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With the pressing demand of climate change mitigation, the rapid technological development and market adoption of battery electric vehicles are imperative. However, the diverse consumption attitudes and their interactions, which are rarely considered, can significantly affect the adoption potential of battery electric vehicles. On the basis of three years of global positioning system data of 1.6 million people, we estimated the travel and adoption demands of battery electric vehicles in Japan considering diverse consumption attitudes. Under the current construction conditions for public charging systems and charging technologies, the adoption potential of battery electric vehicles may not be as promising as previously expected, and the government still faces great pressure to respond to the market share target. Given the current level of battery technology, technical and policy improvements such as fast charging, reducing the production cost, perfecting the public charging infrastructure, and increasing purchasing subsidies were found to be more effective than improving the battery capacity at increasing the adoption potential of battery electric vehicles. [ABSTRACT FROM AUTHOR]

Recent years, the energy consumption and carbon emissions released from the residential sector have increased rapidly due to the improvement of living standards. Japan, as one of the most developed countries worldwide, is found to be the fourth largest CO2 emitter in the world. Meanwhile, Japan is currently promoting social electrification from varied aspects. And this action further brings the household residential consumption to the vital place for overall energy conservation and emission reduction plans. Although electricity consumption and prediction analysis have been widely discussed. However, previous studies mainly focused on the estimation and analysis of energy consumption at the national level, without enough discussion from prefecture-level insights. To bridge this knowledge gap, this study established a factor-based bottom-up model to estimate the electricity consumption and carbon emissions during the 2015–2040 periods in the Japanese residential sector, considering the prefectural characteristics including per capita gross domestic product, population size, household size, residential floor space area, lifestyle, weather condition, and transition effect of appliance in the future. Nine scenarios that combine three levels of household size and three levels of growth of per capita gross domestic product are taken into account to estimate the electricity consumption for space heating and cooling, water heating, cooking, and appliances. Results indicate that the total residential electricity consumption will reach a peak during 2020s. The total carbon emissions will keep decreasing by 51.14–72.16 Mt between 2015 and 2040. Based on the results of this paper, policy recommendations are given for the Japanese government. • A factor-based bottom-up model is established for electricity consumption estimation. • The electricity consumption and carbon emissions are analyzed at a prefecture-level in Japan. • Each component of the residential sector electricity consumption is quantified completely and accurately. • The estimation results consider the lifestyle, weather conditions, and the transition effect of appliances in Japan. [ABSTRACT FROM AUTHOR]

• This study investigated city-level energy-related household consumption. • City-scale input-output table and consumption inventories are applied. • Ten age-based household groups and their corresponding emissions were calculated. • Temperature decrease is responsible for the increased emission in older households. City is the main place to consume goods and services throughout the world. Among the various consumption terminals, household-level consumption is highly behavior driven, which can be affected by various factors such as household income level, age, living environment etc. However, city-level household emissions characteristics are still not fully understood due to the complexity of consumption behaviors and the lack of the supply chain's data. To include the environmental responsibility embodied in residential consumption and reveal how it varies among household type and season, this study investigates city-level household consumption as it relates to energy demand using a city-scale input-output model and urban residential consumption inventories. Importantly, age- and month-based emission are analyzed from different aspects such as emission type, source, fuel types and consumption items. Findings indicate that (1) household emissions differ substantially among the various household age groups; older households generally produce higher emissions levels on a per capita basis; (2) decreases in temperature are the main reason for the increased emissions in older households, while this is not a significant factor in younger households; (3) the high per capita household emissions in older households indicate inefficient energy usage among elder citizens, which strongly suggests that aging societies will face long-term emissions increases if appropriate measures are not taken. [ABSTRACT FROM AUTHOR]

City-level carbon footprint has been recognized as a useful measure of anthropogenic impact on climate change associated with citizens' activities within the administrative boundary. Although the promotion of consumer responsibility suggests rethinking urban indirect emissions, the detailed methodology is far from satisfactory for realistic applications. Due to the lack of multi-regional input-output tables for most cities, there is a wide application of single regional input-output tables. However, there still lacks further discussion on if there will be an obvious evaluation bias by applying city-level single-regional tables rather than multi-regional ones. To visualize the table coverage on its application consequence, both single- and multi-regional input-output tables were employed to compare disparities in the carbon footprint accounting in the case of Tokyo, Japan. Our analysis shows that the gap of emissions driven by Tokyo's final demand between single- and multi-regional input-output tables was considerably large. Furthermore, the results of multi-regional table were found to be 8.11 MtC higher for coal-generated emissions, 7.83 MtC for crude oil-generated emissions and 2.90 MtC for natural gas-generated emissions than those of the single-regional table. The largest deviation in emissions accounting was observed in the power, gas and heating supply sector, the construction sector and the private service sector. The gap between these two input-output tables was notable for all three types of fossil fuels (coal, crude oil and natural gas). These indicated that coal-generated emissions have been largely ignored by single-regional input-output table. The study highlighted the difference of carbon footprint accounting between these two types of input-output tables. Our findings are intended to assist policymakers and scholars in pinpointing and reallocating sectors that are likely to yield severely biased evaluation of emissions embodied in trade when a multi-regional table is not available., Competing Interests: Declaration of competing interest There is no conflict of interest., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Pedestrian trajectory prediction under crowded circumstances is a challenging problem owing to human interaction and the complexity of the trajectory pattern. Various methods have been proposed for solving this problem, ranging from traditional Bayesian analysis to Social Force model and deep learning methods. However, most existing models heavily depend on specific scenarios because the trajectory model is constructed in absolute coordinates even though the motion trajectory as well as human interaction are in relative motion. In this study, a novel trajectory prediction model is proposed to capture the relative motion of pedestrians in extremely crowded scenarios. Trajectory sequences and human interaction are first represented with relative motion and then integrated to our model to predict pedestrians' trajectories. The proposed model is based on Long Short Term Memory (LSTM) structure and consists of an encoder and a decoder which are trained by truncated back propagation. In addition, an anisotropic neighborhood setting is proposed instead of traditional neighborhood analysis. The proposed approach is validated using trajectory data acquired at an extremely crowded train station in Tokyo, Japan. The trajectory prediction experiments demonstrated that the proposed method outperforms existing methods and is stable for predictions of varying length even when the model is trained with a controlled short trajectory sequence.