The latest decades have been marked by rapid climate change and global warming due to the release of greenhouse gas emissions into the atmosphere. Environmental taxes have emerged as a cost-effective way to tackle environmental degradation. However, the effectiveness of environmental taxes in reducing pollution remains a topic of ongoing debate. The purpose of this paper is to examine empirically the effects of various environmental tax categories (energy, pollution, resource and transport) on CO2 emissions in 34 OECD countries between 1995 and 2019. The dynamic panel threshold regression developed by Seo and Shin (2016) is implemented to assess whether the impact of environmental taxes on CO2 emissions depends on a given threshold level. The locally weighted scatterplot smoothing analysis provides evidence for a nonlinear association between environmental taxes and CO2 emissions. The analysis indicates the existence of one significant threshold and two regimes (lower and upper) for all environmental tax categories. The dynamic panel threshold regression reveals that the total environmental tax, energy tax and pollution tax reduce CO2 emissions in the upper regime, i.e., once a given threshold level is reached. The threshold levels are 3.002% of GDP for the total environmental tax, 1.991% for the energy tax and 0.377% for the pollution tax. Furthermore, implementing taxes on resource utilization may be effective but with limited environmental effects. Based on the research results, it is recommended that countries in the OECD implement specific environmental taxes to reduce greenhouse gas emissions.

Background: Environmental degradation has been annoying, pressuring enterprises to look for innovative ways to improve their operations, methods and products.Aim: This research identifies the key factors contributing to developing innovative behaviour among small enterprises in Saudi Arabia and their effect on environmental performance (EP).Method: The study collected a sample of 284 from different types of small enterprises operating in Saudi Arabia. The data collected were analysed using the partial least square structural equation modelling (PLS-SEM).Results: The study revealed interesting results. It was found that green entrepreneurial motivation (GEM) can positively and significantly influence green innovation (GI) as well as environmental performance. It was also found that green innovation can positively and significantly affect environmental performance. Finally, green innovation could mediate the relationship between green entrepreneurial motivation and environmental performance. Also, Knowledge sharing (KS) could moderate the relationship between green entrepreneurial motivation and green innovation.Conclusion: The study concluded by providing several recommendations for the policymakers in Saudi Arabia.

This paper investigates the forecasting accuracy of alternative time series models when augmented with partial least-squares (PLS) components extracted from economic data, such as Federal Reserve Economic Data, as well as Monthly Database (FRED-MD). Our results indicate that PLS components extracted from FRED-MD data reduce the forecasting error of linear models, such as ARIMA and SARIMA, but produce poor forecasts during high-volatility periods. In contrast, conditional variance models, such as ARCH and GARCH, produce more accurate forecasts regardless of whether or not the PLS components extracted from FRED-MD data are used.