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Carbon Presentation

Carbon Emissions 2009 Analysis using statistical software SAS.

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Carbon Presentation

  1. 1. Do wealthy people around the world produce high levels of carbon emissions?16th March 2012 Statistics in Economics - Gurpreet Sidhu 1
  2. 2. Motivation• Sympathetic to reducing personal carbon footprint• World needs to work together to slow down the green house effect• Understand how different classes of society around the world are contributing to this effectBackground• Carbon Emissions (GHG) – Combustion of Wood, Coal, Oil and Natural Gas• 2009 – 41.5% of Total Carbon emission by United States and China• 22 tonnes per person between US and China! http://en.wikipedia.org/wiki/Greenhouse_gas16th March 2012 Statistics in Economics - Gurpreet Sidhu 2
  3. 3. More Motivation – 1 tonne of CO2 http://www.freja.com/FRONTPAGE/Environment 16th March 2012 Statistics in Economics - Gurpreet Sidhu 3
  4. 4. Data Collection - 2009Variable Unit SourceCountry Name http://tonto.eia.doe.gov/cfapps/ipdbproject/IEDIndex3.CO2 Emissions Million Metric Tonnes cfm?tid=90&pid=44&aid=8 http://en.wikipedia.org/wiki/List_of_countries_and_outCountry Area KM2 lying_territories_by_total_area http://www.photius.com/rankings/population/populatiPopulation on_2009_0.html http://www.enotes.com/topic/List_of_countries_by_GGDP per Capita $ DP_%28nominal%29_per_capita http://epi.yale.edu/epi2012/rankingsEnvironmental Ranking Score 0 - 100 http://en.wikipedia.org/wiki/Environmental_PerformanPerformance Index (EPI) ce_Index http://www.cru.uea.ac.uk/~timm/cty/obs/TYN_CY_1_1.Average Temperature Degrees Celsius html http://www.prb.org/pdf09/09wpds_eng.pdfPopulation Growth Rate % per Year http://databank.worldbank.org/ddp/home.do?Step=12Life Expectancy Years &id=4&CNO=2 http://www.unicef.org/statistics/index_step1.phpUrban Living Population % http://en.wikipedia.org/wiki/Developed_countryDeveloped Country Binary (0-No, 1-Yes)Wealth of Country Binary by Quantile GDP per Capita (Poorest in Intercept, Poor, Wealthy, Wealthiest) 16th March 2012 Statistics in Economics - Gurpreet Sidhu 4
  5. 5. Data Summary – Proc Univariates• Y variable is CO2 emissions• Data collected for top 100 CO2 emitting countries worldwide X Variable Mean Min Max Skewness Kurtosis land 936696.7 347 17098242 4.976018 28.670831 pop 45703222 109825 1166079217 7.965223 71.43029 gdpcap 19060.62 100 80943 1.0939 1.4609 epi 53.10206 25.30 76.7 -0.302 -0.342 temp 17.430 -5 28.8 -0.464 -0.747 popgrowth 1.2288 -0.8 10.3 2.9525 17.282 life 73.2164 46 83 -1.6396 3.3631 urbanpop 65.46 14 100 -0.5113 -0.2711 dev 0.3711 0 1 0.5419 -1.7427• Skewness and Kurtosis = 0 if perfect Normal Distribution• Of interest land, population and popgrowth• With foresight investigation needed into land, population and gdpcap (R2 and beta) 16th March 2012 Statistics in Economics - Gurpreet Sidhu 5
  6. 6. Scatterplots Min and Max Values Skewed Distribution China United States 16th March 2012 Statistics in Economics - Gurpreet Sidhu 6
  7. 7. Scatterplots – transforming variables by taking logs Min and Max Values Better Distribution China United States Collinearity 16th March 2012 Statistics in Economics - Gurpreet Sidhu 7
  8. 8. Mutliple Linear Regression• 99 Observations• Should drop ‘lnland’ due to collinearity but will double check with p-value first• Possible Interactions in the data I have added: devlnland=dev*lnland devlnpop=dev*lnpop 16th March 2012 Statistics in Economics - Gurpreet Sidhu 8
  9. 9. Mutliple Linear Regression 2• p-values have improved across variables• still many insignificant p-values above 0.05 16th March 2012 Statistics in Economics - Gurpreet Sidhu 9
  10. 10. Mutliple Linear Regression 3• still many insignificant p-values above 0.05 16th March 2012 Statistics in Economics - Gurpreet Sidhu 10
  11. 11. Mutliple Linear Regression 4• Good model• 99 Observations• R2 is 0.3241• EPI p-value = 0.07 is questionable but we leave it in for now with benefit of foresight• Use model to calculate studentized residuals for all observations 16th March 2012 Statistics in Economics - Gurpreet Sidhu 11
  12. 12. Studentized Residual – Boxplot and ExtremesStudentized Residual = Residual / Standard Deviation of Residual Outliers China and US need to be removed so that errors will be more normally distributed China United States 16th March 2012 Statistics in Economics - Gurpreet Sidhu 12
  13. 13. Mutliple Linear Regression 5 – No Outliers• 97 Observations• R2 now 0.5205, previously 0.3241• p-value for epi much better 16th March 2012 Statistics in Economics - Gurpreet Sidhu 13
  14. 14. Mutliple Linear Regression 6 – Heteroskedasticity• Parameter estimates all lie within Heteroskedasticity consistent 95% CI• To fix this we use new standard errors to put into our regression model 16th March 2012 Statistics in Economics - Gurpreet Sidhu 14
  15. 15. Conclusion CO2 = -3239.86 + 147.20lnpop + 130.74lngdpcap – 4.87epi (31.43) (40.29) (2.78) *** *** *** At 5% level there is significant evidence of: • Each 1% increase of lnpop, CO2 increases by 147/100 • Each 1% increase in lngdpcap, CO2 increases by 130/100 • Each 1 unit increase in EPI, CO2 decreases by 4.87 • gdpcap is an indicator for measuring wealth • So in answer to our original question, wealthy people do emit more CO2 16th March 2012 Statistics in Economics - Gurpreet Sidhu 15

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