CO 2 EMISSIONS, ENERGY CONSUMPTION, INCOME AND TRADE OPENNESS: A SOUTH AFRICAN PERSPECTIVE.

1. CO2EMISSIONS, ENERGY CONSUMPTION, INCOME AND TRADE OPENNESS: A SOUTH AFRICAN PERSPECTIVE. Marcel Kohler University of KwaZulu-Natal (kohler@ukzn.ac.za)

2. Background: • Empirically, two dominant research streams have emerged on the subject of economic growth, energy consumption and environmental pollution: • first area, focuses on relationship between: pollutant emissions and income related to testing the validity of the EKC hypothesis.  tested by Grossman and Krueger (1991). • second area, focuses on relationship between: energy consumption and output growth . This link has been examined extensively in the literature since the seminal work of Kraft and Kraft (1978) but with conflicting results. The direction of causality may therefore not be determined a priori. • Recently, work by Ang (2007) and Soytas et al. (2007) analyses both sets of relationships in the same framework.

3. The New Approach: • The multivariate framework analyses: economic growth, energy consumption & environmental pollutants together in one relationship! • This research adopts the new approach, and like Halicioglu (2009) and Nasir & Rehman (2011) extends this framework by including the additional impact of foreign trade! • Theory identifies 3 mechanisms through which trade impacts on emissions: scale, technique & composition effects. • For the Composition Effect: 2 competing theories, Heckscher-Ohlin Theory –vs- Pollution Havens Hypothesis. • Note: trade involves movement of goods produced in one country and consumed in another!

4. South African Emissions: Source: IEA (2010)

5. Methodology: • The Model: ct= 0+ 1 et+ 2 yt+ 3 y2t+ 4 ft+ t where: ctis CO2 emissions per capita, etis commercial energy use per capita, ytis per capita real income, y2tis square of per capita real income, ftis openness ratio which is used as a proxy for foreign trade, and etis the regression error term.  to test the validity of the EKC hypothesis

6. Theoretical expectations: • 1 : positive (because a higher level of energy consumption should result in greater economic activity which stimulates CO2 emissions); • 2 : positive; • 3 : negative (but if statistically insignificant, it indicates a monotonic increase in the relationship between per capita CO2 emissions and per capita income); • 4 : positive or negative (depending on the level of income and stage of economic development of a country). •  For a rich country: “negative” •  For a poor country: “positive”

7. Econometric Methodology: • The long run coefficients of the relationship are modelled using the ARDL approach to co-integration technique of Pesaran et al. (2001)  instead of OLS because variables are non-stationary. • The short-run estimates are obtained through the VECM. • in contrast to the unrestricted Vector Autoregression (VAR), the VECM is a restricted VAR where the restriction of a long-run relationship is imposed. • Causality Testing, • using a modified version of the Granger test proposed by Toda & Yamamoto (1995) which is valid regardless of whether a series is I(0), I(1) or I(2).

8. Results: • Show the existence of a long-run co-integrating relationship among the series under consideration. • According to the ECM ,we find that:  previous CO2 emissions and the income variables are insignificant factors in explaining current changes in CO2 ­emissions.  energy usage is significant and positive as theory predicts.  Surprisingly the trade openness variable is also significant but negative. This suggests that an increase in SA’s openness to trade will in fact serve to decrease CO2 emissions. • Suggestion:  South Africa behaves as a developed nation contrary to the predictions of the PHH but in support of the FEH. • The error correction term is statistically significant and of the expected negative sign. The ECM indicates that following a deviation from the long-run trend 86% of that deviation will be corrected within a year.

9. Granger causality tests: • causality runs in both directions between carbon emissions & energy consumption (contrary to findings by Menyah & Wolde-Rufael (2010). • results are consistent with the results from the long-run ECM, confirming the importance of energy consumption as a major determinant of carbon emissions in South Africa. • unidirectional causality running from carbon emissions to income without feedback (confirming results of Menyah & Wolde-Rufael (2010) in this regard); • unidirectional causality running from carbon emissions to foreign trade without feedback. • In both later cases, the sum of the lagged coefficients of carbon emissions is positive suggesting that higher carbon emissions promote both SA income and foreign trade.

10. Conclusions: • The long-run elasticity of carbon emissions with respect to energy consumption is computed as 0.95. The impact of foreign trade on carbon emissions seems to be noticeable at -0.18. • South Africa’s industries are found to be highly capital and energy intensive and given the country’s dependence on coal as its major energy source, surprisingly the pollutant emissions outcome is diminished in the presence of foreign trade . • The expansion of renewable, cleaner energy sources might further help reinforce the positive environmental impact, namely that of reduced CO2 emissions of increased trade liberalisation found in this study.

11. References: • Ang, J.,2007. CO2 emissions, energy consumption,and output in France. Energy Policy 35, 4772–4778. • Grossman, G., Krueger, A., 1991. Environmental impacts of a North American free trade agreement. National Bureau of Economics Research Working Paper, No. 3194, NBER, Cambridge. • Halicioglu, F., 2009. An econometric study of CO2 emissions, energy consumption, income and foreign trade in Turkey. Energy Policy 37, 1156-1164. • Kraft, J., Kraft, A., 1978. On the relationship between energy and GNP. Journal of Energy Development 3, 401–403. • Menyah, K., Wolde-Rufael, Y., 2010. Energy consumption, pollutant emissions and economic growth in South Africa. Energy Economics 32, xxx-xxx. • Pesaran, M.H., Shin, Y., Smith, R.J., 2001. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 16, 289–326. • Soytas, U., Sari, R., Ewing, T., 2007. Energy consumption, income, and carbon emissions in the United States. Ecological Economics 62, 482–489. • Toda, H.Y., Yamamoto, T., 1995. Statistical inference in vector autoregressionswithpossibly integrated processes. Journal of Econometrics 66, 225–250.

12. Thank You!