Is there an Environmental Kuznets Curve for Energy Use

and Carbon Emissions?

Amy K. Richmond and Robert K. Kaufmann

US Association for Ecological EconomistsSaratoga Springs, NY

May 22, 2003

http://cybele.bu.edu/people/arr.html

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Talk Overview

• Omitted variable bias– Energy Mix

• Model specification– Quadratic, Semi-Log, Double-Log

• Tests of predictive accuracy• Questions

– Does the inclusion of energy mix influence turning points?

– Is there a turning point?• Conclusion

– Omission of energy variables effects turning point– Quadratic specification is best

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Environmental Kuznets Curve

• Reasons for EKC include income driven changes in: – composition of

production and consumption;

– preference for environmental quality;

– institutions that are needed to internalize externalities;

– increasing returns to scale associated with pollution abatement

Income

Natural Resources Use and\ or Emission of Wastes

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Turning Points

Income

Natural Resources Use and\ or Emission of Wastes

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Energy Omission

• E/GDP influenced by energy mix

• Different energy types have different CO2 emissions

• Statistical effects of omitted variable bias

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Methodology: Data• Panel of International Data

– 36 nations• 20 developed countries• 16 developing countries

– 1973-1997• Total Economic activity measured by GDP

in 1996 US dollars, converted using PPP indices

• Carbon emissions (kg/ million BTU)• Total energy use (BTU’s)• Final energy consumption (BTU’s)

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Basic Model

ittjitit ZXY )ln(

• Yij is a measure of energy per capita (TE/Pop) or carbon emissions per capita (CO2/Pop) by nation i at time t

• X is per capita GDP

• Z is a vector of fuel shares (PCTCOAL, PCTPET, PCTELC)

• µ is the regression error

• α, β, Φ, are regression coefficients

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Model Specifications

itititit ZXY )ln()ln(

Quadratic Specification:

ititititit ZXXY )ln(221

• EKC if β1 > 0 and β2 < 0• Turning point = –(β1/ 2β2)

Semi Log Specification:

• Diminishing returns

itititit ZXY )ln()ln()ln(Double Log Specification:

• Constant elasticity

1.

3.

2.

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Omitted Variable Bias: Fuel Share

• Expect coefficient associated with PCTCOAL to be positive and coefficients associated with PCTPET and PCTELC to be negative.

• Diminishing Returns

PCTCOAL = ln(FINCOAL/TE) PCTPET = ln((FINOIL+FINGAS)/ TE) PCTELC = ln((HYRDO+NUCLEAR)/TE)

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Estimation Techniques

• Regression techniques:– Pooled OLS – Fixed Effects or Random Effects Estimator

– Random Coefficient Model (Swamy, 1970)

• Cointegration (Pedroni, 1999)

itititit ZXY

Yit i Xit Zit it

Yit i iX it iZit it

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Tests of predictive accuracy (Diebold and Mariano, 1995)

S2a I dt 0.5N

t1

N

0.25N

S3a I dt rank( dt )

N N 1 4t1

N

N N 1 (2N 1)24

I(dt ) 1 if dt 00 otherwise

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Results

• Variables generally have correct sign and statistically significant

• GDP variables have correct sign, quadratic term not statistically significant

• All variables contain stochastic trend (indicates modeling variables using time trends is not sensible)

• Quadratic specification cointegrates• Energy shares allow diminishing returns

specifications to cointegrate

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Relation between income and energy consumption

(corrected for changes in energy mix)

Quadratic Model Semi Log Model Double Log Model

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Relation between income and CO2 emissions

(corrected for changes in energy mix)

Semi Log Model Double Log ModelQuadratic Model

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Effect of Energy Mix on Turning Points

• Energy use– Turning point without fuel shares: $43,767*– Turning point with fuel shares: $52,296*

• CO2 emissions

– Turning point without fuel shares: $110,600– Turning point with fuel shares: $29,700

* Calculated even though quadratic term not statistically significant

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Why do Energy Mix Variables Effect Turning Points?

FractionCoal

FractionPetroleum

FractionElectricity

GDP 0.00443(2.89)

-0.031(8.3)

0.00197(8.2)

GDP2 -46.53E-04(1.7)

0.00127(0.8)

--

Turningpoint

$3,390 $12,070 --

Panel ADF -3.14 -5.45 -6.51

Group ADF -4.40 -7.13 -9.12

Sit 1Xit 2Xit2 it

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Conclusion• Omission of energy variables effects turning

point

• Quadratic specification generates a more accurate out of sample forecast

• Modeling relationship between energy use and income using time trends is not sensible

http://cybele.bu.edu/people/arr.html