The environmental Kuznet’s Curve is an “n” shaped curve on a graph between productive output and pollution output that shows a distinction between developed and developing nations on the matter of sustainable resource use. The irony in this evolution is that it not only turns up (increasing pollution with production) for underdeveloped and developing nations, and then down (reduced pollution with respect to production) for developed nations, creating the “n” shape; But in many well-developed nations this curve forms an imposing “N” (upper case N) showing a return to unsustainable environmental practices. In my thesis on the EKC, I looked farther into the inconsistency of the curve using higher education and proxies R&D per GDP, and renewables per total energy use.
I proposed to suggest that with higher education comes both research and development AND renewables and, therefore environmentally sustainable practices via knowledge and policy. The findings had an interesting result in which, in the study of 119 countries divided among developed ‘regions and less developed ‘regions”, where regions contain both OECD and/or developed countries while less developed regions are SIDS, LLDC, and LDC heavy. Both sets of data were run with output CO2 in kilotons and inputs log of GDP, tertiary education per GDP, log of R&D per GDP, and renewables per total energy use. The findings were, as hypothesized, that in less developed regions CO2 is reduced by tertiary education (at the 5 percent level of significance) and proxies renewables (at the 10 percent level of certainty) and R&D. The regression passes with an R stat of 82.96 percent explanatory power and an F stat of 30.9. The inconsistency between the latter and the developed region analysis shows that tertiary education no longer passes significance test and the relationship changes to positive relationship to CO2 output, while significance of renewable changes to surpass the 1 percent level of significance and confirms reduction in CO2 measured in kilotons. This regression shows an R stat of 68.99 explanatory power with a F stat of 63.4. As the model is a dynamic one there are further tests to assess the residual error, however the Newey West test was run to determine robustness. The model with corrected Newey West standard errors improves over a standard panel model. The Augmented Dickey Fuller shows that the model has a time series trend to the input variables and a residual unit root. The residual time trend can be seen in the plotted residual. As this is a work in progress, further time series’ analysis is forthcoming.
Granger causality tests showed bidirectional Granger causality between tertiary education and CO2 in developing regions with probability of 0.558 and 0.501, while in developed regions CO2 shows unidirectional Granger causality from tertiary education to CO2 , but weakly so with probability 0.098. While R&D and GDP showed bidirectional causation in both regions, renewables showed bidirectional causation only in developing regions and in developed regions, renewables appeared to cause CO2 . I have assed both the need for further time series and residual analysis and the model is undergoing ARIMA testing and testing of priors for trends between years of political and economic shocks and variance of input and output variables. Confounding hypotheses may be present as well such as cultural diffusion and timing of UN SDG agenda. Policy and infrastructure will clearly be a factor with a time trend in world developments. The beginning work with data is embedded below.