Environmental Kuznets curve in South Africa: To confirm or not to confirm?

One of the most severe problems of the modern world is the climate change and its important negative consequences to the environment. Human activity, particularly the consumption of energy, has been considered being one of the main factors contributing to the changing of climate in the last decades (IPCC, 2007). To tackle the future changes of the environment, among other measures, a change in the current ways of generating energy is imperative. Traditional generation techniques such as coal-burning have detrimental effects to the environment and hence, internationally, countries have turned towards more environmentally-friendly generation techniques from renewable sources such as solar and wind that are also in synergy with many aspects of sustainable development (Stiglitz, 2002). Developed countries promote renewable energies (RE) with ultimate purpose to strengthen the energy security and control their greenhouse gas (GHG) emissions (Moselle, 2011); while the developing economies see solutions in the use of RE to the challenges of rural electrification and lack of access to electricity (Munasinghe 1990, Pereira et al. 2010). The Environmental Kuznets Curve (EKC) illustrates the hypothesis that a country is performing environmentally worse at the early stages of economic growth and development but subsequently, as the economic growth rises, the environmental quality improves. Although in theory, the hypothesis can be justified, the results of the empirical studies remain inconclusive. Possible reasons to explain this phenomenon can be: (a) the transition of the economies from clean agricultural economies, to high polluting secondary sector-based economies and finally to clean service-based economies and (b) at higher income levels, people do not worry about their surviving needs and tend to improve their preference for environmental quality. Examining the existence of EKC for the South African case and the rest of Africa will be particularly interesting and relevant for South Africa and other African economies that are already in a certain path of growth and development but in parallel they are also committed internationally to reduce emissions and promote clean and renewable energies. This paper will be able to answer the following research questions: • What has the recent international literature concluded on the EKC hypothesis? • Theoretically, where does the hypothesis stand on? • Does the choice of the indicator for environmental performance have an impact on the findings? • Which is the most appropriate methodology to be used in an empirical analysis of EKC for South Africa and the African countries? • Using this methodology, is the EKC hypothesis confirmed or rejected for South Africa? What are the reasons behind the findings? • Are there interventions that can promote a “tunnel-through” for South Africa? What can the country learn from international best practice? • What are the policy implications of the findings and how should the policy makers use them? References IPCC (2007). Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland Moselle, B. (2011). Why support renewables?. EPRG spring research seminar. University of Cambridge. UK. Munasinghe, M. (1990). Rural electrification in the Third World. Power Engineering journal: 189-202. Pereira, M.G.,Freitas, M.A.V. and Silva, N.F. (2010). Rural electrification and energy poverty: empirical evidences from Brazil. Renewable and Sustainable Energy Reviews: 14:1229-1240. Stiglitz, J. (2002). Globalization and its discontents. Penguin Books Ltd. London, UK. Numerous studies have estimated EKCs for certain air and water pollutants as well as other indicators proxying environmental performance. As noted Arrow et al. (1995) and Stern et al. (1996), these estimated regressions are reduced-form relationships which mean that they reflect correlation rather than a causal relationship. Nevertheless, these studies provide evidence that, for at least those pollutants involving local short-term health hazards, market and institutional mechanisms have eventually brought about a reduction in environmental damage during the course of economic growth (Cole et al. 1997). Cole et al (1997) extended past empirical studies by including more environmental indicators such as carbon dioxide, methane, and others. “The employment of a reasonably comprehensive data set permits the examination of a number of hypotheses relating to the association between economic growth and the environment. First, that pollutants with a local short-term impact (e.g., suspended particulate matter) will have estimated turning points at lower per capita income levels than those environmental indicators whose impact is more global in nature (e.g., carbon dioxide)” (Cole et al. 1997). In our analysis, we will follow Cole et al. (1997) with the idea of using different indicators for South Africa and compare the results. The basic model is: Et= f( Yt, Xt) where Et denotes the environmental indicator in per capita form in the country at year t, Yt denotes per capita income in the country at year t, and Xt represents exogenous factors, such as trade intensity and the level of technology in the country at year t. Two alternative functional forms are employed for estimating equation (1) from a cross-country/regional panel set: quadratic in levels and quadratic in logarithms. These are written as: Et= (α + μ F) + β Yt+ γ Yt2+ Xt +et (1) and ln Et= (λ +κ F) + η lnYt + θ(lnYt)2+Xt+ εt (2) A cubic function can also be considered, even though that Cole et al. (1997) pointed that the fact that every cubic relationship necessarily extends to plus or minus infinity was deemed to be unrealistic. An environmental quality path exists if there is a statistically significant relationship between an environmental indicator and income. A path displays a turning point if β> 0 and γ<0 in equation (1) and η>0 and θ<0 in equation (2). Income at the turning point, denoted by Y* is Y*=(-β/2γ) in equation (1) and Y*=exp(-η/2θ) in equation (2). A priori, the quadratic logs function would seem to provide a more realistic income–environmental quality path than the quadratic levels function because of the symmetrical nature of the latter. If, for example, pollution is considered, this symmetry implies, first, that pollution levels will fall at the same rate as they increased and, second, that these pollution levels will become negative, probably in a short space of time. This is in contrast to the quadratic logs function which falls away only gradually, once it passes the turning point, as the curve asymptotically approaches zero. I will critically evaluate various econometric methodologies that would seem appropriate to quantify the above discussed theoretical framework. Among them, we will consider cointegration techniques such as ones using time series data (cointegration as the ones proposed by Granger, Johannsen or the ARDL), Vector AutoRegression (VAR) and Vector Error Correction Models (VECM), or panel data techniques such as the Generalised Method of Moments (GMM) or Seemingly Unrelated regressions (SUR). References Arrow, K., B. Bolin, R. Costanza, P. Dasgupta, C. Folke, C.S. Holling, B.-O. Jansson, S. Levin, K.-G. Maler, C. Perrings and D. Pimentel (1995), ‘Economic growth, carrying capacity and the environment’, Ecological Economics 15(2): 91–95. Cole, M.A., Rayner, A.J. and Bates, J.M. (1997). The environmental Kuznets curve: and empirical analysis. Environment and Development Economics, 2: 401-406. Stern, D.I., M.S. Common and E.B. Barbier (1996), ‘Economic growth and environmental degradation: The environmental Kuznets curve and sustainable development’, World Development 24(7): 1151–1160. The results of this paper will confirm or not the EKC hypothesis and also, make some useful suggestions with regards to the proxies to be used in such papers in the future as well as the appropriate methodologies.