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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 lnPOP0 _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