DP

gro

wth

(short

run)

Ang

(2008)

Mala

ysi

a(1

971–1999)

GD

PC

O2

emis

sions

Com

mer

cialen

ergy

use

Coin

tegra

tion

Gra

nger

causa

lity

CO

2and

ener

gy

use

are

posi

tivel

yre

late

dto

GD

Pin

the

long-r

un.

Causa

lity

from

GD

Pto

ener

gy

consu

mpti

on

gro

wth

,both

inth

esh

ort

-run

and

long-r

un

Asa

fu-A

dja

ye

(2000)

India

(1973–1995)

Indones

ia(1

973–1995)

Philip

pin

e(1

971–1995)

Thailand

(1971–1995)

GD

PE

ner

gy

consu

mpti

on

Ener

gy

pri

ces

Coin

tegra

tion

Gra

nger

causa

lity

Bid

irec

tionalca

usa

lity

inP

hilip

pin

eand

Thailand

Unid

irec

tionalca

usa

lity

(ener

gy

cause

sG

DP

)in

India

and

Indones

ia

Fri

edland

Get

zner

(2003)

Aust

ria

(1960–1999)

GD

PC

O2

emis

sions

Tem

per

atu

reIm

port

sV

alu

eadded

inth

ese

rvic

ese

ctor

Coin

tegra

tion

Gro

wth

ofth

ete

rtia

ryse

ctor

(ser

vic

ese

ctor)

cete

ris

pari

bus

reduce

sC

O2

emis

sions

Import

sas

an

auxilia

rym

easu

refo

r‘e

xport

ing’C

O2-e

mit

ting

indust

ries

als

ore

duce

CO

2em

issi

ons

The

main

dri

vin

gfo

rce

beh

ind

CO

2

emis

sions

isG

DP

gro

wth

Chen

gand

Lai(1

997)

Taiw

an

(1955–1993)

GD

PE

ner

gy

consu

mpti

on

Coin

tegra

tion

Ganger

causa

lity

Unid

irec

tionalca

usa

lity

from

GD

Pto

ener

gy

consu

mpti

on

Ero

land

Yu

(1987)

Ger

many

(1952–1982)

Italy

(1952–1982)

Canada

(1952–1980)

Fra

nce

(1952–1982)

U.K

.(1

952–1982)

Japan

(1952–1982)

GN

PE

ner

gy

consu

mpti

on

Gra

nger

causa

lity

Bid

irec

tionalca

usa

lity

for

Japan

Ener

gy

consu

mpti

on

cause

sG

NP

(Canada)

GN

Pca

use

sen

ergy

consu

mpti

on

(Ger

many

and

Italy

)N

oca

usa

lity

for

Fra

nce

and

U.K

.

Ebohon

(1996)

Nig

eria

(1960–1984)

Tanza

nia

(1960–1981)

GD

PG

NP

Ener

gy

consu

mpti

on

Gra

nger

causa

lity

Sim

ult

aneo

us

causa

lity

bet

wee

nen

ergy

and

econom

icgro

wth

for

both

countr

ies

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142 H. E. Chebbi

Table

1.

(Continued

)

Auth

ors

Countr

ies

and

data

per

iods

Vari

able

sM

ethods

Main

Res

ult

s

Gla

sure

and

Lee

(1997)

Kore

a(1

961–1990)

Sin

gapore

(1961–1990)

GD

PE

ner

gy

consu

mpti

on

Coin

tegra

tion

Gra

nger

causa

lity

No

causa

lity

for

South

Afr

ica

GD

Pca

use

sen

ergy

consu

mpti

on

inSin

gapore

Ghali

and

El-Sakka

(2004)

Canada

(1961–1997)

Ener

gy

consu

mpti

on

GD

PC

apit

alst

ock

.Tota

lem

plo

ym

ent

Coin

tegra

tion

Gra

nger

causa

lity

Vari

ance

dec

om

posi

tion

Bid

irec

tionalca

usa

lity

Short

-run

flow

ofca

usa

lity

isru

nnin

gin

both

dir

ecti

ons

bet

wee

nG

DP

gro

wth

and

ener

gy

use

Jum

be

(2004)

Mala

wi(1

970–1999)

GD

PE

lect

rici

tyco

nsu

mpti

on

Agri

cult

ura

lG

DP

Non-

agri

cult

ura

lG

DP

Gra

nger

causa

lity

Coin

tegra

tion

Bid

irec

tionalca

usa

lity

bet

wee

nel

ectr

icity

consu

mpti

on

and

GD

PU

nid

irec

tionalca

usa

lity

from

Non-

agri

cult

ura

lG

DP

toel

ectr

icity

consu

mpti

on

(Gra

nger

causa

lity

)U

nid

irec

tionalca

usa

lity

from

GD

Pand

Non-

agri

cult

ura

lG

DP

toel

ectr

icity

consu

mpti

on

(EC

M)

Masi

hand

Masi

h(1

997)

Kore

a(1

955–1991)

Taiw

an

(1952–1992)

GN

PE

ner

gy

consu

mpti

on

Consu

mer

pri

cein

dex

Coin

tegra

tion

Vari

ance

dec

om

posi

tion

Impuls

ere

sponse

funct

ion

Bid

irec

tionalca

usa

lity

Oh

and

Lee

(2004)

Kore

a(1

981–2000)

GD

PE

ner

gy

consu

mpti

on

Ener

gy

pri

ceC

apit

al

Labor

Coin

tegra

tion

Gra

nger

causa

lity

No

causa

lity

bet

wee

nen

ergy

and

GD

P(s

hort

run)

Unid

irec

tionalca

usa

lre

lati

onsh

ipfr

om

GD

Pto

ener

gy

(long

run)

Ste

rn(2

000)

U.S

.(1

948–1994)

GD

PE

ner

gy

input

Capit

alin

put

Labor

input

Coin

tegra

tion

Gra

nger

causa

lity

Ener

gy

cause

sG

DP

Mid

dle

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Economic Growth, Energy Consumption and CO2 Emissions in Tunisia 143

Table

1.

(Continued

)

Auth

ors

Countr

ies

and

data

per

iods

Vari

able

sM

ethods

Main

Res

ult

s

Soy

tas

and

Sari

(2003)

Arg

enti

na

(1950–1990)

Italy

(1950–1992)

Kore

a(1

953–1991)

Turk

ey(1

950–1992)

Fra

nce

(1950–1992)

Ger

many

(1950–1992)

Japan

(1950–1992)

GD

PE

ner

gy

consu

mpti

on

Coin

tegra

tion

Gra

nger

causa

lity

Bid

irec

tionalca

usa

lity

(Arg

enti

na)

Unid

irec

tionalca

usa

lity

(GD

Pca

use

sen

ergy

consu

mpti

on

inIt

aly

and

Kore

a)

Unid

irec

tionalca

usa

lity

(ener

gy

consu

mpti

on

cause

sG

DP

inTurk

ey,

Fra

nce

,G

erm

any

and

Japan)

Soy

tas

etal.

(2007)

U.S

.(1

960–2004)

GD

PC

apit

alst

ock

Tota

lla

bor

forc

eE

ner

gy

use

CO

2em

issi

ons

Toda-Y

am

am

oto

appro

ach

toG

ranger

causa

lity

Gen

eralize

dim

puls

ere

sponse

funct

ion

Vari

ance

dec

om

posi

tion

GD

Pdoes

not

cause

CO

2em

issi

ons

(long

run)

Ener

gy

use

cause

CO

2em

issi

ons

Lis

eand

Van

Montf

ort

(2007)

Turk

ey(1

970–2003)

GD

PTota

lpopula

tion

Ener

gy

consu

mpti

on

Coin

tegra

tion

Gra

nger

causa

lity

Unid

irec

tionalca

usa

lity

from

GD

Pto

ener

gy

consu

mpti

on

Yang

(2000)

Taiw

an

(1954–1997)

GD

PE

ner

gy

consu

mpti

on

Coalco

nsu

mpti

on

Oil

consu

mpti

on

Natu

ralgas

consu

mpti

on

Ele

ctri

city

consu

mpti

on

Coin

tegra

tion

Gra

nger

causa

lity

Bid

irec

tionalca

usa

lity

bet

wee

nto

talen

ergy

consu

mpti

on

and

GD

PB

idir

ecti

onalca

usa

llinka

ges

bet

wee

nG

DP

and

both

coaland

elec

tric

ity

consu

mpti

on

Unid

irec

tionalca

usa

lity

from

GD

Pto

oil

consu

mpti

on

Unid

irec

tionalca

usa

lity

from

natu

ralgas

consu

mpti

on

toG

DP

Mid

dle

Eas

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ded

from

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144 H. E. ChebbiTable

1.

(Continued

)

Auth

ors

Countr

ies

and

data

per

iods

Vari

able

sM

ethods

Main

Res

ult

s

Zhang

and

Chen

g(2

009)

Chin

a(1

960–2007)

GD

PG

ross

fixed

capit

al

form

ati

on

Ener

gy

consu

mpti

on

CO

2em

issi

ons

Urb

an

popula

tion

Toda-Y

am

am

oto

appro

ach

toG

ranger

causa

lity

Unid

irec

tionalca

usa

lity

runnin

gfr

om

GD

Pto

ener

gy

consu

mpti

on

and

from

ener

gy

consu

mpti

on

toca

rbon

emis

sions

inth

elo

ng

run

Evid

ence

show

sth

at

nei

ther

carb

on

emis

sions

nor

ener

gy

consu

mpti

on

leads

econom

icgro

wth

Zam

ani(2

007)

Iran

(1967–2003)

GD

PIn

dust

rialva

lue

added

Agri

cult

ura

lva

lue

added

Ener

gy

consu

mpti

on

Gas

consu

mpti

on

Pet

role

um

consu

mpti

on

Indust

rialen

ergy

consu

mpti

on

Ele

ctri

city

consu

mpti

on

Agri

cult

ura

len

ergy

consu

mpti

on

Coin

tegra

tion

Gra

nger

causa

lity

Long-r

un

bid

irec

tionalre

lati

onsh

ipbet

wee

nG

DP

and

gas

and

GD

Pand

pet

role

um

Unid

irec

tionalca

usa

lity

from

GD

Pto

ener

gy

for

the

whole

econom

yU

nid

irec

tionalca

usa

lity

from

indust

rial

valu

eadded

toin

dust

rialto

talen

ergy,

gas,

pet

role

um

pro

duct

sand

elec

tric

ity

consu

mpti

on,and

from

gas

consu

mpti

on

tova

lue

added

Bid

irec

tionalre

lati

on

hold

bet

wee

nagri

cult

ura

lva

lue

added

and

tota

len

ergy,

pet

role

um

pro

duct

sand

gas

consu

mpti

on

Short

-run

causa

liti

esfr

om

GD

Pto

tota

len

ergy

and

tota

lpet

role

um

consu

mpti

on,

and

from

indust

rialva

lue

added

toin

dust

rialen

ergy

and

pet

role

um

pro

duct

sco

nsu

mpti

on

Jaliland

Mahm

ud

(2009)

Chin

a(1

975–2005)

GD

PC

O2

emis

sions

Com

mer

cialen

ergy

use

Tra

de

open

nes

sra

tio

Auto

regre

ssiv

edis

trib

ute

dla

gm

odel

(AR

DL)

Gra

nger

causa

lity

Unid

irec

tionalca

usa

lity

from

GD

Pto

CO

2

CO

2em

issi

ons

are

main

lydet

erm

ined

by

GD

Pand

ener

gy

consu

mpti

on

inth

elo

ng

run

Tra

de

has

(posi

tive)

insi

gnifi

cant

impact

on

CO

2em

issi

ons

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dle

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Economic Growth, Energy Consumption and CO2 Emissions in Tunisia 145

This paper follows closely the empirical approach of recent studies (Ang 2007;Soytas et al. 2007; Ang 2008; Halicioglu 2009; Jalil and Mahmud 2009; and Zhangand Cheng 2009) that embrace two nexus, output-energy and output-environmentaldegradation, in a single multivariate framework. In addition, since the empiricalapproach adopted (i.e., linking the two nexus) is not a structural one, the gen-eralized impulse response analysis is used in this study to compute short–rundynamics.

Furthermore, an attempt is made to examine the dynamic relationships betweenCO2 emissions, energy consumption and economic growth by considering the mainsectoral components of output growth (agriculture, industry, and services). Tunisiaappears to be an interesting case study given that it is one of the highest growtheconomies in the Middle East and North Africa (MENA) region and energy supplyin this country is insufficient to meet increasing demand.

The rest of this study is organized as follows. Section 2 briefly describes theTunisian energy context. Section 3 sets out the data used in this study and theirstochastic characteristics. Long–run equilibrium relationships are analyzed in Sec. 4.Section 5 depicts the empirical findings from the short–run dynamics using thegeneralized impulse response functions. Finally, some concluding remarks and somepolicy implications are outlined.

2. Tunisian Economic and Energy Situations

With annual growth of Gross Domestic Product (GDP) exceeding 5% since 1995,Tunisia is a North African country with a strong growth potential. The improvementof Tunisia’s major macroeconomic indicators is the result of a series of economicreforms and a prudent macroeconomic management (principally since the adoptionand implementation of the structural adjustment programs). The Tunisian economyhas been diversified and is now less vulnerable than in the past to external shockssuch as climate hazards.

At the sectoral level, agriculture accounted for 12% of GDP in 2006. The manu-facturing sector accounted for more than 60% of industrial production, about 20%of the working population and 18.2% of GDP. The services sector represents about40% of GDP and half of the working population (OECD 2008). It has expandedsignificantly in the past few years and has driven Tunisian growth upwards. Growthin the last years was driven by strong domestic and European demand. It was pri-marily stimulated by services (telecommunications in particular), the machineryand electricity industries, and construction and civil engineering. Over the years,the manufacturing and tourist sectors have gained a few percentage points of GDPto the detriment of the primary sector (agriculture, oil and phosphates).

In Tunisia, demand for energy, notably electricity, has been rising sharply dur-ing the last few years. Household consumption is the main engine of economicgrowth; it represented 63.8% of GDP in 2006 (up 8.8% from 2005). The increaseof total primary energy consumption for 1990–2005 period was very strong. This

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Table 2. Energy resources and consumption in Tunisia.

1990 1995 2000 2001 2002 2003 2004 2005

Petroleum (Thousand Barrels per Day)

Total oil production 97.7 90.1 80.5 72.6 78.5 77.4 81.7 76.9Crude oil production 93.0 89.2 78.7 69.6 75.8 75.0 79.8 75.0Consumption 63.2 70.0 84.5 87.2 87.9 87.7 89.4 90.0Net exports/imports 34.5 20.0 −4.0 −14.6 −9.3 −10.3 −7.7 −13.1

Natural Gas (Billion Cubic Feet)

Production 12.0 11.7 66.4 79.5 75.9 80.9 84.8 88.3Consumption 54.0 57.6 108.8 135.3 135.6 130.3 130.7 151.9Net exports/imports −42.0 −45.9 −42.4 −55.8 −59.7 −49.4 −45.9 −63.6

Electricity (Billion Kilowatt-hours)

Net generation 5.2 6.9 10.0 10.7 11.1 11.7 12.3 12.8Net consumption 4.6 6.2 8.8 9.5 9.8 10.3 10.7 11.2

Total Primary Energy (Quadrillion Btu)

Production 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3Consumption 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.4

Source: Energy Information Administration (EIA, 2007).

is attributed to the fact that Tunisia has experienced rapid economic growth dueto the expansion of the tourism and transportation activities, increased industrialactivity and an increase in the standard of living of the Tunisian population.c

The evolution of annual energy consumption and resources in Tunisia during theperiod 1990–2005 is shown in Table 2. Based on the 2005 values, the consumptionof primary energy exceeded 8.5 Mtoe (million tonnes oil equivalent) in Tunisia,supplied primarily by crude oil and petroleum products at 50%, while natural gasis today well represented at 38%. Thanks to the switch to natural gas since themid-1980s, its role is growing and is now the second largest source of fuel as well asbeing a main source for the industrial and electricity sectors. Biomass is essentiallyused in rural areas and represents 12% of primary energy consumption. Lastly,the contribution of renewable energies (hydropower, wind and solar water heating)accounts for 46 ktoe (kilo tonnes of oil equivalent) and represents only 0.6% of theprimary energy balance for 2005.

The energy consumption composition by sectors in Tunisia has not changed since2000. The household sector is the leading consumer (29%), followed by transporta-tion (25%), industry (16%) and agriculture (4%). Although crude oil is the leadingexport product in value, national production is far from covering the country’sneeds. In fact, Tunisia is a hydrocarbon importer in the absence of any significantdiscovery and has initiated a program to reduce oil-deficiency.d This objective was

cWith population growth slowing down, GDP per capita in 2007 was USD9401 in purchasingpower parity, which placed Tunisia just behind Romania and well ahead of Morocco.dSince the end of the 1960s, Tunisia has benefited from a relatively secure energy balance sur-plus; but the 1980s saw the advent of the era of energy dependency. In 1994 for the first time,Tunisia recorded a deficit in its energy balance (Amous 2007). Following the extension of the gaspipeline between Algeria and Italy and the start-up of operations in the Miskar gas mine in 1996,

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expressed in the national energy plan ‘Energy 21’ based on energy saving and theincreased utilization of renewable energy sources.e

3. Data Selection and Stationarity Tests

An expanded view of the linkages between the growth process, energy use and envi-ronmental degradation can be appreciated by considering the sectoral componentsof economic growth (agriculture, industry and services).f Many authors argue that,when investigating the linkages between output growth, energy consumption, andenvironmental degradation in a single country study, dividing by population onlyscales the variable down (Soytas et al. 2007, Zhang and Cheng 2009 and Friedland Getzner 2003).g Keeping this in mind, annual data for total energy use (EU);carbon dioxide emissions (CO2) as proxy for the level of pollution and environ-mental degradation; real gross domestic product of industry (INDGDP); real grossdomestic product of services (SERGDP); and real gross domestic product of agri-culture (AGRGDP) are collected from the World Development Indicators (WorldBank 2009). The sample period covers 1971 to 2005. The series are transformedinto logarithms so that they can be interpreted in growth terms after taking firstdifference. In order to illustrate the change in trend of variables in the same scale,we construct an index for each series (before taking logarithm) using 1986 as thebase year. Figure 1 suggests that the selected variables tend to move together overtime and a long–run or cointegrating relationship is likely to be present.h

The first step of this empirical work is to investigate the stationarity propertiesand establish the order of integration of the selected variables (EU; CO2; INDGDP;SERGDP; and AGRGDP). When the number of observations is low, unit root testshave limited power (Blough 1992). For this reason we have examined the resultsfrom two different tests: the Augmented Dickey-Fuller (ADF) (Dickey and Fuller1979, 1981), which tests the null of unit root, and the KPSS (Kwiatkowski et al.1992), which tests the null of stationarity.

surplus was restored, but as of 2001, deficits appeared again as a result of increasing demand andstagnating supply.eThe 10th Tunisian Development Plan (2002–2006) contains specific provisions on sustainabledevelopment and is based on four pillars: (i) the integration of the environmental dimension in theprocess of development, (ii) the protection of natural resources and the fight against desertification,(iii) the fight against pollution and the improvement of living standards and (iv) the contributionof the environment to development.fWe use this alternative measure, taking into account the lack of time series data of energy useby sector in Tunisia.gIn this paper, we use total CO2 emissions rather than CO2 intensities. This choice is driven by thenature of international protocols which relate to percentage decreases in the levels of greenhousegas (GHG) emissions (and not decreases in emissions per unit of GDP or per capita emissions). Asindicated by various studies, total CO2 can increase even if the emissions per unit of GDP decrease(the scale effect of economic growth can “dominate” the composition effect and the technologicaleffect).hIn addition figure 1 reveals that the different sectoral components of GDP and the CO2 emissionshave a linear relationship so that a quadratic specification is not required.

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0

50

100

150

200

250

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

EU CO2

0

50

100

150

200

250

19

71

19

73

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77

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79

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95

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97

19

99

20

01

20

03

20

05

INDGDP SERGDP AGRGDP

Source: World Development Indicators (World Bank, 2008).

Fig. 1. Trends of the indexed series (basis 100 = 1986).

The results of both tests for the individual time series and their first differencesare shown in Table 3. The ADF statistics suggest that all variables in levels arenon-stationary and are I(1) (integrated of order one). The KPSS test rejects the nullhypothesis of level and trend stationarity for both lag truncation parameters. TheKPSS statistics do not reject the I(0) hypothesis for the first-differenced series atconventional levels of statistical significance. Therefore, the combination of the unitroot tests results (see Table 3) suggests that the five series involved in the estimationprocedure are integrated of order one (i.e., I(1)). This implies the possibility ofcointegrating relationships.

4. Long–Run Relationships Study: A Cointegration Analysis

The next step is to investigate whether the series are cointegrated since the variables(EU, CO2, INDGDP, SERGDP, and AGRGDP) were I(1). In this work, cointegra-tion analysis has been conducted using the general technique developed by Johansen(1988) and Johansen and Juselius (1990). These authors proposed a maximum like-lihood estimation procedure which allows researchers to estimate simultaneouslythe system involving two or more variables to circumvent the problems associatedwith the traditional regression methods. Further, this procedure is independent ofthe choice of the endogenous variables and allows researchers to estimate and testfor the presence of more than one long–run relationship(s) in the multivariate sys-tem and how variables in the system adjust to deviations from long–run equilibriumrelationship(s).

The base-line econometric specification for multivariate cointegration is a Vec-tor Autoregressive (VAR) representation of a k-dimensional time series vector Yt

reparameterized as a Vector Error Correction Model (VECM):

∆Yt = µDt + Γ1∆Yt−1 + · · · + Γp−1∆Yt−p+1 + ΠYt−1 + et (1)

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Economic Growth, Energy Consumption and CO2 Emissions in Tunisia 149

Table 3. Results of the ADF and KPSS tests.

Panel A: ADF test (the null hypothesis is non-stationarity)

Level form First difference

Intercept and Intercept, noVariables time trend time trend Intercept, no time trend

LEU −1.84 −3.09 −7.66LCO2 −1.74 −2.93 −8.66LINDGDP −3.05 −3.01 −5.31LSERGDP −2.64 −0.53 −3.82LAGRGDP −2.80 −1.41 −4.82

Critical values

Intercept and time trend Intercept, no time trend

1% −3.96 −3.435% −3.41 −2.8610% −3.13 −2.57

Panel B: KPSS test (the null hypothesis is stationarity)

Level form First difference

l = 1 l = 3 l = 1 l = 3

Variables ηµ ητ ηµ ητ ηµ ητ ηµ ητ

LEU 1.79 0.32 0.96 0.19 0.59 0.07 0.52 0.08LCO2 1.73 0.39 0.93 0.22 0.64 0.07 0.50 0.07LINDGDP 1.70 0.32 0.94 0.19 0.52 0.16 0.46 0.18LSERGDP 1.81 0.19 0.98 0.12 0.09 0.08 0.12 0.11LAGRGDP 1.74 0.13 0.97 0.13 0.03 0.03 0.05 0.05

Critical values

Level stationarity Trend stationarity

1% 0.74 0.225% 0.46 0.1510% 0.35 0.12

Note: The lag length for the ADF tests to ensure that the residuals were white noise has beenchosen based on the Akaike Info Criterion. The KPSS statistics test for lag-truncation parametersone and three (l=1 and l=3) since it is unknown how many lagged residuals has been used toconstruct a consistent estimator of the residual variance.

where Yt is a (k × 1) column vector of the endogenous variables; Dt is a vector ofdeterministic variables (intercepts, trend. . .); µ is the matrix of parameters associ-ated with Dt; Γi are (k × k) matrices of short–run parameters (i = 1, . . . , p − 1)where p is the number of lags; Π = −(I − Π1 · · · − Πp) is a (k × k) matrix oflong–run parameters; and et is the vector of disturbances assumed to be normal,independent, and identically distributed (NIID).

Granger’s representation theorem asserts that if the matrix Π has reduced rank(i.e., there are r < k cointegration vectors present), then Π can be decomposedinto a matrix of loading coefficients (α) and a matrix of cointegrating vectors (β),that is Π = αβ′. Here, r is the number of cointegrating relations; β is a (k × r)matrix representing the cointegrating vectors which are commonly interpreted asmeaningful long–run equilibrium relations between the Yt variables; while α is a

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150 H. E. Chebbi

(k× r) matrix representing the speed of adjustment to equilibrium. Thus under theI(1) hypothesis, the cointegrated VAR model is given by:

∆Yt = µDt + Γ1∆Yt−1 + · · · + Γp−1∆Yt−p+1 + αβ′Yt−1 + et (2)

where β′Yt−1 is a (r × 1) vector of stationary cointegration relations.The general procedure outlined above has been applied to the system including

the five variables (EU; CO2; INDGDP; SERGDP; and AGRGDP). However, inempirical applications, the choice of r is frequently sensitive to: (a) the deterministicterms included in the system (such as a constant and/or a trend) and on the wayin which such components interact with the error-correction term; and (b) theappropriate lag length to ensure that the residuals are Gaussian. In the presentwork, the model was estimated including two lags and an unrestricted constant anda trend component in the cointegration space.i Multivariate tests for autocorrelation(Godfrey 1988) and normality (Doornik and Hansen 1994) have been carried outto check for model statistical adequacy before applying the reduced rank tests.Diagnostic tests on the residuals indicated that the model could be consideredcorrectly specified.j

The Johansen cointegration approach is applied to determine the number ofcointegrating vectors. Table 4 shows the results of Johansen’s likelihood ratio tests.As can be observed, for the 5% and 1% levels of significance, respectively, thetrace statistics do not reject the null hypothesis that there are two cointegratingrelations between the variables (r = 2). The presence of two cointegrating vectors inour system suggests an inherent movement in the system to revert towards long–runequilibrium path subsequent to a short–run shock.

In this paper, the two estimated cointegrating vectors have been normalized byenergy use (EU) and carbon dioxide emissions (CO2), respectively. The estimatedβ and α parameters are presented in Table 5.

Table 4. Results of cointegration tests.

Critical values

H0 : r LR Trace (90%) (95%) (99%)

0 137.41∗∗∗ 84.27 88.55 96.971 80.67∗∗∗ 60.00 63.66 70.912 38.96 39.73 42.77 48.873 15.71 23.32 25.73 30.674 5.46 10.68 12.45 16.22

iThe lag length has been determined by the Akaike’s information criterion. The maximum numberof lags is set to be three given the reduced sample size.jThe result from multivariate first-order autocorrelation test was 23.431, which was well belowthe critical value at the 5% level of significance (χ2

25 = 37.652). Also, the result from multivariatenormality test was 11.034, which was well below the critical value at the 5% level of significance(χ2

10 = 18.307).

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Economic Growth, Energy Consumption and CO2 Emissions in Tunisia 151

Table 5. Loading coefficients (α) and normalized cointegration relations β.

α =

266666666664

−0.337(−2.186)∗∗

0.271(2.609)∗∗∗

−0.044(−0.185)

0.079(0.489)

−0.559(−3.496)∗∗∗

0.531(4.929)∗∗∗

0.463(5.191)∗∗∗

−0.315(−5.249)∗∗∗

−1.196(−2.511)∗∗

0.568(1.771)∗

377777777775

β′ =

24

1.000 −−− −1.065(−6.488)∗∗∗

3.744(6.664)∗∗∗

1.896(7.258)∗∗∗

−0.228(−7.326)∗∗∗

−−− 1.000 −2.012(−8.715)∗∗∗

6.181(7.820)∗∗∗

2.339(6.362)∗∗∗

−0.314(−7.171)∗∗∗

35 .

0BBBBBBBBB@

LEUt−1

LCO2t−1

LINDGDPt−1

LSERGDPt−1

LAGRGDPt−1

Trend

1CCCCCCCCCA

Note: (*), (**) and (***) indicate 10%, 5% and 1% level of significance, respectively; and figuresin the parentheses indicate t-ratio.

As can be observed, all the parameters of the long–run equilibrium relationshipsare statistically significant. The first cointegration vector reveals a positive linkagebetween Tunisian Industrial output growth and energy use and the second vectorindicates that CO2 emissions and energy consumption are positively related.k

On the other hand, in this type of analysis, it is also convenient to consider theestimated αij (i indicates the row and j the column) parameters as they providevaluable information about the long–run causal relationship.

Hence, we test for weak exogeneity of the variables (EU; CO2; INDGDP;SERGDP; and AGRGDP) within the cointegrating relationship which can bethought of as a long–run non-causality test (Johansen 1992; Johansen and Juselius1992; Ericsson and Irons 1994). The long–run weak exogeneity test, requires sat-isfying the null H0 : αij = 0 for (j = 1, . . . , r). It is based on a likelihood ratiotest which follows a χ2 distribution. Table 6 includes the results of weak exogeneitytests imposing zero loading restrictions.

Statistical results show that the αij coefficients of EU, INDGDP, SERGDPand AGRGDP are significant in the long–run speed adjustment, suggesting thatjoint deviation by energy use and the sectoral components of GDP from the steadystate position gradually disappears with the variables eventually returning to anequilibrium position. On the other hand, only the carbon emissions variable isweakly exogenous indicating that CO2 does not adjust in the long–run.l The finding

kIn this paper, we do not interpret the normalized cointegration relationships as long–run elas-ticities (as in conventional regression context), since such an interpretation ignores the dynamicinteractions among the variables (Juselius 1999 and Lutkepohl 2005).lIn this sense, EU; INDGDP; SERGDP and AGRGDP are simultaneously determined but CO2

can be marginalized from the statistical process.

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152 H. E. Chebbi

Table 6. Results of weak exogeneity tests

Variables H0 : αij = 0

LEU 9.4195∗∗∗LCO2 2.6306LINDGDP 39.5878∗∗∗LSERGDP 20.0471∗∗∗LAGRGDP 15.7747∗∗∗

Note: (*), (**) and (***) indicate the rejectionof the null hypothesis at 10%, 5% and 1% levelof significance, respectively.

indicates that CO2 influences the long–run movements of the other variables, butis not affected by the other variables.m

The combined results of the long–run analysis provide some support for long–runcausation from CO2 emissions growth (degradation of the environment) to outputmovements by sector (agriculture, industry and services). This pattern of devel-opment is consistent with the experiences of many developing countries and mayindicate that economic policy in Tunisia seems to be more oriented to supportingeconomic growth than to encouraging the control of CO2 emissions.

In addition, the combined results provide some support of a mutual causaland feedback relationship in the long–run between energy use and output growth.Indeed, the results do not support the view that energy consumption and outputare neutral with respect to each other in Tunisia.

5. Generalized Impulse Response Functions

Once the VECM has been estimated, short–run dynamics can be examined by con-sidering the impulse response functions (IRF). These functions show the response ofeach variable to the effect of a shock in one of the endogenous variable on the entireendogenous system (Gil et al. 2009). In other words, the impulse response functionsmap out the time path of the reactions. The IRF are calculated from the MovingAverage (MA) representation of the VECM (Lutkepohl 1993 and Pesaran and Shin1998):

Yt =∞∑

i=0

Biεt (3)

where matrices Bi(i = 2, . . . , n) are recursively calculated using the followingexpression: Bn = Φ1Bn−1 + Φ2Bn−2 + · · · + ΦpBn−p; B0 = Ip; Bn = 0 for n < 0;Φ1 = I + Π + Γ1; and Φi = Γi − Γi−1(i = 2, . . . , p).

Following Pesaran and Shin (1998) the scaled generalized impulse response func-tions (GIRF) of variable Yi with respect to a standard error shock in the jth equation

mThis result is in line with Ang (2008) who found long–run causality running from the CO2

emissions growth to the economic growth in the long–run for the Malaysia data.

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Economic Growth, Energy Consumption and CO2 Emissions in Tunisia 153

can be defined as:

GIRF(Yit, Yjt, h) =e′iBh

∑ej√

σjj; h = 0, . . . , n (4)

where es(s = i, j) is the sth column of the identity matrix.The generalized impulse response analysis is an alternative to orthogonalization

of the shocks (i.e., coping with the problems of composition dependence, historydependence and shock dependence).n The GIRF are unique and invariant to theordering of the variable of the system. The GIRF coincide with the orthogonalizedimpulse responses if the covariance matrix, Σ, is diagonal and j = 1. Standarddeviations of generalized impulse responses are obtained following Pesaran andShin (1998).

In this paper, to analyze the short–run dynamics between CO2, INDGDP,SERGDP, and AGRGDP, we have applied a sequential elimination strategy todelete those regressors in the VECM (all the loading coefficients and Γi param-eters) with the smallest absolute values of t-ratios until all t-ratios (in absolutevalue) are greater than some specified threshold value (Bruggemann and Lutkepohl2001). The value of the statistic for the reduced model was 20.792 which was underthe critical value (χ2

20 = 31.410) at the 5% level of significance. This result indicatesthat it was not possible to reject the null (H0: restricted model).o The generalizedimpulse response functions are plotted out in Fig. 2.

The results show that the initial impact of one period standard deviation shockin energy consumption (EU) is positive and statistically significant for CO2, INDand SER. This result indicates that industry and services may be vulnerable toenergy shortage in the short–run. In addition, the responses of CO2 appear to belarger than those of IND and SER. These responses may provide some evidence of“inefficient use” of energy in Tunisia, since CO2 emissions (environmental degra-dation) tend to rise more rapidly than output growth in the short–run. Note alsothat the response of AGR is negative and only significant two horizons after theinitial shock, indicating a causal negative effect of energy consumption growth onthe Tunisian agricultural sector in the short–run.

Short–run dynamics also indicate that the initial impact of a positive shock inCO2 is positive and significant for EU and IND, but insignificant for SER and AGR.Moreover, the responses of EU and IND are positive and significant two years afterthe initial shock in CO2 emissions. These results provide some support for onlypositive causation running from CO2 emissions growth to industrial output growthin the short–run.

nIRF are correlated if the error terms in the VAR are contemporaneously correlated and this makesimpulse responses difficult to interpret. The usual way around this problem is to impose a previousorthogonalization (identification) of the shocks in order to give an economic interpretation for thesource of the shock. This identification can be achieved by using either a Cholesky or a structuraldecomposition and implicitly assumes a recursive contemporaneous structure (the called Waldcausal chain). The resulting IRF will not be unique, and it will depend on the ordering of thevariables in the model.oAnnex 1 shows the lagged endogenous term (Γi) used for the simulation of the generalized impulseresponse functions.

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1 3 5 7 9 11 13 15

-0.0

20

.02

0.0

60

.10 Shock in LEU

1 3 5 7 9 11 13 15

-0.0

20

.02

0.0

60

.10 Shock in LCO2

1 3 5 7 9 11 13 15

0.0

00

.01

0.0

20

.03

0.0

4 Shock in LIND

1 3 5 7 9 11 13 15

-0.0

3-0

.01

0.0

10

.03

Shock in LSER

1 3 5 7 9 11 13 15

0.0

00

.04

0.0

80

.12

Shock in LAGR

Responses of LEUResponses of LCO2Responses of LINDResponses of LSERResponses of LAGR

Note: Responses marked with a square indicate 5% level of significance.

Fig. 2. Responses of variables.

Finally, the responses of EU and CO2 to one shock in the different sectoral com-ponents of GDP (IND, SER, and AGR) are insignificant,p suggesting the existenceof only unidirectional causality from energy consumption to industrial and servicessectors in the short–run.

pIn part, these results contradict the findings of Sari and Soytas (2007) who find that a shock toreal income growth affects energy consumption growth in the case of Tunisian data.

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Economic Growth, Energy Consumption and CO2 Emissions in Tunisia 155

6. Summary and Some Policy Implications

The aim of this country specific study is to explore long and short–run linkagesbetween economic growth, energy consumption and carbon dioxide emissions usingTunisian data over the period 1971–2005. Since these linkages were largely under-considered and unanswered for policy makers in Tunisia, this empirical researchattempts to present some findings to better integrate the environment into economicdevelopment decisions. Furthermore, an attempt is made to examine the dynamicrelationships between CO2 emissions, energy consumption and economic growth, byconsidering the main sectoral components of output growth (agriculture, industryand services).

Results of the long–run analysis suggest the presence of two cointegrating rela-tionships. The first cointegration vector reveals a positive linkage between Tunisianindustrial output growth and energy use and the second vector indicates that CO2

emissions and energy consumption are positively related. In addition, statisticalresults from weak exogeneity indicate that CO2 emissions do not adjust in the long–run and may indicate that economic policy in Tunisia seems to be more orientedto supporting economic growth than to encouraging the control of CO2 emissions.In addition, the results also provide some support of a mutual causal and feedbackrelationship in the long–run between energy use and output growth. Indeed, theresults do not support the neutrality hypothesis between energy consumption andsectoral output growth in Tunisia.

Results from short–run dynamics indicate that linkages between energy con-sumption and economic growth, as well as economic growth and environmentalpollution are not uniform across sectors (agriculture, industry and services) inTunisia. These outcomes suggest that prudent energy and environmental policiesshould distinguish the differences in the relationship between energy consumptionand output growth by sectors. In fact, results from short–run dynamics reveal thatCO2 emissions (environmental degradation) tend to rise more rapidly than outputgrowth. These results have important implications for policy makers in Tunisia whoshould be mindful that a persistent decline in environmental quality may exert neg-ative externalities to the economy through depressing the tourism sector and alsoaffecting human health and thereby reduce productivity and growth in the future.

Since statistical results confirm that energy consumption stimulates growth ofindustry and services sectors (i.e., the growth hypothesis) and an increase in pollu-tion level induces growth in the industrial sector and in order not to adversely affectgrowth, more efforts must be made to encourage Tunisian industry to adopt technol-ogy that minimizes pollution as a serious environmental policy, even though Tunisiahas no commitment to reduce Greenhouse Gas (GHG) emissions. In Tunisia, thepotential exists for the development of renewable energies (since renewable ener-gies represent less than 1% of the primary energy use in Tunisia) although furtherefforts would require additional financing by policy makers.

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Finally, future research on the relationship between the disaggregated energysources within each sector and growth by sector may assist in the development ofeffective energy and environmental policies for Tunisia.

Acknowledgments

I thank Lyn Squire, Ibrahim Elbadawi and two anonymous referees for highlyuseful comments that led to further improvements in the paper. I gratefullyacknowledge feedback received on earlier version of this research from discussantsand participants at the Congress of the European Association of AgriculturalEconomists (EAAE) 2008, the Annual Global Development Network (GDN) Con-ference 2009 and the International Association of Agricultural Economists (IAAE)2009 Conference.

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Annex 1. Lagged endogenous term (Γ) used for the GIRF.

Γp−1 =

2666666666664

— 0.309(3.220)∗∗∗

−0.151(−2.220)∗∗∗

−0.393(−3.253)∗∗∗

—

0.810(5.806)∗∗∗

— — — —

0.556(3.567)∗∗∗

— −0.240(−2.566)∗∗∗

−1.659(−8.119)∗∗∗

−0.140(−3.463)∗∗∗

−0.286(−3.878)∗∗∗

0.224(3.480)∗∗∗

— — —

— — — — —

3777777777775

.

0BBBBBBBBBBB@

∆LEUt−p+1

∆LCO2t−p+1

∆LINDGDPt−p+1

∆LSERGDPt−p+1

∆LAGRGDPt−p+1

1CCCCCCCCCCCA

LR-test (H1: unrestricted model): χ220 = 20.792

Note: (*), (**) and (***) indicate 10%, 5% and 1% level of significance, respectively; and figuresin the parentheses indicate t-ratio.

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