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Currently, the adoption of electric vehicles (EV) draws much attention, as the environmental issue of reducing carbon emission is increasing worldwide. However, different countries face different challenges during this transition, particularly developing countries. This research aims to create a framework for the transition to EV in Indonesia through Agent-Based Modeling (ABM). The framework is used as the conceptual design for ABM to investigate the effect of agents’ decision-making processes at the microlevel into the number of adopted EV at the macrolevel. The cluster analysis is equipped to determine the agents’ characteristics based on the categories of the innovation adopters. There are 11 significant variables and four respondents’ clusters: innovators, early majority, late majority, and the uncategorized one. Moreover, Twitter data analytics are utilized to investigate the information engagement coefficient based on the agents’ location. The agents’ characteristics which emerged from this analysis framework will be used as the fundamental for investigating the effect of agents’ specific characteristics and their interaction through ABM for further research. It is expected that this framework will enable the discovery of which incentive scheme or critical technical features effectively increase the uptake of EV according to the agents’ specific characteristics.

Indicators of sustainability and environmental performance can be useful for comparing modes, discerning trends, and formulating appropriate policies. This paper considers the performance of U.S. transportation service sectors through use of 1992 and 1997 benchmark input–output models. Use of these models permits assessment of not only the direct performance of the sectors but also the supply chain impacts required for operation of the transportation sectors. Consideration of indirect impacts is critical for assessment of the overall costs and impacts of particular products or services. Six transportation service sectors (air, rail, water, truck, transit, and pipeline) are examined. Economic impact, energy, greenhouse gas emissions, and toxic emissions are examined. The transportation sectors use large amounts of energy, both in total and per dollar of output and on a per service basis. Pipeline and water transportation have particularly large energy requirements per dollar of output, likely reflecting higher energy intensity and lower labor intensity in these modes. Truck transportation is the most energy intensive of the freight transportation modes per ton-mile of service, but it has a trend toward greater energy efficiency. For greenhouse gas emissions, truck, water, and air transportation have the highest emissions per dollar of output. Water transportation freight rates are sufficiently low that emissions on a per ton-mile basis would be correspondingly low. Finally, the supply chain (indirect) toxic emissions per dollar of output are highest for rail and pipeline transportation. There is considerable work to be done to improve the overall sustainability of the different transportation modes.

In this study, a mixed integer linear programming model that integrates multimodal transport—truck and rail—into the switchgrass-based bioethanol supply chain was formulated. The objective of this study was to minimize the total cost for cultivation and harvesting, infrastructure, the storage process, bioethanol production, and transportation. Strategic decisions, including the number and location of intermodal facilities and biorefineries, and tactical decisions, such as the amount of biomass shipped, processed, and converted into bioethanol, were validated by using North Dakota as a case study. It was found that the multimodal transport scenario was more cost effective than a single mode of transport (truck) and resulted in a lower cost for bioethanol. A sensitivity analysis was conducted to demonstrate the impact of key factors in the decision to use multimodal transport in a switchgrass-based bioethanol supply chain and on the cost of bioethanol.

Traffic congestion is a perpetual problem for the sustainability of transportation development. Traffic congestion causes delays, inconvenience, and economic losses to drivers, as well as air pollution. Identification and quantification of traffic congestion are crucial for decision-makers to initiate mitigation strategies to improve the overall transportation system’s sustainability. In this paper, the currently available measures are detailed and compared by implementing them on a daily and weekly traffic historical dataset. The results showed each measure showed significant variations in congestion states while indicating a similar congestion trend. The advantages and disadvantages of each measure are identified from the data analysis. This study summarizes the current road traffic congestion measures and provides a constructive insight into the development of a sustainable and resilient traffic management system.

Abstract Airports are moving toward utilizing clean energy technologies along with the implementation of practices that reduce local emissions. This includes replacing fossil fuel-based with electricity-based operations. These changes would significantly impact the energy demand profile of airports. Electrically-conductive concrete (ECON) is currently a focus of heated pavement design for replacing conventional snow removal practices. ECON heated pavement systems (HPSs) use electricity to heat the pavement surface. Since experimental studies are resource intensive and ECON HPS performance depends on weather conditions, developing a field data-validated numerical model enables its long term energy performance evaluation. In this research, a finite element (FE) model is developed and experimentally-validated using two proposed model-updating methods for full-scale ECON HPS test slabs constructed at Des Moines International Airport (DSM) in Iowa. The model predicts energy demands and average surface temperatures within 2% and 13% respectively. The estimated power demand ranges from 325 to 460 W/m2 for different weather conditions. The results of this study provide a validated tool that can be used to evaluate the energy demand of ECON HPS. Studying the energy demand of ECON HPS opens the way for developing control strategies to optimize its energy use which will contribute to developing sustainable communities.

In the context of achieving carbon peaking and carbon neutrality goals, focusing on coordinated efficiency in loss and carbon reduction, and promoting comprehensive green transformation of economic and social development are critical strategies. Line loss is an economic and technical indicator for measuring losses in a power system, and loss reduction is one of the important ways to achieve the carbon peaking and carbon neutrality goals in the power system. However, with the continuous increase in the power grid scale and the increasingly complex operation mode of the system, it is difficult to clearly quantify the carbon reduction benefits brought by system loss reduction. In order to synergize grid loss reduction and system carbon reduction, and generate economic and environmental benefits at the same time, this paper proposes a carbon market trading model that considers multi-layer reactive power compensation strategies. Based on the carbon emission flow model, a node carbon cost pricing is formed, and multi-layer reactive power compensation measures are set in the distribution network nodes to obtain an optimal loss reduction strategy, with the carbon market trading cost minimization as the objective. The effectiveness of the model is verified by simulating and analyzing four scenarios. Compared with the original system that does not consider carbon trading and reactive compensation, the model proposed in this paper can reduce losses by 20% and reduce carbon emissions by 5.7%. This paper is of great value for reactive power loss reduction management in distribution networks of a low-carbon background.

We have modelled the market for new cars in Ireland with the aim of quantifying the values placed on a range of observable car characteristics. Mid-sized petrol cars with a manual transmission sell best. Price and perhaps fuel cost are negatively associated with sales, and acceleration and perhaps range are positively associated. Hybrid cars are popular. The values of car characteristics are then used to simulate the likely market shares of three new electric vehicles. Electric vehicles tend to be more expensive even after tax breaks and subsidies are applied, but we assume their market shares would benefit from an "environmental" premium similar to those of hybrid cars. The "environmental" premium and the level of subsidies would need to be raised to incredible levels to reach the government target of 10% market penetration of all-electric vehicles.