Yazar "Konat, Gokhan" seçeneğine göre listele

Listeleniyor 1 - 6 / 6
  • Küçük Resim Yok
    Öğe
    Analyzing Türkiye's ecological footprint: the impact of air transportation, renewable energy, and R&D using a non-linear ARDL approach
    (Iop Publishing Ltd, 2025) Konat, Gokhan; Yilanci, Veli; Tatar, Havanur Ergun; Han, Aysegul
    This study investigates the complex relationship between air transportation, research and development (R&D) expenditures, renewable energy consumption, economic growth, and the ecological footprint in T & uuml;rkiye, utilizing annual data from 1990 to 2021. Employing both linear and non-linear Autoregressive Distributed Lag (ARDL) models, the study assesses the validity of the Environmental Kuznets Curve (EKC) hypothesis and explores the short- and long-run dynamics of the ecological footprint. Both the linear and non-linear ARDL models provide support for the EKC hypothesis, suggesting that economic growth may decouple from environmental degradation in the long run. Furthermore, robustness checks corroborate these findings. In the long term, air transportation exhibits asymmetric effects; while its positive components do not have a direct impact, its negative components contribute to environmental degradation in T & uuml;rkiye. Renewable energy consumption mitigates environmental pressure, whereas the impact of R&D expenditures is not statistically significant. The findings underscore the substantial influence of air transportation on T & uuml;rkiye's ecological footprint, highlighting the necessity for sustained efforts toward sustainable practices and technological advancements within the aviation sector. Moreover, the study emphasizes the importance of investments in R&D and renewable energy for achieving environmental sustainability, while also acknowledging their complex and multifaceted impacts. The paper also discusses policy recommendations and future research directions focused on achieving a balance between economic development and environmental protection in T & uuml;rkiye.
  • Küçük Resim Yok
    Öğe
    Financial investments in AI-based technologies and carbon footprint in selected advanced industrial economies
    (Bmc, 2026) Konat, Gokhan; Salihoglu, Esengul; Han, Aysegul
    Artificial intelligence (AI) has rapidly expanded across multiple industries and technologies, driving economic growth and offering innovative solutions to structural challenges. However, its environmental impact remains contested. While firms investing in AI aim to lower its carbon footprint, its widespread use continues to generate significant emissions. This study examines the environmental effects of AI investments, particularly on carbon emissions, while also accounting for human and economic development indicators. The analysis employs the Panel ARDL-PMG approach using data from 2012-2023 for nine technologically advanced economies characterized by extensive use of robotics (South Korea, Japan, Germany, the United States, China, Singapore, Sweden, Italy, and France). The findings reveal the existence of a stable long-run equilibrium among the variables. The negative and significant ECT indicates that about 32% of short-term imbalances are corrected each year, suggesting that the system steadily moves toward its long-run equilibrium. In the long run, per capita GDP and renewable energy consumption reduce carbon emissions, whereas AI investments (AIINV), Foreign Direct Investment (FDI), and the Human Development Index (HDI) increase them. The results show that AIINV and FDI do not contribute to reducing carbon emissions. In this context, the findings suggest that investments in the energy sector are not directed toward encouraging the transformation of energy sources. These results highlight the environmental risks posed by the growing prevalence of AI. However, AIINV and FDI have the potential to help reduce carbon emissions if they are aligned with the transformation of energy sources. Thus, aligning AI with green innovation and sustainable environmental policies is essential. This study emphasizes the importance of enabling the energy transition to reduce carbon emissions arising from AIINV and FDI in the sector. Promoting eco-efficient technologies and sustainable innovation processes can help mitigate the carbon-intensive effects of digital transformation.
  • Küçük Resim Yok
    Öğe
    Impact of green innovation on environmental issues: Findings from BRICS-T countries based on spatial analysis
    (Taylor & Francis Inc, 2025) Han, Aysegul; Pehlivan, Ceren; Konat, Gokhan; Koncak, Ahmet
    This study examines the impact of green innovations on environmental sustainability in BRICS-T countries between 2008 and 2021. Using indicators such as renewable energy consumption, R&D investments, industrial design activities, and carbon emissions, the analysis employs both conventional and spatial panel estimation techniques. The findings reveal that investments in renewable energy play a crucial role in reducing carbon emissions and positively contribute to environmental sustainability. However, the effectiveness of environmental innovation policies and R&D expenditures in mitigating emissions remains limited, particularly in the short term. Additionally, industrial design activities are identified as a significant contributor to rising emissions, highlighting the need for a transition toward more sustainable industrial practices. The study also uncovers spatial dependencies among countries, demonstrating that carbon emissions in one nation can influence those in neighboring countries, emphasizing the necessity of regional cooperation and coordinated environmental policies. Based on these findings, the study provides policy recommendations to enhance the effectiveness of green innovations, stressing the importance of increasing investments in renewable energy, aligning R&D expenditures with environmentally sustainable technologies, and promoting sustainable industrial practices across BRICS-T countries.
  • Küçük Resim Yok
    Öğe
    Investigation of the role of technological innovation in reducing carbon dioxide damage in Turkey with Fourier tests: Testing the Kuznets curve hypothesis
    (Springer, 2025) Coskun, Muhammet Fatih; Konat, Gokhan; Yilanci, Veli
    Rising global environmental concerns have intensified the need to understand the relationship between technological innovation, economic growth, and environmental degradation, particularly in rapidly industrializing economies. This study examines these relationships in Turkiye within the framework of the Environmental Kuznets Curve (EKC) hypothesis. Using annual data from 1984 to 2019, we employ Fourier-based econometric techniques, including unit root tests, cointegration analysis, and causality testing, to account for potential structural breaks and nonlinearities. Our findings reveal that while technological innovation currently contributes to increased carbon dioxide emissions, with a 1% increase in innovation leading to a 0.061% rise in environmental degradation, there exists an inverted U-shaped relationship between economic growth and environmental degradation, supporting the EKC hypothesis for Turkiye. Causality tests indicate unidirectional relationships flowing from environmental degradation to both technological innovation and economic growth. These results suggest that Turkiye requires strategic policy interventions focusing on green technologies and sustainable innovation to transition toward environmental sustainability while maintaining economic growth.
  • Küçük Resim Yok
    Öğe
    Is Real Gross Domestic Product (GDP) Series Stationary in EU Countries? Evidence from the RALS-CIPS Test
    (Economics Bulletin, 2021) Konat, Gokhan; Zeren, Fatma
    The purpose of this study is to propose a new residual-based unit root test and then apply it to examine the stationarity of gross domestic product (GDP) for EU membership countries. For this purpose, the CIPS test proposed by Pesaran (2007) has been extended to a structure that takes into account the knowledge of the non-normally distributed residuals. For this, the residual augmented least squares (RALS) estimators proposed by Im and Schmidt (2008) were included in the CIPS test. The second and third moments of the error terms are added to the cross-sectionally augmented ADF (CADF) regression that constitutes the CIPS test process. When calibrated under the behavior of the residues non-normally distributed residuals during the data generation process, it is seen that the panel unit root test specific to the series in which the residuals are not normally distributed has higher power and more appropriate size than CIPS test. According to the results of empirical analysis, it was concluded that the CIPS test was stationary only at the 10% level, while according to the RALS-CIPS test it was concluded that it was stationary at the 1% significance level. It can be interpreted that the RALS-CIPS test is stronger because it used additional information consisting of residual moments. The test offers a simple way to have good size and power properties for non-normal errors.
  • Küçük Resim Yok
    Öğe
    Testing Unemployment Hysteresis with Multi-Factor Panel Unit Root: Evidence from OECD Countries
    (Russian Acad Sciences, Ural Branch, Inst Economics, 2022) Konat, Gokhan; Coskun, Muhammet Fatih
    Hysteresis is a dominant feature of unemployment in numerous countries. According to the hysteresis hypothesis, it is a well-known fact that high unemployment may persist and remain an economic threat in the long run if policy measures are not taken. In this study, it is tested whether the unemployment rates for 10 selected countries of the Organisation for Economic Co-operation and Development (OECD) (Belgium, Canada, Czech Republic, Estonia, France, Japan, Netherlands, Spain, Britain and the USA) contain unit root or not, in other words, whether the hysteresis effect is valid for these countries. For this purpose, this study utilises the concept of the multi-factor panel unit root test proposed by Pesaran, Smith and Yamagata. This method measures cross-section dependence through factors. The test analyses whether the unit root is valid or not, using information about a sufficient number of additional explanatory variables. The characteristic of these additional variables is that they must share a common factor with the variable whose stationarity is tested. It is accepted that this common factor causes cross-sectional dependence. We have taken tax wedge, trade union density and minimum wage as factors that cause cross-sectional dependency and affect unemployment hysteresis. In this test developed by the authors, in the case of a multi-factor error structure, the test procedure is completed by using the information contained in 3 additional variables. The study explores not only the validity of unemployment hysteresis but also the factors that affect the rigidity of the unemployment rate. However, the research was unable to encompass the entire OECD countries and all times because of the lack of data. The results showed that the hysteresis is valid for 10 selected OECD countries.