Is there an Environmental Kuznets Curve for Energy Use and Carbon Emissions?

1. Is there an Environmental Kuznets Curve for Energy Use and Carbon Emissions? Amy K. Richmond and Robert K. Kaufmann US Association for Ecological Economists Saratoga Springs, NY May 22, 2003 http://cybele.bu.edu/people/arr.html

2. 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

3. Natural Resources Use and\ or Emission of Wastes Income 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

4. Turning Points Natural Resources Use and\ or Emission of Wastes Income

5. Energy Omission • E/GDP influenced by energy mix • Different energy types have different CO2 emissions • Statistical effects of omitted variable bias

6. 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)

7. Basic Model • 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

8. 1. Quadratic Specification: • EKC if β1 > 0 and β2 < 0 • Turning point = –(β1/ 2β2) 2. Semi Log Specification: • Diminishing returns 3. Double Log Specification: • Constant elasticity Model Specifications

9. Omitted Variable Bias: Fuel Share PCTCOAL = ln(FINCOAL/TE) PCTPET = ln((FINOIL+FINGAS)/ TE) PCTELC = ln((HYRDO+NUCLEAR)/TE) • Expect coefficient associated with PCTCOAL to be positive and coefficients associated with PCTPET and PCTELC to be negative. • Diminishing Returns

10. Estimation Techniques • Regression techniques: • Pooled OLS • Fixed Effects or Random Effects Estimator • Random Coefficient Model (Swamy, 1970) • Cointegration (Pedroni, 1999)

11. Tests of predictive accuracy (Diebold and Mariano, 1995)

12. 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

13. Quadratic Model Relation between income and energy consumption (corrected for changes in energy mix) Semi Log Model DoubleLog Model

14. Relation between income and CO2 emissions (corrected for changes in energy mix) Quadratic Model Semi Log Model DoubleLog Model

15. 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

16. Why do Energy Mix Variables Effect Turning Points?

17. 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