Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

The carbon market is recognized as the most effective means for reducing global carbon dioxide emissions. Effective carbon price forecasting can help the carbon market to solve environmental problems at a lower economic cost. However, the existing studies focus on the carbon premium explanation from the perspective of return and volatility spillover under the framework of the mean-variance low-order moment. Specifically, the time-varying, high-order moment shock of market asymmetry and extreme policies on carbon price have been ignored. The innovation of this paper is constructing a new hybrid model, NAGARCHSK-GRU, that is consistent with the special characteristics of the carbon market. In the proposed model, the NAGARCHSK model is designed to extract the time-varying, high-order moment parameter characteristics of carbon price, and the multilayer GRU model is used to train the obtained time-varying parameter and improve the forecasting accuracy. The results conclude that the NAGARCHSK-GRU model has better accuracy and robustness for forecasting carbon price. Moreover, the long-term forecasting performance has been proved. This conclusion proves the rationality of incorporating the time-varying impact of asymmetric information and extreme factors into the forecasting model, and contributes to a powerful reference for investors to formulate investment strategies and assist a reduction in carbon emissions.

Recent Advances of Photocatalytic Hydrogenation of CO2 to Methanol

Constantly increasing hydrocarbon fuel combustion along with high levels of carbon dioxide emissions has given rise to a global energy crisis and environmental alterations. Photocatalysis is an effective technique for addressing this energy and environmental crisis. Clean and renewable solar energy is a very favourable path for photocatalytic CO2 reduction to value-added products to tackle problems of energy and the environment. The synthesis of various products such as CH4, CH3OH, CO, EtOH, etc., has been expanded through the photocatalytic reduction of CO2. Among these products, methanol is one of the most important and highly versatile chemicals widely used in industry and in day-to-day life. This review emphasizes the recent progress of photocatalytic CO2 hydrogenation to CH3OH. In particular, Metal organic frameworks (MOFs), mixed-metal oxide, carbon, TiO2 and plasmonic-based nanomaterials are discussed for the photocatalytic reduction of CO2 to methanol. Finally, a summary and perspectives on this emerging field are provided.

The Impact of Technological Innovation, Research and Development, and Energy Intensity on Carbon Emissions: An Experience from BRICS and OECD Countries

The impact of technological innovation, research and development, and energy intensity on carbon dioxide emissions is examined in this study. A panel data econometric analysis of relevant variables extracted from the OECD and World Development Indicators databases for 36 OECD and 5 BRICS countries from 2005 to 2018 reveals that the Kao panel cointegration test revealed all countries, BRICS countries, and OECD countries exhibited cointegrated relationships regarding the selected variables. At this point, the correlation matrix shows that none of the independent variables has a strong correlation coefficient with the dependent variable. We also used two regression methods to evaluate the long-run association between the study's variables; the two-stage least square (2SLS) and panel generalized method of moments (GMM) both provide similar results, indicating that they are robust. According to the findings, technological innovation and R&D have a positive association with CO2 emissions, but energy intensity has a negative relationship with CO2 emissions.

Mathematical modelling and simulation of the thermo-catalytic decomposition of methane for economically improved hydrogen production

The demand for low-emission hydrogen is set to grow as the world transitions to a future hydrogen economy. Unlike current methods of hydrogen production, which largely derive from fossil fuels with unabated emissions, the thermo-catalytic methane decomposition (TCMD) process is a promising intermediate solution that generates no direct carbon dioxide emissions and can bridge the transition to green hydrogen whilst utilising existing gas infrastructure. This process is yet to see widespread adoption, however, due to the high catalyst turnover costs resulting from the inevitable deactivation of the catalyst, which plays a decisive role in the feasibility of the process. In this study, a feasible TCMD process was identified and a simplified mathematical model was developed, which provides a dynamic estimation for the hydrogen production rate and catalyst turnover costs over various process conditions. The work consisted of a parametric study as well as an investigation into the different process modes. Based on the numerous simulation results it was possible to find the optimal process parameters that maximise the hydrogen pro- duction rate and minimise the catalyst turnover costs, therefore increasing the economic potential of the process and hence its commercial viability.