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.

Small businesses are thought to be largely responsible for environmental pollution despite the fact that businesses of all shapes and sizes contribute to this issue. This research explores how important factors such as knowledge sharing (KS) and green entrepreneurial intention (GEI) might help small businesses in Saudi Arabia develop and implement green innovation (GI). It also seeks to determine whether GI is a mediating variable that explains the connection between GEI, KS, and social performance (SP). Accordingly, an online survey was used to collect responses from 284 small entrepreneurs in Saudi Arabia engaged in various types of business activities. The study used partial least squares structural equation modelling for data analysis and hypothesis testing. The results show that GI considerably influences SP while also having a significant link with both GEI and KS. Further, the study reveals that the relationship between GEI, KS, and SP is mediated by GI. The study offers a plethora of suggestions to various stakeholders generally and to Saudi authorities specifically.

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 study examines the impact of renewable energy on sustainable development in the Kingdom of Saudi Arabia from 2000 to 2019 and analyzes the kingdom’s most essential practices in this field to achieve sustainable development. The Cobb–Douglas production function is used in this study to investigate the impact of renewable energy on sustainable development using ordinary least squares (OLS) estimation. According to the findings, renewable energy consumption has a negative but insignificant effect on GDP. Additionally, traditional energy consumption has a major negative influence on GDP. The findings also demonstrate that fixed capital formulation and technical progress have a significant positive impact on Saudi Arabia’s sustainable development. Furthermore, while the labor force has a positive impact on GDP, this effect is not statistically significant. This report provides some recommendations for Saudi government policymakers to reform the country’s energy efficiency and consumption technologies in order to reduce energy waste and satisfy the goals of sustainable development. While the labor force is recognized as having a positive influence on GDP, it is notable that this result lacks statistical significance. The suggestions of these findings are mainly applicable to Saudi policymakers, and we present recommendations to improve energy competence and utilization technologies. Specially, our suggestions are intended to reduce energy dissipation and to support the objectives of sustainable development.

This study aims to compare the effectiveness of renewable energy and circular economy practices in reducing environmental damage and poverty in China and the European Union (EU) by using the Autoregressive Distributed Lag (ARDL) approach and the Vector Error Correction Model (VECM) technique. Analyzing data from 1995 to 2022, we compared the effectiveness of these approaches in both regions. This study offers several fresh perspectives on the fight against environmental damage and poverty, differentiating itself from existing research in a few key ways. In fact, most studies focus on a single region’s approach to sustainability. However, this research directly compares China and the European Union (EU), highlighting how factors like economic structure and development stages influence the effectiveness of renewable energy and circular economy practices. Also, this study shows how renewable energy and circular practices can simultaneously tackle both environmental damage (emissions) and social issues (poverty). Our findings reveal that factors like poverty, fossil fuel reliance, economic growth, and international trade all contribute to rising carbon emissions in both China and the EU, though their influence varies. Interestingly, China experienced greater success in curbing emissions through solar power, circular economy measures, and wind power compared to the EU. Additionally, the impact of these strategies on poverty reduction differed significantly between the two regions. Based on these results, we recommend that policy makers in both regions develop tailored strategies for tackling carbon emissions and poverty. Prioritizing renewable energy sources and circular practices is key, as they can simultaneously reduce emissions and alleviate poverty. However, it is crucial to focus on approaches that work best in each specific region, considering social, economic, and environmental factors. This study suggests that a one-size-fits-all approach will not work for fighting climate change and poverty. While renewable energy and circular economies are effective, China saw greater success with solar and wind power compared to the EU. Policy makers need to design strategies specific to their region’s economic, social, and environmental context. Further research can refine these approaches, and public education is crucial to promote sustainable behavior and empower policy makers. It is also needed to refine and improve the implementation of these strategies. Finally, public awareness and education are essential to promote sustainable behavior and empower policy makers in their fight against climate change and poverty. This study highlights the importance of a region-specific approach to energy transition and poverty alleviation. By comparing China and the EU, we gain valuable insights into the effectiveness of different strategies in tackling these interconnected challenges. The findings provide valuable guidance for policy makers looking to create a more sustainable and equitable future.

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.