Carbon Emissions 2009 Analysis using statistical software SAS.

1. Do wealthy people around
the world produce high
levels of carbon emissions?
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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 effect
Background
• 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_gas
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3. More Motivation – 1 tonne of CO2
http://www.freja.com/FRONTPAGE/Environment
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4. Data Collection - 2009
Variable Unit Source
Country 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_out
Country Area KM2 lying_territories_by_total_area
http://www.photius.com/rankings/population/populati
Population on_2009_0.html
http://www.enotes.com/topic/List_of_countries_by_G
GDP per Capita $ DP_%28nominal%29_per_capita
http://epi.yale.edu/epi2012/rankings
Environmental Ranking Score 0 - 100 http://en.wikipedia.org/wiki/Environmental_Performan
Performance 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.pdf
Population Growth Rate % per Year
http://databank.worldbank.org/ddp/home.do?Step=12
Life Expectancy Years &id=4&CNO=2
http://www.unicef.org/statistics/index_step1.php
Urban Living Population %
http://en.wikipedia.org/wiki/Developed_country
Developed Country Binary (0-No, 1-Yes)
Wealth of Country Binary by Quantile GDP per Capita (Poorest in Intercept, Poor,
Wealthy, Wealthiest)
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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)
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6. Scatterplots
Min and Max Values
Skewed Distribution
China
United States
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7. Scatterplots – transforming variables by taking logs
Min and Max Values
Better Distribution
China
United States
Collinearity
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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
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9. Mutliple Linear Regression 2
• p-values have
improved across
variables
• still many insignificant
p-values above 0.05
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10. Mutliple Linear Regression 3
• still many
insignificant p-values
above 0.05
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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
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12. Studentized Residual – Boxplot and Extremes
Studentized 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
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13. Mutliple Linear Regression 5 – No Outliers
• 97 Observations
• R2 now
0.5205, previously
0.3241
• p-value for epi much
better
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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
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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
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