• Energy Research
• 2021-2025close
• TR close
• Istanbul University close

The present study is motivated by the need to decouple economic growth from environmental degradation given the new wave of chase for higher economic growth trajectories comes with its environmental cost implications, especially among developing blocs like the Emerging 7 (E7) countries. There is a consistent trade-off between economic growth versus environmental quality. Government apparatus are perpetually on the chase for low-carbon emission policies via the pursuit for green economy. To this end, this present study extends the conventional environmental Kuznets curve (EKC) argument by incorporating the role of institution in emerging industrialized economies (E7) and using second-generation panel analysis methods like mean group (MG), augmented mean group (AMG), common correlated effects mean group (CCEMG), and the Dumitrescu and Hurlin causality test for more robust estimates and inferences. To this end, we explore the long-run and causality relationship between economic growth, quadratic form of economic growth, institutional quality, trade flow, investment in energy sector, and financial development in an EKC environment. Empirical analysis established a long-run equilibrium relationship among the outlined variables over the study period. The long-run regression shows the presence of EKC in the E7. Thus, suggesting the preference for GDP growth over environmental quality at the earlier stage of growth curve. Interestingly, investment in energy, trade flow dynamics across the blocs, and financial development dampens the detrimental effect of environmental pollution as we observed negative relationship with the ecological footprint. On the contrary, quality of institution is weak as institutional quality increase (worsen) the quality of environment in the E7 economies. From a policy perspective, this current study proposed the need for more stringent environmental treaties and regulations and promotion of green economy without compromising economic growth. In the conclusion part of the study, more details and specifics about the policy blueprint are presented.

The present study is motivated by the need to decouple economic growth from environmental degradation given the new wave of chase for higher economic growth trajectories comes with its environmental cost implications, especially among developing blocs like the Emerging 7 (E7) countries. There is a consistent trade-off between economic growth versus environmental quality. Government apparatus are perpetually on the chase for low-carbon emission policies via the pursuit for green economy. To this end, this present study extends the conventional environmental Kuznets curve (EKC) argument by incorporating the role of institution in emerging industrialized economies (E7) and using second-generation panel analysis methods like mean group (MG), augmented mean group (AMG), common correlated effects mean group (CCEMG), and the Dumitrescu and Hurlin causality test for more robust estimates and inferences. To this end, we explore the long-run and causality relationship between economic growth, quadratic form of economic growth, institutional quality, trade flow, investment in energy sector, and financial development in an EKC environment. Empirical analysis established a long-run equilibrium relationship among the outlined variables over the study period. The long-run regression shows the presence of EKC in the E7. Thus, suggesting the preference for GDP growth over environmental quality at the earlier stage of growth curve. Interestingly, investment in energy, trade flow dynamics across the blocs, and financial development dampens the detrimental effect of environmental pollution as we observed negative relationship with the ecological footprint. On the contrary, quality of institution is weak as institutional quality increase (worsen) the quality of environment in the E7 economies. From a policy perspective, this current study proposed the need for more stringent environmental treaties and regulations and promotion of green economy without compromising economic growth. In the conclusion part of the study, more details and specifics about the policy blueprint are presented.

Beyond the anticipated experience associated with tourism destinations, the United Nations World Tourism Organization (UNWTO) has further tasked (especially the destination countries) on the importance of tourism to achieving the 2030 Sustainable Development Goals (SDGs). From this dimension, this study employed the ecological footprint of the 10 most visited countries (France, Spain, United States, China, Italy, Mexico, United Kingdom, Turkey, Germany, and Thailand) over the period 1995-2016. Specifically, the study employed an econometric approach and found that increase in tourism arrivals and globalization is detrimental to the attainment of sustainable environmental quality in a long term. Precisely, a 1% increase in international arrivals and globalization is responsible for a 0.18 and 0.89% increase in ecological footprint in the long-run. These impacts of tourism activities and globalization are detrimental to the environmental quality of the destination countries. Meanwhile, the real income per capita and biocapacity in the destination countries improve the environmental quality of the panel of destination countries in the long-run. In addition, the study found significant evidence of Granger causality from tourism and real income to ecological footprint without feedback, the globalization-ecological footprint Granger causality nexus is with feedback. Moreover, potentially effective policies for government and other stakeholders especially toward attaining Global goals were proffered in the study.

Beyond the anticipated experience associated with tourism destinations, the United Nations World Tourism Organization (UNWTO) has further tasked (especially the destination countries) on the importance of tourism to achieving the 2030 Sustainable Development Goals (SDGs). From this dimension, this study employed the ecological footprint of the 10 most visited countries (France, Spain, United States, China, Italy, Mexico, United Kingdom, Turkey, Germany, and Thailand) over the period 1995-2016. Specifically, the study employed an econometric approach and found that increase in tourism arrivals and globalization is detrimental to the attainment of sustainable environmental quality in a long term. Precisely, a 1% increase in international arrivals and globalization is responsible for a 0.18 and 0.89% increase in ecological footprint in the long-run. These impacts of tourism activities and globalization are detrimental to the environmental quality of the destination countries. Meanwhile, the real income per capita and biocapacity in the destination countries improve the environmental quality of the panel of destination countries in the long-run. In addition, the study found significant evidence of Granger causality from tourism and real income to ecological footprint without feedback, the globalization-ecological footprint Granger causality nexus is with feedback. Moreover, potentially effective policies for government and other stakeholders especially toward attaining Global goals were proffered in the study.

Today, data accumulated during internet use have become an important source of information for people’s behaviour, issues, and needs, and due to real-time data acquisition, Google search data have become a focal point for researchers. As a result, it has been become more common to use GT data, which have been included in forecasting models for many economic indicators, including unemployment rate forecasting. Therefore, this study aims to determine whether including Google search data in forecasting models can improve the model’s performance in forecasting the unemployment rate in Turkey. In this context, out-ofsample forecasting was performed in this study using seasonally adjusted monthly unemployment rates for the period between January 2005 and August 2020 and monthly GT data about the topic of unemployment insurance. In addition, the forecasting performance of ARIMA and ARIMAX methods were compared.

Today, data accumulated during internet use have become an important source of information for people’s behaviour, issues, and needs, and due to real-time data acquisition, Google search data have become a focal point for researchers. As a result, it has been become more common to use GT data, which have been included in forecasting models for many economic indicators, including unemployment rate forecasting. Therefore, this study aims to determine whether including Google search data in forecasting models can improve the model’s performance in forecasting the unemployment rate in Turkey. In this context, out-ofsample forecasting was performed in this study using seasonally adjusted monthly unemployment rates for the period between January 2005 and August 2020 and monthly GT data about the topic of unemployment insurance. In addition, the forecasting performance of ARIMA and ARIMAX methods were compared.

Textile azo dyes are one of the pollutants in waste water that adversely affect human and environmental health. Removal of these chemicals from wastewater is important for eco-system and human health. In this study, Bi2O3 nanoflakes and ZnO were synthesized by the co-precipitation method. Adsorption and photocatalytic degradation reactions were carried out to remove dyes (Victoria blue (VB) and Malachite green (MG)) from wastewater with the photocatalysts. In order to improve the activity of catalysts, cetyltrimethylammoniumbromide (CTAB) was added as a surfactant to pure oxide structures, and Bi2O3-CTAB and ZnO-CTAB catalysts were prepared. The structural and morphological properties of these catalysts were determined by BET, XRD, DRS, FTIR, and SEM analysis. It was found that the activity of the catalyst was improved by adding surfactant to the Bi2O3. The total mineralization of VB dye was completed in 60 min under sunlight with Bi2O3-CTAB catalyst. However, the degradation of the MG dye with the same catalyst under UV-C irradiation could be completed in 120 min.

Textile azo dyes are one of the pollutants in waste water that adversely affect human and environmental health. Removal of these chemicals from wastewater is important for eco-system and human health. In this study, Bi2O3 nanoflakes and ZnO were synthesized by the co-precipitation method. Adsorption and photocatalytic degradation reactions were carried out to remove dyes (Victoria blue (VB) and Malachite green (MG)) from wastewater with the photocatalysts. In order to improve the activity of catalysts, cetyltrimethylammoniumbromide (CTAB) was added as a surfactant to pure oxide structures, and Bi2O3-CTAB and ZnO-CTAB catalysts were prepared. The structural and morphological properties of these catalysts were determined by BET, XRD, DRS, FTIR, and SEM analysis. It was found that the activity of the catalyst was improved by adding surfactant to the Bi2O3. The total mineralization of VB dye was completed in 60 min under sunlight with Bi2O3-CTAB catalyst. However, the degradation of the MG dye with the same catalyst under UV-C irradiation could be completed in 120 min.