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The purpose of this study was to determine the relevance of the Environmental Kuznets Curve, which shows that there is an inverted-U shaped relationship between environmental pollution and economic growth. We investigated the relationship between per capita income and the carbon dioxide emissions as indicators of environmental pollution in Developing Countries, OECD, Middle East and OPEC countries for the period of 1970-2016. The contribution of our study is the evaluation and comparison of Developing Countries, OECD, Middle East and OPEC countries together in the context of EKC. We employ the fixed effect and GMM techniques in this study and results obtained from cubic models indicate that the N-shaped relationship for Developing, Middle East countries and OECD countries and inverted N-shaped relationship for OPEC countries exist. Considering these conclusions, we draw some serious policy implications for the policy makers in these countries. Governments should closely follow the industries that generate CO2 emissions as after some point environmental degradation increases again as income increases. In addition adopting clean energies including wind and solar systems and making these technologies widespread across countries might reduce CO2 emissions. Another alternative way to reduce CO2 emissions might be a carbon tax which should be implemented for polluters.

PurposeThere is extensive literature on the effect of military expenditure on economic growth. However, there is also a wide gap in the literature on the relationship between productivity and innovation, which is considered the driving force of economic growth and military expenditures. To this end, this study examines the effect of military expenditures on economic growth, innovation and labor productivity for the period 1995–2019 in most militarized countries.Design/methodology/approachThe tests used in the study's empirical analysis are techniques that take into account cross-sectional dependence and heterogeneity. The stationarity of the variables was tested with the Pesaran’s (2007) unit root test. Then, empirical findings were revealed based on the analysis through Westerlund’s (2008) cointegration test and Emirmahmutoglu and Kose’s (2011) panel causality test.FindingsAccording to the empirical results, there is a long-run relationship, in other words, a cointegration between military expenditures and productivity, innovation and economic growth. Additionally, there are causality relationships between military expenditures and productivity, innovation and economic growth.Practical implicationsThese results support the arguments of military Keynesianism and the Benoit hypothesis.Originality/valueDespite the widespread theoretical debate, no empirical study tests the effect of military expenditure on productivity and innovation to the author's best knowledge. Hence, this study aims to fill this gap in the literature. Moreover, the fact that the econometric method used is based on second generation tests and the timeliness of the period range makes the study's findings more significant.

The aim of this study is to study the impact of the development of civil aviation on Turkey's exports for the period of 2011-2020 by using data retrieved from 51 African countries as part of Turkish Airlines' African opening. In the study, an empirical analysis was conducted to reveal the effects of the role played by Turkish Airlines and the sector policies it implemented. The empirical analysis was generally conducted within the framework of linear time series models and analysis techniques. An econometric model has been established in which exports are selected as the dependent variable and the number of passengers as the independent variable. The analysis is based on monthly observations from the 2011-2020 period. The variable number of passengers, which is considered representative of the activities carried out by Turkish Airlines for Africa, is also based on monthly observations. The results of the cointegration test reveals that there is cointegration relationship between variables and so they are move together in the long run. According to the cointegration coefficient one unit increase in passenger variable lead to 0,341 unit increase in FMOLS method, 0,291 unit increase in DOLS method and 0,340 unit increase in CCR method. It can be inferred that passenger variable affect exports variable positively. Lastly according to Granger causality analysis it shows that there is one way causality linkage goes from number of passengers toward exports variables.