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The problem of global warming is getting more serious, and vehicle emission is the main cause. In recent years, the number of locomotives in China has been increasing at a rate of more than 20% per year, and the problem of automobile pollution is becoming more serious. The transportation industry is the main source of fossil fuel combustion and environmental pollution. Therefore, in this paper, we propose a multi-view deep neural network (MVDNN) to analyze the key factors affecting the fuel consumption of automobiles. The experiments show that the introduction of human input improves the prediction accuracy and the root mean square error (RMSE) achieves 0.993. In addition, this paper also finds that for drivers, driving habits, driving frequency, and safety awareness are the most important factors affecting the fuel consumption of vehicles by combining Lasso regression with MVDNN. Finally, by comparing the prediction accuracy of different experiments, relevant policy suggestions are made. [ABSTRACT FROM AUTHOR]

This study investigates the environmental consequences of consumers opting for traditional internal combustion engine vehicles (ICEVs), resulting in heightened pollution. Given the critical importance of addressing environmental degradation and its impact on societal welfare, our research aims to provide insights into effective policymaking strategies. In an ideal scenario, a benevolent planner would equitably distribute wealth, forming the first-best policy. Recognizing the impracticality of this ideal, we propose two viable second-best policies: an income-based tax and the implementation of a carbon emission quota. Theoretical analysis highlights the efficacy of the tax policy in pollution reduction, particularly with the growing number of mid-income electric vehicle consumers. The optimal tax policy adeptly navigates the delicate balance between pollution and income effects. Furthermore, we establish that the optimal pollution quota under the carbon emission policy is lower than in both competitive and first-best scenarios. A numerical comparison of the four equilibrium outcomes underscores these findings. In addition, we advocate for a blockchain-based decentralized carbon emission quota trading platform to digitize and optimize allocation and compliance processes, fostering more efficient and transparent environmental management. • Explore the ramifications of consumer preferences upon excess carbon emissions. • Evaluate policy effectiveness in curbing pollution and improving welfare. • Analyze carbon emissions quota as alternative tools for environmental governance. [ABSTRACT FROM AUTHOR]