This study aims to investigate the relationship of economic development, measured as economic growth, energy use, trade and foreign direct investment, on the one hand, and environmental degradation (carbon dioxide (hereafter CO2) emissions), on the other hand, in eleven emerging Eastern European and Central Asian countries during the period of 1990 to 2014. The empirical results give an evidence of a carbon emission Kuznets curve for these emerging economies. The current income level indicates that not every country has reached the turning point for CO2 emissions reductions. Income elasticities for CO2 are positive for all eleven countries. The paper concludes that within the group, Ukraine and Kazakhstan have the most sensitive change in economic growth in respect to CO2. In addition, it concludes that there is a negative effect of total energy consumption on environment as such consumption increases CO2 emissions. The results also show a positive effect of foreign direct investment (FDI) on CO2 emissions in Eastern European and Central Asian countries. It is expected that the innovative transition to a low-carbon economy offers great opportunities for economic growth and job creation. Technological leadership (the initiative Industry 4.0) should be accompanied by the development and introduction of new technologies throughout Eastern European and Central Asian countries, hence, the paradigm of “sustainable development” should be considered as fatal. Furthermore, Eastern European and Central Asian economies should consider the experience of policy making implications made by other developing countries in gaining sustainable growth. Econometric analyses prove the existence of different impact on energy consumption of the ICT sector, which plays a key supporting role for intelligent manufacturing. Thus, there is a need for further investigations of the relationship between technology use and CO2 emissions.
Imports, remittances, direct foreign investment and economic growth in Republic of the Fiji Islands: An empirical analysis using ARDL approach
