The Impact of Population Age Structure on C02 Emissions in NigeriaBy

AJIDE,K.B (PhD),Department of EconomicsUniversity of Lagos, Lagos

Nigeria

Introduction

• Climate change remains the most challenging threats to all living creatures

• World CO2 emissions had reached a threateningly high historical maximum of 30,600 millions of tonnes in 2010.

• 2ºC has been identified as the threshold above which irreversible and dangerous impacts of climate change will become unavoidable.

Introduction

• A study commissioned by the British government estimated that the overall costs and risks of inaction on climate change would be equivalent to losing 5 to 20 percent of GDP each year.

• However, provision of concrete solutions becomes feasible only if certain underlying causative factors can be adequately uncovered.

Introduction

• European Commission on Trends in Global C02 emissions (2012) report indicates that Global emissions of carbon dioxide (CO2) is the main cause of global warming

• the increased concentrations are the consequence of human activities around the globe.

• Among these anthropogenic factors, the principal ones which often called referred to as ‘‘driving forces’’ include population, economic activity, technology, political and economic institutions, and attitudes and beliefs.

Introduction

• Against this background, empirical studies are replete examining the impact of likely causative factors on C02 emissions in both developed and developing countries.

• A particular strand of empirical studies that specifically looked into the underlying impact of population age structure on carbon-dioxide emission is still at its infancy, most especially within the context of developing economies’ experiences.

Introduction

• In the light of the foregoing, the paper is interested in contributing to the debate as well as adding to the repository of existing knowledge by examining how the impact of population structure contributes to C02 emissions in Nigeria.

STYLIZED FACTSAnnual Averages

Population(In Millions)

C02 Emissions(Kilotons)

Per Capita C02 (Metric ton per capita)

Pop0-14 Pop15-64 Popabove65

1970-79 64740846 47891.02 0.73174 43.37447 53.46238 3.163145

1980-89 85021324 64520.13 0.765692 44.73577 52.13482 3.129412

1990-99 1.09E+08 46272.77 0.429402 44.03816 52.77413 3.187705

2000-08 1.37E+08 92385.99 0.676805 42.90261 53.808 3.289388

STYLIZED FACTS

19701972

19741976

19781980

19821984

19861988

19901992

19941996

19982000

20022004

20062008

0

20000000

40000000

60000000

80000000

100000000

120000000

140000000

160000000

Fig.1: Trend of Population in Nigeria (1970-2010)

pop

Valu

e in

Mill

ion

STYLIZED FACTS

19701972

19741976

19781980

19821984

19861988

19901992

19941996

19982000

20022004

20062008

0

20000

40000

60000

80000

100000

120000

Fig.2: Trend of C02 Emissions in Nigeria (1970-2008)

c02

Kilo

tons

STYLIZED FACTS

19701972

19741976

19781980

19821984

19861988

19901992

19941996

19982000

20022004

20062008

0

20

40

60

80

100

120

0

0.2

0.4

0.6

0.8

1

1.2

Fig.3: Trend of Population Age Structure and Per Capita C02 Emissions in Nigeria

pc02popab65pop15-64pop0-14

Perc

enta

ge o

f To

tal

Literature Review

• The first studies considered demographic factors to explain the sources of air pollution were based on cross-sectional data for only one time period.

• In this line, Cramer (1998, 2002) and Cramer and Cheney (2000) evaluated the effects of population growth on air pollution in California and found a positive relationship only for some sources of emissions but not for others.

Literature Review

• Dietz and Rosa (1997) and York, Rosa, and Dietz (2003) studied the impact of population on carbon dioxide emissions and energy use within the framework of the IPAT model. Impact=Population .Affluence. Technology (IPAT). The results from these studies indicate that the elasticity of CO2 emissions and energy use with respect to population are close to unity.

Literature Review

• Onozaki,K.(2009) employed graphical method to explore the relationship between population and global carbon dioxide. In the study, population was plotted against atmospheric C02 concentration.

• Apparently , most of the studies that had been conducted on the impact of population on carbon dioxide emissions are largely cross country and cross sectional studies in nature.

Methodology

• Dietz and Rosa (1997) formulated a stochastic version of the IPAT equation with quantitative variables containing population size (P), affluence per capita (A), and the weight of industry in economic activity as a proxy for the level of environmentally damaging technology (T).

• These authors designated their model with the term, STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology).

(1)I P A T

ln ln ln ln (2)I P A T

0 1 2 3 4 5lnC02 ln lnPGDP lnPOP 0 _14 ln 15 _ 64 lnPOP POP ENERINT

6 (3)LNURBAN

Empirical ResultsCO2 POP PGDP POP15 POP64 POPAB65 ENERINT URBAN

 Mean  62008.03  97875130  379.0889  43.78481  53.02526  3.189926  52.57690  36302898

 Median  60061.79  95133496  368.1039  43.70029  53.16806  3.180143  50.36082  32916190

 Maximum  104043.8  1.51E+08  492.3429  45.05093  54.04872  3.365278  68.30929  72861947

 Minimum  21539.96  57357275  293.5969  42.74192  51.79949  3.097634  0.000000  13020101

 Std. Dev.  20880.49  27772602  46.63749  0.789039  0.742459  0.067283  11.59387  17825259

 Skewness  0.325907  0.265856  0.407456  0.254551 -0.339711  0.912384 -2.241760  0.479212

 Kurtosis  2.224206  1.902499  2.817502  1.661357  1.698440  3.336967  11.86271  2.046146

 Jarque-Bera  1.668419  2.416743  1.133256  3.333121  3.502965  5.595400  160.3056  2.971175

 Probability  0.434218  0.298683  0.567436  0.188896  0.173516  0.060950  0.000000  0.226369

 Sum  2418313.  3.82E+09  14784.47  1707.608  2067.985  124.4071  2050.499  1.42E+09

 Sum Sq. Dev.  1.66E+10  2.93E+16  82652.12  23.65817  20.94732  0.172024  5107.874  1.21E+16

 Observations  39  39  39  39  39  39  39  39

Regression resultsIndependent Variables Model I : Coefficients (without

correction for autocorrelation)Model II : Coefficients (with

correction for autocorrelation I)Model III : Coefficients (with correction for autocorrelation II)

Constant -1266.16(-1.893)*

-99.102(-0.140)

355.66(0.520)

POP 61.134(3.381)***

54.543(3.296)***

52.926(3.419)***

PGDP -0.488(-0.746)

4.275(1.711)*

4.610(1.974)*

% POP0-14 87.381(1.363)

-45.854(-0.644)

-98.282(-1.415)

% POP15-64 102.74(1.289))

-47.667(-0.557)

-107.40(-1.293)

ENERINT 0.256(3.107)***

-5.040(-2.083)*

-5.731(-2.535)**

% URBAN -33.646(-3.360)***

-30.896(-3.403)***

-30.258(-3.562)***

AR(1) - 0.037(1.809)*

-

AR(2) 0.023(1.741)*

Regression resultsR-squared 0.584 0.653 0.661

Adjusted R-squared 0.506 0.572 0.579

Durbin-Watson stat 0.935 1.449 1.609

F-statistic 7.475 8.072 8.073

Prob(F-statistic) 0.0000 0.0000 0.0000

Diagnostic Statistics

X2 Normal 1.751[0.417] 1.540[0.463] 1.917[0.383]

X2 White 0.478[0.036] 0.478[0.877] 0.299[0.969]

X2 Arch 0.193[0.663] 0.259[0.614] 0.142[0.709]

X2 Reset 2.543[0.121] 6.924[0.013] 0.973[0.332]

X2 Serial 6.253[0.005] 1.301[0.288] 0.947[0.401]

Concluding Remarks

• The study examines the impact of population structure on C02 emissions in Nigeria using annual time series data from 1970 through 2008. From the empirical findings, the contributory factors of affluence (measured by per capita GDP), population, energy intensity and urbanization are clearly brought out on the one hand. On the other hand, population age structure does not appear as important factor causing environmental degradation as one would expect .

Policy Prescriptions

• Government should henceforth embark on enlightening programmes as well as educating people about environmental impacts of having excessive population on the health of the economy;

• Need to be more proactive on policy relating to wage and salaries increase since more income may suggest acquiring more environmental damaging items and

• Environmental polluters should severely sanctioned

Suggestions for Future Studies

• For future research in this area, it is therefore suggested that more important variables that are more likely to contribute to environmental degradation should be considered and a more robust econometric methods should be applied.

Remarks

Thanking you all for listening