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ArticleYalnız künye2026/r/ebyu/10677
Change-point detection and trend magnitude estimation in long-term streamflow records of a transboundary river basin under climate change
Tanas Savaş, Gülçin; Ertugay, Neşe; Katipoğlu, Okan Mert
Global climate change is reshaping basin hydrology by altering precipitation timing, evaporative demand, runoff generation, and groundwater recharge, which makes long-term streamflow trend detection essential for sustainable water resources management and climate adaptation. In this study, monthly, seasonal, and annual streamflow trends from three gauging stations in the upper Euphrates basin (D21A001, E21A019, and E21A051) were evaluated using an integrated non-parametric framework that combines Mann–Kendall (MK), Spearman’s rank correlation (SRC), Sequential Mann–Kendall (SMK), innovative trend analysis (ITA), and ITA slope (ITAS). This multi-method design allowed consistent identification of trend direction, magnitude, and temporal transition behavior, while also improving interpretive robustness across stations with different streamflow characteristics. The results show a clear common signal: streamflow weakening is most pronounced in the spring to early summer period, although the strength and timing vary by station. E21A019 shows a relatively coherent declining trend across methods, particularly during April–June and at seasonal and annual scales, indicating a stronger tendency toward streamflow reduction compared to the other stations. E21A051 shows a seasonally contrasting regime, with increasing tendencies in winter and late summer/early autumn (notably January–February and August–September), but decreasing trends during spring and early summer, implying intra-annual redistribution rather than uniform change. D21A001 presents weaker trend magnitudes overall, yet decreasing tendencies are still evident in late spring, early summer, and autumn. Across methods, MK and SRC consistently support the trend directions, while ITA/ITAS better highlight magnitude and seasonal contrast. Overall, the findings provide robust evidence of long-term streamflow change and hydrological pressure in the upper Euphrates basin. They can support adaptive and transboundary water management planning, while attribution to specific climatic or anthropogenic drivers requires direct analysis of the relevant forcing variables.
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ArticleYalnız künye2026/r/ebyu/10673
Comparative environmental performance of high-speed rail and air transport on Japan's domestic short-haul routes: A case-based evaluation using EUROCONTROL's IMPACT
Çam, Ömer; Kılıç, Uğur; Tanrıverdi, Gökhan
Air transportation offers significant speed advantages, particularly on medium- and long-haul routes, yet it contributes disproportionately to environmental pollution, especially on short-haul domestic flights. In contrast, high-speed rail (HSR) represents a more sustainable alternative. This study examines CO2 emissions from narrow-body and wide-body aircraft in comparison with Shinkansen trains on selected Japanese domestic routes, where air and HSR services directly compete, focusing on the Haneda–Osaka and Haneda–Fukuoka corridors. Japan is notable for having the highest use of wide-body aircraft on domestic routes worldwide, which makes this context particularly relevant for examining the environmental impact of aircraft type. Emission data for each aircraft type were obtained using the integrated aircraft noise and emissions modeling platform (IMPACT) developed by EUROCONTROL. The results indicate that wide-body aircraft emit at least twice as much CO2 as narrow-body jets, highlighting the critical influence of aircraft type on short-haul emissions. Shinkansen trains, particularly the N700 series, demonstrate the lowest per-passenger emissions, achieving approximately 20% greater efficiency than the older 700 series. The study provides policymakers with a meaningful perspective, emphasizing that deploying wide-body aircraft on short-haul routes is inconsistent with environmental sustainability goals.
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ArticleAçık Erişim2026/r/ebyu/10670
Machine Learning-Based Performance Analysis of Solar Thermal Storage Tanks with Fin-Configured Phase Change Materials
Çolak, Andaç Batur; Kılınç, Cuma
Solar thermal energy storage systems play a vital role in bridging the gap between intermittent solar availability and continuous energy demand; however, their efficiency is severely constrained by the inherently low thermal conductivity of phase change materials. Integrating physical heat transfer enhancements, such as radial fins, offers a practical solution, but evaluating these non-linear thermal dynamics across diverse design configurations typically incurs heavy computational costs. To address this challenge, this research investigates an artificial intelligence-based predictive framework capable of accurately modeling complex phase change dynamics in fin-configured storage tanks. Utilizing high-fidelity 2D Computational Fluid Dynamics simulation data of a stainless-steel double-tube storage tank filled with RT-50 paraffin wax across 10, 20, and 29 fin configurations, a Multi-Layer Perceptron Artificial Neural Network trained with the Bayesian Regularization algorithm was developed. The model predicts liquid fraction, latent heat distribution, and buoyancy-driven natural convection (Reynolds number) based on fin count and time. The optimal architecture, featuring 30 hidden neurons, achieved exceptional predictive precision, yielding a coefficient of determination of 0.99999, along with individual Mean Squared Error values of 1.29 × 10−3 for liquid fraction, 5.73 × 10−1 for latent heat distribution, and 2.64 × 10−5 for Reynolds number, with average prediction deviation rates consistently below 0.5%. These results demonstrate that high-precision surrogate modeling can effectively replace computationally intensive numerical simulations, offering significant practical implications for the real-time thermal monitoring, rapid design optimization, and intelligent control of advanced solar energy storage technologies.
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ArticleAçık Erişim2026/r/ebyu/10599
The Mediating Role of Climate Change Anxiety in the Effect of Nurses’ Environmental Behaviors on Their Carbon Footprint Awareness
Özdemir, Hicran; Tarsuslu, Sinan; Agaoglu, Ferhat Onur
Background: Climate change and related environmental issues are among the major global threats affecting health systems. While nurses’ eco-friendly behaviors are critical for reducing the environmental impact of health care services, the psychological mechanisms through which these behaviors influence carbon footprint awareness have not been sufficiently explained. Objective: The aim of this study is to examine the effect of nurses’ eco-friendly behaviors on carbon footprint awareness and to assess the mediating role of climate change anxiety in this relationship. Methods: The study was conducted using a cross-sectional and analytical design. The sample consisted of 386 nurses selected through convenience sampling based on voluntary participation from among nurses working in secondary and tertiary healthcare facilities in Turkey. Results: It was determined that environmentally friendly behavior significantly and positively predicted carbon footprint awareness (B = 0.542, p < 0.001) and climate change anxiety (B = 0.602, p < 0.001). In addition, climate change anxiety was found to have a significant positive effect on carbon footprint awareness (B = 0.458, p < 0.001). Bootstrap analysis showed that climate change anxiety played a significant partial mediating role in the effect of eco-friendly behavior on carbon footprint awareness (indirect effect = 0.176, 95% CI = 0.165–0.369). Conclusions: Nurses’ eco-friendly behaviors increase carbon footprint awareness both directly and through climate change anxiety. The findings highlight the importance of practices that support eco-friendly behaviors and educational programs that strengthen climate change awareness in the development of sustainable health services.
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ArticleAçık Erişim2026/r/ebyu/10544
Adaptive Weighting Ensemble Approach for High-Fidelity PM10 Forecasting via Evolutionary TCN and Bidirectional GRU
Kilinc, Huseyin Cagan; Dalkilic, Huseyin Yildirim; Yurtsever, Adem; Ates, Sinan; Yeşilyurt, Sefa Nur
Rapid socioeconomic development has intensified air pollution, making accurate air quality forecasting essential for effective pollution management and public health protection. However, short-term PM10 prediction remains challenging because of complex temporal dependencies, nonlinear pollutant–meteorological interactions, and rapidly changing pollution episodes. To address these challenges, this study proposes the AWE-Bi-GRU-EA-TCN-LR hybrid framework, where Bi-GRU captures bidirectional temporal dependencies, EA-TCN extracts multi-scale temporal patterns through enhanced attention, and Linear Regression (LR) statistically calibrates residual relationships. The framework is evaluated using hourly air quality and meteorological observations from an industrialized urban area. As a consequence of the estimate, the RMSE value of 17.740 obtained with the comparison model, the persistence model, was decreased to 12.228 in the proposed model, while the R2 values were obtained as 0.786 and 0.899 in the same models, respectively. A number of different models were used in this research for the purpose of predicting PM10, and the Enhanced Temporal Convolutional Network (EA-TCN) model was found to have a root mean square error (RMSE) value of 12.804. Consequently, this proposed synergistic model has the capability to generate accurate results while maintaining robustness in air quality forecasting using complex datasets in regions with different environmental characteristics and meteorological variables.
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ArticleAçık Erişim2026/r/ebyu/10543
Waste Management Strategies for Environmental Wellbeing: Exploring the Roles of Environmental Regulations and Green Finance in Reducing Carbon Intensity
Caglar, Abdullah Emre; Destek, Mehmet Akif; Demirdag, Ismail; Manga, Muge; Aydın, Sercan
Rising industrial activity, urban expansion, and economic growth continue to intensify waste generation and greenhouse gas emissions, placing growing pressure on environmental sustainability. In this setting, reducing the carbon intensity of electricity generation has become a key policy priority, particularly in advanced economies pursuing long-term decarbonization. Grounded in Ecological Modernization Theory and the Porter Hypothesis, this study examines the effects of waste management, environmental regulations, green finance, and economic growth on the carbon intensity of electricity generation in the G-6 economies, namely France, Germany, Italy, Japan, the United Kingdom, and the United States. This article contributes to the literature in two ways. First, the paper ensures a focused assessment of the direct roles of waste management, environmental regulations, and green finance in shaping carbon intensity in advanced economies. Second, it investigates whether the effectiveness of green finance depends on the stringency of environmental regulation. Utilizing panel data of the 2000–2020 period, drawn from Our World in Data, the World Development Indicators, the OECD, and the IEA, the analysis employs the CS-ARDL and AMG estimators to address issues of cross-sectional dependence and heterogeneity. The findings show that waste management, environmental regulations, and economic growth reduce carbon intensity in the long run, whereas green finance on its own does not have a statistically significant effect. However, green finance becomes effective when combined with stricter environmental regulations. These findings underscore the importance of coordinated policy design and offer practical insights to advance progress toward SDGs 11, 12, and 13.
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ArticleYalnız künye2026/r/ebyu/10493
The role of air transportation on CO2 emissions in BRICS countries: Novel findings from MMQR methodology
Gümüş Akar, Pınar; Balli, Esra; Manga, Müge; Çatik, Abdurrahman Nazif
This article examines the impact of air passenger and freight transportation, economic growth, energy consumption, trade openness, and FDI on the CO2 emissions in BRICS countries for the 1993–2021 period, utilising panel Fully Modified Ordinary Least Square (FMOLS) and Dynamic Ordinary Least Square (DOLS) estimators and Method of Moments Quantile Regression (MMQR) methodologies. According to the findings, air passenger transportation, economic growth, energy consumption and FDI have a positive impact on CO2 emissions, while squared economic growth and trade openness have a negative impact. Findings indicate that air freight transportation has no statistically significant effect on CO2 emissions. According to the causality analysis, there is a bidirectional causality between air passenger-CO2 emissions and GDP-CO2 emissions. There is a unidirectional causality from air freight to CO2 emissions, from energy consumption to CO2 emissions and from CO2 emissions to trade openness.
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ArticleAçık Erişim2026/r/ebyu/10439
Robust Monthly Lake Water Level Forecasting Using an Ensemble of Singular Spectrum Analysis Based Deep Learning and Categorical Boosting Models
Katipoğlu, Okan Mert; Kartal, Veysi; Tuğrul, Türker; Yavuz, Veysel Süleyman; Ariman, Sema
This study developed an innovative hybrid modelling approach to predict one-month-ahead water levels (LWL (T + 1)) for two major lakes in Türkiye (Eber and Eğirdir), aiming to model complex dynamics of hydrological systems and optimize sustainable water resources management. In the proposed methodological framework, Singular Spectrum Analysis (SSA) was employed to decompose hydrometeorological time series into sub-components, while Mutual Information (MI) and Partial Autocorrelation Function (PACF) techniques were utilized to determine optimal input variables. The hybrid model architecture, incorporating Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and CatBoost algorithms, was optimized using 70% of the data for training and 30% for testing, and its performance was evaluated through deterministic and probabilistic metrics. The analyses revealed that SSA-based data preprocessing significantly improved the performance of prediction models, and depending on lake characteristics, CNN-based models demonstrated superior performance for Lake Eber, while DNN architecture excelled for Lake Eğirdir. The developed hybrid approach presents an original methodology that can be integrated into decision support systems for water resources management, contributing to the development of sustainable water management strategies and adaptability to similar hydrological systems.
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ArticleAçık Erişim2026/r/ebyu/10423
Future Climate Change and Bioclimatic Suitability of Two Endemic Balkan Water Frogs
Papežík, Petr; Rusinko, Adam; Sari, İdris; Mikulíček, Peter; Brus, Jan; Benovics, Michal; Iannella, Mattia
Aim: To evaluate the impacts of future climate change on the bioclimatic suitability of two endemic water frogs of the southwestern Balkans, focusing on species- and lineage-level responses and implications for conservation. Location: Southwestern Balkans, including Albania, Greece and Montenegro. Methods: We applied an ensemble bioclimatic suitability modelling framework based on multiple algorithms, using selected bioclimatic predictors to estimate current suitability and to project future distributions under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) for 2050 and 2070. Intraspecific patterns were assessed by integrating mitochondrial lineage data to evaluate stability for both species and lineage levels. Results: Both species are predicted to experience an overall decline in bioclimatic suitability under future climate scenarios, indicating broadly similar responses to climate change. However, the magnitude of these changes differs substantially. Pelophylax shqipericus is predicted to undergo more pronounced range contraction and fragmentation, whereas P. epeiroticus shows relatively higher spatial persistence, with suitable areas remaining primarily in lowland coastal regions. Lineage-level analyses reveal uneven patterns of vulnerability, with greater losses generally affecting phylogenetic lineages occupying more restricted or environmentally sensitive parts of the range. Main Conclusions: Regardless of the differences in their realized bioclimatic niches, both species respond in a similar direction to projected climate change. This suggests that shared climatic constraints outweigh niche differentiation in shaping future distributions. However, differences in the magnitude of response highlight species-specific and lineage-specific sensitivity to environmental change. These findings emphasize the importance of incorporating intraspecific diversity into climate vulnerability assessments and conservation planning in biodiversity hotspots.
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ArticleAçık Erişim2026/r/ebyu/10418
Fighting against energy consumption risk by diversifying energy sources
Kuziboev, Bekhzod; Radulescu, Magdalena; Çatık, A. Nazif; Makhmudov, Samariddin; Balli, Esra; Allayarov, Sardor
Currently economic turbulence and geopolitical fluctuations are causing high prices and supply disruptions in energy markets. As a result, energy consumption risks are becoming more severe. Diversifying energy sources is one way to address these challenges. This study examines how energy source diversification influences energy consumption risk in 36 developed countries from 2000 to 2018, using Method of Moments Quantile Regression (MMQR) and Panel Threshold Regression (PTR) Models. The MMQR captures heterogeneous effects across various levels of energy consumption risk, offering insights into how diversification strategies differ in effectiveness across the risk distribution. The PTR Model identifies potential nonlinear relationships and regime-dependent effects based on economic development levels, enabling an analysis of how the diversification-risk relationship changes across different stages of economic advancement. These methods are well-suited for the non-normal distribution of variables and the potential structural breaks in the relationship between energy diversification and consumption risk. The findings align with theory, revealing that the effects of energy source diversification on energy consumption risk are negative across all quantiles, from 10 % to 90 %. Furthermore, PTR results reveal that energy source diversification reduces energy consumption risk in both the lower and upper regimes of economic development. The study's results provide valuable guidelines for policymakers.
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ArticleAçık Erişim2026/r/ebyu/10414
Model-Based Exergy and Partial Techno-Economic Screening of Hydrogen, Ammonia, E-Diesel, and Waste Cooking Oil Biodiesel Pathways for Heavy-Duty Internal Combustion Engines
Demirci, Oğuz Kürşat
Five pathways—diesel, renewable hydrogen, green ammonia, Fischer–Tropsch e-diesel, and B100 waste cooking oil fatty acid methyl ester (WCO-FAME)—were compared on a common service basis using a reduced-order engine exergy model, a partial techno-economic cost model, and 10,000 Latin hypercube simulation samples; each alternative was paired with diesel by sample index. The results were normalised to 1 kWh of net brake work (kWhb) and 1000 tonne-kilometres of freight work (1000 tkm). A carbon balance back-calculation from European Commission Vehicle Energy Consumption Calculation Tool (VECTO) monitoring data for subgroup 5-Long Haul (5-LH; a 4 × 2 tractor–semitrailer) yielded 89.31 kWhb/1000 tkm; first-order payload penalties increased this requirement to 94.79 kWhb/1000 tkm for hydrogen and 91.97 kWhb/1000 tkm for ammonia. Deterministic central-case exergy efficiencies were 43.02% for hydrogen, 39.67% for e-diesel, 39.35% for diesel, 37.56% for B100, and 33.81% for ammonia. The study-specific partial cost was expressed in 2025 EUR per 1000 tkm and included delivered fuel, maintenance, and annualised incremental vehicle capital; common fleet costs, including driver, tyre, toll, insurance, and tax costs, and externalities were excluded. Therefore, this metric was not a full freight cost or tariff. Deterministic central-case costs were EUR 25.85/1000 tkm for diesel, EUR 26.72/1000 tkm for B100, EUR 58.64/1000 tkm for hydrogen, EUR 59.84/1000 tkm for ammonia, and EUR 67.44/1000 tkm for e-diesel. Hydrogen exceeded diesel in exergy efficiency in 96.50% of the paired simulation samples; all three electrofuel pathways were costlier than diesel in every paired simulation sample, whereas B100 was cheaper than diesel in 35.62% of the paired simulation samples. Thus, hydrogen led in exergy efficiency, diesel in cost under the defined partial economic boundary, and B100 was the closest cost competitor. These are conditional model-screening results rather than a completed comparative life-cycle assessment: no quantitative life-cycle impact assessment (LCIA), environmental ranking or marginal abatement cost result is reported.
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ArticleAçık Erişim2026/r/ebyu/10404
Nonlinear effects of financial development on energy infrastructure: Insights from nonlinear panel models
Helmi, Mohamad Husam; Balli, Esra; Çatik, Abdurrahman Nazif; Kuziboev, Bekhzod; Ruzmetova, Maxbuba; Makhmudov, Samariddin
This study examines the impact of financial development on energy infrastructure needs in a sample of 85 countries over the period 2000–2023. The analysis combines linear panel estimators with advanced nonlinear methods, including Method of Moments Quantile Regression (MMQR), the Instrumental-Variable Partially Linear Functional-Coefficient model (IVPLFC), and the dose–response function. These methods allow us to examine heterogeneity, dynamic persistence, and nonlinear marginal effects. A key contribution of the study is the use of the Energy Infrastructure Needs index, which captures deficiencies across electricity generation, transmission, distribution, and storage, rather than relying on narrower traditional proxies. The results show that stronger financial development significantly reduces energy infrastructure needs. This effect is strongest at earlier stages of financial development, while diminishing marginal effects emerge as financial systems mature. The MMQR results reveal distributional differences across levels of energy infrastructure needs, while the IVPLFC and dose–response results confirm clear nonlinear effects. Overall, the findings show that financial development plays an important but uneven role in reducing energy infrastructure needs. Financially underdeveloped economies benefit most from expanding access to credit, strengthening banking intermediation, and improving liquidity, whereas more financially developed economies require complementary institutional and regulatory reforms to achieve further progress.
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ArticleYalnız künye2026/r/ebyu/10385
Data-driven forecasting of photovoltaic performance with explainable artificial intelligence: Experimental validation using environmental and thermal predictors
Karakoyun, Yakup; Katipoğlu, Okan Mert; Dogan, Ahmet; Canbaz, Ali
PV systems meet energy demand, but environmental and operational factors affect their performance. This study used a three-scenario ablation framework to predict PV power from baseline meteorological and operational variables to under-panel temperature and finally to the full thermal configuration, including water temperature (a newly studied variable). Scenario 1 included time, air temperature, panel angle, relative humidity, and average wind speed; Scenario 2 added under-panel temperature; and Scenario 3 added water temperature. Direct thermal predictors were evaluated separately from meteorological inputs using this design. Four sophisticated ML algorithms, Random Forest (RF), CatBoost, Support Vector Machine (SVM), and one-dimensional Convolutional Neural Network (1D CNN), were refined by Randomized Search (RS) and evaluated using metrics. According to the first group of power prediction results under the full Scenario 3 configuration, the CatBoost-RS model demonstrated superior performance with RMSE = 10.61, MAE = 8.41, AIC = 222.22, KGE = 0.957, and R2 = 0.99. For the second group, the SVM-RS model outperformed others, achieving RMSE = 8.02, MAE = 5.50, AIC = 200.36, KGE = 0.994, and R2 = 0.992. Comparative analysis showed that CatBoost-RS was the most robust predictor, while RF exhibited proficiency in managing nonlinearity in this experimental setting. One-way ANOVA, paired t -test, Wilcoxon signed-rank test, bootstrap confidence intervals, and effect size (Cohen’s d) analyses across both groups show that optimised ensemble and kernel-based models outperform the convolutional approach in predictive performance. Experimental validation and high predictive accuracy improve PV performance prediction in the proposed method.
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ArticleAçık Erişim2026/r/ebyu/10350
Contrastive self-supervised learning for streamflow forecasting: a multi-station comparison with supervised models
Dalkilic, H. Yıldırım; Kilinc, H. Cagan; Yurtsever, Adem; Katipoğlu, O. Mert; Yeşilyurt, S. Nur
Daily streamflow forecasting is complicated by the fact that the predictive value of recent observations shifts considerably depending on the hydrological character of the catchment. This study develops a regime-aware, window-adaptive framework to compare conventional supervised learning algorithms — XGBoost, Random Forest, LSTM, and GRU — against four contrastive self-supervised (SSL) approaches: SimCLR, TS2Vec, CPC, and TempContrast. Experiments were conducted at four gauging stations in Türkiye, each representing a distinct hydroclimatic regime. The SSL stage employs a shared LSTM encoder pretrained with hydrology-informed augmentations and is evaluated across window lengths of 7, 14, 30, and 60 days, allowing a direct assessment of how context length interacts with catchment memory. A bias-regularised loss function is applied during fine-tuning to reduce systematic prediction errors — an aspect that has received limited attention in prior SSL-based hydrological studies. Model performance was quantified using R2, NSE, KGE, Willmott’s d, RMSE, MAE, MBE, and PBIAS. No single model emerged as universally superior. At the strongly seasonal Demirkapı station, supervised models reached Test R2 = 0.96–0.97, while SSL peaked near 0.91. At the low-flow, repetitive Beyşehir station, short-window SSL outperformed all supervised baselines, achieving Test R2 ≈ 0.93–0.94 against 0.89–0.90. At the noisier Akçay and Karadere stations, short-lag supervised models proved more reliable. These results point to a consistent pattern: the effectiveness of any given approach depends heavily on the memory structure and signal regularity of the basin in question, rather than on algorithmic complexity alone.
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ArticleAçık Erişim2026/r/ebyu/10283
Estimation of the pan evaporation coefficient in semi-arid climate conditions via machines learning models
Achite, Mohammed; Katipoğlu, Okan Mert; Vishwakarma, Dinesh Kumar; Pandey, Kusum; Salem, Ali; Elbeltagi, Ahmed
In this study, class A pan coefficient (KPan) values were simulated via five machine learning models, namely the ANN, the AAN-REPTree, the ANN-SMO SVM, the ANN-Linear regression, and the ANN-Bagging model, by using daily meteorological data of the meteorological station of Ouled Ben Abdelkader region, which has semi-arid microclimate in the northwest region of Algeria. To determine the optimal combination of inputs, a variety of input-target pairs were tested by a variety of machine learning models, resulting in seven possible input scenarios: Tmean, RHmin, RHmax and Wind Speed were found to be the best input combinations. The results of models were analyzed (i.e., correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE)) to find their accuracy. As the best optimal condition, four input variables were introduced for the models ANN, ANN-REPTree, ANN-SMO SVM, ANN-Linear regression, and ANN-Bagging, with R = 0.9941 and 0.984, MAE = 0.0018 and 0.0037, RMSE = 0.0068 and 0.0016, RAE = 3.8863 and 7.6858, and RRSE = 10.9301 and 18.0686 in the training and testing phases, respectively. This hybrid model (ANN-Bagging) has demonstrated its utility in a scenario where there is a strong connection among the variables which includes KPan, and its feasibility to display the model in a feasible state.
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ArticleAçık Erişim2026/r/ebyu/10258
Revealing the effectiveness of hydropower innovations on emission level in Japan: evidence from machine-learning algorithm
Özkan, O.; Destek, M.A.; Khan, Z.; Manga, M.
This study explores how oil efficiency and hydropower innovation affect carbon emissions in Japan. In this direction, the novel multivariate quantile on quantile regression technique is applied to explore the period from 2000 to 2020. In addition, to prevent omitted variable bias, the environmental implications of industrialization, urbanization, and economic growth are also observed. The empirical findings indicate that oil efficiency, industrialization, and hydropower innovations are all lower carbon emissions. However, urbanization and economic growth harm the environmental quality. Lastly, a robustness check is performed using a kernel-based regularized least squares technique based on machine learning. The results of this investigation support the claim that oil efficiency and hydropower innovation can reduce emissions. Furthermore, it is found that the emission-reducing impact of both gets stronger as the emission level rises.
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ArticleAçık Erişim2026/r/ebyu/10242
Energy, exergy and exergoeconomic analysis of a CI engine fuelled with waste frying oil biodiesel blended with waste tyre pyrolysis oil and higher alcohols (Heptanol and Octanol): A waste-to-energy approach
Gülcan, Halil Erdi; Demirci, Oğuz Kürşat; Erol, Derviş; Tunçer, Erdal
This study evaluates a waste-to-energy diesel concept that co-valorises waste cooking oil and end-of-life tyres by formulating biodiesel–tyre pyrolysis oil blends with small additions of higher alcohols (1-heptanol and n-octanol). Five fuels (B100, B90T10, B85T10H5, B85T10O5 and B80T10H5O5) were tested in a single-cylinder diesel engine. Engine performance, emissions and combined energy–exergy–exergoeconomic indicators were used to assess thermodynamic and economic viability. The quaternary blend B80T10H5O5 achieved the highest mean energy and exergy efficiencies (26.5 % and 24.6 %) and reduced brake-specific fuel consumption by about 5 % relative to neat biodiesel, owing to improved volatility and fuel oxygenation. This benefit was accompanied by higher NOx emissions and the largest specific exergy cost (≈1430 USD GJ−1) at full load. By contrast, the binary blend B90T10 preserved biodiesel-like efficiency while lowering the specific exergy cost of shaft power by roughly 10 %, emerging as the most cost-effective option. Overall, modest shares of tyre pyrolysis oil (10 %) and long-chain alcohols (≤5 %) in waste-oil biodiesel can provide an attractive compromise between energy efficiency, emissions and exergy-based cost, supporting circular-economy-oriented diesel combustion.
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ArticleAçık Erişim2026/r/ebyu/10167
The Impact of Agricultural Value Added and Energy Consumption on Economic Growth in Türkiye: An Empirical Analysis; [Türkiye’de Tarımsal Katma Değer ile Enerji Tüketiminin Ekonomik Büyümeye Etkileri: Ampirik Bir Analiz]
Akın, Fatih; Dinçer, Selin
Climate change, global warming, natural disasters, wars, and global crises, which threaten the world's food supply, have made the agricultural sector fragile and strategically important, especially over the last century. Accordingly, it has become even more crucial for countries to have a strong agricultural infrastructure to ensure food security, maintain social stability, and reduce external dependence. In developing countries, in particular, the agricultural sector plays a critical role through functions such as job creation, reducing income inequality, and contributing to economic growth. For the Türkiye economy, the agricultural sector has historically been one of the cornerstones of the economic structure, providing raw materials to the industrial sector, supporting rural development, creating employment, and generating exports and foreign exchange earnings. On the other hand, energy consumption has become an indispensable element of production processes today. This study examines the impact of agricultural value added, gross fixed capital investment, and energy consumption on economic growth in Türkiye during the period 1968–2023 using the Fourier Engle-Granger cointegration test, Fourier Fully Modified Ordinary Least Squares (FMOLS), Fourier Dynamic Ordinary Least Squares (DOLS), and Fourier Canonical Cointegration Regression (CCR) methods. The results of the Fourier Engle-Granger cointegration test reveal a long-term relationship among the variables. The Fourier FMOLS, Fourier DOLS, and Fourier CCR estimation results indicate that agricultural value added, gross fixed capital investment, and energy consumption have positive and statistically significant effects on economic growth in both the short and long term.
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ArticleAçık Erişim2026/r/ebyu/10059
Global Tea Production Forecasting Using ARIMA Models: A Multi-Country Time-Series Analysis (1961–2028)
Kumbasaroglu, Hediye
Understanding the long-term dynamics of global tea production is essential for assessing supply stability, climate sensitivity, and producer competitiveness. This study examines annual tea production data for major producing countries—China, India, Kenya, Sri Lanka, Türkiye, Vietnam, and other producer groups—over the period 1961–2023 and provides production forecasts for 2024–2028 using country-specific ARIMA models. Unlike most existing studies focusing on single countries or short-term horizons, this research offers a unified multi-country and long-term comparative framework that integrates time-series forecasting with market concentration indicators. The results reveal pronounced cross-country heterogeneity in production behavior, with China exhibiting strong structural growth, while other producers display more moderate or climate-sensitive patterns. Forecasts suggest a continued increase in global tea production toward 2028, although projections are subject to uncertainty, as reflected by model-based confidence intervals. Overall, the study contributes robust, statistically validated insights to support evidence-based strategies for sustainable tea supply and international market planning. Forecasts suggest a continued increase in global tea production toward 2028, although projections are subject to uncertainty, as reflected by model-based confidence intervals. These forecasts highlight a robust upward trend in global tea supply due to both technological advancements and market expansion.
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ArticleAçık Erişim2025/r/ebyu/19572
Mıst ülkelerinde sürdürülebilir kalkınma ve yeşil enerjinin yeşil üretime etkisi
Akçayır, Ömer; Akın, Fatih
Çalışmada, MIST (Meksika, Endonezya, Güney Kore ve Türkiye) ülkelerinde sürdürülebilir kalkınma (HDI) ve yenilenebilir (REN) enerji tüketiminin yeşil üretim (CO2) üzerindeki etkilerini araştırmak amacıyla 1990-2021 yılları verileri kullanılarak panel veri analizi yöntemleri kullanılmıştır. CIPS birim kök testinin kullanıldığı analizde tüm serilerin birinci farkta durağan oldukları belirlenmiş, LM Bootstrap eşbütünleşme yöntemiyle eşbütünleşme ilişkisi tespit edilmiştir. Modelin katsayıları Artırılmış Ortalama Grup (AMG) yöntemi ile tahmin edilmiş ve nihayetinde Dumitrescu & Hurlin (2012) panel nedensellik testi ile analiz tamamlanmıştır. Panelin genelinde ve ülke bazında istatistiksel olarak anlamlı sonuçlar ortaya çıkmış, HDI ve yenilenebilir enerji tüketimi CO2’yi azaltırken, kontrol değişken olan GDP ve yenilenemeyen enerji tüketimi CO2’yi artırmaktadır. Sürdürülebilir kalkınmanın yeşil üretim üzerindeki etkisinin yenilenebilir enerjinin etkisinden daha fazla olduğu tespit edilmiştir. Ayrıca bağımsız değişkenlerin her birinden bağımlı değişkene (CO2) doğru, doğrudan ya da diğer değişkenler aracılığıyla dolaylı olarak nedensellik ilişkisi tespit edilmiştir.
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