Yıldırım, Metin; Destek, Mehmet Akif; Manga, Müge

This research examines whether environmental regulations have a moderating effect on the link between foreign direct investment and the environment, as well as the effect of foreign capital investments on environmental quality for BRICS nations. In this approach, using second-generation panel data methodologies for the period 1992–2020, the impacts of foreign direct investments, real national income, consumption of renewable energy, and environmental stringency index on the load capacity factor are explored in the base empirical model. In order to test if there is any evidence of a potential parabolic link between economic growth and environmental quality, the model also includes the square of real national income. In addition, in the robustness model, the moderating role of environmental policy on foreign investment and environmental quality is checked. Empirical results show a U-shaped association between environmental quality and economic development. The usage of renewable energy and the environmental stringency index is also shown to improve environmental quality, although foreign direct investments decrease it. Finally, it is determined that environmental regulations are effective in undoing the negative impacts of foreign capital investments on environmental quality, demonstrating the validity of their moderating function.

Çatık, Abdurrahman Nazif; Bucak, Çağla; Ballı, Esra; Manga, Muge; Destek, Mehmet Akif

This paper investigates the impacts of renewable and nonrenewable energy consumption, income inequality, and globalization on the ecological footprints of 49 countries for the period of 1995–2018. Panel cointegration test reveals a long-run relationship between the variables. Long-run parameter estimates derived from AMG and CCEMG, increasing income and nonrenewable energy consumption, have a significant positive impact on the ecological footprint, while countries that consume more renewable energy have seen an improvement in the quality of the environment. Conversely, neither income inequality nor globalization has a significant effect on national EFs. Evidence from the estimation of the panel threshold error correction model, where GDP growth is used as the transition variable, indicates a significant threshold effect, which supports a nonlinear relationship among the variables by identifying two distinct growth regimes: lower and upper. For the estimation sample, the positive and significant parameter estimates for economic growth in both growth regimes do not support the EKC hypothesis. The results indicate that renewable and nonrenewable energy consumption has a larger impact on the EF in the upper than lower growth regime. The threshold estimates are in line with the linear long-run estimates that do not indicate that income inequality has a significant impact on ecological footprint. However, globalization appears to negatively affect environmental quality in the lower growth regime.

Manga, Muge; Cengiz, Orhan; Destek, Mehmet Akif

This paper investigates the role of export quality in climate action goal of the sustainable development goals in emerging Asian countries. For this purpose, the empirical model that observes the impact of real GDP, energy use and export quality index on carbon emissions is constructed and is analyzed by ARDL bounds testing approach for the period from 1970 to 2014. We also include the square of real GDP as independent variable to observe the existence of environmental Kuznets curve (EKC) hypothesis which implies the parabolic relationship between economic growth and environmental degradation. The findings show that increase in export quality leads to a fall in CO2 emissions for China and India. In contrast, the effect of increasing export quality increases CO2 emissions in Thailand and the Philippines. Lastly, our asymmetric causality results show that the positive shocks of export quality causes positive shocks of CO2 emissions in Thailand and Indonesia. Furthermore, we found that positive export quality shocks cause negative CO2 emissions shocks in India while negative export quality shocks cause positive CO2 emissions shocks in China. We also confirm the inverted U-shaped EKC hypothesis in China and Thailand.

Ozkemahli, Gizem; Erkekoglu, Pinar; Ercan, Ayse; Zeybek, Naciye Dilara; Yersal, Nilgun; Kocer-Gumusel, Belma

Endocrine disrupting chemicals (EDCs) may affect many biological processes like growth and stress response. Bisphenol A (BPA) is a plasticizer that is used to harden plastics and polycarbonates. Phthalates are used to add flexibility to polyvinyl chloride containing plastics. The main metabolite of di(2-ethylhexyl) phthalate (DEHP) is mono(2-ethylhexyl) phthalate (MEHP) and it is even more toxic than the parent compound. Humans are usually exposed to these chemicals in mixtures by different routes starting from fetal period. However, there are not many studies in literature that investigate the combined effects of these chemicals. The aim of this study is to investigate toxic effects of BPA and/or MEHP on HepG2 cell line. We have evaluated cytotoxicity, cytomorphological, apoptotic changes, oxidative stress, oxidant/antioxidant status alterations, and endoplasmic reticulum (ER) stress. Combined exposure to BPA and MEHP caused alterations in oxidant/antioxidant status and ER stress marker proteins in both cytoplasmic and nuclear cellular fractions. We can suggest that combined exposure to EDCs may cause serious toxicological outcomes and more mechanistic studies are needed to determine the combined toxic effects. Graphical abstract: [Figure not available: see fulltext.]

Katipoğlu, Okan Mert; Keblouti, Mehdi; Mohammadi, Babak

Streamflow estimation is important in hydrology, especially in drought and flood-prone areas. Accurate estimation of streamflow values is crucial for the sustainable management of water resources, the development of early warning systems for disasters, and for various applications such as irrigation, hydropower production, dam sizing, and siltation management. This study developed the ANN algorithm by optimizing with an artificial bee colony (ABC). Then, the ABC-ANN hybrid model, which was established, was combined with different signal decomposition techniques to evaluate its performance in streamflow estimation in the East Black Sea Region, Türkiye. For this purpose, the lagged streamflow values were divided into subcomponents using the local mean decomposition (LMD) with the empirical envelope and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) signal decomposition techniques presented to the ABC-ANN algorithm. Thus, the success of the novel hybrid LMD-ABC-ANN and CEEMDAN-ABC-ANN approaches in streamflow prediction was evaluated. The outputs are reliable strategies and resources for water resource planners and policymakers.

Katipoğlu, Okan Mert

Hotter and drier weather conditions due to climate change negatively affect water resources and agricultural production. For this reason, it is vital to analyze the change in potential evapotranspiration (PET) values, which is one of the most important parameters related to plant growth and agricultural irrigation planning. This study analyses the trends and changes in monthly and annual PET values between 1965 and 2018 at Erzincan, Bayburt and Gümüşhane meteorological stations in Turkey. For this purpose, monotonic trends in PET values were determined by Spearman’s rho (SR), Mann–Kendall (MK), Şen slope (SS), and innovative trend analysis (ITA) tests and change points were analyzed with the sequential MK (SQMK) test. The Hargreaves equation was used to calculate the PET values. As a result of the study, according to MK and SR tests, while increasing trends at 95% and 99% significance levels were dominant at Erzincan and Bayburt stations, no statistically significant trends were found at Gümüşhane station except in February. ITA generally detected more than 5% increasing trends in PET data’s low, medium, and high values. According to ITA slope analysis, there are significant increase trends in PET values at all periods, trend 1% significance level. According to the SQMK test, it was revealed that the trend started in PET values, especially in 1995, 2005, and 2010. The findings emphasized the importance of taking measures against decreased agricultural production and managing water resources effectively.

Balli, Esra; Sigeze, Ciler; Ugur, Mehmet Sedat; Çatık, Abdurrahman Nazif

This paper investigates the relationship between CO2 emissions, energy consumption, economic growth, and foreign direct investment for a sample of Asia-Pacific Economic Cooperation Countries (APEC) countries from 1981:Q1 to 2021:Q1 employing panel data methodology. We identify cross-sectional dependence and hence utilize the cross-sectional augmented Dickey-Fuller panel unit root test for appropriate estimation. The cointegration test developed by Westerlund (2008) reveals a long-run equilibrium between CO2 emissions, energy consumption, economic growth, and foreign direct investment. Long-run parameter estimates based on Common Correlated Effect Mean Group indicate that an increase in FDI inflows has a negative impact on air quality, supporting the pollution haven hypothesis. The cointegration test results also show that the impact of Gross Domestic Product (GDP) on CO2 emissions varies by country in the estimation sample. In contrast to the mixed evidence on the effects of other variables, the increase in energy consumption is positively and significantly affecting CO2 emissions in all APEC countries. Emirmahmutoglu and Kose Econ Model 28:870-876, (2011)’s panel causality test results show a bidirectional relationship between FDI and CO2 emissions in Japan. Furthermore, there is a bidirectional causal relationship between GDP and energy consumption in Australia, China, Japan, and Singapore. Overall, empirical evidence suggests that APEC countries should adhere to strict regulations and invest in environmental-friendly clean technologies to attract foreign direct investment.

Katipoğlu, Okan Mert

Revealing the dynamic link between rainfall and runoff, which are the main components of the hydrological cycle, is significant for the planning and managing water resources, disaster risk management, and construction of water structures. This study used feed-forward neural network (FFNN), adaptive neuro-fuzzy inference system (ANFIS), and long short-term memory (LSTM) network to model the rainfall-runoff relationship. Various variations of lagged precipitation, temperature, relative humidity, and flows were presented as inputs, and the flow values of Munzur River were estimated as outputs. During the selection of input parameters, variables with high correlation to flow values were utilized. The model’s success was tested using several statistical indicators, including the coefficient of correlation (R), coefficient of determination (R2), and root mean square error (RMSE). When measuring values and model results are compared, FFNN and ANFIS models show accurate predictive results with high accuracy, while LSTM prediction results are not satisfactory. However, it was concluded that the FFNN model with the hyperbolic tangent sigmoid transfer function and Levenberg-Marquardt training algorithm made a slightly more accurate estimation. In addition, it was revealed that the best ANFIS-Sugeno model was obtained with a hybrid learning algorithm, Gaussmf membership function, and eight subsets. As a result of the analysis, it has been found that FFNN is superior to ANFIS in flow prediction. These results provide policymakers and planners with helpful information for developing flood and drought management strategies.

Katipoğlu, Okan Mert; Sarıgöl, Metin

With the effect of global warming, the frequency of floods, one of the most important natural disasters, increases, and this increases the damage it causes to people and the environment. Flood routing models play an important role in predicting floods so that all necessary precautions are taken before floods reach the region, loss of life and property in the region is prevented, and agricultural lands are protected. This research aims to compare the performance of hybrid machine learning models such as least-squares support vector machine technique hybridized with particle swarm optimization, empirical mode decomposition, variational mode decomposition, and discrete wavelet transform processes for flood routing estimation models in Ordu, Eastern Black Sea Basin, Türkiye. In addition, it is aimed to examine the effect of data division in flood forecasting. Accordingly, 70%, 80%, and 90% of the data were used for training, respectively. For this purpose, the flood data of 2009 and 2013 in Ordu were used. The performance of the established models was evaluated with the help of statistical indicators such as mean bias error, mean absolute percentage error, determination coefficient, Nash–Sutcliffe efficiency, Taylor Diagrams, and boxplot. As a result of the study, the particle swarm optimization least-squares support vector machine technique was chosen as the most successful model in predicting flood routing results. In addition, the optimum data partition ratio was found to be Train:70:Test:30 in the flood routing calculation. The findings are essential regarding flood management and taking necessary precautions before the flood occurs.

Katipoğlu, Okan Mert

Accurate estimation of wind speed (WS) data, which greatly influences meteorological parameters, plays a vital role in the safe operation and optimization of the power system and water resource management. The study’s main aim is to combine artificial intelligence and signal decomposition techniques to improve WS prediction accuracy. Feed-forward back propagation neural network (FFBNN), support vector machine (SVM) and Gaussian processes regression (GPR) models, discrete wavelet transform (DWT), and empirical mode decomposition (EMD) were used to forecast the WS values ​​1 month ahead in Burdur meteorology station. Statistical criteria such as Willmott’s index of agreement, mean bias error, mean squared error, determination coefficient, Taylor diagram and regression analysis, and graphical indicators were used to evaluate the prediction success of the models. As a result of the study, it was determined that both wavelet transform and EMD signal processing increased the WS prediction performance of the stand-alone ML model. The best performance was obtained with the hybrid EMD-Matern 5/2 kernel GPR with test (R2:0.802) and validation (R2:0.606). The most successful model structure was obtained using input variables with a delay of up to 3 months. The study’s results contribute to wind energy-related institutions in terms of practical use, planning, and management of wind energy.