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The participants of the public–private partnership projects have different parts in developing the concessions, hence different perspectives and goals. Besides, the public–private partnership concession agreement has many parameters and components, and a change in one component will have a considerable impact on other components. Thus, determining the optimal value for different concession parameters by providing a series of feasible contribution options was investigated in this paper using two different multi-objective optimization models: genetic algorithm and Thompson sampling. Two case studies (the US I-495 and the I-4 Ultimate) were used to construct and validate the results of the model. The model results showed that the socio-economic sustainability performance increases as the private equity increases and the public equity decreases. The results also showed that the socio-economic sustainability performance increases as the concession price (user-fee) decreases. Having these contribution options could facilitate the decision-making process for both the public and private parties. The genetic algorithm model obtained faster optimization results when compared to the Thompson sampling model; however, the Thompson sampling obtained better results. Moreover, the use of the model could improve the socio-economic sustainability performance of the public–private partnership projects.

Purpose Efficient management of earthmoving equipment is critical for decision-makers in construction engineering management. Thus, the purpose of this paper is to prudently identify, select, manage and optimize the associated decision variables (e.g. capacity, number and speed) for trucks and loaders equipment to minimize cost and time objectives. Design/methodology/approach This paper addresses an innovative multiobjective and multivariable mathematical optimization model to generate a Pareto-optimality set of solutions that offers insights of optimal tradeoffs between minimizing earthmoving activity’s cost and time. The proposed model has three major stages: first, define all related decision variables for trucks and loaders and detect all related constraints that affect the optimization model; second, derive the mathematical optimization model and apply the multiobjective genetic algorithms and classify all inputs and outputs related to the mathematical model; and third, model validation. Findings The efficiency of the proposed optimization model has been validated using a case study of earthmoving activities based on data collected from the real-world construction site. The outputs of the conducted optimization process promise the model’s originality and efficiency in generating optimal solutions for optimal time and cost objectives. Originality/value This model provides the decision-maker with an efficient tool to select the optimal design variables to minimize the activity's time and cost.

Connected and automated vehicles (CAVs) are emerging technologies expected to bring important environmental, social, and economic improvements in transportation systems. Given their implications in terms of air quality and sustainable and safer movement of goods, heavy-duty trucks (HDTs), carrying the majority of U.S. freight, are considered an ideal domain for the application of CAV technology. An input–output (IO) model is developed based on the Eora database—a detailed IO database that consists of national IO tables, covering almost the entire global economy. Using the Eora-based IO model, this study quantifies and assesses the environmental, economic, and social impacts of automated diesel and battery electric HDTs based on 20 macro-level indicators. The life cycle sustainability performances of these HDTs are then compared to that of a conventional diesel HDT. The study finds an automated diesel HDT to cause 18% more fatalities than an automated electric HDT. The global warming potential (GWP) of automated diesel HDTs is estimated to be 4.7 thousand metric tons CO2-eq. higher than that of automated electric HDTs. The health impact costs resulting from an automated diesel HDT are two times higher than that of an automated electric HDT. Overall, the results also show that automation brings important improvements to the selected sustainability indicators of HDTs such as global warming potential, life cycle cost, GDP, decrease in import, and increase in income. The findings also show that there are significant trade-offs particularly between mineral and fossil resource losses and environmental gains, which are likely to complicate decision-making processes regarding the further development and commercialization of the technology.

Renewable energy has gained popularity as an alternative to fossil fuels, which regularly emit large amounts of Greenhouse Gases and consume/withdraw large amounts of water, but renewable energy market penetration is still limited while fossil fuels are still the U.S.‘s dominant power source. This is due to resistance in the market, or in this case, the failure of renewable energy policies to achieve long-term environmental sustainability due to neglected external factors (economic, societal, etc.). No available literature analyzes potential sources and/or effects of this policy resistance, so this research investigates the underlying mechanisms in the renewable energy generation market by utilizing a system dynamics model. A two-alternative Generalized Bass Model was developed to simulate the renewable energy market (specifically with respect to solar PV and wind energy), including the environmental, societal, and economic concerns associated with each of the alternatives evaluated in this study, so as to identify and address possible causes of policy resistance and its subsequent effects on environmental impacts (esp. GHG emissions and water withdrawal rates). Based on this model, three separate policy areas (solar PV investments, wind power investments, and the elimination of fossil fuel subsidies) and various combinations thereof were proposed and tested within the context of the model. Based on the results of this study, it is highly recommended to invest as generously as possible into multiple renewable energy industries, reduce fossil fuel subsidies (in turn freeing up funding for renewable energy investments), and seek further advancement in renewable energy technologies (e.g. enhancing the useable lifetimes of wind turbines). A balanced policy have potential to increase the share of renewable's up to roughly 40% in the U.S. by 2050, as well as 17% and 32% GHG and water withdrawal reduction potential by 2050.

Heavy-duty trucks are the main carrier of the most of freight in the United States today. The U.S. Department of Energy’s projections show that, under the reference case, the truck vehicle-miles-travelled (VMT) on the national highway system will further increase in the near future. This outlook with regard to U.S. Class 8 Heavy-Duty Trucks (HDTs) raises concerns regarding environmental, economic, and social impacts of these vehicles and HDT fleets. However, the transition to sustainable trucking is a challenging task for which multiple sustainability objectives must be considered and addressed, such as minimizing the life-cycle costs (LCCs), life-cycle GHGs (LCGHGs), and life-cycle air pollution externality costs (LCAPECs) of trucks while composing a truck fleet. This study proposes a hybrid life-cycle assessment-based robust Pareto optimal approach to developing a HDT fleet mix, accounting for the sector-specific average payloads of 5 U.S. economic sectors. The results of this study indicate that battery-electric, hybrid, and diesel HDTs make up most of the fleet mixes in the studied sectors in order to optimize their environmental, economic, and social impacts. It is therefore concluded that, given the relevant objectives and constraints, the current techno-economic circumstances in the U.S. and current forms of electricity generation should both be improved in order for HDT fleet mixes to achieve greenhouse gas emission reductions of 30% or greater. The findings of the study will support decision-making processes by public and private organizations and help them to develop environmentally, economically, and socially optimized fleet mixes for their operations.

This research aims to provide important insights regarding the environmental and socioeconomic impacts of the world’s largest food producing countries based on four sustainability metrics: energy use, carbon footprint, value-added and compensation of employees by low, medium and high-skill groups. World Input-Output Database is used as a detailed and intercountry and sector economic database. To compare the results between global databases, Eora and EXIOBASE are also used for comparative analysis. Three statistical analysis techniques such as Mann-Kendal trend test, matching index and k-means clustering algorithm are applied to provide a further insight from the analysis. The results are presented for three categories: regional on-site, regional supply chain, and global supply chain. The agriculture industry has the largest environmental footprints in food supply chains. Based on the Mann-Kendall trend test, there is a statistically significant trend in carbon, energy, and employment indicators. The maximum value of the matching–index of the overall impact (0.92) is achieved between the EXIOBASE and WIOD databases. China and USA are positioned in different clusters based on total sustainability performance when using different MRIO databases.

Today, increasing levels of water demand become a particularly serious challenge for many countries, especially since water is an essential element for production of transportation fuels. Unfortunately, no research efforts as of now have been directed specifically toward understanding the fundamental relationship between the adoption of electric vehicles (EVs) and water demand. This research aims to fill this knowledge gap by analyzing the water consumption and withdrawal impacts resulting from the increased usage of alternative vehicle technologies in the United States. 5 vehicle types - Internal Combustion Vehicles (ICVs), Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEV20, PHEV40) and Battery Electric Vehicles (BEVs) - are analyzed across 50 U.S. states with 3 different electricity generation mix profiles: the state-based average electricity generation mix, the state-based marginal electricity generation mix, and a hypothetical electricity generation mix consisting entirely of solar-powered charging stations. The well-to-wheel (WTW) life cycle analysis is used for the water footprint calculations. In worst case, BEVs may consume up to 70 times more water than ICVs. BEVs with solar charging have the lowest levels of water consumption and withdrawal and can reduce transportation water footprint by up to 97%. In most of the states, the marginal electricity generation mix has higher water consumption and withdrawal values than those of the average electricity generation mix. In particular, the authors suggest the use of BEVs with solar charging for states with the highest water-stressed areas (California (CA), Arizona (AZ), Nevada (NV), Florida (FL), etc.), and recommend the inclusion of incentives by federal and state governments for these states. ? 2018 Elsevier Ltd U.S. Department of Transportation U.S. Department of Transportation Scopus

When operated at low speeds, electric and hybrid vehicles have created pedestrian safety concerns in congested areas of various city centers, because these vehicles have relatively silent engines compared to those of internal combustion engine vehicles, resulting in safety issues for pedestrians and cyclists due to the lack of engine noise to warn them of an oncoming electric or hybrid vehicle. However, the driver behavior characteristics have also been considered in many studies, and the high end-prices of electric vehicles indicate that electric vehicle drivers tend to have a higher prosperity index and are more likely to receive a better education, making them more alert while driving and more likely to obey traffic rules. In this paper, the positive and negative factors associated with electric vehicle adoption and the subsequent effects on pedestrian traffic safety are investigated using an agent-based modeling approach, in which a traffic micro-simulation of a real intersection is simulated in 3D using AnyLogic software. First, the interacting agents and dynamic parameters are defined in the agent-based model. Next, a 3D intersection environment is created to integrate the agent-based model into a visual simulation, where the simulation records the number of near-crashes occurring in certain pedestrian crossings throughout the virtual time duration of a year. A sensitivity analysis is also carried out with 9000 subsequent simulations performed in a supercomputer to account for the variation in dynamic parameters (ambient sound level, vehicle sound level, and ambient illumination). According to the analysis, electric vehicles have a 30% higher pedestrian traffic safety risk than internal combustion engine vehicles under high ambient sound levels. At low ambient sound levels, however, electric vehicles have only a 10% higher safety risk for pedestrians. Low levels of ambient illumination also increase the number of pedestrians involved in near-crashes for both electric vehicles and combustion engine vehicles.

Refuse collection trucks, due to the frequent stop-and-go nature of their operation, have considerably lower fuel economy and larger amounts of tailpipe emissions. In this study environmental burdens of refuse collection vehicles, including diesel, compressed natural gas, hydraulic hybrid, and plug-in battery electric powered trucks are analyzed. A hybrid life cycle assessment consisting of an environmentally extended input–output analysis and a process-based analysis is conducted to evaluate the life cycle carbon footprints and energy consumption levels of different types of refuse collection trucks. To comprehensively reflect the real-world performance or impacts of the operation of refuse collection trucks, a Monte Carlo Simulation is used to take into account the inherent uncertainties associated with each of the key parameters in this analysis. In addition, the influence of the U.S.’s various regional electric power mixes on the upstream emissions of battery electric trucks is also considered, so as to explore whether or not each particular region is suitable for the adoption of electric trucks. The results indicate that both the all-electric and the CNG refuse trucks generate approximately 1200 tons of GHG emissions over their respective life cycles, while the GHG emissions of the diesel truck amount to slightly less than 1000 tons. The hydraulic hybrid truck demonstrates the best overall environmental performance, while the CNG truck has significant impacts in terms of energy consumption (more than 25 trillion joules). In addition, the regional analysis indicates that the electricity source(s) available in any given region are the primary deterministic factor for the performance of an all-electric truck.

The purpose of Corporate Average Fuel Economy (CAFE) Standards is to enhance the fuel efficiency of passenger vehicles in the United States. Although these standards had been set constant for years, the National Highway Traffic Safety Administration (NHTSA) set increasing CAFE standards in 2011 based on vehicle's footprint, requiring vehicle manufacturers to improve the fuel economy of the vehicles they produce. This resulting improvement in vehicle fuel economy is likely to influence consumers’ decisions regarding new vehicle purchases, while the stringent CAFE standards are also likely to affect manufacturers’ production costs and benefits. In addition, the government provides various incentives to support the adoption of alternative fuel vehicles (AFVs), including electric vehicles (EVs), which in turn will likewise influences consumers’ decisions regarding purchasing a new vehicle. An agent-based model is developed in this paper to estimate the potential future market shares of EVs considering the existing inherent uncertainties under different policy scenarios, including the footprint-based CAFE regulation. The results show that, if implemented effectively in conjunction with the available government incentives, the CAFE regulation can accelerate EV market penetration and help the U.S. to move away from conventional vehicles, thus reducing fossil fuel dependency.

Sport utility vehicles typically have lower fuel economy due to their high curb weights and payload capacities as well as their potential to cause serious environmental impacts. In light of this fact, a life cycle assessment is carried out in this study to assess their cradle-to-grave environmental impacts for life cycle phases ranging from manufacturing to end-of-life recycling. A hybrid economic input-output life cycle assessment (EIO-LCA) method is used in this research paper to estimate the environmental impacts (greenhouse gas emissions, energy consumption, and water withdrawal) of sport utility vehicles. This life cycle assessment is also supplemented with a sensitivity analysis, using a Monte Carlo simulation to estimate the possible ranges for total mileage of operation and fuel economy, and to account for the sensitivity of the EIO-LCA output. The operation phase is the major contributor to the overall life cycle impact of sport utility vehicles in each fuel/power category. Furthermore, among the selected vehicles in this study, the battery electric vehicle has the lowest greenhouse gas emissions (77.2 tonnes) and the lowest energy consumption (1046.8 GJ) even though the environmental impact indicators for the battery manufacturing process are significantly large. The plug-in hybrid vehicle, on the other hand, demonstrates an optimal performance between energy use and water withdrawal (1172.9 GJ of energy consumption and 1370 kgal of water withdrawal). In addition, all of the fuel-powered vehicles demonstrated similar environmental performances in terms of greenhouse gas emissions, which ranged between 100 and 110 tonnes, but the hydrogen fuel cell vehicle had a significantly large water withdrawal (2253.2 kgal). Since the majority of the overall impact stems from the operation of the vehicle in question, their complete elimination of tailpipe emissions and their high energy efficiency levels make battery electric vehicles a viable green option for sport utility vehicles. However, there are certain uncertainties beyond the scope of this study that can be considered in future studies to improve upon this assessment, including (but not limited to) regional differences in source of electricity generation and socio-economic impacts.

This study explores suitability of battery electric vehicles in the United States by considering their potential market share and operations costs as well as the state-specific variations in electricity generation profiles, given current government policies and the social acceptability of the technology. A performance assessment is developed to compare each state and identify major policy efforts that are needed to increase the environmental and economic competitiveness of electric vehicles. A novel multi-criteria decision-support framework, integrating Life Cycle Assessment, Data Envelopment Analysis, and Agent Based Modeling, is developed. To this end, the environmental and economic impacts of battery electric vehicles are calculated based on three scenarios: an average electricity generation mix, a marginal electricity generation mix, and a solely renewable energy mix with 100% solar. The states are classified, each requiring different policy strategies, in accordance with their performance scores. The results provide important insights for advancing transportation policies and a novel framework for multi-criteria decision-making in the future analyses.

The integration of wind energy in the electricity sector and the adoption of electric vehicles in the transportation sector both have the potential to significantly reduce greenhouse gas emissions individually as well as in tandem with Vehicle-to-Grid technology. This study aims to evaluate the greenhouse gas emission savings of mitigating intermittency resulting from the introduction of wind power through Vehicle-to-Grid technologies, as well as the extent to which the marginal electricity consumption from charging an electric vehicle fleet may weaken this overall environmental benefit. To this end, the comparisons are conducted in seven independent system operator regions. The results indicate that, in most cases, the emission savings of a combination of wind power and Vehicle-to-Grid technology outweighs the additional emissions from marginal electricity generation for electric vehicles. In addition, the fluctuations in newly-integrated wind power could be balanced in the future using EVs and V2G technology, provided that a moderate portion of EV owners is willing to provide V2G services. On the other hand, such a combination is not favorable if the Vehicle-to-Grid service participation rate is less than 5% of all electric vehicle owners within a particular region.

Attempting to gain insights from how alternative fuel technologies employed in heavy-duty trucks (HDTs) differ with respect to their life-cycle emissions, costs, and externalities presents an important opportunity to develop a more holistic overall analysis of future HDTs. To this end, this study uses a hybrid life-cycle assessment method to analyze and compare alternative fuel-powered Class 8 HDTs. To account for the uncertainty in the data a Monte Carlo simulation is also applied. The HDTs considered in this analysis (biodiesel (B20), compressed natural gas (CNG), hybrid, and BE HDTs) are compared to the diesel HDT (conventional HDT). The results show that BE HDTs outperform all other types of trucks overall, despite their incremental costs and electricity generation-related emissions. Furthermore, if such a BE truck were to run on electricity generated in the Northeast Power Coordinating Council (NPCC) NERC region, fuel-consumption related GHGs emissions from BE HDTs could decrease by as much as 63 percent. It has also been found that, although there is a slight difference between the life-cycle costs (LCCs) of conventional HDTs and CNG-powered HDTs, the latter emits 33% more GHGs than the former. Moreover, this study concludes that CNG trucks yield no improvements in either HDT's life-cycle environmental impacts or LCCs compared to their conventional counterparts. Providing that electricity is generated from renewable energy sources, the use of BE trucks would significantly improve the life-cycle performance of a truck as well as ambient air quality.

International audience; Urban passenger transportation in the U.S. has been heavily dependent on car modes, mainly due to prevailing trends in urban development. However, transportation mode choice studies are currently limited to micro-level and regional-level boundaries, lacking of presenting a complete picture of the issues and the root causes associated with urban passenger transportation choices in the U.S. To this end, further analysis from a system perspective is required to investigate the interdependencies among system parameters more thoroughly, thus revealing the underlying mechanisms contributing or causing the low public transportation use in the U.S. Hence, system dynamics modeling approach is utilized to capture complex causal relationships among the critical system parameters affecting public transportation ridership in the U.S. as well as to identify possible policy areas to improve public transportation ridership rates. Considering the high degree of uncertainties inherent to the problem, multivariate sensitivity analysis is utilized to explore the effectiveness of existing and possible policy implications up to the year 2050 in the terms of their potential to increase transit ridership and locating critical parameters that influences the most on mode choice and emission rates. Transportation mode choice behavior is projected to change slightly and reach up to a maximum of 7.25% of public transportation ridership until 2050. Analysis results reveal that the effects of trip length and rate are by far the most influential factors. Both parameters are 99% sensitive compared to all other factors including the effects of fuel tax policies, federal funds for public transportation, use of alternative green bus technologies, increasing private vehicle occupancy rates, etc. on negative environmental, economic, and social impacts of transportation. This finding highlights how important urban structures are to secure the future of public transportation in the U.S. as the existing urban structures and the shared-idea in the minds of the society about how urban transportation should be (the prevailing paradigm) are the root causes of excessive trip generation and increasing average trip lengths. Thus a paradigm-shift, a radical change in the shared-idea in the minds of the society about existing urban structures, is needed. © 2016 Elsevier Ltd

Most of the global multiregional studies have looked at material footprint (MF) of either a nation or a region. A product-specific MF analysis, particularly when it comes to Alternative Fuel Vehicles (AFVs), carried out using a multiregional input-output (MRIO)-based life-cycle assessment model creates a valuable knowledge contributing to both the global and national efforts to develop cleaner production. The EXIOBASE v.2 is used in order to analyze global life cycle material footprints of five types of passenger vehicles based on 10 metals (ores of iron, bauxite and aluminum, copper, lead, nickel, tin, uranium and thorium, zinc, precious metal, and other metals) and 9 minerals (chemical and fertilizer materials, clays and kaolin, limestone, gypsum, chalk, dolomite, salt, slate, other industrial minerals, building stones, gravel and sand, and other construction minerals). The novelty of this research is to develop a MRIO-based life cycle assessment approach, utilized for estimating the material footprint of each vehicle alternative considering regional and global supply chains. The results show that the manufacturing phase dominates the life-cycle material footprints of vehicles. The study concludes that the alternative fuel vehicles considered in this study have larger material footprint compared to conventional vehicles under all circumstances assumed by the study. The findings showed that 63% of all material footprints related to entire life cycle of electric vehicles are found in the U.S. territorial boundary. Battery manufacturing places a huge burden on the material footprint of electric vehicles, accounting for over 65% of the direct impacts, and more than half of the total material footprint under the current techno-economic circumstances. The sources of supply of raw materials that are critical to deployment of alternative fuel vehicles should be diversified if the U.S. is to safeguard the sustainable future of the transportation sector.

As part of sustainable urban planning, the potential of public transportation modes has been studied to reduce CO2 emissions and energy consumption and to increase roadway safety. Increasing public transportation ridership shares compared to drive alone transportation mode would therefore be a giant step toward more environmentally friendly and stress-free cities, and so this study aims to propose possible public transportation policies to be adopted by policy makers or urban planners. Although public transportation is one of the sub-sections of urban planning, it has a wide variety of aspects that affect local societies and the environment, so a system dynamics approach is used to model and simulate the most realistic and practical CO2 mitigation scenarios for U.S. cities by adopting public transportation policies for future years. Based on historical data and applicable model validation processes, the behavior of the U.S. commuters' transportation mode choice and the potential of transit transportation to mitigate CO2 emissions are both forecasted for future years up to 2050 under several possible policy scenarios. The results indicate that, in order to decrease fuel consumption and CO2 emission trends, marginal and ambitious scenarios should be implemented. For instance, increasing public transportation ridership by 9% has the potential to reduce CO2 emissions by 766,000 tonnes annually in 2050, whereas a 25% increase in ridership could potentially reduce cumulative CO2 emissions by 61.3 million tonnes.

Parcel delivery trucks, due to their intensive stop-and-go operational patterns, have relative lower fuel efficiency and higher environmental impacts to urban areas. The adoption of alternative fuel trucks may mitigate the environmental impacts, however, the first cost of these trucks is higher than those of traditional diesel trucks. To this end, based on environmental, social, and economic indicators, a model that provides optimized solutions for a fleet consists of 30 commercial delivery trucks is studied in this paper. An economic input–output based hybrid life cycle assessment is performed in conjunction with Multi-Objective Linear Programming to evaluate various delivery truck fleet combinations and to provide a comprehensive analysis of fleet performance. Furthermore, six specific scenarios, representing different utilization levels and fuel economy levels, have been taken into consideration to reflect the sensitivity of fleet performance with respect to real word operation. The performances of the vehicles are evaluated based on three criteria: economic aspects, environmental concerns, and public health impacts. Furthermore, the results are considered from two perspectives, the first being a case in which no constraints are taken into account, and the second case being considered under tailpipe emission constraints. The results indicate that when fuel economy is high and annual mileage is low, current diesel trucks are able to fulfill the requirement in both cases with reasonably low costs. Conversely, in scenarios with low fuel economy and high utilization levels, hybrid vehicles are preferred. However, the optimization model selects more electric trucks when tailpipe emission constraints are accounted.

95 percent of American households own at least one car, and with the help of newly introduced Vehicle to Home technology, it is now possible for buildings, vehicles, and renewable energy sources to work together as a single techno-ecological system to meet the requirements of a net zero energy building. Vehicle to Home technologies use idle electric vehicle battery power as a grid storage tool to mitigate fluctuations from renewable electric power sources and to help supply backup power in the event of an emergency. This study aims to investigate the role of Vehicle to Home technology in satisfying the energy requirements for a net zero energy building. For this purpose, an optimization analysis is performed first to select the best design alternatives for an energy-efficient building under the relevant economic and environmental constraints. Next, solar photovoltaic sources are used to supply the building’s remaining energy demand and thereby minimize the building’s grid reliance. Finally, Vehicle to Home technology is coupled with the renewable energy source as a substitute for power from the grid. The results indicate that, with the help of this system, it is possible to not only lower the monetary value of the required grid electricity for such a building to zero in certain months of the year but also to earn money to compensate for the installation costs of solar panels and other technologies necessary for a net zero energy building, while grid electricity consumption for the rest of the year can still be effectively reduced by up to 68% compared to that of a conventional building design.

Alternative vehicle technologies have a great potential to minimize the transportation-related environmental impacts, reduce the reliance of the U.S. on imported petroleum, and increase energy security. However, they introduce new uncertainties related to their environmental, economic, and social impacts and certain challenges for widespread adoption. In this study, a novel method, uncertainty-embedded dynamic life cycle sustainability assessment framework, is developed to address both methodological challenges and uncertainties in transportation sustainability research. The proposed approach provides a more comprehensive, system-based sustainability assessment framework by capturing the dynamic relations among the parameters within the U.S. transportation system as a whole with respect to its environmental, social, and economic impacts. Using multivariate uncertainty analysis, likelihood of the impact reduction potentials of different vehicle types, as well as the behavioral limits of the sustainability potentials of each vehicle type are analyzed. Seven sustainability impact categories are dynamically quantified for four different vehicle types (internal combustion, hybrid, plug-in hybrid, and battery electric vehicles) from 2015 to 2050. Although impacts of electric vehicles have the largest uncertainty, they are expected (90% confidence) to be the best alternative in long-term for reducing human health impacts and air pollution from transportation. While results based on deterministic (average) values indicate that electric vehicles have greater potential of reducing greenhouse gas emissions, plug-in hybrid vehicles have the largest potential according to the results with 90% confidence interval.

Intuitionistic Fuzzy Set theory can be used in conjunction with environmentally extended input–output based life cycle assessment (EE-IO-LCA) models to help decision makers to address the inherent vagueness and uncertainties in certain sustainable energy planning problems. In this regard, the EE-IO-LCA model can be combined with an intuitionistic fuzzy set theory for a multi-criteria decision making (MCDM) application with a set of environmental and socio-economic indicators. To achieve this goal, this study proposes the use of the Technique for Order of Preference by Similarity to Ideal Solution method to select the best wind energy alternative for a double layer MCDM problem, which requires expert judgments to simultaneously apply appropriate weighting to each life cycle phase and sustainability indicator to be considered. The novelty of this research is to propose a generic 9-step fuzzy MCDM method to solve sustainable energy decision-making problems using a combination of three different techniques: (1) an intuitionistic fuzzy entropy method to identify the individual importance of phases and criteria; (2) an IFWGA operator to establish a sub-decision matrix with the weights applied to all relevant attributes; and (3) an IFWAA operator to build a super-decision matrix with the weights applied to all of the life-cycle phases considered. This proposed method is then applied as a case study for sustainable energy planning, specifically for the selection of V80 and V90 onshore and offshore wind turbines to be installed in the United States. It is strongly believed that this methodology will provide a vital guidance for LCA practitioners in the future for selecting the best possible energy alternative under an uncertain decision-making scenario.

Due to frequent stop-and-go operation and long idling periods when driving in congested urban areas, the electrification of commercial delivery trucks has become an interesting topic nationwide. In this study, environmental impacts of various alternative delivery trucks including battery electric, diesel, diesel-electric hybrid, and compressed natural gas trucks are analyzed. A novel life cycle assessment method, an environmentally-extended multi-region input-output analysis, is utilized to calculate energy and carbon footprints throughout the supply chain of alternative delivery trucks. The uncertainties due to fuel consumption or other key parameter variations in real life, data ranges are taken into consideration using a Monte Carlo simulation. Furthermore, variations in regional electricity mix greenhouse gas emission are also considered to present a region-specific assessment for each vehicle type. According to the analysis results, although the battery electric delivery trucks have zero tailpipe emission, electric trucks are not expected to have lower environmental impacts compared to other alternatives. On average, the electric trucks have slightly more greenhouse emissions and energy consumption than those of other trucks. The regional analysis also indicates that the percentage of cleaner power sources in the electricity mix plays an important role in the life cycle greenhouse gas emission impacts of electric trucks.

The uncertainty in the results of input–output-based life cycle assessment models makes the sustainability performance assessment and ranking a challenging task. Therefore, introducing a new approach, fuzzy data envelopment analysis, is critical; since such a method could make it possible to integrate the uncertainty in the results of the life cycle assessment models into the decision-making for sustainability benchmarking and ranking. In this paper, a fuzzy data envelopment analysis model was coupled with an input–output-based life cycle assessment approach to perform the sustainability performance assessment of the 33 food manufacturing sectors in the United States. Seven environmental impact categories were considered the inputs and the total production amounts were identified as the output category, where each food manufacturing sector was considered a decision-making unit. To apply the proposed approach, the life cycle assessment results were formulated as fuzzy crisp valued-intervals and integrated with fuzzy data envelopment analysis model, thus, sustainability performance indices were quantified. Results indicated that majority (31 out of 33) of the food manufacturing sectors were not found to be efficient, where the overall sustainability performance scores ranged between 0.21 and 1.00 (efficient), and the average sustainability performance was found to be 0.66. To validate the current study's findings, a comparative analysis with the results of a previous work was also performed. The major contribution of the proposed framework is that the effects of uncertainty associated with input–output-based life cycle assessment approaches can be successfully tackled with the proposed Fuzzy DEA framework which can have a great area of application in research and business organizations that use with eco-efficiency as a sustainability performance metric.

Concerns regarding the fuel costs and climate change impacts associated with petroleum combustion are among the main driving factors for the adoption of electric vehicles. Future commercial delivery truck fleets may include Battery Electric Vehicles (BEVs) and Extended Range Electric Vehicles (EREVs); in addition to savings on fuel and maintenance costs, the introduction of these grid accessible electric vehicles will also provide fleet owners with possible Vehicle to Grid (V2G) opportunities. This study investigates the potential net present revenues and greenhouse gas (GHG) emission mitigation of V2G regulation services provided by electric trucks in a typical fleet. The total cost of ownership and the life-cycle GHG emissions of electric trucks are also analyzed and compared to those of traditional diesel trucks. To account for uncertainties, possible ranges for key parameters are considered instead of only considering fixed single data values for each parameter. The results of this research indicate that providing V2G regulation services for electric trucks could yield considerable additional revenues ($20,000–50,000) and significant GHG emission savings (approximately 300 ton CO2) compared to conventional diesel trucks.

This research involves two novel elements to advance the body of knowledge in existing sustainability assessment frameworks for alternative vehicle technologies. First, we developed an input–output based hybrid life cycle sustainability assessment model using several macro-level social, economic, and environmental indicators, taking into consideration the manufacturing of vehicles and batteries, operation, and end-of-life phases. Second, the results of a hybrid life cycle sustainability assessment for different conventional and alternative vehicles technologies (internal combustion electric vehicles, hybrid electric vehicles, plug-in-hybrid electric vehicles, and battery electric vehicles) are incorporated into the Technique for Order-Preference by Similarity to Ideal Solution and Intuitionistic Fuzzy Sets. Two policy scenarios are considered in this analysis, with Scenario 1 being based on existing electric power infrastructure in the U.S. with no additional infrastructure requirements, while Scenario 2 is an extreme scenario in which the electricity to power electric vehicles is generated exclusively via solar charging stations. The Intuitionistic Fuzzy Multi-Criteria Decision Making and Technique for Order Preference by Similarity to Ideal Solution methods are then utilized to rank the life cycle sustainability performance of alternative passenger vehicles. Furthermore, since expert judgments play an important role in determining the relative performance of alternative vehicle technologies, a sustainability triangle analysis is also presented to show how the weighting applied to each dimension affects the selection of different alternatives. The results indicate that hybrid and plug-in hybrid electric vehicles are the best alternatives for both Scenarios 1 and 2 when all of the indicators are considered. On the other hand, the ranking of vehicles changes significantly when each of the environmental, economic, and social indicators are evaluated individually. This proposed method can be a useful decision making platform for decision-makers to develop more effective policies and guide the offering of incentives to the right domains for sustainable transportation.

Vehicle to Grid technologies utilize idle EV battery power as a grid storage tool to meet fluctuating electric power demands. Vehicle to Grid systems are promising substitutes for traditional gas turbine generators, which are relatively inefficient and have high emissions impacts. The purpose of this study is to predict the future net revenue and life cycle emissions savings of Vehicle to Grid technologies for use in ancillary (regulation) services on a regional basis in the United States. In this paper, the emissions savings and net revenue calculations are conducted with respect to five different Independent System Operator/Regional Transmission Organization regions, after which future EV market penetration rates are predicted using an Agent-Based Model designed to account for various uncertainties, including regulation service payments, regulation signal features, and battery degradation. Finally, the concept of Exploratory Modeling and Analysis is used to estimate the future net revenue and emissions savings of integrating Vehicle to Grid technology into the grid, considering the inherent uncertainties of the system. The results indicate that, for a single vehicle, the net revenue of Vehicle to Grid services is highest for the New York region, which is approximately $42,000 per vehicle on average. However, the PJM region has an approximately $97 million overall net revenue potential, given the 38,200 Vehicle to Grid-service-available electric vehicles estimated to be on the road in the future in the PJM region, which is the highest among the studied regions.

Reflective Cracking (RC) has been a daunting challenge in pavement maintenance and rehabilitation (M&R), yet, still, after several decades of research, no exclusive solution prevails. Moreover, RC mitigation methods have shown significant variation in in situ performance. Therefore, a technique tailored to select an effective RC mitigation method is essential for the success of pavement M&R. In this study, a life cycle cost (LCC) and multi-criteria decision-making (MCDM) analyses were conducted to evaluate the effectiveness of currently available RC mitigation methods and to select the optimal method for an asphalt concrete overlay above flexible pavements. The MCDM includes three components: LCC, performance, and materials (recyclability). These criteria determine the selection ranking of each RC mitigation method. In addition, the effects of the priority level including cost, performance, and recyclability on the final decision were evaluated by conducting a series of sensitivity analysis under ...

The Shin-Meishin Expressway is being constructed from Nagoya to Kobe in Japan, and the section from Takatsuki to Kobe is currently under construction as a viable alternative route as opposed to the Meishin Expressway. However, highway construction is usually considered destructive to the natural environment due to the deforestation required during construction, as well as subsequent increases in CO2 emissions upon completion of new highways as new traffic is brought to the area. By applying a system dynamics methodology with respect to the Meishin and Shin-Meishin Expressways, this study analyzes two major effects of new highway construction (CO2 absorption and CO2 emissions) as well as the potential influence of felling, replanting, and/or maintaining the surrounding forest area on the overall CO2 absorption capacity, net CO2 emissions, and total CO2 stock in the analyzed construction area. Regarding CO2 absorption, it was found that, if the portion of the forest felled during construction is very old, new highway construction can help to recover the lost CO2 absorption capacity by planting new trees along the highway, even if the area of planting is much smaller than the area of felling trees. As for CO2 emissions, it was found that mitigating traffic congestion and decreasing the required driving distance can reduce CO2 emissions from the highway(s) in question. Therefore, this study concludes that new highway construction does not always harm the environment regarding CO2 pollution, as the harmful impacts commonly associated with highway construction can be mitigated with other technological and/or ecological mitigation methods.

Quantitative life cycle sustainable assessment requires a complex and multidimensional understanding, which cannot be fully covered by the current portfolio of reductionist-oriented tools. Therefore, there is a dire need on a new generation of modeling tools and approaches that can quantitatively assess the economic, social, and environmental dimensions of sustainability in an integrated way. To this end, this research aims to present a practical and novel approach for (1) broadening the existing life cycle sustainability assessment (LCSA) framework by considering macrolevel environmental, economic, and social impacts (termed as the triple bottom line), simultaneously, (2) deepening the existing LCSA framework by capturing the complex dynamic relationships between social, environmental, and economic indicators through causal loop modeling, (3) understanding the dynamic complexity of transportation sustainability for the triple bottom line impacts of alternative vehicles, and finally (4) investigating the impacts of various vehicle-specific scenarios as a novel approach for selection of a macrolevel functional unit considering all of the complex interactions in the environmental, social, and economic aspects. To alleviate these research objectives, we presented a novel methodology to quantify macrolevel social, economic, and environmental impacts of passenger vehicles from an integrated system analysis perspective. An integrated dynamic LCSA model is utilized to analyze the environmental, economic, and social life cycle impact as well as life cycle cost of alternative vehicles in the USA. System dynamics modeling is developed to simulate the US passenger transportation system and its interactions with economy, the environment, and society. Analysis covers manufacturing and operation phase impacts of internal combustion vehicles (ICVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). In total, seven macrolevel indicators are selected; global warming potential, particulate matter formation, photochemical oxidant formation, vehicle ownership cost, contribution to gross domestic product, employment generation, and human health impacts. Additionally, contribution of vehicle choices to global atmospheric temperature rise and public welfare is investigated. BEVs are found to be a better alternative for most of sustainability impact categories. While some of the benefits such as contribution to employment and GDP, CO2 emission reduction potential of BEVs become greater toward 2050, other sustainability indicators including vehicle ownership cost and human health impacts of BEVs are higher than the other vehicle types on 2010s and 2020s. While the impact shares of manufacturing and operation phases are similar in the early years of 2010s, the contribution of manufacturing phase becomes higher as the vehicle performances increase toward 2050. Analysis results revealed that the US transportation sector, alone, cannot reduce the rapidly increasing atmospheric temperature and the negative impacts of the global climate change, even though the entire fleet is replaced with BEVs. Reducing the atmospheric climate change requires much more ambitious targets and international collaborative efforts. The use of different vehicle types has a small impact on public welfare, which is a function of income, education, and life expectancy indexes. The authors strongly recommend that the dynamic complex and mutual interactions between sustainability indicators should be considered for the future LCSA framework. This approach will be critical to deepen the existing LCSA framework and to go beyond the current LCSA understanding, which provide a snapshot analysis with an isolated view of all pillars of sustainability. Overall, this research is a first empirical study and an important attempt toward developing integrated and dynamic LCSA framework for sustainable transportation research.

One of the most promising strategies recommended for increasing energy security and for mitigating transportation sector emissions is to support alternative fuel technologies, including electric vehicles. However, there is a considerable amount of uncertainty regarding the market penetration of electric vehicles that must be accounted for in order to achieve the current market share goals. This paper aims to address these inherent uncertainties and to identify the possible market share of electric vehicles in the United States for the year 2030, using the developed Electric Vehicle Regional Market Penetration tool. First, considering their respective inherent uncertainties, the vehicle attributes are evaluated for different vehicle types, including internal combustion engine, gasoline hybrid, and three different electric vehicle types. In addition, an agent-based model is developed to identify the market shares of each of the studied vehicles. Finally, market share uncertainties are modeled using the Exploratory Modeling and Analysis approach. The government subsidies play a vital role in the market adoption of electric vehicle and, when combined with the word-of-mouth effect, may achieve electric vehicle market share of up to 30% of new sales in 2030 on average, with all-electric vehicles having the highest market share among the electric vehicle options.

This research aims to advance the existing sustainability assessment framework for alternative passenger cars with a combination of life-cycle sustainability assessment and multi-criteria decision-making. To this end, sixteen macro-level sustainability impacts are evaluated for seven different vehicle types: internal combustion vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles with all-electric ranges of 16, 32, 48, and 64 km of electric powered drive, and battery electric vehicles. Additionally, two battery charging scenarios are considered in this analysis with respect to plug-in hybrid electric vehicles and battery electric vehicles; Scenario 1 is based on existing electric power infrastructure in the U.S., while Scenario 2 is an extreme scenario in which electricity to power battery electric vehicles and plug-in hybrid electric vehicles is generated entirely via solar charging stations. In this study, optimal vehicle distributions are calculated based on the environmental, social, and economic impacts of all vehicle types for each scenario. Various distributions are presented in accordance with the relative importance assigned to each indicator, with different weighting scenarios applied to account for variability in decision-makers' priorities, such as the assignment of higher weights to socio-economic indicators (e.g. maximizing employment) and lower weights to environmental indicators (e.g. minimizing greenhouse gas emissions). In a balanced weighting case (i.e. when environmental and socio-economic indicators have equal importance) under Scenario 1, hybrid electric vehicles have the largest fleet share, comprising 91% of the optimal U.S. passenger car fleet, while internal combustion vehicles dominate the optimal fleet with 99.5% of the optimal fleet share when only socio-economic indicators are given priority. On the other hand, in a balanced weighting case under Scenario 2, the optimal U.S. passenger car fleet consists entirely (100%) of plug-in hybrid electric vehicles with 16 km of all-electric range. In the majority of the considered weighting scenarios, battery electric vehicles were not given any share of the optimal vehicle fleet. The proposed framework can be used as a practical decision-making platform when deciding which vehicle type to promote given each vehicle type's respective environmental, social, and economic impacts. Considering that decision makers are often highly influenced by the “silo effect”, i.e. a lack of communication among different agencies and departments (national or international), the proposed framework provides a holistic system-based approach to minimize the silo effect and can enhance the efficiency of future inter/cross/trans-disciplinary works. Furthermore, the outcomes of this study can pave the way for advancement in the state-of-the-art and state-of-the-practice of current sustainability research.

The vehicle-to-grid system is an approach utilizing the idle battery capacity of electric vehicles while they are parked to provide supplementary energy to the power grid. As electrification continues in light duty vehicle fleets, the application of vehicle-to-grid systems for commercial delivery truck fleets can provide extra revenue for fleet owners, and also has significant potential for reducing greenhouse gas emissions from the electricity generation sector. In this study, an economic input–output based hybrid life cycle assessment is conducted to analyze the potential greenhouse gas emissions emission savings from the use of the vehicle-to-grid system, as well as the possible emission impacts caused by battery degradation. A Monte Carlo simulation was performed to address the uncertainties that lie in the electricity exchange amount of the vehicle-to-grid service as well as the battery life of the electric vehicles. The results of this study showed that extended range electric vehicles and battery electric vehicles are both viable regulation service providers for saving greenhouse gas emissions from electricity generation if the battery wear-out from regulation services is assumed to be minimal, but the vehicle-to-grid system becomes less attractive at higher battery degradation levels.

Public transportation is one of the most promising transportation modes to reduce the environmental emissions of the transportation sector in the U.S. In order to mitigate the environmental impacts brought by the transit bus system, new energy buses are introduced into the vehicle market. The goal of this study is to find an optimal bus fleet combination for different driving conditions to minimize life cycle cost, greenhouse gas emissions, and conventional air pollutant emission impacts. For this purpose, a Multi-Objective Linear Programming approach is used to select the optimum bus fleet combinations. Given different weight scenarios, this method could effectively provide solutions for decision makers with various budget constraints or emission reduction requirements. The results indicate that in heavily congested driving cycles such as the Manhattan area, the battery electric bus is the dominant vehicle type, while the hybrid bus has more balanced performances in most scenarios because of its lower initial investment comparing to battery electric buses. Petroleum powered buses have seldom been selected by the model. The trade-off analysis shows that the overall greenhouse gas impact performance is sensitive to the life cycle cost after certain points, which could provide valuable information for the bus fleet combination planning.

Recent advances in sustainable construction and development methods have allowed urban developers to significantly reduce urban environmental problems caused by buildings such as increased energy consumption and greenhouse gas emissions, urban runoff, and the urban heat island effect. Newer forms of green infrastructure, including alternative roofing (green roofs, solar BIPV systems, etc.), have emerged as potential solutions to these urban environmental problems. However, a number of practical and socioeconomic factors are currently limiting their market penetration to a marginal share. This study employs a System Dynamics modeling approach to analyze these hindrances in Orlando’s alternative roofing market, as well as the potential of societal, economic, and developmental investments in different alternative roofing markets to address these issues. Conventional roofs have been found to remain predominant in Orlando’s roofing market under all policy scenarios, with solar roof system being the most mature out of the three alternative roofing markets considered, and consequently has the most potential for future market penetration. It has been found that the studied alternative roof systems (ARSs) reach only a marginal market share in the year 2040, and for this reason, could not result in significant improvements in the studied impact categories at the city level. However, the studied ARSs yield important reductions in all of the impact categories when analyzed at the building level. The results of this study indicate that the alternative roofing market has significant growth potential, but further research, development, and improvement will be essential to achieve more effective, long-term benefits in Orlando.

Adoption of alternative vehicle technologies such as electric vehicles (EVs), plug-in hybrid electric vehicles (PHEVs), and hybrid electric vehicles (HEVs) have the potential of reducing some of the environmental impacts and reducing oil-dependency of the U.S transportation sector. However, this potential depends on the regional driving patterns and the source of the electricity generation to power PHEVs and EVs. In this study, state-specific electricity generation mix scenarios and driving patterns in Alabama, Florida, and Hawaii are considered to calculate regional impacts associated with alternative vehicle technologies (HEVs, PHEVs, EVs) compared to internal combustion vehicles (ICVs). Three electricity generation mix scenario are evaluated, which are namely; average electricity generation mix, marginal electricity generation mix, and 100% solar electricity generation mix. Well-to-wheel carbon, energy, and water footprint of these vehicles are quantified for each state and potential environmental reductions are evaluated. According to comparative evaluation for the proposed scenarios, shifting to low carbon, energy, and water intensive electricity generation mix by utilization of solar energy is crucial to achieve environmental friendly transportation in the U.S. https://doi.org/10.2991/apte-18.2019.23

The life cycle cost and environmental impacts of electric vehicles are very uncertain, but extremely important for making policy decisions. This study presents a new model, called the Electric Vehicles Regional Optimizer, to model this uncertainty and predict the optimal combination of drivetrains in different U.S. regions for the year 2030. First, the life cycle cost and life cycle environmental emissions of internal combustion engine vehicles, gasoline hybrid electric vehicles, and three different Electric Vehicle types (gasoline plug-in hybrid electric vehicles, gasoline extended range electric vehicle, and all-electric vehicle) are evaluated considering their inherent uncertainties. Then, the environmental damage costs and the water footprint of the studied drivetrains are estimated. Additionally, using an Exploratory Modeling and Analysis method, the uncertainties in the life cycle cost, environmental damage cost, and water footprint of studied vehicle types are modeled for different U.S. electricity grid regions. Finally, an optimization model is coupled with Exploratory Modeling and Analysis to find the ideal combination of different vehicle types in each U.S. region for the year 2030. The findings of this research will help policy makers and transportation planners to prepare our nation's transportation system for the influx of electric vehicles.

Alternative fuel options are gaining popularity in the vehicle market. Adopting alternative fuel options for public transportation compared to passenger vehicles contributes exponentially to reductions in transportation-related environmental impacts. Therefore, this study aims to present total air pollutant emissions and water withdrawal impacts through the lifetime of a transit bus with different fuel options. In consideration of market share and future development trends, diesel, biodiesel, compressed natural gas (CNG), liquefied natural gas (LNG), hybrid (diesel-electric), and battery electric (BE) transit buses are analyzed with an input-output (IO)-based hybrid life cycle assessment (LCA) model. In order to accommodate the sensitivity of total impacts to fuel economy, three commonly used driving cycles are considered: Manhattan, Central Business District (CBD), and Orange County Transit Authority (OCTA). Fuel economy for each of these driving cycles varies over the year with other impacts, so a normal distribution of fuel economy is developed with a Monte Carlo simulation model for each driving cycle and corresponding fuel type. Impacts from a solar panel (photovoltaic, PV) charging scenario and different grid mix scenarios are evaluated and compared to the nation’s average grid mix impacts from energy generation to accommodate the lifetime electricity needs for the BE transit bus. From these results, it was found that the BE transit bus causes significantly low CO2 emissions than diesel and other alternative fuel options, while some of the driving cycles of the hybrid-powered transit bus cause comparable emissions to BE transit bus. On the other hand, lifetime water withdrawal impacts of the diesel and hybrid options are more feasible compared to other options, since electricity generation and natural gas manufacturing are both heavily dependent on water withdrawal. In addition, the North American Electricity Reliability Corporation’s (NERC) regional electricity grid mix impacts on CO2 emissions and water withdrawal are presented for the BE transit bus. As an addition of current literature, LCA of alternative fuel options was performed in this paper for transit buses with the consideration of a wide variety of environmental indicators. Although the results indicate that BE and hybrid-powered buses have less environmental emissions, the US’s dependency on fossil fuel for electricity generation continues to yield significant lifetime impacts on BE transit bus operation. With respect to water withdrawal impacts, we believe that the adoption of BE transit buses will be faster and more environmentally feasible for some NREC regions than for others.

Electric vehicles (EVs), plug-in hybrid electric vehicles (PHEVs), and hybrid electric vehicles (HEVs) are often considered as better options in terms of greenhouse gas emissions and energy consumption compared to internal combustion vehicles. However, making any decision among these vehicle options is not a straightforward process due to temporal and spatial variations, such as the sources of the electricity used and regional driving patterns. In this study, we compared these vehicle options across 50 states, taking into account state-specific average and marginal electricity generation mixes, regional driving patterns, and vehicle and battery manufacturing impacts. Furthermore, a policy scenario proposing the widespread use of solar energy to charge EVs and PHEVs is evaluated. Based on the average electricity generation mix scenario, EVs are found to be least carbon-intensive vehicle option in 24 states, while HEVs are found to be the most energy-efficient option in 45 states. In the marginal electricity mix scenario, widespread adoption of EVs is found to be an unwise strategy given the existing and near-future marginal electricity generation mix. On the other hand, EVs can be superior to other alternatives in terms of energy-consumption, if the required energy to generate 1 kW h of electricity is below 1.25 kW h.

This research evaluated the implementation of transit signal priority (TSP) on a test corridor along International Drive in Orlando, Florida, to see whether the implementation was successful and justified expansion to a regional implementation of TSP for bus tie-ins to the new regional SunRail commuter rail in Central Florida. TSP is a technology that provides preferential treatment to buses at signalized intersections. This research demonstrated the effectiveness of TSP in improving bus corridor travel time in a simulated environment by using real-world data for the International Drive corridor. Evaluation was conducted with microsimulation to compare unconditional and conditional TSP with the no TSP scenario. This evaluation looked at performance metrics (for buses and all vehicles), including average speed profiles, average travel times, average number of stops, and crossing street delay. Different conditional TSP scenarios of activating TSP when a bus is 3 or 5 min behind schedule were considered. The simulation demonstrated that conditional TSP significantly improved bus travel times with little effect on crossing street delays. Unconditional TSP resulted in significant crossing street delays at some intersections with only minor improvement to bus travel time compared with both conditional TSP scenarios. The results also showed that using TSP technology reduced the environmental emissions in the International Drive corridor. With a benefit–cost ratio of 7.92 in the International Drive corridor, conditional TSP 3 min behind schedule was determined to be the most beneficial and practical TSP scenario for real-world implementation at corridor and regional levels.

Purpose In Hot Mix Asphalt (HMA) overlays, the existing cracks in the underlying pavements can propagate upward to the new added overlay and may cause Reflective Cracks (RC). These cracks allow water infiltration to the underlying layers and causes further moisture damage as well as weakening the unbound layers. Over the years, several methods have been developed for mitigating the RCs. This study aims to investigate the current reflective cracking mitigation methods and develop a methodology for the selection of appropriate mitigation technique. The developed model is then applied to a case study in the state of Florida. Method To accomplish this goal, a nationwide literature review was conducted to better understand the current in practice methods in the United States. Moreover, a life cycle cost analysis (LCCA) in five different road types was performed to find the annuity of roadway rehabilitation for each of the mitigation methods. The uncertainty in the LCCA results is represented using Exploratory Modeling and Analysis (EMA) method. Then through a Multi Criteria Decision Making (MCDM) model, a stochastic optimization model was developed to find the appropriate reflective cracking mitigation solution under Florida’s climate and road conditions, based on different cost and performance weights. Results Based on the available data for the state of Florida, the LCCA results indicate that the annuity of maintaining the roadway with Fabrics and ISAC are lower compared to other methods. However, the results of stochastic optimization model reveal that while looking at the performance and cost at the same time, different methods would be more feasible. For instance, while the cost of the used method does not matter at all and only performance matters, STRATA® is more probable to be the appropriate mitigation technique. The findings of this research are critical for decision makers to better understand the most cost-effective mitigation technique in different conditions.

The U.S. final demand categories such as household consumption, private fixed investments, government purchases and investments, and export of goods and services have a wide range of environmental, economic, and social impacts. Analysis of these impacts, termed as the Triple Bottom Line (TBL), stimulated a tremendous interest by policy makers over the last decade. Therefore, current research aims to analyze the TBL of U.S. final demands from a systems perspective. To accomplish this goal, the supply and use tables published by the U.S. Bureau of Economic Analysis are merged with a range of environmental, economic, and social metrics. The results show that household consumption has the largest indirect TBL sustainability impacts compared to other final demand categories with shares that range between 43% and 88%. Industrial sectors including manufacturing, utilities, agriculture, construction, transportation, and mining are generally found to be responsible for the highest impacts for most of the environmental impact categories. Service sectors generally have the highest impacts on the economic and social indicators of sustainability. Analysis results also indicate that while meeting the household demand, agriculture, utilities, and manufacturing sectors have relatively more environmental impacts than their contributions to gross domestic product (GDP), whereas service sectors contribute to GDP with a higher share than their environmental burdens. Furthermore, it is envisioned that significant reductions in environmental footprints of U.S. households can be achieved if environmental policies that aim to reduce the household consumption are also supported with sustainable growth through greener and resource efficient economy.

Energy consumption in residential buildings is one of the major sources of greenhouse gas (GHG) emissions in the U.S. Most of the efforts to minimize these emissions contemplate on construction of new high performance green buildings rather than retrofitting the existing residential building stock, which has the greatest emission reduction potential. In this paper, rapidly increasing GHG emissions trend associated with the U.S. residential building stock is addressed. The objective is to reduce or stabilize the increasing GHG emissions trend as a result of sprawling residential building stock across the country. System Dynamics (SD) is utilized to study the mid and long term impacts of green building related policies on the GHG emissions stock. SD model is built based on stock and flow diagram, which is derived from causal loop diagram that consists of 12 endogenous and 2 exogenous variables and causal relationships. Three important action areas are considered for policy making, namely: high performance green building construction, building retrofitting, and net zero building construction. From the three policy fields, a total of 19 policy strategies (7 single and 12 hybrid) is developed and the impacts of the policies on GHG emissions trend are experimented until 2050. Among the proposed policies, retrofitting-focused policies are found to be more effective on stabilizing the GHG emissions trend compared to the policies related to the construction of new net zero and high performance green buildings. On the other hand, hybrid implementation of policies from the three policy fields provided the greatest reduction in the GHG emissions trend. One of the most important outcomes of this study is that focusing on increasing the construction rate of net zero or high performance green buildings alone does not help with stabilizing/reducing the GHG emissions trend unless the retrofitting of existing residential building stock is seriously considered as a strict policy along with green building policies. Analysis results also revealed that the residential green building movement itself is found to be far from being the driver policy in stabilizing the rapidly increasing GHG emissions trend in the long run.

According to the U.S. Department of Energy’s wind energy scenario, 20% share of the U.S. energy portfolio is to come in from wind power plants by the year 2030. This research aims to quantify the direct and supply chain related indirect environmental impacts of onshore and offshore wind energy technologies in the United States. To accomplish this goal, a hybrid life cycle assessment (LCA) model is developed. On average, offshore wind turbines produce 48% less greenhouse gas emissions per kWh produced electricity than onshore wind turbines. It is also found that the more the capacity of the wind turbine, the less the environmental impact when the turbine generates per kWh electricity.