1. The Impact of Population Age Structure on C02 Emissions in Nigeria By AJIDE,K.B (PhD), Department of Economics University of Lagos, Lagos Nigeria

2. Introduction Climate change remains the most challenging threats to all living creatures World CO 2 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. STYLIZED FACTS Annual Averages Population (In Millions) C02 Emissions (Kilotons) Per Capita C02 (Metric ton per capita) Pop0-14Pop15-64Popabove65 1970-796474084647891.020.7317443.3744753.462383.163145 1980-898502132464520.130.76569244.7357752.134823.129412 1990-991.09E+0846272.770.42940244.0381652.774133.187705 2000-081.37E+0892385.990.67680542.9026153.8083.289388

8. STYLIZED FACTS

11. 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.

12. 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.

13. 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.

14. 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).

16. Empirical Results CO2POPPGDPPOP15POP64POPAB65ENERINTURBAN 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

17. Regression results Independent 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)*

18. Regression results R-squared 0.5840.6530.661 Adjusted R-squared 0.5060.5720.579 Durbin-Watson stat 0.9351.4491.609 F-statistic 7.4758.0728.073 Prob(F-statistic) 0.0000 Diagnostic Statistics X 2 Normal 1.751[0.417]1.540[0.463]1.917[0.383] X 2 White 0.478[0.036]0.478[0.877]0.299[0.969] X 2 Arch 0.193[0.663]0.259[0.614]0.142[0.709] X 2 Reset 2.543[0.121]6.924[0.013]0.973[0.332] X 2 Serial 6.253[0.005]1.301[0.288]0.947[0.401]

19. 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.

20. 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

21. 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.

22. Remarks Thanking you all for listening