carbon presentation

1

Do wealthy people around the world produce high

levels of carbon emissions?Statistics in Economics - Gurpreet Sidhu16th March 2012

• 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!

• 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 effect

Statistics in Economics - Gurpreet Sidhu16th March 2012 2

Motivation

Background

http://en.wikipedia.org/wiki/Greenhouse_gas

Statistics in Economics - Gurpreet Sidhu 316th March 2012

More Motivation – 1 tonne of CO2

http://www.freja.com/FRONTPAGE/Environment


Data Collection - 2009Variable Unit Source

Country Name

CO2 Emissions Million Metric Tonnes http://tonto.eia.doe.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=90&pid=44&aid=8

Country Area KM2 http://en.wikipedia.org/wiki/List_of_countries_and_outlying_territories_by_total_area

Population http://www.photius.com/rankings/population/population_2009_0.html

GDP per Capita $ http://www.enotes.com/topic/List_of_countries_by_GDP_%28nominal%29_per_capita

Environmental Performance Index (EPI)

Ranking Score 0 - 100 http://epi.yale.edu/epi2012/rankingshttp://en.wikipedia.org/wiki/Environmental_Performance_Index

Average Temperature Degrees Celsius http://www.cru.uea.ac.uk/~timm/cty/obs/TYN_CY_1_1.html

Population Growth Rate % per Year http://www.prb.org/pdf09/09wpds_eng.pdf

Life Expectancy Years http://databank.worldbank.org/ddp/home.do?Step=12&id=4&CNO=2

Urban Living Population % http://www.unicef.org/statistics/index_step1.php

Developed Country Binary (0-No, 1-Yes) http://en.wikipedia.org/wiki/Developed_country

Wealth of Country Binary by Quantile GDP per Capita (Poorest in Intercept, Poor, Wealthy, Wealthiest)

http://tonto.eia.doe.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=90&pid=44&aid=8

http://tonto.eia.doe.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=90&pid=44&aid=8

http://en.wikipedia.org/wiki/List_of_countries_and_outlying_territories_by_total_area

http://en.wikipedia.org/wiki/List_of_countries_and_outlying_territories_by_total_area

http://www.photius.com/rankings/population/population_2009_0.html

http://www.photius.com/rankings/population/population_2009_0.html

http://www.enotes.com/topic/List_of_countries_by_GDP_(nominal)_per_capita

http://www.enotes.com/topic/List_of_countries_by_GDP_(nominal)_per_capita

http://epi.yale.edu/epi2012/rankings

http://en.wikipedia.org/wiki/Environmental_Performance_Index

http://en.wikipedia.org/wiki/Environmental_Performance_Index

http://www.cru.uea.ac.uk/~timm/cty/obs/TYN_CY_1_1.html

http://www.cru.uea.ac.uk/~timm/cty/obs/TYN_CY_1_1.html

http://www.prb.org/pdf09/09wpds_eng.pdf

http://databank.worldbank.org/ddp/home.do?Step=12&id=4&CNO=2

http://databank.worldbank.org/ddp/home.do?Step=12&id=4&CNO=2

http://www.unicef.org/statistics/index_step1.php

http://en.wikipedia.org/wiki/Developed_country


• 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

Data Summary – Proc Univariates

• 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)


Scatterplots

Min and Max Values

Skewed Distribution

China

United States


Scatterplots – transforming variables by taking logs

China

United States

Better Distribution

Min and Max Values

Collinearity


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*lnlanddevlnpop=dev*lnpop


Mutliple Linear Regression 2• p-values have

improved across variables

• still many insignificant p-values above 0.05


Mutliple Linear Regression 3• still many

insignificant p-values above 0.05


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


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


Mutliple Linear Regression 5 – No Outliers• 97 Observations• R2 now 0.5205,

previously 0.3241• p-value for epi much

better


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


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

Conclusion

carbon presentation

Education

economics gurpreet sidhu5

economics gurpreet sidhu6

mutliple linear regression

epi pvalue

co2 emissions data

tonne of co2 http

co2 decreases

epi muchbetter16th