Search Results (1,158)

Environmental protection and the reduction of carbon dioxide (CO₂) emissions are central priorities within European climate policy. This study analyses and forecasts annual CO₂ emissions in Greece using a univariate time-series framework. Annual data from 1960 to 2024, sourced from Our World in Data, enable the analysis to capture both the historical expansion of emissions and the recent decarbonization phase of the Greek energy system. Using the Box–Jenkins methodology, multiple ARIMA specifications were evaluated based on information criteria and diagnostic tests. To examine the stationarity properties of the series, the Augmented Dickey–Fuller (ADF) unit root test is applied. The findings indicate that the ARIMA (1,1,1) model most accurately represents the stochastic dynamics of the emissions series. The estimated autoregressive and moving-average coefficients, 0.9404 and −0.7165, respectively, are statistically significant at the 1% level. Residual diagnostics confirm the absence of serial correlation, approximate normality, and no significant heteroskedasticity. Forecast evaluation for the 2020–2024 holdout period demonstrates satisfactory predictive performance, with a mean absolute percentage error (MAPE) of approximately 6%. Dynamic forecasts for 2025 to 2030 indicate a gradual decline in national CO₂ emissions, reaching an estimated 45.5 million tonnes by 2030. Overall, the study demonstrates that parsimonious ARIMA models offer a transparent and empirically reliable benchmark for national emissions forecasting. These models provide a reproducible tool for monitoring climate policy outcomes and for supporting evidence-based environmental decision-making. This study contributes to the environmental forecasting literature by providing an updated, diagnostically rigorous univariate benchmark model for Greece’s CO₂ emissions that encompasses both the pre- and post-decarbonization phases of the national energy transition. Full article

The rapid transition to Industry 4.0/5.0 has widened the gap between graduates’ skill sets and labor market expectations; this study aimed to profile student competencies and align academic pathways with inclusive and adaptive AI-driven learning. A quantitative design was applied: an online survey of n = 126 students (engineering and economics, February–March 2025), expert evaluations from 5 faculty and 5 employers on a 5-point scale, framed by T-shaped competencies, 4C skills, and Bloom’s taxonomy. Analysis was performed in Python 3.11; future demand until 2035 was forecasted using ARIMA and Prophet models trained on publicly available labor market data (OECD, WEF, Eurostat 2015–2024); competency prioritization employed K-Means clustering and Random Forest models. Strengths included cooperation 4.2, critical thinking 3.9, communication 3.8, and creativity 3.6. Deficits were programming 2.8, project management 3.2, and solution development 3.2; employers rated programming at 2.5 (−0.7 compared to faculty). Forecast 2025–2035 showed growth in demand for programming +56% (3.2 → 5.0), data analytics +39% (3.6 → 5.0), project management +34% (3.2 → 4.3), digital literacy +30% (3.7 → 4.8), and critical thinking +15% (3.9 → 4.5). Clustering identified critical (programming, analytics, project management), supporting (creativity, communication, teamwork), and optional (narrow theoretical depth) competencies. Curriculum adjustment with practice-oriented modules, AI-enabled adaptive learning, and systematic university–employer feedback is essential; the proposed AI-supported profiling model is scalable and enhances inclusiveness. Full article

Europe’s fossil-based electricity mix has shifted rapidly in recent years, raising a practical question: can we model competitive substitution among fuels with a framework that is both predictive and interpretable? We address this by combining a compact neural network (NN) with a three-dimensional Lotka–Volterra (LV) system to study monthly EU coal, natural gas, and oil-fired generation shares from the second semester of 2017 to 2023. After converting the series to row-wise shares that sum to one, we use the first $70\%$ of the sample to learn smooth trajectories and data-driven derivatives with the NN and then estimate the LV interaction coefficients through a constrained nonlinear fit. We advance the calibrated LV system over the final $30\%$ holdout with a fourth-order Runge–Kutta (RK4) scheme and evaluate forecasts using the RMSE and MAE for each fuel share series. For comparison, we report the results against both a neural network-only forecasting baseline and a classical ARIMA benchmark, both trained on the same $70\%$ window and evaluated on the same $30\%$ holdout. The hybrid NN–LV model achieves competitive forecast errors while yielding interpretable interaction patterns consistent with substitution pressures (for example, negative cross-effects between coal and gas). Finally, we run counterfactual shock experiments to illustrate how a change in one fuel’s share propagates through the mix under the learned LV dynamics, highlighting the usefulness of embedding a simple mechanistic structure within a data-driven estimator. Full article

Understanding the long-term production trends of MDFLAM panels, which are widely used in panel furniture manufacturing, is important for evaluating the sector’s competitiveness and environmental performance. In this study, MDF/HDF production data for Türkiye covering the period 1995–2024 were analyzed. The observations for 1995–2019 were used for model estimation, while the period 2020–2024 was reserved for out-of-sample validation. Production projections for 2025–2030 were generated using the ARIMA time series model. The relationships between fiberboard production and selected socio-economic variables (population, GDP per capita, forest area, and number of enterprises) were evaluated through correlation analysis. While strong correlations were observed in the level data, additional analysis using first-differenced (growth rate) series indicated that these relationships are weak and statistically insignificant in the short term, suggesting that the observed associations are largely influenced by common time trends. Assuming that approximately 60% of total fiberboard production consists of MDFLAM, future GWP values were estimated using verified EPD data. The results indicate that production is expected to continue increasing in the coming years. Although negative GWP values are observed due to biogenic carbon storage during the production stage, this reflects temporary carbon sequestration rather than a permanent reduction in atmospheric emissions. Emissions are expected to increase during end-of-life stages as the stored carbon is released. Overall, the study provides a forward-looking framework by integrating time-series forecasting with EPD-based environmental indicators, offering a useful basis for sustainability assessment and policy-oriented decision-making in the wood-based panel sector. Full article

The management of municipal solid waste in intermediate cities exhibits high daily variability and source heterogeneity, which hinders operational sizing and material recovery. Reliable predictions are required from heterogeneous and often-scarce data. However, studies that compare multiple machine learning algorithms with temporal validation on short time series in intermediate cities are still limited. This study compares fourteen machine learning algorithms to predict the daily generation of organic and inorganic waste in La Joya de los Sachas, Ecuador, formulating the problem as a multi-output regression problem. An adapted CRISP-DM design was employed, using primary data from a waste characterization campaign, temporal feature engineering, variable encoding, and an expanding-window backtesting protocol against lag-7 persistence and ARIMA. Tree-based ensembles achieved the best performance. AdaBoost provided the best organic forecasts ($R^2$$=0.985$, $\mathrm{RMSE}$ $=0.081$, $\mathrm{MAE}$$=0.061$ in rate space), while Random Forest was best for inorganic ($R^2$$=0.965$, $\mathrm{RMSE}$ $=0.049$, $\mathrm{MAE}$$=0.040$). Linear models were stable but slightly inferior, and other approaches (SVR, KNN, MLP, Lasso, ElasticNet) showed lower generalization capacity. The study provides a multi-output regression protocol with temporal validation for municipal contexts with short time series, comparative evidence across fourteen algorithms, and a conversion from rates to kilograms for operational use. Full article

Despite the well-documented acceleration of climate-related economic losses in Europe, existing research has largely treated these damages as isolated stochastic events rather than as structurally embedded fiscal risks. This gap leaves EU fiscal governance frameworks inadequately prepared for the persistent, spatially concentrated, and temporally dependent nature of such losses. This study addresses this gap by investigating the systemic transformation of climate-related economic risks within the European Union, arguing that climate losses have evolved from unpredictable stochastic shocks into a persistent, structural burden on the European economy. Adopting a comprehensive multi-methodological approach, the research quantifies this transition by integrating spatial concentration metrics (HHI), advanced time-series modelling (OLS, ARIMA, ETS), and anomaly detection techniques to analyse loss patterns across the EU-27 from 1980 to 2023. The empirical results demonstrate three critical systemic dimensions: (1) a statistically significant upward shift in the baseline of economic damages; (2) a high geographical concentration of losses, with Germany, Italy, and France consistently bearing the largest share of climate-driven fiscal pressure; and (3) the emergence of volatility clustering, indicating that climate risks are becoming increasingly non-linear and embedded in the macroeconomic environment. The study contributes to the literature by reframing climate-related economic losses as a systemic fiscal phenomenon requiring structural governance reform, rather than ad hoc disaster response. The findings suggest that existing reactive policy frameworks are insufficient to address the scale of these structural risks. Consequently, the paper advocates for a paradigm shift in EU climate policy—moving toward anticipatory fiscal instruments, harmonised resilience financing, and monitoring systems designed to mitigate systemic volatility and cross-country economic asymmetry rather than merely responding to isolated disaster events. Full article

Inference after model selection is a very important problem. Model selection algorithms for ARIMA time series, with criteria such as AIC and BIC, tend to select an inconsistent model with positive probability, making data-splitting inference for testing and confidence intervals unreliable. One technique was fairly reliable for sample sizes greater than 600, and a modification also worked. Model selection is often useful for prediction, since the selected submodel tends to have fitted values and residuals that are highly correlated with those of the full model. A few prediction intervals perform fairly well even after model selection. A useful technique for handling outliers is to replace the outliers with missing values. Full article

Background: Acute hepatitis E (AHE) poses escalating risks to older adults (≥65 years), compounded by immunosenescence and comorbidities. Using Global Burden of Disease (GBD) 2021 data, this study analyzes global AHE burden, trends, and projections in aging populations. Methods: Age-standardized rates (ASIR, ASMR, ASDR) for AHE in adults ≥ 65 years were extracted from GBD 2021 across 204 countries (1990–2021). Frontier analysis assessed gaps between observed burdens and sociodemographic index (SDI)-based theoretical minima. Age-period-cohort (APC) modeling evaluated age/period/cohort effects. Bayesian (BAPC), NORDPRED, and ARIMA models projected trends to 2050. Results: Global ASIR increased by 1.5% annually (1990–2021), with ASMR and DALYs declining significantly. Middle SDI regions showed the steepest ASIR rise (net drift: 0.064%/year), while high SDI areas had volatile trends. Age effects peaked in ≥95-year-olds. Frontier analysis revealed persistent ASIR-SDI gaps, particularly in low-middle SDI regions. Projections indicate a ASIR rise by 2050 (113.04/100,000), contrasting with declining ASMR (0.056/100,000) and ASDR (1.31/100,000) and the NORDPRED, ARIMA, and EAPC models exhibit analogous global predictive trends. Conclusions: Diverging trends of rising incidence and falling mortality highlight unmet prevention needs. High-burden regions require SDI-stratified strategies, prioritizing vaccination programs (e.g., HEV 239), zoonotic transmission control, and enhanced surveillance. The Sustainable Development Goals (SDGs) envision hepatitis elimination by 2030 (Target 3.3). However, our analysis projects ongoing AHE burden in aging populations through 2050, indicating the need for post-2030 policy adaptations. Full article

The Longmen Shan Fault Zone is marked by intricate geological structures and frequent seismic activity, which gives rise to persistent seismic hazards. To tackle the challenge of capturing the multi-temporal characteristics of earthquake frequency, this study combines machine learning with time series analysis to conduct earthquake frequency prediction research. Based on the 1970–2023 seismic dataset from the China Earthquake Networks Center, the seismic records were structured into four temporal scales: daily, weekly, monthly and quarterly. The minimum completeness magnitude (Mc) was determined as M3.0 by applying the G–R relationship. After conducting white noise tests and data normalization, ACF and PACF were utilized to select the optimal time-step parameters for the LSTM model. Considering the inherent characteristics of the seismic data, the 99th percentile of the frequency series was set as the threshold, and an auxiliary parameter was introduced to label high-frequency earthquake days for the construction of the LSTM model. Upon the completion of LSTM model fitting, heteroscedasticity tests were performed on the residuals between the predicted and observed values. Confirming the presence of significant heteroscedasticity, the GARCH model was incorporated to process these residuals, thus establishing a complete LSTM-GARCH coupled model. The results reveal that seismic activity in this region is normally low-frequency with occasional high-frequency occurrences. The proposed model achieves R² above 0.80 across all four temporal scales, accompanied by superior performance in all error metrics. This study validates that the LSTM-GARCH model can effectively extract the multi-scale patterns of earthquake frequency, with the best performance observed at the daily scale. Ablation experiments further demonstrate that this coupled model outperforms both the ARIMA and single LSTM models, providing reliable technical support for short-to-long-term earthquake prediction and regional disaster risk assessment. Full article

Modeling nonstationary time series in financial and energy markets remains challenging due to nonlinear dynamics, volatility clustering, and frequent regime shifts that distort the underlying probabilistic structure of the data. This study introduces a novel probabilistic–statistical decomposition framework, termed Robust Adaptive Decomposition (RAD), designed to preserve probabilistic symmetry between deterministic and stochastic components. In this context, symmetry refers to maintaining statistical balance—particularly in the means, variances, and distributional structures—between the extracted modes and the residual series, thereby preventing artificial bias or variance distortion during decomposition. The RAD framework adaptively determines the optimal number of modes needed to effectively separate short-term fluctuations from long-term structural movements. Unlike conventional techniques, such as Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD), and CEEMDAN, the proposed method incorporates a robustness mechanism that mitigates mode mixing and reduces distortions induced by extreme shocks and regime transitions. The empirical evaluation is conducted on six oil-related energy commodities—Brent crude oil, kerosene, propane, sulfur diesel, heating oil, and gasoline—whose price dynamics exhibit pronounced nonlinearity and structural volatility. When integrated with ARIMA forecasting models, the RAD-based framework consistently outperforms benchmark decomposition approaches. Across all datasets, RAD–ARIMA achieves reductions of approximately 65–90% in MAE, 60–85% in RMSE, and up to 95% in MAPE relative to CEEMDAN-based models. These results demonstrate that RAD provides a mathematically rigorous and computationally efficient preprocessing mechanism that preserves statistical equilibrium while effectively disentangling deterministic structures from stochastic noise. Beyond oil markets, the framework offers broad applicability in econometric modeling, financial forecasting, and risk management, contributing to probability- and statistics-driven symmetry analysis in complex dynamic systems. Full article

This study proposes a hybrid landslide displacement prediction model based on multi-strategy integrated optimization to address high nonlinearity and limited accuracy. An improved SFOA with Lévy flight, dynamic exploration adjustment, and stagnation detection enhances global search and convergence. The optimized SFOA (OSFOA) is employed to optimize CEEMDAN using minimum envelope entropy, reducing hyperparameter subjectivity and decomposing cumulative displacement into multi-scale components. The trend term is predicted by a Bayesian-optimized ARIMA, while periodic and stochastic terms are further decomposed by VMD and predicted using Bayesian-optimized SVR. GRA-MIC is applied to select key influencing factors and optimize model inputs. Results show that the proposed method improves accuracy and stability, reducing RMSE by about 82% and 52% compared with SSA-SVR and the baseline single decomposition model, respectively. The study further identifies monthly rainfall change and two-month reservoir level variation as the dominant driving factors for the displacement evolution, providing an effective and interpretable approach for complex landslide early warning. Full article

We forecast monthly Canadian real GDP growth using machine learning models trained on Official macroeconomic indicators and Google Trends (GT) data. Predictors are selected dynamically in each rolling window using PDC-SIS, with cross-validation-based tuning to support real-time forecasting and avoid data leakage. The evaluation is conducted on the latest-available (final-vintage) series and should be interpreted as a pseudo out-of-sample forecasting exercise rather than real-time vintage nowcasting. We evaluate GBM, XGBoost, LightGBM, CatBoost, and Random Forest against an ARIMA baseline. Official data deliver the strongest performance at short and medium horizons, while combining Official and GT data yields the clearest improvement at the longest horizon. With GT data alone, LightGBM is the only ML model maintaining positive out-of-sample R² across all horizons. Diebold–Mariano tests corroborate these patterns: LightGBM dominates other ML models under GT-only predictors, whereas with Official and combined data, the horizon-specific best models significantly outperform ARIMA, with smaller differences among leading tree-based methods. Full article

This study compares classical and deep learning models (ARIMA, Random Forest, RNN, LSTM, CNN, and Transformer) for forecasting one-day-ahead log returns $r_{t+1}=ln(P_{t+1}/P_t)$ using daily data for six U.S.-listed equities (NVDA, TSLA, SMCI, GOOGL, PYPL, SNAP) from 2014 to 2024. Predictors include lagged price/return information, lagged macroeconomic variables (CPI, policy rate, GDP) to reflect information availability, and technical indicators (SMA, RSI, MACD) computed using rolling windows ending at day t to avoid look-ahead bias. Performance is evaluated in a walk-forward out-of-sample design, with hyperparameters selected using time-series validation within each training window. Empirically, results are asset-dependent: ARIMA and Random Forest remain strong baselines; deep learning models show asset-dependent performance, with LSTM occasionally competitive in some settings, and the Transformer competitive but not uniformly dominant. For context, this study also reports a rule-based SMA(10/50) crossover benchmark evaluated net of transaction costs. Overall, the findings suggest that predictive signals in daily equity returns, when present, are modest and must be assessed under strict leakage controls and realistic evaluation protocols. Full article

Renewable energy sources (RES) will be the main source of energy in the future. The main goal of this study was to analyze and elaborate a prognosis for the development of renewable energy sources in Poland. Specific objectives included: evaluation of the prognosis developed as part of Poland’s energy policy (PEP), development of our own forecast of the share of renewable energy sources, and comparison of the forecast developed for Poland’s energy policy with our own forecast. We have also elaborated a hypothesis that the prognosis for the development of renewable energy sources for Poland prepared by PEP, and our own prognosis based on Autoregressive Moving Average (ARIMA) models, are both promising and confirm the development of the renewable energy sector in the future. We used the Augmented Dickey–Fuller (ADF) test as well as ARIMA models. Moreover, we compared own RES prognosis with prognoses proposed by the European Union. Cumulative capital expenditures from 2021 to 2040, including financing costs, will amount to PLN 300 billion, of which PLN 195 billion go towards renewable energy sources alone. Photovoltaics (PV) will account for the largest share of energy production, reaching 16 GW of achievable capacity, followed by onshore wind farms with 9.7 GW. Solid biomass accounts for the largest share of renewable energy consumption in heating and cooling, although its share is gradually decreasing from 98.6% in 2005 to a projected 75% in 2040. Heat pumps, which had no share in 2005, are expected to increase their share to a projected 11.8% in 2040. Solar technology has also increased from 0% in 2005 to a projected 5.6% in 2040. The share of renewable energy in this energy sector is increasing from 22.1% in 2020 to 31.8% in 2030 and 39.7% in 2040. The prognosis elaborated by PEP for 2025–2040 are very optimistic and the prognosis elaborated based on ARIMA models is also promising. Both prognoses predict the development of RES in the future and the transformation of the energy sector in Poland. Full article

Hybrid energy storage system (HESS) is the preferred energy source for hybrid electric vehicles (EVs). Extending system lifespan and improving energy management efficiency are critical factors in enhancing the availability and sustainability of EVs. This study develops a predictive deep reinforcement learning energy management strategy using vehicle historical data and considering the battery life effect during the power optimization process. First, the Autoregressive Integrated Moving Average (ARIMA) model processes the vehicle’s historical data to predict short-term future speed and road gradient changes. Second, a battery life-aware predictive deep Q-Network (LAP-DQN) energy management strategy (EMS) is introduced, and the battery aging effect is incorporated during training to achieve a synergistic optimization of energy consumption and battery lifespan. Finally, the effectiveness of the proposed method is validated via comparative simulations against CD-CS and PMP via three cycles. The results demonstrated that LAP-DQN significantly extended battery life by 8.76% while improving UC utilization ratio by 17.91% in overall performance. This study offers new insight into EMS for EVs and shows promising prospects for engineering sustainability applications and the circular economy. Full article