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This study focuses on the resistance to geothermal energy projects in the Aegean region in Turkey. It explores, drawing on the analysis of documents and the fieldwork conducted in four Aegean cities, why there is a widespread local resistance to geothermal energy, which is widely promoted as environmentally benign and renewable and, as such, critical for the low-carbon energy transition. Examining the resistance from a political ecology perspective, I show how the power/resistance nexus in the field of renewable energy is shaped in those contexts where authoritarianism, populism, and geographically specific forms of capital accumulation operate in and through each other. Specifically, I demonstrate that the formulation of geothermal policy and practices to perpetuate and consolidate the power of the authoritarian populist AKP government laid the groundwork for the generation of widespread resistances by threatening to create new environmental injustices and to deepen existing class, and gender inequalities. The study also shows that geothermal energy may be as destructive as fossil fuel-based energy when not planned, regulated, or monitored effectively. It is concluded therefore that the practice of grouping renewable energy sources in a single 'clean energy' category should be reconsidered. [ABSTRACT FROM AUTHOR]

Recently, interest in industrialisation has increased again, and reindustrialisation policies have begun to be discussed. Because empirical and theoretical evidence indicates that industrialisation plays a significant role in economic growth and development. Nevertheless, studies analysing the factors affecting industrialisation are limited. In consequence, the main purpose of this paper is to analyse the dynamics of industrialisation in Türkiye over the period 1990-2020. For this purpose, the effects of foreign direct investments (FDI), trade globalisation, and renewable energy, which are thought to affect industrialisation significantly, are investigated. The ARDL bounds test, and the Toda-Yamamoto (1995) causality test are employed in this respect. The findings prove that FDI, trade globalisation, and renewable energy consumption positively impact industrialisation in the long-run. At the same time, Toda-Yamamoto's (1995) causality test results indicate a unidirectional causality from FDI to industrialisation, bidirectional causality between renewable energy and industrialisation and no causality between trade globalisation and industrialisation. In this regard, for sustainable industrialisation, effective policies are necessary for FDI, trade globalisation, and renewable energy. In addition, FDI inflows should be directed to specific sectors. [ABSTRACT FROM AUTHOR]

While renewable energy sources are getting high interest, amounts of biogas plants have visible increases. In addition to energy, these facilities also produce a nutrient-rich organic material called digestate, which can be utilized as a crop fertilizer. Scientific research on digestate usage has increased with the new environmental protection laws for direct discharges, particularly after the 2000s. The nutrient composition of digestate provides a variable range of applications, rendering it a valuable commodity. In this study, the energy production capacity of Turkey and Poland from biogas power plants are compared, and the case study was used to estimate the amount of digestate produced for both countries. Two recovery technologies, such as organomineral fertilizer production and an innovative technology of super-heated steam torrefaction to produce valuable products and energy from digestate, were presented with their energy potentials. The outcomes of the case study indicate that harnessing digestate as an energy source holds the capability to make a 3.64% to 4.26% contribution to the overall energy consumption of Turkey and Poland through torrefied biochar energy. As per the results derived from the fertilizer case study, the application of digested biomass as a source of organomineral fertilizer holds the capacity to yield approximately 16,142,806 tons and 9,648,142 tons of fertilizer for Turkey and Poland, correspondingly. [ABSTRACT FROM AUTHOR]

With the increasing need for energy, issues related to using energy efficiently in buildings and employing renewable energy technologies are gaining significance. The building production process is intricate, involving numerous stakeholders, multiple decisions, and a combination of qualitative and quantitative data. This process necessitates decision making based on specific requirements. The objective of this study is to identify effective criteria in decision making concerning the use of renewable energy technologies (RETs) in buildings in Turkey. It aims to highlight the importance of these criteria and compare them, and it also aims to define a recommendation decision-making model for the widespread adoption of renewable energy technologies. This study employed qualitative and quantitative research methods. Based on information gathered from the literature, the main criteria and sub-criteria for RET utilization were determined through in-depth interviews with an expert group, including individuals influencing the building design process (architects, engineers, consultants, employers, and users). A recommendation model was developed using the analytic hierarchy process method to highlight the significance of the identified criteria, compare the criteria and technologies, and facilitate the selection of the most-appropriate technology. This study demonstrates that the decision-making model can be utilized in determining RET-related criteria in the building production process, establishing their weights, and make informed decisions regarding the appropriate technology. [ABSTRACT FROM AUTHOR]

The increase in the use of Renewable Energy Sources (RES) provides many advantages such as reducing the environmental problems and sustainability. In this study, a long-term optimum RES settlement strategic plan is conducted for 81 provinces in Turkey by considering real data. Biomass energy, solar energy, hydroelectric energy, geothermal energy, and wind energy are considered RES sources. Energy consumption until the 2050 year is estimated with the regARIMA method, and then a weighted goal programming model was developed in which the outputs of the regARIMA method and risk analysis are integrated. The results of the regARIMA method are tested, and the test results indicate that an R2 value close to 1 indicates that the model is suitable, a low and negative MPE value is neutral, and a MAPE value below 4% indicates high accuracy of the model. Using GAMS 23.5 optimization software program, the developed weighted goal programming model is solved optimally. In this integrated model developed, the objectives of minimizing the installation time, minimizing the investment cost, minimizing the annual cost, maximizing the carbon emission reduction, maximizing the usage time, and minimizing the risk are considered. When the results obtained regarding the number of installations according to the model are examined, the decisions are made for 53% wind energy, 23% biomass energy, 13% hydroelectric energy, 9% solar energy, and 2% geothermal energy. Computational results show that the effective solutions are obtained by minimizing the sum of goals values, covering all provinces in Turkey, and considering real data. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of renewable energy consumption, total factor productivity (TFP), income, imports, and exports on consumption-based CO2 emissions in Turkey from 1990 to 2019, utilizing the Autoregressive Distributed Lagged (ARDL) method. The findings indicated that the use of renewable energy, total factor productivity and exports negatively influence CO2 emissions. On the other hand, the positive impact of income and imports on CO2 emissions was found. The negative impact of renewable energy consumption and total factor productivity highlights the importance of implementing environmentally friendly measures in the energy sector (particularly raising the proportion of renewable energy in overall energy consumption) and technological innovation. [ABSTRACT FROM AUTHOR]

Copyright of Ege Journal of Fisheries & Aquatic Sciences (EgeJFAS) / Su Ürünleri Dergisi is the property of Ege Journal of Fisheries & Aquatic Sciences (EgeJFAS) / Su Urunleri Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Izmir Journal of Economics is the property of Dokuz Eylul University, Faculty of Economics & Administrative Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, growth in environmental awareness and increasing non‐renewable energy costs have augmented the interest in hydropower generation as a renewable energy. The volume of water stored in a reservoir system is crucial in hydropower production and has deep uncertainty. The subject of this investigation is the estimation of the amount of future hydropower generation in reservoir systems, some of which have multi‐purpose use. This investigation was carried out in Turkey at Atatürk, Keban, Karakaya and Demirköprü Dams. In the estimation of the hydropower generation for future, the decision matrices were devised and run separately for each reservoir system. In the verification of the validity of the model, the amounts of energy generated in reality in the last three years, which were not included in the modelling process, were used for the reservoirs investigated. In the verification process, when the model results and the amounts of energy produced in reality are compared, similar satisfactory results were obtained which are compatible with the properties of the regions where the reservoirs were built. It is expected that the proposed model will provide a decision support tool especially in multi‐objective planning of constrained water resources. [ABSTRACT FROM AUTHOR]

Animal waste can be converted into a renewable energy source using biogas technology. This process has an impact on greenhouse gas emissions and is a sustainable source of energy for countries. It can reduce the effects of climate change and protect the planet for future generations. Tier1 and tier2 approaches are commonly used in the literature to calculate emissions factors. With boosting algorithms, this study estimated each animal category's biogas potential and CH4 emissions (tier1 and tier2 approach) for 2004–2021 in all of Turkey's provinces. Two different scenarios were created in the study. For scenario-1, the years 2020–2021 were predicted using data from 2004 to 2019, while for scenario-2, the years 2022–2024 were predicted using data from 2004 to 2021. According to the scenario-1 analysis, the eXtreme Gradient Boosting Regressor (XGBR) algorithm was the most successful algorithm with an R2 of 0.9883 for animal-based biogas prediction and 0.9835 and 0.9773 for animal-based CH4 emission predictions (tier1 and tier2 approaches) for the years 2020–2021. When the mean absolute percentage error was evaluated, it was found to be relatively low at 0.46%, 1.07%, and 2.78%, respectively. According to the scenario-2 analysis, the XGBR algorithm predicted the log10 values of the animal-based biogas potential of five major cities in Turkey for the year 2024, with 11.279 for Istanbul, 12.055 for Ankara, 12.309 for Izmir, 11.869 for Bursa, and 12.866 for Antalya. In the estimation of log10 values of CH4 emission, the tier1 approach yielded estimates of 3.080, 3.652, 3.929, 3.411, and 3.321, respectively, while the tier2 approach yielded estimates of 1.810, 2.806, 2.757, 2.552 and 2.122, respectively. [ABSTRACT FROM AUTHOR]

Sustainable and environmental technologies for renewable energy have become more significant as countries get to consume more energy and be more sensitive to environmental issues due to regulations and public awareness. Reserves of fossil fuels are slowly being exhausted as the energy demand increases, but investments in renewable energy technologies (RETs) accelerate. The objective of this research is to offer a new robust decision framework for the assessment of RETs under an uncertain decision environment from a sustainable and environmental point of view. Evaluation of RETs driven from different natural resources is a complex problem involving the interaction of multiple criteria at the same time. Thereby, additive ratio assessment (ARAS) method, a distance-based multi-criteria decision-making (MCDM) technique, is applied to quantitatively assess candidate RETs to make use of the technique's ability to compensate for others' limitations to achieve a more convincing and comprehensive outcome. To describe the inconsistent, incomplete, ambiguous, and fuzzy information of real life, Fermatean fuzzy set (FFS) is used as an objective world setting. The interval-valued environment is a rather operational concept to cope with imprecision by considering the functions of membership and non-membership degrees in an interval and thus further capable of solving and modeling complex problems. The MCDM process can also be enriched with group decision-making (GDM) setting to combine individual decision makers' (DMs) opinions into group judgments, as well as by interval-valued FFSs (IVFF) to better simulate human opinions. Thus, this article proposes an MCDM method that integrates the IVFF–ARAS MCDM algorithm into GDM as a first in the literature and presents its application for the RETs as a decision support tool. Its feasibility and effectiveness are demonstrated in a case study for regional assessment and prioritization of RETs in Turkey. [ABSTRACT FROM AUTHOR]

In recent years, high energy costs, increases in carbon emissions, and concerns about energy supply security have led countries to prioritize renewable energy sources in their sustainable energy policies. The selection and ranking of alternative renewable energy sources is a critical issue in establishing an effective energy policy and ensuring environmental improvement at the national and global levels. This study evaluates Turkey's best renewable energy options using the institutional fuzzy assessment based on distance to mean solution (IF-EDAS) method and aims to find Turkey's best renewable energy alternative. The decision model alternatively uses wind, solar, geothermal, biomass, wave, hydroelectric, and hydrogen energy options. According to the study's empirical findings, while the best alternative renewable energy source for Turkey is solar energy, the best criterion in terms of criteria evaluation is "Technology Maturity". The study also carried out sensitivity analysis, and the results were shared. [ABSTRACT FROM AUTHOR]

Recently, wind energy has been utilized globally as a renewable, sustainable, and eco-friendly energy source. However, wind energy's unpredictable and stochastic nature influences its entry into the national electrical grid. An effective wind speed prediction is required to meet these challenges. In this article, the Nonlinear Autoregressive Neural Network (NARNN) model is used and investigated for short-term wind speed forecasting by taking a dataset from the Kandıra wind farm in Kocaeli- Türkiye. The crux of the paper is to improve the actual application of the existing NARNN model with factual data using a different number of neurons of the hidden layer, delays, and training functions in the learning phase called the model's hyperparameters. The mean squared error (MSE) and determination coefficient (R2) are used as performance measures. As a result, the hyperparameter optimization for wind speed prediction using the NARNN increased the forecasting performance. Suggested NARNN model is compared with its exogenous version (NARXNN) using three extra inputs. It is observed that NARNN is not falling behind NARXNN because they provide close results, and NARNN has been shorter to run. Likewise, the learning algorithms were also compared, and it turned out that Bayesian Regularization (BR) is the best learning algorithm. Still, Levenberg Marquardt (LM) algorithm is much faster to execute and provides close results to BR. [ABSTRACT FROM AUTHOR]

This study investigates the potential for renewable energy-based electricity generation using existing wave, wind, and solar energies in Türkiye. A significant part of Türkiye's energy needs is still met using fossil fuels. Considering the country's resources, renewable energy sources appear as an alternative source to meet these needs. The objective of this study is to find an effective, efficient, economical, environmentally friendly, and sustainable way to produce electricity to reach net-zero targets and transition towards low-carbon and carbon-free energy systems. To be able to make a deep investigation about the relevant issue, six provinces from different regions of Türkiye (Antalya, Çanakkale, İstanbul, İzmir, Kırklareli, and Muğla) are assessed in terms of wave, wind, and solar energy potential, including wave data, wind speeds, sunshine duration, and global radiation values. Wind, wave, and solar energy data of the selected regions were taken from the ERA5 database, which is the weather forecast model of the European Center for Medium-Term Weather Forecasts (ECMWF), and the Ministry of Energy and Natural Resources of the Republic of Türkiye and the General Directorate of Meteorology. Calculations were made using monthly data for the last 5 years. Considering the coastal lengths in the determined regions, the annual total electrical power produced from wave, solar, and wind energies was calculated. In these calculations, the coastal length parameter was assumed to be uniform across all cities, and the electrical power potential from these energy sources was analyzed. Within the framework of these analyses, the number of houses in the selected regions whose electricity needs can be met was calculated. As a result, the potential electrical power and the amount of affordable housing units in the selected regions were compared. As an important result of the studies, it was determined that the characteristic features of the selected regions, such as wavelength, wave height, and wind speed, were directly related to the applicable coast length. The power obtained from wave energy was higher than that from other renewable energy sources, considering the determined coast lengths. Wave energy was followed by parabolic solar collector, wind, and photovoltaic solar energy systems. According to the model, the power obtained from renewable energy systems was at the highest level in the Kırklareli/Demirköy province compared to other locations. Kırklareli was followed by İstanbul, Antalya, İzmir, Muğla, and Çanakkale. It was also found that the electricity needs of 763,578 houses were met in the Kırklareli/Demirköy region, and the electricity needs of 470,590 houses were met in the Çanakkale/Ayvacık region. The statistically optimized factors using the Response Surface Methodology (RSM) for wind, photovoltaic, parabolic solar collector, and wave power were reported as 995.278, 4529.743, 2264.546, and 276,495.09, respectively. The optimal factors aim to achieve a total electricity generation rate of 2.491 × 109 (kWh/year), a total number of houses of 682,590.55 (number/year), and a total cost of USD 813,940,876. In line with the results obtained, the Kırklareli/Demirköy region becomes favorable when considering wave and wave-integrated wind and solar energies. The proposed system has the potential to meet the entire electricity demand of the Kırklareli province based on data from the Republic of Türkiye Energy Market Regulatory Authority (EMRA). [ABSTRACT FROM AUTHOR]

In recent years, wind energy has become remarkably popular among renewable energy sources due to its low installation costs and easy maintenance. Having high energy potential is of great importance in the selection of regions where wind energy investments will be made. In this study, the wind power potential in Çanakkale Province, located in the northwest of Türkiye, is examined, and the wind speed is estimated using hourly and daily data over a one-year period. The data, including 12 different meteorological parameters, were taken from the Turkish State Meteorological Service. The two-parameter Weibull and Rayleigh distributions, which are the most widely preferred models in wind energy studies, are employed to estimate the wind power potential using hourly wind speed data. The graphical method is implemented to calculate the shape (k) and scale (c) parameters of the Weibull distribution function. Daily average wind speed estimation is performed with artificial neural network–genetic algorithm (ANN-GA) and ANN–particle swarm optimization (ANN-PSO) hybrid approaches. The proposed hybrid ANN-GA and ANN-PSO algorithms provide correlation coefficient values of 0.94839 and 0.94042, respectively, indicating that the predicted and measured wind speed values are notably close. Statistical error indices reveal that the ANN-GA model outperforms the ANN-PSO model. [ABSTRACT FROM AUTHOR]

In 2023, the Turkish government set ambitious targets for hydrogen production, aiming to reach a price of $2.40 per kilogram by 2035 and halve it by 2050. This article explores the development potential of hydrogen energy in Turkey based on current data. The analysis encompasses critical elements, such as research and development, governmental hydrogen strategy, economic & energy advantages, production & application technologies. There is also a SWOT analysis of the current hydrogen energy landscape in Turkey. Overcoming various challenges and increasing investments in scientific research and infrastructure development are crucial for the successful expansion of the hydrogen economy. In summary, the development of hydrogen green energy is pivotal for enhancing energy security, reducing environmental impact, and meeting international climate commitments, thus strengthening Turkey's economic position in the global energy arena. [Display omitted] • The latest data of hydrogen research has been comprehensively analyzed. • Turkey has the potential to promote green hydrogen energy. • Turkey aims to cut hydrogen costs and expand electrolyser capacity by 2053. • A SWOT analysis of the current hydrogen energy landscape in Turkey was conducted. [ABSTRACT FROM AUTHOR]

Due to global warming, countries have turned to renewable and clean energy sources. Among these sources, they have started to prefer hydrogen energy and green hydrogen production as the cleanest type of energy in producing hydrogen energy. In this study, an innovative adaptive hybrid forecasting model based on deep learning was proposed to be used in green hydrogen production forecasting. The energy required for the electrolysis method used in hydrogen production was planned to be provided by wind energy. A new perspective was introduced in determining hydrogen production with the proposed model. In particular, the wind regime in 9 provinces selected for the case study was determined and the most suitable wind turbine power for the system was calculated according to this regime by applying the decomposition method to wind speeds, the most important input of wind energy. Depending on the determined turbine powers, the amount of hydrogen that can be produced was estimated. In the prediction process, 70 % of the data was used as training data (24528), 5 % as validation data (1752) and 25 % as test data (8760). The highest accuracy rate obtained in hydrogen energy forecasting among the selected provinces is 92.045 %. The lowest error and the highest regression rates (The root mean square error (RMSE): 0.600, Mean absolute percentage error (MAPE): 0.100 and the coefficient of determination (R2): 0.990) in wind speed prediction were realized in İzmir province. The highest wind turbine power was calculated as 225 kW. • A new adaptive forecasting method was presented for hydrogen production. • The advantages of decomposition and merging technologies were used together. • Analysis of green hydrogen production and regulations is very important. • More accurate and consistent results were obtained with the proposed method. • A new perspective in determining hydrogen production was introduced. [ABSTRACT FROM AUTHOR]

The effect of industrialization and technological developments and the rate of population growth have begun to disrupt the ecological balance in the world. A large share of the deterioration of this balance is due to the rapidly increasing energy demands of people. Fossil fuels and renewable energy sources are used to obtain the energy that is needed by human beings. Most of the world's energy needs are met by fossil fuels such as coal, oil, and natural gas. These resources, which we call fossil fuels, cause many parallel environmental problems, such as global warming, climate change, and carbon emissions, for the world and nature. The most affected by all these experiences, of course, is the entire production sector, which is dependent on energy. However, textile and apparel, which is a pioneer in taking steps towards harmonization with the Green Agreement, is one of the sectors that started the transition to green energy within the scope of the European Union and brands' net-zero targets. Within the scope of the Green Agreement, Turkey has participated and started to work for a 70% carbon reduction, which is the target for 2030, and carbon neutrality, which is the target for 2050. Therefore, within the scope of these targets, the textile sector of Çukurova Region, which has the highest export rate in Turkey, was chosen. Within the scope of this study, carbon emission, which is one of the global problems, was examined within the framework of the ISO 14067-ISO Product Based Carbon Footprint (CF) standard by examining the production of a textile company, and the results were analyzed in detail. The main innovation of this article is to follow all stages of the fabric called Tricia, which is the most produced product in the textile industry, from its entry as fiber to its exit as fabric in the factory, and to calculate and analyze the amount of carbon that is released into nature. The dynamic and experimental results showed that it was determined that 6.00 tons of carbon dioxide carbon were released in the time it took for the fabric to go to the sewing room as a fabric. [ABSTRACT FROM AUTHOR]

This article aims to search the technical, environmental, and economic model of an off‐grid hybrid power generation system that supplies electricity to a seawater reverse osmosis (RO) system. Net present cost (NPC) and levelized cost of electricity (LCOE) values were used to determine the optimal system sizing powering a reverse osmosis desalination system for different sites where is located south and west coast of Türkiye. In the proposed power systems, PV panels, wind turbines, diesel generators, lead‐acid batteries, and converters were used. In the instance where the lowest LCOE of 0.301$/kWh is calculated, the optimal system comprises of a 25.7 kW PV array, one wind turbine (rated at 10 kW), 152 kWh LA batteries, and a 6.76 kW converter. The levelized cost of water (LCOW) value for this case was calculated as 1.168 $/m3. The LCOE value was calculated as 0.529 $/kWh for the power system, which is considered as a base case and consists of only a diesel generator, where no renewable energy source is used. For the base case, the carbon footprint of electricity generation is 35,127 kg/year. According to CO2 sequestration analysis result, the number of trees (Pinus Brutia) to be planted was calculated as approximately 164 tree/year over the lifetime of the power system for base case. [ABSTRACT FROM AUTHOR]

In response to technological advances, environmental concerns, and the depletion of conventional energy sources, the world is increasingly focusing on renewable energy sources (RES) as a means of generating electricity in a more sustainable and environmentally friendly manner. Türkiye, with its advantageous geographical location, long hours of sunshine, and favourable climatic conditions, has a high potential for the use of solar energy. The objective of this study was to identify an energy system that minimizes investment costs while optimizing the levelized cost of energy (LCOE) and minimizing greenhouse-gas (GHG) and carbon dioxide emissions. To achieve this, the study used the concept of nanogrids (NGs) and carried out different evaluations for electric vehicle charging stations (EVCS) at different energy levels connected to the grid. The research focused on classic apartment buildings and multistory condominium-style buildings in Istanbul, Türkiye. Using HOMER Grid 1.11.1 version software, the study identified two optimal configurations: a PV–GRID system with 7 kW photovoltaic capacity and a PV–WT–GRID system with 90 kW PV capacity and 6 kW wind-turbine capacity. These configurations had a significantly lower LCOE compared to the cost of electricity from the conventional grid. When examining the sensitivity to economic factors, it was observed that the net present cost (NPC) and LCOE values fluctuated with electricity prices, inflation rates, and equipment costs. In particular, the two optimal configurations did not include a battery energy-storage system (BESS) due to the low energy demand in the PV–GRID system and the efficiency of the wind turbines in the PV–WT–GRID system. This highlights the need to tailor energy solutions to specific consumption patterns and resource types. In conclusion, the adoption of PV–GRID and PV–WT–GRID systems in Istanbul's urban buildings demonstrates economic viability and environmental benefits, highlighting the importance of renewable energy sources, particularly solar PV, in mitigating energy-related environmental challenges, such as reducing CO2 emissions and reducing dependence on conventional grid electricity. [ABSTRACT FROM AUTHOR]

Renewable energy is currently a part of the energy supply. Türkiye, which has a significant potential in terms of renewable energy, offers various supports for biogas energy production. Biogas plants should be established at appropriate locations based on their legal requirements and efficiency. In this study, the application of the Best Worst Method (BWM) and Geographic Information System (GIS) integration for determining suitable locations for biogas plant installation in Türkiye, is presented. First, a suitable region for facilities was determined in a nationwide study. Subsequently, six criteria were determined for the selection of appropriate locations in line with the opinions of experts in this region. By overlay analysis using a GIS software, six criteria were combined using weight values and a site selection suitability map was generated. This map was divided into four classes as unsuitable, low suitability, moderate suitability and high suitability. When the spatial distribution of the suitable areas for the establishment of a biogas facility was examined, it was determined that the high suitability areas were 19931 ha (7.17%), the moderate suitability areas were 22805 ha (8.20%), the low suitability areas were 18300 ha (6.58%), and the unsuitable areas were 2786 ha (1%). The restricted area in this study were 219519 ha (77.04%). In this study, the site selection process was carefully planned and local factors, regulations and environmental impacts were taken into consideration. Correct site selection for biogas plants ensures efficient, environmentally friendly and economically sustainable operation of the plant. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper investigates the impact of geopolitical risks on renewable energy generation in MENAT oil-importing countries, namely, Egypt, Tunisia, and Turkey over the period 1990–2020 using the autoregressive distributed lag (ARDL) model. The main findings emphasize that geopolitical risks play a crucial role in inducing renewable energy development in MENAT oil-importing countries in the short and long run. Financial development appears to positively and significantly affect renewable energy generation in the three countries. Furthermore, the speeds of adjustment towards the long-run equilibrium are 36.78%, 66.03%, and 17.81% annually in Egypt, Tunisia, and Turkey, respectively. In today's volatile and turbulent world, dramatically rising geopolitical risks make the transition to renewable energy an inevitable reality. Consequently, it is incumbent upon policymakers and relevant authorities in MENAT oil-importing countries to preemptively redirect their efforts and strategies to conform to the demands of the inevitable transition to renewable energy sources and boost energy self-reliance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sustainability is an important concept for the whole world. Generally, in order to measure the sustainability of the environment, carbon dioxide emissions and ecological footprint indicators are used. However, these variables do not reflect the supply side of natural resources. Therefore, load capacity factor is an important environmental indicator for a sustainability. Environmental assessment based on the load capacity factor is more meaningful. Besides, improved access to financial services can contribute to environmental sustainability. The effect of financial inclusion on the load capacity factor in Türkiye has not been examined in the current literature. In this context, this study analyzes the impact of financial inclusion, hydropower energy consumption, and life expectancy at birth on environmental sustainability from a different perspective by focusing on load capacity factor. To this end, this study used the newly developed Augmented ARDL method to determine the cointegration relationship between the series and measure the values of the long-term coefficients. Based on the Augmented ARDL method, there is a cointegration relationship between the series. In the long run, hydropower energy consumption reduces pollution, while financial inclusion decreases load capacity factor. The effect of life expectancy at birth on pollution is not significant. Moreover, the results reveal that the load capacity curve hypothesis is valid in Türkiye. As a result, the Turkish government should promote renewable energy sources, especially hydropower energy consumption, align financial services with pollution reduction measures, and contribute to the creation of an environmentally conscious society. [ABSTRACT FROM AUTHOR]

The interest in absorption chillers for air conditioning applications has increased recently due to the negligible electricity requirement. Especially in Turkey, where the potential of renewable energy sources such as solar energy is high, it is possible to achieve significant energy savings by utilizing absorption chillers. This study presents comprehensive energy and exergy analyses of a solar-driven single-effect absorption chiller with LiBr + LiCl/H2O (mass ratio 2:1) solution mixture. Thermodynamic optimization is carried out for the first time in this study to determine the optimum generator temperature using different absorber and condenser temperatures to maximize exergy efficiency of the absorption chiller. Also, generator temperature ranges for each absorber and condenser level are determined in this study so that the chiller using solution mixture can operate without crystallization. The coefficient of performance, total exergy destruction rate, and exergy efficiency of solar-driven absorption chiller for solar collector area of 194 m² under a certain optimized operating condition are 0.402, 113.63 kW, and 1.255%, respectively. Thanks to an alternative solution mixture compared to LiBr/H2O, the effective thermodynamic parameters can be improved and become more advantageous. The comparison results demonstrated that the thermodynamic performance of the system not only increased but also the thermal capacities and collector area decreased. [ABSTRACT FROM AUTHOR]

The restored interest now receives renewable energy due to the global decline in greenhouse gas emanations and fossil fuel combustion. The fasted growing energy source, wind energy generation, is recognized as a clean energy source that has grown fast and is used extensively in wind power-producing facilities. This study's short-term wind speed estimations are made using a multivariate model based on an artificial neural network (ANN) that combines several local measurements, including wind speed, wind direction, LV active power, and theoretical power curve. The dataset was received from Turkey's SCADA system at 10-min intervals, and the actual data validated the expected performance. The research took wind speed into account as an input parameter and created a multivariate model. To perform prediction outcomes on time series data, an algorithm such as an artificial neural network (ANN) is utilized. The experiment verdicts reveal that the ANN algorithm produces reliable predicting results when metrics like 0.693 for MSE, 0.833 for RMSE and 0.96 for R-squared or Co-efficient of determination are considered. [ABSTRACT FROM AUTHOR]

In today's world, new solutions are constantly needed to meet the ever-increasing energy demand. One of the methods of meeting energy needs is to support renewable energy. In order to support renewable energy, globally accepted feed-in tariff (FiT) applications are used effectively. Feed-in tariff support policies differs from country to country. With the continued development of smart grid infrastructures, it has become necessary to reorganize renewable energy support policies. The determined conditions of the FiT application completely affect the renewable energy investments and productions in that region. Turkey has a number of economic and geographical advantages, the majority of which are renewable energy resources. Over the last decade, Turkey has made significant progress in renewable energy development and manufacture. The relevant legislation to renewable energy has been improved. Many studies have been conducted on the use of smart grids to increase energy efficiency and to obtain electrical power systems that provide different opportunities to users. In this study, a new variable FiT algorithm has been developed. The aim of this new variable support mechanism is to provide more benefits for both the investor and the electric power system of renewable energy investments. The results obtained with the proposed algorithm are explained. With this algorithm, the importance of more accurate planning and investment has been tried to be revealed. [ABSTRACT FROM AUTHOR]

In recent years, increasing the rate of green energy use in order to meet the increasing energy demand and combat global warming has become one of the important goals of the countries. For this reason, the integration of renewable energy sources as distributed generation has become increasingly popular. In this study, hybrid renewable energy systems were designed for the electrification of a 100-household area in the Sarayköy district of Denizli province, Turkey, and the Hybrid Optimization Model for Electric Renewables program was used to optimize the required component outputs to to achieve the best economic and environmental results. A total of six hybrid renewable energy system designs, three grid-connected and three stand-alone, were created with different combinations of components such as photovoltaic panel, wind turbine, diesel generator, battery energy storage system, and converter. The most economical design was the grid-connected system with only solar energy with a unit energy cost of 0.0362 $/kWh, while the most cost-effective was the stand-alone system containing solar energy, wind energy, and batteries with 1.61 $/kWh. In terms of the environment, on the contrary, off-grid systems emit less carbon dioxide, while on-grid systems emit more carbon dioxide. [ABSTRACT FROM AUTHOR]

Recently, renewable energy generation (REG) forecasting has become a challenging task for effective energy management. To date, a number of time-series models utilizing physical models, statistical techniques, and artificial intelligence algorithms have been proposed to deal with this problem. Fuzzy time series (FTS) models stand out among these methods since they do not need extensive historical data or some statistical assumptions like the normal distribution. Because intuitionistic fuzzy sets incorporate non-membership values in addition to membership values, they provide more information than conventional fuzzy sets do. In this regard, this paper recommends a hybrid methodology that combines the intuitionistic FTS method with the intuitionistic fuzzy c-means method for REG forecasting of Turkey between the years 2000–2020. According to the empirical results, it can be concluded that the presented integrated model showed good performance in terms of accuracy. The suggested framework is a useful tool not only for REG forecasting but also for other time series forecasting problems. [ABSTRACT FROM AUTHOR]

Meteorology and climate data are used as important data source in energy-related project studies. The use of available energy resources for any given region and the effects of these resources on the environment and human health should be considered. In this context, the priority use of new and renewable energy sources is extremely important in terms of their positive effects on carbon emissions and global warming. In this respect, it is necessary to determine the renewable energy potentials for Ardahan province, which can be an alternative to fossil-based and especially foreigndependent energy sources. In addition, meteorology and climate data should be analyzed in detail in order to raise awareness and utilize resources effectively. In this study, data are provided to determine the renewable energy potential such as wind and solar for Ardahan province, especially based on meteorological and climate data. Ardahan is among the provinces with the lowest average temperature in Turkey, with an annual average temperature of 3.80 °C. According to the long-term average of the province, the total daily sunshine duration is 5.67 hours/day, and there is a significant important solar energy potential. Ardahan, which has hydroelectric power plants of various sizes within its borders, has an average of 136 days of precipitation per year, with an annual precipitation of 554.82 kg/m². In some districts of the province, there are various geothermal energy sources in the form of hot springs and mineral waters, and the soil temperature at a depth of 100 cm throughout the province is not below 0 °C in any month of the year. In addition, the annual average wind speed of the province is 1.88 m/s. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Management & Economics Research is the property of Journal of Management & Economics Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Research in Agricultural Sciences is the property of Ataturk University Coordinatorship of Scientific Journals and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Along with the growth and globalization of the whole world economy since the First Industrial Revolution, the production and use of fossil fuels have led to increased CO2 emissions and, ultimately, significant environmental degradation. The impact of globalization, economic growth, and renewable energy sources on CO2 may show trends with different turning points in developing countries, and estimations may need to follow Fourier-type functions to capture the frequency domain. Therefore, the aim of this study is to examine the effects of renewable energy production, economic globalization, and economic growth on CO2 emissions for Turkey in the period 1971–2006 with Fourier autoregressive distribution lag (ADL) cointegration, DOLS, and Fourier-Granger causality tests. The originality of this study is the estimation of a model of CO2 emissions with a Fourier-type function for the first time. The findings indicate a negative relationship between renewable energy production and CO2 emissions and a positive relationship between economic globalization and economic growth and CO2 emissions. In addition, according to the empirical results, there exists a one-way causality relationship between economic globalization to CO2 and economic globalization to renewable energy production, and there is evidence of a bidirectional causality relationship between economic globalization and economic growth in this study. [ABSTRACT FROM AUTHOR]

Since investigating the long-term trends of the renewable energy potential may help in planning sustainable energy systems, this study intends to forecast the renewable energy potential of the East Thrace, Turkey region, in the future based on CMIP6 Global Circulation Models data using the ensemble mean output of the best-performed tree-based machine learning method. To evaluate the accuracy of global circulation models, Kling-Gupta efficiency, modified index of agreement, and normalized root-mean-square error are applied. The best four global circulation models are detected as a result of the comprehensive rating metric, which combines all accuracy performance results into a single metric. Three different machine learning methods, random forest, gradient boosting regression tree, and extreme gradient boosting, are trained using the historical data of the top-four global circulation models and the ERA5 dataset to calculate the multi-model ensembles of each climate variable, and then, the future trends of those variables are forecasted based on the output of ensemble means of best-performed machine learning methods with the lowest out-of-bag root-mean-square error. It is foreseen that there will not be a significant change in the wind power density. The annual average solar energy output potential is found to be between 237.8 and 240.7 kWh/m2/year depending on the shared socioeconomic pathway scenario. Under the forecasted precipitation scenarios, 356–362 l/m2/year of irrigation water could be harvested from agrivoltaic systems. Thereby, it would be possible to grow crops, generate electricity, and harvest rainwater on the same area. Furthermore, tree-based machine learning methods provide much lower error compared to simple mean methods. [ABSTRACT FROM AUTHOR]

Wave energy is recognized as a significant renewable energy source with a high energy density and very low environmental impact. Today, in parallel with the developments in wave energy technologies, the interest of designers, investors and researchers increases rapidly. Knowledge of wave characteristics is needed to construct and plan wave energy generation systems in any basin. In this respect, determining the probability distribution function of the mean wave period and significant wave height is of vital importance for optimum and reliable planning, and installation of wave energy generation systems. The aim of this study is to examine wave power potential in the North Aegean Sea using the Weibull and Rayleigh probability density models in order to achieve the intended results, a data set such as hourly significant wave height, mean wave period, and wave direction covering one year were used. In agreement with the calculation results, the annual average wave power potential, mean wave period, and significant wave height of this buoy measurement point in the North Aegean Sea were obtained as 1.0145 kW/m, 4.1702s, and 0.5573 m, respectively. This information is essential for power plant operators and policy makers to define the development opportunities, benefits, and costs of developing Turkey's wave energy converter industry. [ABSTRACT FROM AUTHOR]

The transportation sector, which is largely dependent on oil, is faced with many problems such as the danger of depletion of fossil fuels that are harmful to the environment. Moreover, the situations such as epidemics and war cause excessive fluctuations in oil prices. Therefore, there is a need for new solutions based on alternative energy sources for a sustainable transportation sector. Hydrogen fuel cell electric vehicles (HFCEV) are one of the significant alternatives for an efficient, zero emissions and sustainable transportation system. Considering the potential investment in HFCEV technology, the need for a cost effective, green, and low risk Hydrogen Supply Chain (HSC) network infrastructure is inevitable. In this study, the HSC design of the Turkish transportation sector over a 25-year period (2026–2050) is investigated. The problem is modeled using a multi-period mixed integer linear programming (MIP) model. Three objectives are addresses: cost, carbon dioxide (CO 2) emissions and safety risk. In order to consider the uncertainty in the hydrogen demand, five different scenarios are analyzed using fuzzy concept. There are four main results. First, unit hydrogen cost is found to be very high due to low demand and high capital cost in the initial period (2026–2031). Second, HSC network is established in a decentralized setting in all scenario solutions. The level of decentralization gets stronger over time and with increasing demand. Third, short-distance road transport is generally preferred for hydrogen transport. Fourth, since the aim is to minimize cost, CO 2 emissions, and risk level, a mixed production strategy based on cost-oriented SMR and zero-emissions-oriented Electrolysis (ELE) is observed in all scenarios. • Turkey's hydrogen supply chain design for 2026–2050 is examined. • Cost, CO 2 emissions and safety risk are balanced in the transportation sector. • A fuzzy logic procedure is applied to handle demand uncertainty. • The network is balanced with low cost (SMR) and low CO 2 (Electrolysis) production. • The network is decentralized in terms of the distribution of production facilities. [ABSTRACT FROM AUTHOR]

Growth and consumption are two closely related concepts that should not be considered separately. Along with population growth, industrialization, and increasing trade, people started to produce more and thus to consume more. Several environmental problems, including air, soil, and water pollution, the consumption of much more significant amounts of raw materials, and excessive use of synthetic products and chemicals have been emerged in order to meet production needs. One of the necessary resources to meet the consumption required for growth is energy. Renewable energy resources of nature, which are regarded as unlimited, sustainable, and regenerative, include wind energy, solar energy, bio-energy, geothermal energy, wave or tidal energy, and hydroelectric energy. One of the main goals of Turkey, within the scopes of its environmental friendliness and energy saving policies, is to increase the share of renewable energy resources in the energy portfolio of the country whose industry and economy are growing and the population is on the rise. Considering the points mentioned, the current study was carried out to analyze renewable energy consumption and economic growth in Turkey. In so doing, the annual data of renewable energy consumption, Gross domestic product, fixed capital formation, and the total labor force for the period 1990-2019 for Turkey were obtained from the World Bank database and analyzed using Toda-Yamamoto causality analysis. The results showed that there is a unidirectional causality running from renewable energy consumption, capital, and labor force to growth. In other words, increases in renewable energy consumption positively affect economic growth. [ABSTRACT FROM AUTHOR]

One of the most remarkable renewable energy applications is the floating photovoltaic (FPV) power plants in recent years. Although it reduces evaporation and increases solar energy production with the thermal cooling effect, determining the location for the installation of the facilities is a significant problem. There is a lack of a common methodology of evaluating the optimum deployment locations for FPV systems for micrositing of water reservoirs in anywhere in the world. Firstly, solar irradiance should be evaluated over the entire reservoir area because of the shading effects. There are also some dynamical constraints through the formation of extreme winds and water waves over the reservoir surface. Consideration of these restrictions provides a geographically suitable site location for FPV installation. In the present study, three essential natural constraints, namely solar irradiance, wind speed, and wave height, were analyzed over the reservoir of a dam located in the southwest of Turkey. The places where the insolation is high, and the wind speed and the wave height are both low in the entire reservoir area are spatially determined with this approachment, which is named as the FPV Convenient Reservoir Surface (FPV-CRS). The goal of this research is to offer a new methodology including essential guideline for micrositing of the FPV facilities. [ABSTRACT FROM AUTHOR]

Energy is an increasing necessity due to the change in needs and the development of the industry with the rapid growth of the world's population. Today, the energy supply usually meet from non-renewable (fossil-based) sources. This situation causes adverse effects globally and a decrease in the fossil resources that future generations need. Sustainable, efficient, and clean energy will only be possible using renewable resources. Installation of renewable energy systems requires large areas, but suitable terrestrial for onshore are not always available. Therefore, in our country, it is necessary to increase floating renewable energy power plants. In the study, the offshore installation potential of solar, wind, and wave renewable energy required for sustainable energy production in Türkiye was examined. A qualitative evaluation was conducted for solar, wind, and wave energy generation. It has been concluded that coastal areas and stagnant water surfaces of Türkiye are suitable for the installation of floating energy systems. This study aims to emphasize the necessity of floating renewable energy systems for Türkiye. It also provides essential contributions to the studies to be carried out on floating energy systems in Türkiye. [ABSTRACT FROM AUTHOR]

The current study investigates if environmental taxation in Turkey significantly impacts stock returns of renewable energy producers. Renewable energy-related firms trading Istanbul Stock Exchange and the overall energy stock index are selected and analyzed using time series methods. Results generally confirm the long-term effects of taxation on renewable energy stock prices. This study demonstrates that variations in national environmental regulations and energy taxation result in significant variations in energy stock performance. It is also found that foreign direct investments in Turkey negatively impact energy stock markets. Implications are provided at the end of this study. [ABSTRACT FROM AUTHOR]

This research work analyzes the relationship between environmental degradation, economic growth, trade openness, primary energy consumption, coal consumption, and hydroelectricity consumption in Turkey from 1971 to 2015 using the autoregressive distributed lag (ARDL) time series approach through the hypothesis of the environmental Kuznets curve (EKC). Carbon dioxide (CO2) emissions and ecological footprint are both used as indicators of environmental degradation, which employs six different models. According to the results found in this study, while trade openness increases CO2 emissions, it decreases ecological footprint in the long-run. Coal consumption raises both CO2 emissions and ecological footprint. While hydroelectric energy reduces CO2 emissions, it has no effect on the environment. The results demonstrate that the EKC hypothesis is correct for both CO2 emissions and Turkey's ecological footprint. The threshold points are investigated as $18,704, $16,361, and $13,571 in models, where CO2 emissions are the dependent variable. In models where the ecological footprint is the dependent variable, the investigated threshold points of $11,824, $11,821, and $15,476 are higher than the gross domestic product (GDP) per capita during the analysis periods. Furthermore, the findings highlight the importance of renewable energy use in reducing environmental degradation and coal use in increasing environmental degradation. These findings can shed light on the importance of transition to renewable energy sources (i.e., hydroelectricity consumption), from fossil fuels (i.e., coal consumption), related to future planning in energy diversification for Turkey. [ABSTRACT FROM AUTHOR]

It is considered that the use of renewable energy sources will replace fossil fuels due to global climate change and accompanying decisions taken by states. In this study, unlike the renewable energy production estimation studies in the literature, a model was created by taking the socioeconomic, environmental and energy time series data of the countries. In the study, Turkey, which did not promise a numerical reduction in greenhouse gas emissions unlike other developing countries but has an increasing energy production from renewable energy sources, was chosen. In the study, the data between 1990 and 2020 were used to receive more realistic results by considering the interval before and after the Kyoto protocol. Artificial neural networks and support vector regression among machine learning methods were used to predict the model. As a result of the study, support vector regression had a 92% and artificial neural networks had a successful predictive power of 89.9% according to the coefficient of determination (R2). In the study, the root mean square error value was 0.071 for artificial neural networks and 0.045 for support vector regression; the mean squared error value was 0.005 for artificial neural networks and 0.002 for support vector regression, which was close to the ideal values. Both methods were statistically successful. It is predicted that the model designed because of these successful results obtained in the study would guide the creation of energy policies and contribute to scientific studies. [ABSTRACT FROM AUTHOR]

Energy is an indispensable requirement for the sustainable development of countries. Turkey has been trying to increase the use of renewable sources in electricity energy production with the policies it has implemented recently. This study investigates the effect of disaggregate energy consumption on economic growth in Turkey via the Augmented ARDL. Robust results are obtained by Augmented ARDL in econometric analysis. In this context, it is to examine the impact of renewable energy, natural gas, and coal consumption. Considering the 2001 crisis in Turkey, we add a dummy variable to the cointegration equation. The paper employs the recently developed augmented ARDL approach in the presence of one structural break to investigate annual time series data during the period 1988–2018. The results obtained in this study showed that all variables were statistically significant eventually. Long-term estimation results suggest that among the energy sources examined in the study, on economic growth has a positive effect coal consumption, natural gas consumption, and renewable energy. Moreover, empirical results indicated that economic growth and energy consumption also contribute to environmental damage. On the contrary, natural gas both increases economic growth and is effective in increasing environmental quality. The fact that the positive effect of renewable energy sources on economic growth eventually is greater than natural gas is the most striking finding of the study. In line with these results, it can be said that Turkey can reduce its energy dependence by increasing the use of domestic and renewable energy sources and providing sustainable economic growth. [ABSTRACT FROM AUTHOR]

Today, most of the increasing energy demand is still supplied by fossil fuel sources containing high amounts of hydrocarbons. This situation leads to many environmental problems, such as air pollution and global warming. In the face of these problems, the world has focused on research and development studies on renewable energy sources. In this study, the utilization statuses and installation costs of renewable energy sources in the world and Turkey were examined with current data. As a result of the study, it was determined that coal, which is a non-renewable energy source, is still the most consumed energy source, with a share of 36% globally. On the other hand, it was observed that the share of total clean energy in electricity generation had reached 38%. In this context, the rapid expansion of renewable energy utilization in the world and Turkey and the rapid reduction of the demand for non-renewable energy is significant in terms of environmental and resource diversity. [ABSTRACT FROM AUTHOR]

Hydroelectric power plants have been in use for more than a century in countries with rich river resources owing to their cheaper installation costs. Hydroelectric power plants, which have particular importance for the economic growth of developing countries, have been generating electricity in Turkiye over many decades, since the early 1900s. The ultimate objective of the study is to observe the relationship between Turkiye's hydroelectric power generation and its economic growth, which is rarely addressed in the literature, by using recent data. Aimed in the empirical analysis are to test the impact of renewable energy use on economic growth in Turkiye between the years 1965 and 2019 and, in particular, to determine the role and importance of hydroelectric energy, which has an important historical background in respect of Turkish economy, in this relationship. In the analysis part of the study, firstly, unit root analyses were performed, and the ARDL estimator was made, which is the most appropriate estimator among the series with different orders of stationarity. In addition to the diagnostic tests showing whether the ARDL estimator was an appropriate estimator, findings with respect to causality between the variables were revealed with the Toda-Yamamoto causality test, which allows for different orders of stationarity of the series. According to the results of the ARDL estimator, which allows to examine the short and long-term results of the independent variables, it was concluded that renewable energy had no significant effect on economic growth in the long and short run, while hydroelectric energy has increased economic growth in the long run in the analyzed period. In Turkiye, which is highly dependent on foreign for energy production, the contribution of energy consumption to economic growth in the long run was found to be negative. [ABSTRACT FROM AUTHOR]

Health expenditures are affected by different macroeconomic variables. This study aims to examine the impact of renewable energy consumption, carbon emissions, the refugee population, and economic growth on Turkey's health expenditures from 1975 to 2019. For this purpose, firstly, the stationarity orders of the variables were examined with the Fourier-based stationarity test. The long-run effects of the variables on health expenditures were also examined using the Fourier-based cointegration test. The results show that there is a long-run relationship between health expenditures and the explanatory variables. In addition, long-run coefficients were calculated, and it was concluded that carbon emissions and the refugee population increased health expenditures while renewable energy consumption decreased. The causality results indicate that there is unidirectional permanent causality from health expenditures to renewable energy consumption and economic growth and bidirectional permanent causality between carbon emissions and health expenditures. Overall, adopting environmentally and renewable energy-friendly policies and controlling the refugee population are essential policy tools in terms of health expenditures. [ABSTRACT FROM AUTHOR]

Energy is an essential requirement for sustainable development, society, and the world. Turkey has been trying to increase the use of renewable sources in electricity energy production with the incentive policies it has implemented in recent years. Accordingly, in the 2019–2023 Strategic Plan of the Ministry of Energy and Natural Resources, targets to increase the installed capacity of renewable energy sources have been determined. In this context, this article examines fort the first time, the effect of installed capacity of renewable energy sources on sectoral electricity demand. Thus, the study aims to evaluate the renewable energy policies implemented in Turkey. For this purpose, firstly, sectoral electricity demands were estimated for the period 1988–2017 using the autoregressive distributed lag bound testing approach. Then, forecasts were made for the period 2019–2023 with the econometric simulation method. The findings show that the Installed Capacity variable significantly and negatively affects electricity demand in all sectors in the long run. According to the forecast results made in the second stage of the study, it was observed that the higher the increase rate in the installed capacity of renewable energy sources, the faster the electricity demand would decrease. According to these results, Turkey needs to increase the share of renewable energy sources in electricity production in terms of economic and environmental sustainability. [ABSTRACT FROM AUTHOR]

Copyright of Çankırı Karatekin University Journal of the Faculty of Economics & Administrative Sciences is the property of Cankiri Karatekin University, Faculty of Economics & Administrative Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The adaptation of nonpowered dams (NPDs) to dams with hydroelectric generation units requires only minor interventions compared with the construction of a completely new dam and power plant. Thus, it is both more economical and has a minimal environmental impact, especially during construction. The aim of this study was to determine the environmental, technical, and economic feasibility of hydroelectric retrofitting projects in the Büyük Menderes basin in Turkey. For this purpose, the economic feasibility and retrofitting potential of 11 NPDs in the basin were investigated using Water Evaluation and Planning (WEAP) software. The results from the energy simulation revealed that the annual average hydropower generation potential and installed capacity for the selected 11 NPDs are 38.7 GWh/year and 4.4 MW, respectively. The total estimated capital investment cost of the 11 retrofit projects was found to be $7.9 million with a total NPV of $25.6 million. In addition, in this study, the leveled electricity cost for each retrofitting project was established and compared with the LCOE values of other renewable energy sources. The findings show that retrofitting NPDs may represent an as yet untapped opportunity to support the global energy transition by providing a cheaper and more environmentally friendly option to pioneer rural electrification. [ABSTRACT FROM AUTHOR]