Search

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]

Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R2) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, namely, reference fuel-consumption-rate value, engine power, and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with the greatest impact are the vehicle brand, engine power, and engine displacement. The average air pressure, average temperature, and sunshine time are the three most important climate factors. [ABSTRACT FROM AUTHOR]