Integrating Truck Emissions Cost in Traffic Assignment

<div class="section abstract"><div class="htmlview paragraph">The transportation sector is responsible for a significant portion of greenhouse gas emissions. Within the sector, truck freight is responsible for a third of the associated emissions. Alternative powertrains are seen as a viable approach to significantly reduce these emissions. Prior to making a large-scale transition, it is important to consider the following questions: will the power grid support a transition to alternative powertrains?; will the transition truly reduce carbon emissions?; and will the transition impose an unnecessary economic burden on companies within the industry? The answer to these questions, however, can vary by geography, maturity/capacity of the energy distribution network or predicted vehicle load. We focus on the latter two questions, investigating the variation in estimated total cost of ownership and carbon emissions across the United States at the zip code level for both heavy-duty battery electric vehicles and heavy-duty fuel cell electric vehicles. As a benchmark, we compare estimated emissions and costs of alternative powertrain vehicles to that of conventional heavy-duty vehicles powered by diesel internal combustion engines. This work highlights areas with electric grids primed for a transition to alternative powertrain vehicles, such as the Pacific Northwest, and areas that require further infrastructure investment in renewables, such as many of the Mountain states, Missouri, and Florida. Additionally, this work illustrates the current advantages in carbon emissions of battery electric vehicles compared to fuel-cell electric vehicles, while providing insights into required regional investments for narrowing the gap.</div></div>

Energy-related activities are a major contributor of greenhouse gas (GHG) emissions. A growing body of knowledge clearly depicts the links between human activities and climate change. Over the last century the burning of fossil fuels such as coal and oil and other human activities has released carbon dioxide (CO2) emissions and other heat-trapping GHG emissions into the atmosphere and thus increased the concentration of atmospheric CO2 emissions. The main human activities that emit CO2 emissions are (1) the combustion of fossil fuels to generate electricity, accounting for about 37% of total U.S. CO2 emissions and 31% of total U.S. GHG emissions in 2013, (2) the combustion of fossil fuels such as gasoline and diesel to transport people and goods, accounting for about 31% of total U.S. CO2 emissions and 26% of total U.S. GHG emissions in 2013, and (3) industrial processes such as the production and consumption of minerals and chemicals, accounting for about 15% of total U.S. CO2 emissions and 12% of total ...

Human activities such as burning fossil fuels, deforestation, and economic growth are increasingly affecting the climate and temperature of the earth. Large amounts of greenhouse gases in the atmosphere have increased the greenhouse effect and global warming. By 2020, the concentration of greenhouse gases in the atmosphere has increased to 48% above its pre-industrial level. The main objectives of this study are to determine the level and the pattern of the relationship between dependent and independent variables. Also, this study examines the long-term and short-term impacts of energy consumption, economic growth, and non-renewable energy on carbon dioxide (CO2) emissions in Malaysia. Due to increased industrialization, Malaysia faces significant problems, such as environmental pollution. This study uses annual time series data from 1986 to 2021 and is analyzed using the Autoregressive Distributed Lag approach. The study suggests that energy consumption, economic growth, and non-renewable energy positively impact carbon dioxide (CO2) emissions. The results through dynamic ARDL indicate that energy consumption, economic growth, and non-renewable energy positively impact Malaysia’s carbon dioxide (CO2) emissions in the short-run and long run. The error correction model (ECM) provides short- run shocks in these variables and establishes equilibrium relations in the long run. Therefore, policymakers should consider implementing a carbon tax to be enforced on polluters to prevent ecological pollution at a minimum for the short-term regulation of carbon dioxide (CO2) emissions.

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Impact of Human Activities on Carbon Dioxide (CO2) Emissions: A Statistical Analysis

Comparison of average particle number emission factors for heavy and light duty vehicles derived from rolling chassis dynamometer and field studies

Alternate weibit-based model for assessing green transport systems with combined mode and route travel choices

Special issue: emerging technologies for intelligent transportation

This paper investigates the effect of heavy-duty (HD) vehicles (primarily road freight) on the traffic congestion–emissions relationship. Unlike previous studies, this research explicitly considers the effects of travel demand elasticity by vehicle class on total emissions. Modeling results show that, even as a small share of the traffic volume, HD vehicles can contribute a large share of total pollution emissions, especially for particulate matter and nitrogen oxides. HD vehicle emission rates are more sensitive to congestion than are light-duty (LD) vehicle emission rates, and thus greater emissions benefits may result from mitigating congestion for these vehicles. Potentially lower travel demand elasticity with respect to speed for HD vehicles further indicates vehicle class–specific benefits from congestion mitigation. Differences between LD and HD vehicles suggest greater air quality benefits from vehicle class–targeted congestion mitigation or lane and capacity management strategies. HD vehicle travel demand elasticity is a key parameter for predicting the net emissions effects of congestion. It is strongly recommended that analysis of emissions effects from congestion mitigation strategies include class-specific volume forecasts. However, the estimation of HD vehicle travel demand elasticity values has received scant attention in the literature.

In modern transportation networks, there is a common phenomenon of overlapping of physical road sections and transport modes. In this study, the overlapping phenomenon in traffic network is defined as the generalized overlapping problem which is related to the failure of many existing models for users' route selection. When modeling the route selection behavior in the super-networks, the available routes are constrained by the overlapping of both the physical segments and the transport modes. In this paper, we propose a multi-level mixed logit model to investigate the correlation of generalized routes. Based on the proposed model, the traffic assignment of super network can be simplified as fixed point problem. By analyzing the error terms of the physical segments and the transport modes, we discuss the overlapping effects on the route choice. The results show that the proposed model can sole the overlapping problem effectively, which has significant effects on the route choice and traffic assignment problems in practice.

Integration Of Ecological Criteria Into The Dynamic Assessment Of Order Penetration Points In Logistics Networks

The optimization of empty container repositioning nets has become an essential problem in low-carbon port cooperation. This paper proposed three optimization models of multi-port low-carbon empty container repositioning considering threshold under input and output of empty containers as random variables. Non repositioning strategy means the highest threshold, and complete-repositioning strategy means the lowest threshold; threshold-repositioning strategy is in the middle. The probability of empty-container inventory in each port and the storage cost, repositioning cost, lease cost, and carbon emission cost of empty containers are calculated. This paper mainly compares each cost of three models. The results have shown that: (1) Compared with the non repositioning strategy, the threshold-repositioning strategy and complete-repositioning strategy can reduce the ports storage costs and lease costs of empty containers and also reduce carbon emissions. The lower the repositioning threshold of empty containers between ports is, the more obvious the advantages of the threshold-repositioning strategy become. (2) When the cost of storage per empty container increases, under three strategies, the total cost, storage cost, lease cost, and carbon emission cost of the port will all increase. The ports proportion of dependence on its own empty-container storage will decrease, and the proportion of dependence on other ports and leasing companies will both increase.

The utilization of Greenhouse gases (GHG) CO2 and CH4 caught Government & Scientists attention due to global warming effects in our climate changes , most of GHG emission are responsible for this changes are coming from the burning fossil fuel. Carbon dioxide classified as one of most reasons where gases from burning fossil fuels is the largest single source of greenhouse gas emissions used by human activities , even the old well known mechanism to convert CO2 to organic species using Sabatier reaction cannot be applied in commercial application due to energy required to provide hydrogen gas which is important to proceed with this reaction ,poisoning of Nickel catalyst or other used catalyst through reaction, minor products produced such as methanol, diethyl ether , formic acid and other hydrocarbons, the limitation of this reaction at low temperature.

Heavy-duty vehicles (HDVs) account for a significant portion of the U.S. transportation sector’s fuel consumption, greenhouse gas (GHG) emissions, and air pollutant emissions. In our most recent efforts, we expanded the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREETTM) model to include life-cycle analysis of HDVs. In particular, the GREET expansion includes the fuel consumption, GHG emissions, and air pollutant emissions of a variety of conventional (i.e., diesel and/or gasoline) HDV types, including Class 8b combination long-haul freight trucks, Class 8b combination short-haul freight trucks, Class 8b dump trucks, Class 8a refuse trucks, Class 8a transit buses, Class 8a intercity buses, Class 6 school buses, Class 6 single-unit delivery trucks, Class 4 single-unit delivery trucks, and Class 2b heavy-duty pickup trucks and vans. These vehicle types were selected to represent the diversity in the U.S. HDV market, and specific weight classes and body types were chosen on the basis of their fuel consumption using the 2002 Vehicle Inventory and Use Survey (VIUS) database. VIUS was also used to estimate the fuel consumption and payload carried for most of the HDV types. In addition, fuel economy projections from the U.S. Energy Information Administration, transit databases, and the literature were examined. The U.S. Environmental Protection Agency’s latest Motor Vehicle Emission Simulator was employed to generate tailpipe air pollutant emissions of diesel and gasoline HDV types.

Carbon dioxide (CO2) emissions result from human activities like burning fossil fuels. CO2 is a greenhouse gas, contributing to global warming and climate change. Efforts to reduce CO2 emissions include transitioning to renewable energy. Monitoring and reducing CO2 emissions are crucial for mitigating climate change. Strategies include energy efficiency and renewable energy adoption. In the past few decades, several nations have experienced air pollution and environmental difficulties because of carbon dioxide (CO2) emissions. One of the most crucial methods for regulating and maximizing CO2 emission reductions is precise forecasting. Four machine learning algorithms with high forecasting precision and low data requirements were developed in this study to estimate CO2 emissions in the United States (US). Data from a dataset covering the years 1973/01 to 2022/07 that included information on different energy sources that had an impact on CO2 emissions were examined. Then, four algorithms performed the CO2 emissions forecast from the layer recurrent neural network with 10 nodes (L-RNN), a feed-forward neural network with 10 nodes (FFNN), a convolutional neural network with two layers with 10 and 5 filters (CNN1), and convolutional neural network with two layers and with 50 and 25 filters (CNN2) models. Each algorithm's forecast accuracy was assessed using eight indicators. The three preprocessing techniques used are (1) without any processing techniques, (2) processed using max-min normalization technique, and (3) processed using max-min normalization technique and decomposed by variation mode decomposition (VMD) technique with 7 intrinsic mode functions and 1000 iterations. The latter with L-RNN algorithm gave a high accuracy between the forecasting and actual values. The results of CO2 emissions from 2011/05 to 2022/07 have been forecasted, and the L-RNN algorithm had the highest forecast accuracy. The L-RNN model has the lowest value of 1.187028078, 135.5668592, and 11.64331822 for MAPE, MSE, and RMSE, respectively. The L-RNN model provides precise and timely forecasts that can help formulate plans to reduce carbon emissions and contribute to a more sustainable future. Moreover, the results of this investigation can improve our comprehension of the dynamics of carbon dioxide emissions, resulting in better-informed environmental policies and initiatives targeted at lowering carbon emissions.