Testing the impact of environmental taxation and IFRS adoption on consumption-based carbon in European countries.

Several governance regulations have been adopted in European countries to promote environmental sustainability, such as environmental taxation and environmental disclosures in financial reports. In this context, this paper examines the linkage between environmental taxation, International Financial Reporting Standards (IFRS), and environmental sustainability in European countries from 1994 to 2018. Unlike earlier empirical studies, the present work is the first to assess the impact of environmental taxation and IFRS adoption on consumption-based carbon emissions. In order to yield valid and reliable outcomes, the modern econometric method that is vigorous to cross-sectional dependence and slope heterogeneity was employed. Likewise, the study uses the novel method of moment quantile regressions (MMQR). The MMQR outcomes illustrated that environmental taxation significantly negatively affects consumption-based emissions in European countries, indicating that environmental taxation has a positive effect on the ecological sustainability. Besides, the findings show that IFRS negatively affects consumption-based emissions, while economic growth positively affects the level of consumption-based emissions. Therefore, European governments must use fiscal and financial policies to mitigate ecological pollution. Moreover, more environmental, social, and governance (ESG) disclosure in European industries could also help promote environmental sustainability in European countries.

Incorporating Russo-Ukrainian war in Brent crude oil price forecasting: A comparative analysis of ARIMA, TARMA and ENNReg models.

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.

Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach.

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.

Do as your neighbours do? Assessing the impact of lockdown and reopening on the active COVID-19 cases in Nigeria.

This paper employs Autoregressive Integrated Moving Average (ARIMA) modelling and doubling time to assess the effect of lockdown and reopening on the active COVID-19 cases (ACC) based on a sample from 29 February to July 3, 2020. Two models are estimated: one with a sample covering post-lockdown period only and another spanning both post-lockdown and post-reopening periods. The first model reveals that the lockdown caused an immediate fall in the daily growth rate of the ACC by 14.30% and 33.26% fall in the long run. The parameters of the second model show that the lockdown had an impact effect of 8.56% and steady state effect of 20.88% reduction in the growth rate of the ACC. The effect of reopening on the ACC is insignificant. However, the doubling time of the ACC has increased after reopening. The study warns against complete reopening until sufficient post-reopening data series is available for exact estimation. The findings in this study can be useful in determining the hospitalisation needs and effectiveness of similar health-related policies.