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In the recent decade, the topic of environmental sustainability and its determinants has gained the attention of environmental policy analysts and academicians. However, some crucial factors that can affect environmental sustainability have not yet been explored in the literature. In this essence, the current study aims to investigate the mediating impact of energy alternatives on the liaison between inflation rate and environmental quality. The empirical analysis was conducted on 24-years data of BRICS countries ranging from 1997 to 2020. The study employed the DOLS and FMOLS estimation models to run the regression among variables and check the robustness by employing ARDL model. The empirical results first reveal the negative significant effect of the inflation rate on CO2 emissions. In the second channel, the inflation rate has an inverse impact on fossil fuel energy while a direct impact on consumption of renewable energy. Third, fossil fuel energy consumption positively determines the CO2 emissions while renewable energy consumption shows a declining trend with CO2 emissions. Lastly, the empirical analysis confirms the mediating impact of energy alternatives in the nexus between inflation rate and environmental quality. In view of results, the policymakers are directed to utilize the inflation rate and energy alternatives as policy tools for managing the environmental quality. This study implies the innovative intuitions regarding the role of energy alternatives and inflation rate in pollution mitigation.

At present, the interpretation of regional economic development (RED) has changed from a simple evaluation of economic growth to a focus on economic growth and the optimization of economic structure, the improvement of economic relations, and the change of institutional innovation. This article uses the RED trend as the research object and constructs the RED index to conduct the theoretical analysis. Then this paper uses the attention mechanism based on digital twins and the time series network model to verify the actual data. Finally, the regional economy is predicted according to the theoretical model. The specific research work mainly includes the following aspects: 1) This paper introduced the development status of research on time series networks and economic forecasting at home and abroad. 2) This paper introduces the basic principles and structures of long and short-term memory (LSTM) and convolutional neural network (CNN), constructs an improved CNN-LSTM model combined with the attention mechanism, and then constructs a regional economic prediction index system. 3) The best parameters of the model are selected through experiments, and the trained model is used for simulation experiment prediction. The results show that the CNN-LSTM model based on the attention mechanism proposed in this paper has high accuracy in predicting regional economies. [ABSTRACT FROM AUTHOR]