Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the best ARIMA model for cumulative COVID-19 cases (CCC) across multi-region countries. Seven different performance criteria were used to evaluate the accuracy of the models. The obtained results justified both types of ARIMA model, with ARIMAGLS and ensemble ARIMA demonstrating superiority to the other models. Among the DL models analyzed, LSTM-M1 emerged as the best and most reliable estimation model, with both RF and LSTM attaining more than 80% prediction accuracy. While the EML of the DL proved merit with 96% accuracy. The outcomes of the two scenarios indicate the superiority of ARIMA time series and DL models in further decision making for FK.

This article investigates the performance of three models - Autoregressive Integrated Moving Average (ARIMA), Threshold Autoregressive Moving Average (TARMA) and Evidential Neural Network for Regression (ENNReg) - in forecasting the Brent crude oil price, a crucial economic variable with a significant impact on the global economy. With the increasing complexity of the price dynamics due to geopolitical factors such as the Russo-Ukrainian war, we examine the impact of incorporating information on the war on the forecasting accuracy of these models. Our analysis shows that incorporating the impact of the war can significantly improve the forecasting accuracy of the models, and the ENNReg model with the inclusion of the dummy variable outperforms the other models during the war period. Including the war variable has enhanced the forecasting accuracy of the ENNReg model by 0.11%. These results carry significant implications regarding policymakers, investors, and researchers interested in developing accurate forecasting models in the presence of geopolitical events such as the Russo-Ukrainian war. The results can be used by the governments of oil-exporting countries for budget policies.

As 2030 approaches, countries are challenged to achieve the Sustainable Development Goals set by the United Nations in 2015. In addition to public policies, innovation is at the core of global sustainable development ambitions. Accordingly, this study examines how technological innovation, economic growth, clean energy use, natural resources, and industrialization affect the load capacity factor (LCF) of the 10 most innovative countries, based on data from 1997 to 2020. Unlike conventional indicators (e.g. ecological footprint, CO₂ emissions), the LCF captures both the demand and supply dimensions of ecosystems and represents a comprehensive environmental indicator. The method of moments quantile regression (MMQR) method is used to estimate these relationships across different quantiles. The results suggest that increase in natural resources, economic growth and industrialization decreases LCF across all quantiles, while clean energy and technological innovation significantly enhance LCF, particularly in lower and middle quantiles. These results imply that policies to promote green innovation and sustainable energy should be paramount in improving environmental quality and guiding countries towards achieving the SDG targets.

Having accurate and effective wind energy forecasting that can be easily incorporated into smart networks is important. Appropriate planning and energy generation predictions are necessary for these infrastructures. The production of wind energy is linked to instability and unpredictability. Wind energy forecasting has traditionally been performed using statistical models, but with the advent of artificial intelligence (AI), research indicates that AI is more accurate than the statical technique. In this study, the nominal power of six wind farms in China was predicted using Kolmogorov-Arnold Networks (KAN) and Multilayer Perceptron (MLP) models. KAN as an alternative to the conventional MLP, has the ability to handle problems with scalability, vanishing gradients, and interpretability associated with MLP. The KAN uses learnable B-Spline as activation functions prompting it to address the issues of the MLP. We employed the Radial Basis Function (RBF) with Gaussian kernels to approximate the 3-order B-spline basis. In most deep learning models stochastic gradient-based optimization algorithms such as Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent (SGD) optimizer are mostly employed, a quasi-Newton optimization technique Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm LBFGS was employed in this work to approximate the Hessian matrix and estimate the parameter space's curvature. Also, in the preprocessing of the data, the Interquartile Range (IQR) technique is used to handle outliers and a clustering-based K-Nearest Neighbor (KNN) imputer to handle missing values. Based on different sites, the KAN-LBFGS shows superior performance based on the performance evaluation metrics with site 5 achieving MSE of 0.0039, RMSE of 0.0622, MAE of 0.0352, and DC of 0.9468. The study highlights the importance of the model’s architecture, preprocessing and optimization techniques.

This study operationalizes the Optimum Currency Area (OCA) to investigate the preparedness of Economic Community of West African States (ECOWAS) members to form a Monetary Union (MU). Inflation and output models are estimated, with the sample 1988:01 to 2017:12 for the former and 1967 to 2016 for the latter. Analyses of ECOWAS convergence criteria, impulse responses, variance decompositions and correlations of shocks of these two models, reveal that the shocks across the ECOWAS members are asymmetric. The conclusion is that ECOWAS members as a whole are not well-prepared and therefore a full-fledged pan-ECOWAS MU is not advisable. It is also found that members of the European Monetary Union (EMU) tend to be a better fit for OCA than the ECOWAS members. The study recommends various courses of action such as fostering coordination among Central Banks of ECOWAS members, and providing a fund to serve as an incentive for countries that may incur cost rather than benefit if the single currency is created. LA MONEDA COMÚN DE LA ECOWAS: ¿CUÁN PREPARADOS ESTÁN SUS MIEMBROS?RESUMENUtilizamos el Área Monetaria Óptima (AMO) para indagar cuán preparados están los miembros de la Comunidad Económica de Estados de África Occidental (ECOWAS, Economic Community of West African States) para formar una Unión Monetaria (UM). Estimamos modelos de inflación y producto con datos de 1988:01-2017 y 1967-2016 respectivamente. Los análisis de criterios de convergencia, impulso-respuesta, descomposición de varianza y correlación de choques de estos modelos revelan que los choques entre estos países son asimétricos. Concluimos que estos países no están bien preparados y, por tanto, una UM pan-ECOWAS no es aconsejable. Además, los integrantes de la Unión Monetaria Europea (UME) tienden a satisfacer mejor una AMO que los de ECOWAS. Nuestro análisis recomienda fortalecer la coordinación entre los bancos centrales de la ECOWAS y un fondo que incentive a los países que incurran en costos en lugar de beneficios si se crea la moneda única.