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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]

To assess the impact of the online car-hailing services on the environment in China, in this paper, we develop a model to evaluate greenhouse gas (GHG) emissions and emission reduction during the production and use of car-hailing, as a result of increased online bookings. Since energy use differs according to type of vehicle, this paper also contains different scenarios that depict degrees in the regulation stringency according to the Chinese Preliminary Rules on the Management of Online Car-Hailing, regional differences, and development of electric vehicles. The results show that, under the three different scenarios, the online car-hailing service would have the highest GHG emissions of 325, 413, or 160 MtCO2 and the highest reduction in emissions of 19.46, 25.1, or 9.48 MtCO2 by 2030. The government needs to encourage the development of online car-hailing to reduce the purchase of private cars, avoid compulsory actions to shut down online car-hailing platforms, and mandate that only electric vehicles (EVs) can be used for online car-hailing. [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]