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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]

To comprehensively investigate how regional household contribute to national carbon emission, this paper evaluates both the direct and indirect Japanese household carbon‐based emissions by using an environmentally extended input‐output table as well as the regional household consumption inventory. The results indicate that the household sector is the key driving force behind Japan's emissions, and it accounts for approximately 80% of the country's total emissions when indirect emissions are included. Moreover, significant regional differences exist in terms of the carbon footprint, which is in part caused by household and fuel‐type differences found across the country. Finally, this paper suggests that the impact of regional household and fuel‐type differences on Japan's overall carbon footprint should be a primary focus when practitioners design customized strategies to decarbonize Japan at the subnational scale. Plain Language Summary: As the major emitters of Greenhouse Gases, households release emissions both directly through combusting fossil fuels, and indirectly through by consuming goods or service. Carbon footprint represents how a household can impact the environment in both ways. Here, household lifestyles have major ramifications on the environment, and lifestyle choices may impact on the carbon footprint gap among different population groups. Our results show that households are responsible for approximately 80% of the country's total emissions. An increment of 4.19 t‐CO2 has been found at per capita level from direct to total accounting. Moreover, significant regional differences exist in terms of the carbon footprint by fuel‐type differences. We suggest that the impact of regional household and fuel‐type differences on Japan's carbon overall footprint should be a primary focus when practitioners design customized strategies to decarbonize Japan at the sub‐national scale. Key Points: This paper evaluates both the direct and indirect Japanese household carbon‐based emissions by fuel typesIndirect emission accounts for approximately 80% of the changes in terms of the country's total emissionsSignificant regional differences exist on household carbon footprint, which caused by fuel‐type consumption differences across the country [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]