CO2 Emissions
and Economic
Development
Binta Sidibe & Amarachi Okorigbo
University of Wisconsin-Superior
Introduction
•
•
•
•
Nations around the
world pay high costs
for their economic
development
Human activities
cause pollution
Pollution is increasing
around the world
When would
development help
reduce pollution?
Research Questions
 Is
there a relationship between pollution
and the level of economic development?
 If the relationship exists, what functional
form characterizes it best?
 Linear/monotonically increasing
 Quadratic/concave
Theoretical Models of Pollution
IPAT

Ehrlich and Holdren (1971)

Monotonically increasing relationship
I= Impact of human activities on
the environment
P= Population Size
A= Affluence, measured here by
RGDPPC
T= Technology used to produce a unit
for consumption

Stern (2003)

Curve Shifts down over time

Grossman and Krueger (1991)



Quadratic relationship
Early stages of economic
development are characterized
by degradation and pollution.
Turning point where they begin
to care more about the
environment
Pollution
Pollution

Environmental Kuznets Curve
Real GDP per capita
Real GDP per capita
Measures of Pollution
 Water and Land
 Territory-specific
Pollution
 Air pollution
 Fewer boundaries
 Negative worldwide externalities of air pollution

Main Measure: CO2 Emissions
Research Hypotheses
1.
There exists a relationship between real
GDP per capita and CO2 emissions
2.
The relationship between the CO2
emissions and real GDP per capita is
quadratic
Data: Descriptive Statistics
Data: Graph Matrix
5
10
15
-10
-5
0
-20
0
-20
0
5
0
lnco2
-5
-10
15
lnrgdppc
10
5
25
20
lnpop
15
10
0
lncars
-5
-10
20
lnforestarea
10
0
0
lncoalrents
-20
5
0
lnoilrents
-5
-10
0
lngasrents
-20
5
0
lnforestrents
-5
-10
-10
-5
0
5
10
15
20
25
0
10
20
-10
-5
0
5
-10
-5
0
5
Empirical Model
ln(C O 2) it =  +  1 lnrgdppc it +  2 lnrgdppc it +
2
 3 lnpop it +  4 lncars it +  5 lnfore starea it +
 6 lncoalrents it +  7 lnoilren ts it +  8 lngasrents it +
 9 lnfore strents it +  i +  t + e it
 If
hypothesis 1 holds, β1 should be positive and
statistically significant
 If hypothesis 2 holds, β2 should be negative and
statistically significant
Empirical Model
CO2 emissions
A
B
Real GDP pc
Empirical Results: Robust LSDV
variable
constant
lnrgdppc
lnrgdppc2
lnpop
lncars
lnforestarea
lncoalrents
lnoilrents
lnforestrents
No of obs.
R-squared
1
2
3
-12.09*** -18.86*** -16.47***
1.54***
1.19***
2.89***
-0.04***
-0.02
-0.14***
0.48***
0.07**
5516
0.9751
5516
0.9756
773
0.998
4
-9.79***
1.60***
-0.4
5
-11.69*
2.69***
-0.13**
-0.28*
-0.16
0.04**
0.06
-0.08
588
0.9773
156
0.9908
***, **, * statistically significant coefficients at 1%, 5%, 10%
Analysis of Results
 There
is a statistically significant positive
relationship between the CO2 emissions and
real GDP per capita
 The quadratic relationship between CO2
emissions and real GDP per capita is
negative and statistically significant in 3
out of 5 regressions
 While there is evidence to support the
Environmental Kuznets Curve theory,
such evidence is not robust
Analysis of Results: Regression 5
CO2 emissions
A
B
$41,942
$83,884