Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 1Citation - Scopus: 1Machine Learning for V2X-Enabled Microgrids: A Bibliometric and Thematic Review of Intelligent Energy Management Applications(Springer Heidelberg, 2026-03-09) Dogan, Yasemin; Unlu, RamazanModern power systems are evolving due to convergence of electric mobility, artificial intelligence, and renewable energy integration. Electric vehicles serve as dynamic, mobile energy storage units playing a vital role in ensuring resilient microgrid operations, via vehicle-to-everything (V2X) technology. However, despite the rise of machine learning (ML) in energy management, much of the existing literature remains fragmented lacking a holistic perspective across all facets of V2X-enabled microgrids. This study fills this gap by conducting a systematic bibliometric and thematic analysis of 310 articles obtained from Web of Science (2013-2024). By combining bibliometric mapping with thematic synthesis, the research identifies dominant and emerging ML techniques-ranging from reinforcement learning to federated learning-and evaluates their roles in microgrid management. The study highlights underexplored areas, including decentralized coordination, encouraging prosumer participation, understanding user behavior, safeguarding cybersecurity, improving real-time optimization, and the effective integration and adaptation of V2X technology within microgrid ecosystems. These gaps emphasize the need for interdisciplinary research and policy frameworks to address the social dimensions of future energy systems. Beyond a comprehensive overview, this paper proposes a research roadmap integrating technical, social, and policy dimensions. It offers actionable guidance for researchers, stakeholders aiming to unlock the potential of intelligent, human-centered, and socially inclusive energy ecosystems. Furthermore, the findings align with UN Sustainable Development Goals (SDG 7, 11, and 13), while also creating a positive impact on humanity by supporting the well-being of both society and the planet. Ultimately, this reinforces the indispensable role of ML in advancing the zero-carbon transition.Article Citation - WoS: 194Citation - Scopus: 209The Role of Economic Policy Uncertainty in the Energy-Environment Nexus for China: Evidence From the Novel Dynamic Simulations Method(Academic Press Ltd- Elsevier Science Ltd, 2021-08) Amin, Azka; Dogan, EyupEven though a great number of researches have explored the determinants of carbon emissions, the impact of economic policy uncertainty (EPU) on the environment has not been fully investigated in the energy-environment literature. Since recent studies show a strong relationship between the external environment and uncertainty, the present study for the first time in the literature aims to explore the function of EPU in the energy-environment nexus for China by using the novel bounds testing with dynamic simulations. The empirical results indicate that increases in the real income and energy intensity contribute to environmental pollution while increases in renewable energy lower the level of emissions. Besides, an increase in EPU causes an increase in the volume of carbon emissions. As EPU increases, the government's attention to implement environmental protection policies decreases, and the execution of the environment-related strategies is likely directed in an expected way. The empirical findings suggest that the government should establish consistency in economic and environmental policies to mitigate environmental pollution and thus to reach environmental sustainability.Article Citation - WoS: 350Citation - Scopus: 393The Influence of Renewable and Non-Renewable Energy Consumption and Real Income on CO2 Emissions in the USA: Evidence From Structural Break Tests(Springer Heidelberg, 2017-03-14) Dogan, Eyup; Ozturk, IlhanThe objective of this study is to explore the influence of the real income (GDP), renewable energy consumption and non-renewable energy consumption on carbon dioxide (CO2) emissions for the United States of America (USA) in the environmental Kuznets curve (EKC) model for the period 1980-2014. The Zivot-Andrews unit root test with a structural break and the Clemente-Montanes-Reyes unit root test with a structural break report that the analyzed variables become stationary at first-differences. The Gregory-Hansen cointegration test with a structural break and the bounds testing for cointegration in the presence of a structural break show CO2 emissions, the real income, the quadratic real income, renewable and non-renewable energy consumption are cointegrated. The long-run estimates obtained from the ARDL model indicate that increases in renewable energy consumption mitigate environmental degradation whereas increases in non-renewable energy consumption contribute to CO2 emissions. In addition, the EKC hypothesis is not valid for the USA. Since we use time-series econometric approaches that account for structural break in the data, findings of this study are robust, reliable and accurate. The US government is advised to put more weights on renewable sources in energy mix, to support and encourage the use and adoption of renewable energy and clean technologies, and to increase the public awareness of renewable energy for lower levels of emissions.Article Citation - WoS: 819Citation - Scopus: 943The Influence of Real Output, Renewable and Non-Renewable Energy, Trade and Financial Development on Carbon Emissions in the Top Renewable Energy Countries(Pergamon-Elsevier Science Ltd, 2016-07) Dogan, Eyup; Seker, FahriDue to tremendous increase in the level of carbon dioxide (CO2) emissions in the last several decades, a number of studies in the energy-growth-environment literature have attempted to identify the determinants of CO2 emissions. A major criticism related to the existing studies, we realize, is the selection of panel estimation techniques. Almost all studies use panel methods that ignore the issue of cross-sectional dependence even though countries in the panel are most likely heterogeneous and cross-sectionally dependent In addition, the majority of existing studies use aggregate energy consumption, and thus fail to identify the impacts of energy consumption by sources on the environment In order to fulfill the mentioned gaps in the literature, this empirical study analyzes the influence of the real income, renewable energy consumption, non-renewable energy consumption, trade openness and financial development on CO2 emissions in the EKC model for the top countries listed in the Renewable Energy Country Attractiveness Index by employing heterogeneous panel estimation techniques with cross-section dependence. We find that the analyzed variables become stationary at their first-differences by using the CADF and the CIPS unit root tests, and the analyzed variables are cointegrated by employing the LM bootstrap cointegration test By using the FMOLS and the DOLS, we also find that increases in renewable energy consumption, trade openness and financial development decrease carbon emissions while increases in non-renewable energy consumption contribute to the level of emissions, and the EKC hypothesis is supported for the top renewable energy countries. (C) 2016 Elsevier Ltd. All rights reserved.Article Citation - WoS: 196Citation - Scopus: 225The Impact of Renewable Energy Consumption to Economic Growth: A Replication and Extension of Inglesi-Lotz (2016)(Elsevier, 2020-08) Dogan, Eyup; Altinoz, Buket; Madaleno, Mara; Taskin, DilvinThis study replicates and extends the results presented in a top-cited article in this journal, Inglesi-Lotz (2016), which analyzes the impact of renewable energy consumption to economic growth for the OECD countries by applying the ordinary least squares with fixed effect estimator on the data from 1990 to 2010. By using the same data and methods, this study first produces and compare empirical results with those reported in the original article. Then, it applies a set of new econometric methods on the same data to address heterogeneity in renewable energy and economic growth across the analyzed group of countries. The panel quantile regression estimation shows that the effect of renewable energy consumption on economic growth is positive for lower and lowmiddle quantiles; however, its effect becomes negative for middle, high-middle, and higher quantiles when renewable energy consumption is proxied by the absolute value. Furthermore, a negative impact of renewable energy on economic growth is observed in almost all quantiles when it is proxied by the share of renewable energy consumption to total energy consumption. These results greatly differ from those of the original study (C) 2020 Elsevier B.V. All rights reserved.Article Citation - WoS: 77Citation - Scopus: 89Revisiting the Nexus of Ecological Footprint, Unemployment, and Renewable and Non-Renewable Energy for South Asian Economies: Evidence From Novel Research Methods(Pergamon-Elsevier Science Ltd, 2022-07) Dogan, Eyup; Majeed, Muhammad Tariq; Luni, TaniaGiven the need to employ novel research methods in the energy-environment nexus, the objective of the present research is to investigate the impacts of real output, unemployment, and renewable and nonrenewable energy on ecological footprint under a STIRPAT theoretical framework by applying the second-generation unit root, cointegration, Granger-causality, and long-run estimation methods on the annual data from 1990 to 2017 for South Asian economies. Empirical results show that increases in unemployment and renewable energy decrease ecological footprint while increases in real income and non-renewable energy hurt the environment. This study confirms the adverse effect of renewable energy on environmental degradation as well as the trade-off between unemployment and pollution through multiple robustness and sensitivity checks. In addition, the causality test supports unidirectional causality from income, renewable energy, and non-renewable energy to ecological footprint. Regarding policy perspectives, the governments of the South Asian region should support the deployment of renewable energy through various channels and regulations. The development of technologies that promote sustainable production and consumption play critical roles for reducing the trade-off unemployment and ecological footprint. Further policy suggestions are discussed in the study.(c) 2022 Elsevier Ltd. All rights reserved.Article Citation - WoS: 292Citation - Scopus: 324Potential Ion Exchange Membranes and System Performance in Reverse Electrodialysis for Power Generation: A Review(Elsevier Science Bv, 2015-07) Hong, Jin Gi; Zhang, Bopeng; Glabman, Shira; Uzal, Nigmet; Dou, Xiaomin; Zhang, Hongguo; Chen, YongshengReverse electrodialysis (RED) is an emerging membrane based energy conversion process used to extract electricity by mixing two water streams of different salinities. This technique utilizes transport of cations and anions during controlled mixing of saltwater and freshwater through selective ion exchange membranes. The development of ion exchange membranes and optimization of system performance are crucial for sustainable energy capture from salinity gradients using RED. Recently, increased attention has been given to the preparation of ion exchange membranes and to understanding the factors that determine the RED power performance. This review evaluates potential ion exchange membrane materials, currently available state-of-the-art RED membranes, and their key properties. Discussion will focus on the electrochemical and physical properties of these membranes (e.g., resistance, permselectivity, and swelling) because of their significant role in RED performance throughout the system, Although an interconnected relationship exists between membrane properties, RED requires high quality membranes that are uniquely tailored to have a low resistance and high permselectivity. Moreover, harnessing this potential technology demands not only carefully optimized components but also a novel RED stack design and system optimization. The key findings and advancements needed to assure proper stack design and optimization are also described. This review paper's goal is to elucidate effective energy conversion from salinity gradients and expedite implementation of RED as the next promising renewable source of power for large-scale energy generation. (C) 2015 Elsevier B.V. All rights reserved.Article Citation - WoS: 22Citation - Scopus: 24Multi-Objective Turbine Allocation on a Wind Farm Site(Elsevier Sci Ltd, 2024-02) Dincer, A. E.; Demir, A.; Yilmaz, K.The Multi-Objective Turbine Allocation (MOTA) method is introduced as a novel approach for wind farm layout optimization and site selection. By incorporating Geographic Information System (GIS) tools and the Analytical Hierarchy Process (AHP), the MOTA method offers a comprehensive solution to balance energy production, cost factors, and environmental impacts. In this study, the MOTA method is applied to Go center dot kceada, Turkiye, for wind farm development. Results show that the MOTA method effectively proposes the optimum wind farm layout by selecting the best site for each turbine. The sequential turbine allocation approach, integration of multiple objectives, and use of GIS tools and AHP are the key capabilities and novelties of the MOTA method. The method allows for flexible investment decisions, considering technical and economic aspects. The outcomes from the Go center dot kceada case study highlight the effectiveness of the MOTA method in maximizing energy production while considering cost factors and environmental impacts. The results indicate that for the selected objective functions, the optimal net profit is attained with the installation of 155 turbines on Go center dot kceada. The MOTA method presents a practical and efficient solution for wind farm development, contributing to sustainable and efficient renewable energy generation.Article Citation - WoS: 150Citation - Scopus: 163Investigating the Spillovers and Connectedness Between Green Finance and Renewable Energy Sources(Pergamon-Elsevier Science Ltd, 2022-09) Dogan, Eyup; Madaleno, Mara; Taskin, Dilvin; Tzeremes, PanayiotisAlthough a few studies have analyzed the nexus of renewable energy and green finance, the literature lacks the use of renewable energy by sources. The other major failure is that it uses only annual and small data. Therefore, this study investigates the connectedness and spillovers relationship between green finance and five types of renewable energy (biofuels, fuel cell, geothermal, solar, and wind) by applying the novel TVP-VAR method of Balcilar et al. [1] to the daily indexes from July 31, 2014, to Feb 4, 2022. The results show that dynamic connectedness, both total and pairwise, is heterogeneous over time and influenced by economic events. Furthermore, wind is found to be the largest transmitter of shocks to green finance, followed by biofuels, while both fuel cell and geothermal receive the least shocks. The findings suggest that green finance is mostly a net receiver of shocks from renewable energy sources and that wind has been a net receiver of shocks during the COVID-19 pandemic. A high interconnectedness between the indexes highlights the safe-haven property for diversification purposes of green finance. Our results are important for energy policymakers, those responsible for the implementation of environmental policies, individual investors, and portfolio managers, while also shedding light on the achievement of COP26 goals.Article Citation - WoS: 165Citation - Scopus: 179Factors Affecting Co2 Emissions in Top Countries on Renewable Energies: A LMDI Decomposition Application(Pergamon-Elsevier Science Ltd, 2018-07) Moutinho, Victor; Madaleno, Mara; Inglesi-Lotz, Roula; Dogan, EyupThis study breaks down carbon emissions into six effects considering the current Top 23 countries group on renewable energies, afterwards divided into two different groups (the TOP countries in Europe and the remaining group entering into the Top 23 countries included in the category Rest of the World). It analyses the effects evolution using a larger available data span that runs from 1985 until 2011, to determine which of the effects had more impact over changes of CO2 emissions. The complete additive decomposition technique was used to examine carbon dioxide (CO2) emissions and its components. Moreover, it is performed a comparative analysis to contrast their performance, and a decoupling analysis is presented. For the 1985-2011 period results point for different positive and negative impacts in the behavioral change of CO2 emissions throughout Europe as compared to the Rest of the World. Moreover, the productivity of renewable sources and the financial development effect in renewable electricity generation per GDP are the main responsible for the total and negative changes of CO2 emissions in the last decade; whereas an increase in total changes of emissions are observed due to the fossil fuel energy consumption effect. The multiplicative cross effect, into these two important effects in CO2 emissions decomposed, indicate an aggregate proxy effect of the energy technology level of a country's economy.