The relationship between energy consumption, CO2 emissions, and economic growth in Turkey: evidence from Fourier approximation.

Given that Turkey has recently committed itself for the first time to reducing its CO2 emissions in the interest of sustainable growth in not only Turkey but also the world as a whole, this paper examines the relationship between energy consumption, CO2 emissions, and economic growth in Turkey for the period 1960-2014. In view of the different findings concerning causality and the character of the relationships between these variables revealed in our review of past studies (in most cases using quite different methods), this paper utilizes several different but related methodological approaches for identifying causal relationships. These include both the Toda and Yamamoto (1995) approach, the Fourier Toda-Yamamoto for Cumulative Frequency approach developed by Nazlioglu et al. (2016), vector error correction model (VECM) methodology, and the asymmetric Granger causality test proposed by Hatemi-J (Empir Econ 43:447-456, Hatemi-j 2012). Our results show that, when we apply the popular Toda-Yamamoto model, causality in these relationships is not confirmed even among any of the relevant variables in Turkey. Yet, when the Fourier Toda-Yamamoto tests for cumulative frequency are employed, we find unidirectional causality running from GDP per capita to emissions of CO2 per capita. Moreover, when we utilize the VECM methodology, the results show that long-run causality exists from GDP per capita and energy to CO2 emissions. When we apply the asymmetric causality tests, the results provide even stronger evidence for a unidirectional causal relationship from GDP per capita to CO2 emissions. As a result, the latter sets of results, based on more realistic conditions, suggest very strongly that, if Turkey is to meet the objectives of its ambitious Climate Change Action Plan commitment to the United Nations to reduce its CO2 per capita emissions relative to its past trends by up to 21% over the coming 2021-2030 decade, it is going to get very serious about the best way to do this as soon as possible.

A Mixed Application of Geographically Weighted Regression and Unsupervised Classification for Analyzing Latex Yield Variability in Yunnan, China.

This paper introduces a mixed method approach for analyzing the determinants of natural latex yields and the associated spatial variations and identifying the most suitable regions for producing latex. Geographically Weighted Regressions (GWR) and Iterative Self-Organizing Data Analysis Technique (ISODATA) are jointly applied to the georeferenced data points collected from the rubber plantations in Xishuangbanna (in Yunnan province, south China) and other remotely-sensed spatial data. According to the GWR models, Age of rubber tree, Percent of clay in soil, Elevation, Solar radiation, Population, Distance from road, Distance from stream, Precipitation, and Mean temperature turn out statistically significant, indicating that these are the major determinants shaping latex yields at the prefecture level. However, the signs and magnitudes of the parameter estimates at the aggregate level are different from those at the lower spatial level, and the differences are due to diverse reasons. The ISODATA classifies the landscape into three categories: high, medium, and low potential yields. The map reveals that Mengla County has the majority of land with high potential yield, while Jinghong City and Menghai County show lower potential yield. In short, the mixed method can offer a means of providing greater insights in the prediction of agricultural production.