Dynamic impact of demographic features, FDI, and technological innovations on ecological footprint: evidence from European emerging economies.

Climate change effect mitigation is a critical priority for top leaders and communities around the globe. Human-induced environmental issues are affecting humankind's standard of living and development potential and the planetary boundaries. Sustainability objectives aim to enhance environmental quality and ensure sustainable development for all by eliminating social inequalities. This study examines the complex relationships between demographic features, foreign direct investment, technological innovation, and ecological footprint, emphasizing the relevance of population aging, population density, and urbanization in this context. The research uses a selection of emerging European economies during 1995-2018. The reasons for countries' selection are related to the increasing rate of population aging in European countries, the attractiveness for foreign direct investment, the economic growth, and the technological advancement potential these emerging countries possess. In order to investigate the long-run relationship between the selected variables, the study tests the cross-section dependence, homogeneity, and cointegration and uses Konya tests to determine panel causality. Based on Konya methodology, differences between countries in the panel are evidenced and discussed accordingly. Our findings confirm the long-run relationship between environment, technological innovation, population aging, and FDI. The results of this research are highly relevant for policymakers in selected countries for identifying the set of correlations and the relevance of various variables in such national economies. Demographic features such as population aging and population density are critical for Europe, and the results show the impact on the ecological footprint.

Sustainable pathways for attaining net-zero emissions in European emerging countries - the nexus between renewable energy sources and ecological footprint.

This study aims to investigate the relationship between renewable energy and ecological footprint during the period of 1994-2018 from selected developing countries in Europe (Czechia, Croatia, Poland, Romania, Romania, and Turkey). In this context, the ecological footprint (EF), which has recently been the most widely used environmental indicator in the literature and is known as the most comprehensive because it includes many environmental factors, has been determined as the dependent variable. As independent variables, renewable energy consumption (REC), energy-related tax revenue (ETR), and energy productivity (EP) are included in the model. GDP and development of environment-related technologies (DET), which affect the ecological footprint in the model, are determined as control variables. As a result of the panel data analysis, according to the Durbin-Hausman cointegration test result, a long-term relationship between the variables was determined. According to the CCE estimator analysis, it can be said that there is a positive relationship between ETR and GDP variables and EF. For the AMG estimator analysis, it can be said that there is a positive relationship between GDP and EP variables and EF. Finally, according to the results of the Konya Causality test, a unidirectional causality relationship is detected from environmental technologies to the ecological footprint in Turkey, and a unidirectional causality relationship from the ecological footprint to GDP in Czechia, Romania, and Turkey. Furthermore, no causality relationship is detected between other variables. Based on the results, several policy implications are suggested.

Causation between energy consumption and climate change in the countries with the highest global climate risk.

The study aims to examine if there is causation between "energy consumption" and "climate change" through the data of ten countries with the highest Climate Risk Index (CRI) scores. The ten highest CRI score countries include Puerto Rico, Myanmar, Haiti, Philippines, Mozambique, The Bahamas, Bangladesh, Pakistan, Thailand, and Nepal. The annual data for the years 2005-2019 was used because of the data constraints. CRI is selected as the dependent variable. As for the independent variables, the ratios of the energy consumption of the key sectors indicated by the International Energy Agency (IEA) to the total energy consumption are chosen. These key sectors in energy consumption are industry (IND), transportation (TRA), trade and public services (TPS), and housing (HOU). Economic growth (EG), which is one of the main factors affecting climate change in the literature, is included in the model as the control variable. According to the results of the Dumitrescu-Hurlin causality test, there is one-way causality from transportation towards CRI, but not any causality between others. It is evaluated that since the transportation sector is heavily dependent on fossil fuels, it has a strong effect on the amount of CO2 emissions and a significant determining role on climate change.