The study aims to compare CO2 emissions, renewable energy, trade openness, gross domestic product (GDP), financial development (FD), and remittance in selected G-20 countries. The study carried out fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) models for estimation covering annual data from the year 1990–2019. LM tests detected the cross-section dependency while stationarity of the variables was checked through Levin-Lin-Chu and Im-Pesaran-Shin tests along with Hansen's Covariate-Augmented Dickey Fuller (CADF) test in the presence of cross-section dependency. The panel unit root tests reported that all variables became stationary after converting them into the first difference. The Panel Cointegration and Wester-Lund test examined the existence of long-run equilibrium nexus among selected variables in the context of G-20 countries. The study's findings show that there is a significant and negative relationship between renewable energy and CO2 emissions. It was proven in two models that the economic growth of selected G-20 countries has a positive relationship with CO2 emissions. Furthermore, findings indicate that the coefficient of financial development is positive and significantly impacts CO2 emissions. The remittances have a significant positive effect on CO2 emissions, while trade openness has an insignificant impact on CO2 emissions in both models. This research will enlighten policymakers, researchers, governments, and environmentalists toward attaining a sustainable environment by wisely consuming remittances and renewable energy resources.
Accurate wind power forecasting is essential to reduce the negative impact of wind power on the operation of the grid and the operation cost of the power system. Day-ahead wind power forecasting plays an important role in the day-ahead electricity spot trading market. However, the instability of the wind power series makes the forecast difficult. To improve forecast accuracy, a hybrid optimization algorithm is established in this study, which combines variational mode decomposition (VMD), maximum relevance & minimum redundancy algorithm (mRMR), long short-term memory neural network (LSTM), and firefly algorithm (FA) together. Firstly, the original historical wind power sequence is decomposed into several characteristic model functions with VMD. Then, mRMR is applied to obtain the best feature set by analyzing the correlation between each component. Finally, the FA is used to optimize the various parameters LSTM. Adding the forecasting results of all sub-sequences acquires the forecasting result. It turns out that the proposed hybrid algorithm is superior to the other six comparison algorithms. At the same time, an additional case is provided to further verify the adaptability and stability of the proposed hybrid model.
At present, electric cars are being developed rapidly in China as emerging carbon emission reduction vehicles, but their proportion in the Chinese automobile market is still small, and a large number of potential consumers are still holding a wait-and-see attitude. Therefore, for the sake of promoting the further development of electric cars in China, this paper based on the TPB (Theory of Planned Behavior) theoretical research framework, investigates potential consumers in typical areas of Beijing and collects a large amount of data through the design of paper and electronic questionnaires. SEM (Structural Equation Modeling) and MNL (Multinomial Logit Model) models are used to analyze key factors affecting consumers’ purchase intention and actual purchasing behavior. The results show that the positive and negative attributes of consumers’ attitude, subjective norm, and perceived behavior control will have different effects on consumers’ actual purchasing behavior. Finally, based on the analysis results, some reasonable suggestions are proposed for the government and EV (Electric Vehicles) enterprise service providers to increase electric vehicle diffusion.
In order to protect the environment and reduce energy consumption, new energy vehicles have begun to be vigorously promoted in various countries. In recent years, the rise of intelligent technology has had a great impact on the supply chain of new energy vehicles, which, coupled with the complexity of the supply chain itself, puts it at great risk. Therefore, it is quite indispensable to evaluate the risk of the new energy vehicle supply chain. This paper assesses the risks faced by China’s new energy vehicle supply chain in this period of technological transformation. First of all, this paper establishes an evaluation criteria system of 16 sub-criterion related to three dimensions: the market risk, operational risk, and the environmental risk. Then, variable weight theory is proposed to modify the constant weight obtained by the fuzzy analytic hierarchy process (FAHP). Finally, a risk assessment of China’s new energy vehicle supply chain is carried out by combining the variable weight and the cloud model. This method can effectively explain the randomness of matters, and avoid the influence of value abnormality on the criteria system. The results show that China’s new energy vehicle supply chain is at a high level. Through the identification of risk factors, mainly referring to the low clustering risk, technical level risk and information transparency risk, this paper can provide a risk prevention reference for corresponding enterprises.
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