Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 204Citation - Scopus: 234The Relationship Between Economic Growth and Electricity Consumption From Renewable and Non-Renewable Sources: A Study of Turkey(Pergamon-Elsevier Science Ltd, 2015-12) Dogan, EyupThe main objective of this study is to analyze the short and long run estimates as well as the causality relationships between economic growth (GR), electricity consumption from renewable sources (RELC) and electricity consumption from non-renewable sources (NRELC) for Turkey in a multivariate model wherein capital (K) and labor (L) are included as additional variables. Using the autoregressive distributed lag (ARDL) approach to cointegration, the Johansen cointegration test and the Gregory-Hansen cointegration test with structural break, we show that GR, RELC, NRELC, K and L are cointegrated. Although NRELC has a long run positive effect on GR, the long run estimate of RELC is negative but insignificant at 5% level of significance. The Granger causality test based on the vector error correction model reveals the evidence of neutrality hypothesis between RELC and GR, and between NRELC and GR in Turkey in the short run. In addition, the Granger causality runs from RELC, NRELC, K and L to GR as well as from GR, RELC, K and L to NRELC in the long run, which supports the existence of growth hypothesis between RELC and GR, and feedback hypothesis between NRELC and GR in the long run. It is advised that policy makers in the Turkish government should continue to reduce the share of electricity consumption from renewable sources and encourage the usage of electricity from non-renewable sources to have sustainable long run growth rates. It is also essential to promote the investment projects to increase the efficiency of electricity generation from non-renewable sources. (C) 2015 Elsevier Ltd. All rights reserved.Article Citation - WoS: 356Citation - Scopus: 404The Impact of Trade Openness on Global Carbon Dioxide Emissions: Evidence From the Top Ten Emitters Among Developing Countries(Elsevier, 2016-08) Ertugrul, Hasan Murat; Cetin, Murat; Seker, Fahri; Dogan, EyupThis study aims to analyze the relationship between carbon dioxide (CO2) emissions, trade openness, real income and energy consumption in the top ten CO2 emitters among the developing countries; namely China, India, South Korea, Brazil, Mexico, Indonesia, South Africa, Turkey, Thailand and Malaysia over the period of 1971-2011. In addition, the possible presence of the EKC hypothesis is investigated for the analyzed countries. The Zivot-Andrews unit root test with structural break, the bounds testing for cointegration in the presence of structural break and the VECM Granger causality method are employed. The empirical results indicate that (i) the analyzed variables are co-integrated for Thailand, Turkey, India, Brazil, China, Indonesia and Korea, (ii) real income, energy consumption and trade openness are the main determinants of carbon emissions in the long run, (iii) there exists a number of causal relations between the analyzed variables, (iv) the EKC hypothesis is validated for Turkey, India, China and Korea. Robust policy implications can be derived from this study since the estimated models pass several diagnostic and stability tests. (C) 2016 Elsevier Ltd. All rights reserved.Article Citation - WoS: 31Citation - Scopus: 31Revisiting the Nexus Among Carbon Emissions, Energy Consumption and Total Factor Productivity in African Countries: New Evidence from Nonparametric Quantile Causality Approach(Elsevier Sci Ltd, 2020-03) Dogan, Eyup; Tzeremes, Panayiotis; Altinoz, BuketThis study aims to contribute to the existing thin body of nonlinear causality literature by applying the new hybrid nonparametric quantile causality approach. In this line, we investigate the non-linear nexus among total factor productivity, energy consumption and carbon emissions for seventeen African countries. From the results, it is remarkable that there are generally strong causalities between the variables in the middle lower, middle upper and middle quantiles. Hence, energy consumption, environmental pollution and total factor productivity are closely linked in African countries. In particular, bidirectional linkage is detected between total factor productivity and energy consumption for Angola, Benin, Botswana, Cote d'Ivoire, Kenya, Morocco, Egypt, Nigeria and Tunisia. Studying the relationship between total factor productivity and emissions again at the middle quantile bidirectional causal ordering is documented almost for all the countries. Lastly and regarding the linkage between energy consumption and carbon emissions, a strong bidirectional ordering between the two variables is confirmed for Angola, Benin, Cote d'Ivoire, Cameroon, Kenya, Morocco, Egypt, Mozambique, Nigeria, Senegal and Tunisia. We can notice that an increase in economic development is critical for these countries; a number of regulatory policies for environmental problems and energy consumption are required during this development.Article Citation - WoS: 63Citation - Scopus: 107Research Article Energy Consumption of On-Device Machine Learning Models for IoT Intrusion Detection(Elsevier, 2023-04) Tekin, Nazli; Acar, Abbas; Aris, Ahmet; Uluagac, A. Selcuk; Gungor, Vehbi CagriRecently, Smart Home Systems (SHSs) have gained enormous popularity with the rapid development of the Internet of Things (IoT) technologies. Besides offering many tangible benefits, SHSs are vulnerable to attacks that lead to security and privacy concerns for SHS users. Machine learning (ML)-based Intrusion Detection Systems (IDS) are proposed to address such concerns. Conventionally, ML models are trained and tested on computationally powerful platforms such as cloud services. Nevertheless, the data shared with the cloud is vulnerable to privacy attacks and causes latency, which decreases the performance of real-time applications like intrusion detection systems. Therefore, on-device ML models, in which the user data is kept locally, have emerged as promising solutions to ensure the security and privacy of the data for real-time applications. However, performing ML tasks requires high energy consumption. To the best of our knowledge, no study has been conducted to analyze the energy consumption of ML-based IDS. Therefore, in this paper, we perform a comparative analysis of on-device ML algorithms in terms of energy consumption for IoT intrusion detection applications. For a thorough analysis, we study the training and inference phases separately. For training, we compare the cloud computing-based ML, edge computing-based ML, and IoT device-based ML approaches. For the inference, we evaluate the TinyML approach to run the ML algorithms on tiny IoT devices such as Micro Controller Units (MCUs). Comparative performance evaluations show that deploying the Decision Tree (DT) algorithm on-device gives better results in terms of training time, inference time, and power consumption.Article Citation - WoS: 298Citation - Scopus: 325Investigating the Impacts of Energy Consumption, Real GDP, Tourism and Trade on CO2 Emissions by Accounting for Cross-Sectional Dependence: A Panel Study of OECD Countries(Routledge Journals, Taylor & Francis Ltd, 2015-12-11) Dogan, Eyup; Seker, Fahri; Bulbul, SerapThe objective of this study is to analyse the long-run dynamic relationship of carbon dioxide emissions, real gross domestic product (GDP), the square of real GDP, energy consumption, trade and tourism under an Environmental Kuznets Curve (EKC) model for the Organization for Economic Co-operation and Development (OECD) member countries. Since we find the presence of cross-sectional dependence within the panel time-series data, we apply second-generation unit root tests, cointegration test and causality test which can deal with cross-sectional dependence problems. The cross-sectionally augmented Dickey-Fuller (CADF) and the cross-sectionally augmented Im-Pesaran-Shin (CIPS) unit root tests indicate that the analysed variables become stationary at their first differences. The Lagrange multiplier bootstrap panel cointegration test shows the existence of a long-run relationship between the analysed variables. The dynamic ordinary least squares (DOLS) estimation technique indicates that energy consumption and tourism contribute to the levels of gas emissions, while increases in trade lead to environmental improvements. In addition, the EKC hypothesis cannot be supported as the sign of coefficients on GDP and GDP(2) is negative and positive, respectively. Moreover, the Dumitrescu-Hurlin causality tests exploit a variety of causal relationship between the analysed variables. The OECD countries are suggested to invest in improving energy efficiency, regulate necessary environmental protection policies for tourism sector in specific and promote trading activities through several types of encouragement act.Article Citation - WoS: 346Citation - Scopus: 388Exploring the Relationship Among CO2 Emissions, Real GDP, Energy Consumption and Tourism in the EU and Candidate Countries: Evidence From Panel Models Robust to Heterogeneity and Cross-Sectional Dependence(Pergamon-Elsevier Science Ltd, 2017-09) Dogan, Eyup; Aslan, AlperA major criticism to the existing energy-growth-environment literature, we notice, is the selection of methodology. Panel estimation techniques that fail to consider both heterogeneity and cross-sectional dependence across countries may cause forecasting errors. The other concern related to the literature is that only a small number of studies analyze the influence of tourism on CO2 emissions even though tourism sector has potential for affecting the environment. To fulfill the mentioned gaps in the literature, this study analyzes the relationship among carbon emissions, real income, energy consumption and tourism for a panel of the EU and candidate countries over the period 1995-2011 by using heterogeneous panel estimation techniques with cross-sectional dependence. Results from the CADF and the CIPS panel unit root tests show that the analyzed variables become stationary at their first-differences. The LM bootstrap panel cointegration test indicates the presence of a long run relationship among the analyzed variables. Results from the OLS with fixed effects, the FMOLS, the DOLS and the group-mean estimator reveal that energy consumption contributes to the level of emissions while real income and tourism mitigate CO2 emissions. The Emirmahmutoglu-Kose panel Granger causality test suggests that there is one-way causality running from tourism to carbon emissions, and two-way causality between CO2 emissions and energy consumption, and between real income and CO2 emissions. Policy implications are further discussed.