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The purpose of this article was to identify the characteristics of HRM (Human Resources Management) portals that can contribute to building a smart, sustainable society in two countries: Poland and Türkiye. The analyzed countries were selected due to their similar economic situation, as well as differences in terms of culture and customs and varying degrees of ICT (Information and Communications Technology) implementation in the economy. Conducting the survey at this particular moment was intended to objectivize users' opinions on the HRM portals and their attributes that may contribute to mitigating the effects of the crisis and facilitating economic development toward the creation of a sustainable society. The survey was conducted in April 2022. It was carried out in both countries and covered a group of more than 850 respondents. The CAWI (Computer Assisted Web Interview) method was used in the survey. The differences between results were identified, and the degree of verification of the hypothesis was established and discussed in this paper. In the first stage of the study, the respondents' environment was examined, as well as their opinions on the use of HRM portals. The second phase consisted of assessing the attributes of the most well-known HRM portals that can contribute to building a smart, sustainable industry. As a result, the finally distinguished features included the characteristics that were most important to the users of the portals and those that supported the development of HRM portals due to their innovativeness. The study fills the research gap regarding the potential impact of HRM portals and their prospective use in first restoring the economic balance of a sustainable society. [ABSTRACT FROM AUTHOR]

This paper describes a newly developed system for harvesting thermoelectric energy from photovoltaic panels. This system helps to power monitoring systems for photovoltaic panels (PVs) in locations where there is no energy source using waste thermal energy from PVs exposed to the sun's rays. In the study described here, the thermal energy from a PV panel was captured and transferred to a thermoelectric generator (TEG). A temperature gradient was created by reducing the temperature using an aluminium heat sink in ambient weather conditions. This temperature gradient was used to generate electricity via two TEGs. In field tests carried out in April, in Aksaray province in central Turkey, the maximum temperature gradient due to solar radiation was measured as 21.08 °C. The harvested energy was increased to a usable level of 4.1 V using a DC-to-DC converter and stored in a li-ion rechargeable battery. The maximum charge current level of the battery was 147 µA. The maximum harvested energy was 458.64 mW, and a stable level of around 350 mW was achieved. The experimental operation of the prototype system was carried out in stable weather conditions; however, weather and climatic conditions greatly affect levels of energy harvested as a result of changing temperature gradients. The energy obtained with the prototype may reduce the battery maintenance costs of PV monitoring systems and lead to the development of new such systems which cannot presently be used due to a lack of energy. [ABSTRACT FROM AUTHOR]

This article is designed to demonstrate that electric roads are an affordable way to electrify all forms of road transport—not only cars, but also buses and trucks. Electric roads represent a way to power electric vehicles without relying solely on batteries. The idea is that when an electric vehicle reaches an electric road, it stops using power from the battery and instead uses power directly from the road itself. The primary challenge for electric vehicles is still the perception of a compromised quality of life in owning an electric vehicle due to a limited range compared with petrol and diesel cars, today. This paper introduces a new technology, currently experiencing rapid development, that can not only overcome range anxiety but make electric vehicles better, in terms of range, than petrol and diesel cars today. Furthermore, not only can this research help to arrange this, but it can also help, for the first time, to cost-effectively electrify heavy-duty transport, such as trucks and buses, which would be a huge breakthrough in terms of sustainability, as it is very important to start supplying electricity to heavy-duty vehicles. The case study provides a very hypothetical example of a trip with and without an electric road, covering a total of 26,011 km of highways and main roads. The results indicate that building electric roads is cheaper than many other alternatives. If a large battery is replaced with a smaller battery for each new vehicle sold, after 3 years, enough savings will be made to electrify all highways and main roads in Turkey. This paper can help transport operators and policymakers develop strategies to accelerate the adoption of electric vehicles by appropriately implementing electric road infrastructure. [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]

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]

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]

Household energy consumption is influenced by a variety of factors, including climate, demographics, and socio-economic conditions. This study is a review based on clustering analysis of data from the 2019 Household Budget Survey in Turkey, and explores the role of dwelling structure, household habits, and possessions as indicators of energy patterns and preferences within distinct household groups contributing to Sustainable Development Goals 1, 7, and 12. The data analysis identifies three distinct clusters with specific features that set them apart. The Urban Majority cluster represents densely populated urban areas where natural gas is a preferred fuel source, and parquet and ceramic tiles are common flooring materials. The Deprived-like Remote Residents cluster portrays areas with less access to natural gas, where wood and coal are frequently used for heating, and the Urban Comfort-Seekers cluster highlights urban areas with high comfort levels, featuring natural gas, floor heating, and various amenities. These clusters reflect energy consumption patterns and social aspects related to poverty and habits. They indicate that Turkey's energy preferences are closely linked to accessibility, user-friendliness, and affordability. Urban households prioritize comfort, while rural areas are more resource-constrained, and contextual factors like location and possessions play a vital role in energy preferences. [ABSTRACT FROM AUTHOR]

This research aimed to analyze the potential amount of electrical energy from biogas energy obtained from animal wastes in Turkey and Poland. Animal waste values were calculated by taking into account the recommended literature values. In determining the biomass energy potential of livestock enterprises in Turkey and Poland, FAO's 2012–2021 data were taken into account. The animal breeds selected as material in this study were cattle, goat, sheep, chicken, duck, goose, turkey, horse, pig, mule and donkey. Considering 10-year calculations, the potential amount of biogas energy that can be obtained from animal wastes for Turkey is 28,845,975 GJ, which is equivalent to 8,105,058 MWh of electrical energy. In Poland, the potential amount of biogas energy that can be generated from animal waste is 13,999,612 GJ, which is equivalent to 3,902,020 MWh of electricity. Moreover, it is estimated that the percentage of the potential amount of electricity to be obtained in 2021 to cover the amount of electricity consumed is 0.303% for Turkey and 0.392% for Poland. For 2021, the amount of economic gains that can be from electricity obtained was also calculated, and it was determined that this value can be 78,650,302 Euro for Turkey and 62,182,435 Euro for Poland. At the same time, it was calculated that the electricity needs of 406,170 houses in Turkey and 171,958 houses in Poland can be met in 2021. As a result, it is thought that the potential electricity to be obtained will contribute to determining energy gains and investment plans for biogas plants. [ABSTRACT FROM AUTHOR]

Medium Neural Networks (MNN), Whale Optimization Algorithm (WAO), and Support Vector Machine (SVM) methods are frequently used in the literature for estimating electricity demand. The objective of this study was to make an estimation of the electricity demand for Turkey's mainland with the use of mixed methods of MNN, WAO, and SVM. Imports, exports, gross domestic product (GDP), and population data are used based on input data from 1980 to 2019 for mainland Turkey, and the electricity demands up to 2040 are forecasted as an output value. The performance of methods was analyzed using statistical error metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared, and Mean Square Error (MSE). The correlation matrix was utilized to demonstrate the relationship between the actual data and calculated values and the relationship between dependent and independent variables. The p-value and confidence interval analysis of statistical methods was performed to determine which method was more effective. It was observed that the minimum RMSE, MSE, and MAE statistical errors are 5.325 × 10−14, 28.35 × 10−28, and 2.5 × 10−14, respectively. The MNN methods showed the strongest correlation between electricity demand forecasting and real data among all the applications tested. [ABSTRACT FROM AUTHOR]

The necessary precautions should be taken in order to prevent service interruption during the maintenance and repairing of electricity networks. Among these measures, emergencies that may occur in the network should be foreseen, hazard scenarios should be created, and solutions should be developed. If these are not done, a blackout, which first follows the local regions and eventually results in the collapse of the national electrical network, may take place. In this study, the national blackout of Turkey that occurred on 31 March 2015 is examined. The information about Turkey's electrical infrastructure and its energy policies was provided, as well as the reliability assessment criteria for power systems and examples of significant blackouts that occurred worldwide. The direct relation between line voltage and system frequency was provided with mathematical derivation by using real data taken from a local industrial zone. Then, a case study is presented to demonstrate this direct relation. The causes, development process, and consequences of the blackout are discussed in detail, and some recommendations are offered to increase the security of the electrical infrastructure and to prevent future occurrences while ensuring the sustainability of the system. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate and compare investment opportunities in the financial markets of Saudi Arabia, a net oil-exporting country, and Turkey, a net oil-importing country, in the Middle East. The international capital asset pricing model (ICAPM) was extended by considering local factors proxied by country risk (CR) and oil price risk exposures of the excess returns of Saudi Arabia and Turkey. In this study, we employed the extended ICAPM in a two-state Markov-switching setting for the sample period of January 2005 to December 2018 to explore whether the risk premium is time-varying. The results suggested that systematic risk is time-varying depending on the state of the financial markets and is affected by both global and local factors. Saudi Arabia offered higher excess returns during the high-volatility regime compared to that of the World Index and enjoyed higher returns during the low-risk regime from oil price shocks. Turkey was negatively affected by oil price shocks and was rather sensitive to the country's risk factor, which varied with both the state of the market and the time factor. These findings will be useful to international investors in diversifying their risks. This study differs from others in estimating the risk premium (beta) by taking into account both the local and global factors and the dynamic nature of systematic risk. [ABSTRACT FROM AUTHOR]

New-generation technologies on vehicles provide many advantages in terms of cost, time, and the environment in the transportation, logistics, freight, and delivery service sectors. This study aimed to measure the effect of using e-scooter vehicles in mail delivery on the energy cost and delivery time in Turkey. Considering the number of test drives in e-scooter applications of potential regions, the amount of energy consumption and driving time data were used. The number of test drives for each e-scooter was assumed as a package or postal delivery amount. The methodology of this study consisted of measuring the effect of input parameters on output variables using the linear response optimization regression method and minimizing the amount of energy consumed and delivery time. The nine input variables and two output variables based on the test drive were analyzed in this study. The distance to the delivery address, region where the delivery address was located, and temperature were found to be statistically significant predictors of the amount of energy required for delivery. The statistical significance levels of time zone, distance, temperature, rainfall, and region factors were calculated as 0.053, 0.001, 0.0033, 0.044, and 0.042, respectively. Driver age, data time zone, distance, wind speed, and delivery region factors affected the time required for delivery with an e-scooter. The statistical significance levels of these factors were calculated as 0.02, 0.001, 0.001, 0.043, and 0.001, respectively. Additionally, N (p; 0.042), NE (p; 0.030), and W (p; 0.057) wind directions directly influenced the delivery time. SE (p; 0.017) was the only wind direction that statistically significantly affected energy consumption. The objective functions were estimated by calculating the optimum values of the input parameters for the minimum energy consumption and delivery time. The optimum values of both input and output variables were calculated based on the desirability values of the optimization models, which were in the optimum solution set. The average data of the optimum values of the objective functions were computed as 2.83 for the number of tests and TRY 0.021 (per 0.098 km) for the energy cost required for delivery. The necessity of using e-scooters, which are more environmentally friendly, economical, and time-saving than traditional delivery vehicles, in postal delivery service is among the prominent suggestions of this study. [ABSTRACT FROM AUTHOR]

In agricultural production, it is important to determine where input usage saving can be implemented by taking energy use into consideration and to analyze the greenhouse gas emissions of agricultural activities. This study has been conducted to review orange (Citrus sinensis L.) production in terms of energy balance and greenhouse gas (GHG) emissions. This study was carried out during the 2015/2016 production season in Adana, a province in Turkey. Energy balance and GHG emissions have been defined by calculating the inputs and outputs of agricultural nature used in orange production. The findings of the study indicate that the distribution of energy inputs in orange production are 11,880 MJ ha−1 (34.10%) of electricity, 10,079.75 MJ ha−1 (28.93%) of chemical fertilizer energy, 7630 MJ ha−1 (21.90%) of chemical energy, 3052 MJ ha−1 (8.76%) of diesel fuel energy, 1348.91 MJ ha−1 (3.87%) of human labor energy, 378 MJ ha−1 (1.09%) of irrigation water energy, 351.22 MJ ha−1 (1.01%) of machinery energy and 118.80 MJ ha−1 (0.34%) of lime energy. In total, input energy (IE) in orange production has been calculated as 34,838.68 MJ ha−1 and the output energy (OE) has been calculated as 95,000 MJ ha−1. Energy use efficiency (EUE), specific energy (SE), energy productivity (EP) and net energy (NE) have been calculated as 2.73, 0.70 MJ kg−1, 1.44 kg MJ−1 and 60,161.32 MJ ha−1, respectively. The total energy input in the production of oranges was divided into: 47.82% direct, 52.18% indirect, 4.96% from renewable sources and 95.04% from non-renewable sources. The GHG emissions figure for orange production was 3794.26 kg CO2–eq ha−1, with electricity having the greatest share, 1983.96 (52.29%); the GHG ratio was 0.08 kg CO2–eq kg−1. According to the results, the production of orange was considered to be profitable in terms of EUE. [ABSTRACT FROM AUTHOR]

In this research, the advantages of the e-scooter tool used in the mail or package delivery process were discussed by considering the Turkish Post Office (PTT) data in the districts of Istanbul (Kadıköy, Üsküdar, Kartal, and Maltepe) in Turkey. The optimization Poisson regression model was utilized to deliver the maximum number of packages or mails with minimum cost and the shortest time in terms of energy consumption, cost, and environmental contribution. Statistical and optimization results of dependent and independent variables were calculated using numerical and categorical features of 100 e-scooter drivers. The Poisson regression analysis determined that the e-scooter driver's gender (p|0.05 < 0.199) and age (p|0.05 < 0.679) factors were not effective on the dependent variable. We analysed that the experience in the profession (tenure), the size of the area responsible, and environmental factors is effective in the e-scooter distribution activity. The number of packages delivered was 234 in a day, and the delivery cost per package was calculated as 0.51 TL (Turkish Lira) for the optimum values of the dependent variables. The findings show that the choice of e-scooter vehicle in the mail or package delivery process is beneficial in terms of time, cost, energy, and environmental contribution in districts with higher population density. As the most important result, the operation of e-scooter vehicles with electrical energy shows that it is environmentally friendly and has no CO2 emission. The fact that the distribution of packages or mail should now turn to micro-mobility is emerging with the advantages of e-scooter vehicles in the mail and package delivery. Finally, this analysis aims to provide a model for integrating e-scooters in package or mail delivery to local authorities, especially in densely populated areas. [ABSTRACT FROM AUTHOR]

The study compares the prediction performance of alternative machine learning algorithms and time series econometric models for daily Turkish electricity prices and defines the determinants of electricity prices by considering seven global, national, and electricity-related variables as well as the COVID-19 pandemic. Daily data that consist of the pre-pandemic (15 February 2019–10 March 2020) and the pandemic (11 March 2020–31 March 2021) periods are included. Moreover, various time series econometric models and machine learning algorithms are applied. The findings reveal that (i) machine learning algorithms present higher prediction performance than time series models for both periods, (ii) renewable sources are the most influential factor for the electricity prices, and (iii) the COVID-19 pandemic caused a change in the importance order of influential factors on the electricity prices. Thus, the empirical results highlight the consideration of machine learning algorithms in electricity price prediction. Based on the empirical results obtained, potential policy implications are also discussed. [ABSTRACT FROM AUTHOR]

In this study, maximum power point tracking (MPPT) control of a grid-connected doubly fed induction generated (DFIG)-based wind energy conversion system (WECS) at variable wind speed was designed and analyzed. The real wind speed data of the Edremit/Balıkesir region in Turkey was used as the wind speed profile. A N90/2.5 MW wind turbine model of Nordex Company was used in the study. Firstly, a conventional PI controller was applied to both rotor and grid side converters. The rotor-side converter (RSC) controls the power generated from the DFIG, whereas the grid-side converter (GSC) controls the DC bus voltage. An MPPT controller was applied to the RSC to generate reference torque at instant variable wind speeds. Thus, the system's response time, electromagnetic torque, generated power, and grid-side currents parameters were improved. In the MPPT controller, the reference torque value is produced by using the angular velocity and reference angular velocity values of the DFIG. The proposed system was modeled and simulated in Matlab/Simulink. Generated power, DC bus voltage, response time, electromagnetic torque, and grid side currents results were obtained. The results of the conventional PI controller and the results of the PI controller with MPPT were compared. The results of the proposed control were also compared with the related studies. The results showed that the proposed system is reliable, applicable, and valid for the grid-connected DFIG at variable wind speeds. [ABSTRACT FROM AUTHOR]

This study examines the long-term energy capacity investment problem of a power generation company (GenCo), considering the drought threat posed by climate change in hydropower resources in Turkey. The mid-term planning decisions such as maintenance and refurbishment scheduling of power plants are also considered in the studied investment planning problem. In the modeled electricity market, it is assumed that GenCos conduct business in uncertain market conditions with both bilateral contracts (BIC) and day-ahead market (DAM) transactions. The problem is modeled as a fuzzy mixed-integer linear programming model with a fuzzy objective and fuzzy constraints to handle the imprecisions regarding both the electricity market (e.g., prices) and environmental factors (e.g., hydroelectric output due to drought). Bellman and Zadeh's max-min criteria are used to transform the fuzzy capacity investment model into a model with a crisp objective and constraints. The applicability of methodology is illustrated by a case study on the Turkish electric market in which GenCo tries to find the optimal power generation investment portfolio that contains five various generation technologies alternatives, namely, hydropower, wind, conventional and advanced combined-cycle natural gas, and steam (lignite) turbines. The results show that wind turbines with low marginal costs and steam turbines with high energy conversion efficiency are preferable, compared with hydroelectric power plant investments when the fuzziness in hydroelectric output exists (i.e., the expectation of increasing drought conditions as a result of climate change). Furthermore, the results indicate that the gas turbine investments were found to be the least preferable due to high gas prices in all scenarios. [ABSTRACT FROM AUTHOR]

Wind power represents a promising source of renewable energies. Precise forecasting of wind power generation is crucial to mitigate the challenges of balancing supply and demand in the smart grid. Nevertheless, the major difficulty in wind power is its high fluctuation and intermittent nature, making it challenging to forecast. This study aims to develop efficient data-driven models to accurately forecast wind power generation. Crucially, the main contributions of this work are listed in the following major elements. Firstly, we investigate the performance of enhanced machine learning models to forecast univariate wind power time-series data. Specifically, we employed Bayesian optimization (BO) to optimally tune hyperparameters of the Gaussian process regression (GPR), Support Vector Regression (SVR) with different kernels, and ensemble learning (ES) models (i.e., Boosted trees and Bagged trees) and investigated their forecasting performance. Secondly, dynamic information has been incorporated in their construction to further enhance the forecasting performance of the investigated models. Specifically, we introduce lagged measurements to enable capturing time evolution into the design of the considered models. Furthermore, more input variables (e.g., wind speed and wind direction) are used to further improve wind prediction performance. Actual measurements from three wind turbines in France, Turkey, and Kaggle are used to verify the efficiency of the considered models. The results reveal the benefit of considering lagged data and input variables to better forecast wind power. The results also showed that the optimized GPR and ensemble models outperformed the other machine learning models. [ABSTRACT FROM AUTHOR]

With heightening global concerns about the impacts of climate change, the debate around different carbon mitigation options is gaining momentum. A widespread argument is between those for and against utilizing nuclear energy as a low-carbon energy source. This debate is also relevant for Turkey, a country that is set to introduce nuclear energy to its electricity generation mix over the coming years. The purpose of the study is to assess the potential merits and drawbacks of the utilization of nuclear energy in the country versus the increased utilization of renewable energy sources. A fundamental merit order model is used for modeling the Turkish electricity market until 2025 under three scenarios. The comparative effects of renewable energy sources and nuclear energy are evaluated in terms of their impact on electricity generation costs, CO2 emissions and energy security. The results of the study show that the increased utilization of renewable energy has a more effective role in achieving the country's energy targets in comparison to nuclear energy. [ABSTRACT FROM AUTHOR]

In the literature, the linkage between income, energy, and carbon emissions has been widely examined and most of the empirical studies have not investigated the impact of the real estate market on their empirical models. Our study endeavors to present a novel topic by investigating the influence of the real estate market on Turkey's environmental quality, using an advanced method of the Bootstrap Autoregressive Distributed Lag (BARDL). We estimate that consumption of renewable energy contributes significantly to CO2 emissions, while real income increases the environmental degradation in both the short and long run. Furthermore, our study demonstrates that the real estate market contributes negatively to the deduction of carbon emissions in Turkey. A one percent increase in the real estate market will cause a rise in Turkey's carbon level by 0.010% and 0.009% in the short and long term, respectively. Our research suggests that Turkey should design new strategies for sustainable real estate markets to improve the environmental quality by supporting green investment projects. [ABSTRACT FROM AUTHOR]

In this study, the performance of an organic dye obtained from the bark of the red pine (Pinus brutia) tree growing in Muğla/Turkey as an interface layer in the Au/n-Si Schottky diode (SD) structure was evaluated. For this purpose, at first, the optimized molecular structure, the highest occupied molecular orbital (HOMO), and the lowest unoccupied molecular orbital (LUMO) simulations of the organic dye were calculated by the Gauss program and it was theoretically proven that the dye exhibits semiconducting properties. Then, the electrical and photodiode variables such as ideality factor, effective barrier height, series resistance, interface states density distribution, photosensitivity, and photo responsivity were evaluated employing current-voltage measurements under dark and different illumination densities. Additionally, C-V measurements were used to demonstrate that the fabricated device has capacitive features and this capability varies as a function of the frequency. Under these measurements, the possible conduction mechanism for the organic dye-based Au/n-Si device was investigated and the results showed that Au/Pinus brutia/n-Si may be a good candidate for optoelectronic applications. [ABSTRACT FROM AUTHOR]

Many empirical studies have tested the linkage among CO 2 emissions, economic growth, and consumption of energy; however, most have not tested the possible influence of insurance market development on their frameworks. This research aims to provide new perspectives on the empirical literature by exploring the role of insurance market development on environmental degradation. The study utilizes a new technique of the bootstrap Autoregressive Distributed Lag (ARDL) test as introduced by (McNown et al., 2018). The ARDL testing approach is utilized to explore the short and long linkage between the examined variables. Furthermore, the research utilized the Granger causality to explore the of causality linkage among the selected variables. The findings illustrate that economic growth and consumption of nonrenewable energy have positive influence on CO 2 emissions. Furthermore, the findings illustrate that the insurance market development has a positive influence on the levels of Turkey's carbon emissions; this finding is also confirmed through the economic growth channel. The outcomes of the current study suggested that the Turkish policy makers should make strategies and policies to ensure the sustainable development of insurance markets, to reduce environmental degradation by supporting the projects, and to invest in clean energy sources. [ABSTRACT FROM AUTHOR]

Each country must determine the Grid Code conditions and apply these criteria to integrate distributed generation (DG) systems into the existing electricity grid and to ensure a stable power system. Thus, experimental studies are required to provide an effective, national, and specific Grid Code. In this study, the Turkish Grid Code's electrical criteria were examined, and the application of these criteria was carried out on a developed PV-based DG. A real-time energy management system (RTEMS) was proposed in the study. Electrical parameters on the developed DG were monitored in real-time by considering IEEE 1547, IEEE 929–2000, and Turkey's electrical criteria. A practical grid code study was firstly investigated in detail about the Turkish Grid Code by a developed real-time monitoring-control and protection system. The proposed RTEMS method in the study is implemented as an inverter-resident system; thus, it provides advantages over many energy management systems embedded in the inverter. The degradation in power quality and non-detection zone (NDZ) problems encountered in active and passive island mode detection methods developed embedded in the inverter are eliminated in the proposed method. With the RTEMS method, where under and over-voltage, under and over voltage frequency, and unintentional island mode events can be detected in real-time, both the existing grid-code requirements are met, and the existing power quality and NDZ problem is eliminated with the recommended inverter-independent RTEMS method. [ABSTRACT FROM AUTHOR]