Search

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

With the increasing concerns related to integration of social and economic dimensions of the sustainability into life cycle assessment (LCA), traditional LCA approach has been transformed into a new concept, which is called as life cycle sustainability assessment (LCSA). This study aims to contribute the existing LCSA framework by integrating several social and economic indicators to demonstrate the usefulness of input–output modeling on quantifying sustainability impacts. Additionally, inclusion of all indirect supply chain-related impacts provides an economy-wide analysis and a macro-level LCSA. Current research also aims to identify and outline economic, social, and environmental impacts, termed as triple bottom line (TBL), of the US residential and commercial buildings encompassing building construction, operation, and disposal phases. To achieve this goal, TBL economic input–output based hybrid LCA model is utilized for assessing building sustainability of the US residential and commercial buildings. Residential buildings include single and multi-family structures, while medical buildings, hospitals, special care buildings, office buildings, including financial buildings, multi-merchandise shopping, beverage and food establishments, warehouses, and other commercial structures are classified as commercial buildings according to the US Department of Commerce. In this analysis, 16 macro-level sustainability assessment indicators were chosen and divided into three main categories, namely environmental, social, and economic indicators. Analysis results revealed that construction phase, electricity use, and commuting played a crucial role in much of the sustainability impact categories. The electricity use was the most dominant component of the environmental impacts with more than 50 % of greenhouse gas emissions and energy consumption through all life cycle stages of the US buildings. In addition, construction phase has the largest share in income category with 60 % of the total income generated through residential building’s life cycle. Residential buildings have higher shares in all of the sustainability impact categories due to their relatively higher economic activity and different supply chain characteristics. This paper is an important attempt toward integrating the TBL perspective into LCSA framework. Policymakers can benefit from such approach and quantify macro-level environmental, economic, and social impacts of their policy implications simultaneously. Another important outcome of this study is that focusing only environmental impacts may misguide decision-makers and compromise social and economic benefits while trying to reduce environmental impacts. Hence, instead of focusing on environmental impacts only, this study filled the gap about analyzing sustainability impacts of buildings from a holistic perspective.

Analyzing building related carbon emissions remains as one of the most increasing interests in sustainability research. While majority of carbon footprint studies addressing buildings differ in system boundaries, scopes, GHGs and methodology selected, the increasing number of carbon footprint reporting in response to legal and business demand paved the way for worldwide acceptance and adoption of the Greenhouse Gas Protocol (GHG Protocol) set by the World Resources Institute (WRI) and World Business Council for Sustainable Development (WBCSD). Current research is an important attempt to quantify the carbon footprint of the U.S. residential and commercial buildings in accordance with carbon accounting standards and Scopes set by WRI, in which all possible indirect emissions are also considered. Emissions through the construction, use, and disposal phases were calculated for the benchmark year 2002 by using a comprehensive hybrid economic input–output life cycle analysis. The results indicate that emissions from direct purchases of electricity (Scope 2) with 48% have the highest carbon footprint in the U.S. buildings. Indirect emissions (Scope 3) with 32% are greater than direct emissions (Scope 1) with 20.4%. Commuting is the most influential activity among the Scope 3 emissions with more than 10% of the carbon footprint of the U.S. buildings overall. Construction supply chain is another important contributor to the U.S. building's carbon footprint with 6% share. Use phase emissions are found to be the highest with 91% of the total emissions through all of the life cycle phases of the U.S. buildings.

This study aims to quantify the socio-economic and environmental impacts of producing electricity by wind power plants for the US electricity mix. To accomplish this goal, all direct and supply chain-related impacts of different onshore and offshore wind turbines are quantified using a hybrid economic input-output-based triple bottom line (TBL) life cycle assessment model. Furthermore, considering TBL sustainability implications of each onshore and offshore wind energy technology, a multi-criteria decision-making tool which is coupled with Monte Carlo simulation is utilised to find the optimal choice of onshore and offshore wind energy. The analysis results indicate that V90-3.0 MW wind turbines have lower impacts than V80-3.0 MW for both socio-economic and environmental indicators. The Monte Carlo simulation results reveal that when environmental issues are more important than socio-economic impacts, V90-3.0 MW offshore is selected among the alternatives.

Due to increasing concerns related to emerging environmental problems as a result of industrial activities, sustainable manufacturing has become a topic of considerable interest worldwide. In this study, Economic Input-Output Life Cycle Assessment (EIO-LCA) and Data Envelopment Analysis (DEA), a linear programming-based mathematical optimization model, were integrated to analyze the eco-efficiency of manufacturing sectors in the United States. This integration was achieved by aggregating different environmental pressures into a single eco-efficiency score. First, greenhouse gas emissions, energy use, water withdrawals, hazardous waste generation, and toxic releases of each manufacturing sector were quantified using the EIO-LCA model. Second, an input-oriented DEA multiplier model was developed. Third, eco-efficiency scores and rankings, target and performance improvement values of each environmental category were determined. Finally, the sensitivity of each environmental impact category was analyzed. Analysis results showed that five industrial sectors, such as “Petroleum and Coal Products Manufacturing”, “Food Manufacturing”, “Printing and Related Support Activities”, “Ordinance and Accessories Manufacturing”, and “Motor Vehicle Manufacturing” were 100% eco-efficient compared to other manufacturing sectors. On the other hand, approximately 90% of U.S. manufacturing sectors were found to be inefficient and require significant improvements in their life cycle performance. Among the environmental impact categories, energy use had the highest sensitivity on the eco-efficiency of U.S. manufacturing sectors, and therefore improved energy efficiency in industrial processes and successful policy making toward increasing the share of renewable energy utilization were highly recommended.

The construction industry has considerable impacts on the environment, economy, and society. Although quantifying and analyzing the sustainability implications of the built environment is of great importance, it has not been studied sufficiently. Therefore, the overarching goal of this study is to quantify the overall environmental, economic, and social impacts of the U.S. construction sectors using an economic input–output-based sustainability assessment framework. In this research, the commodity-by-industry supply and use tables published by the U.S. Bureau of Economic Analysis, as part of the International System of National Accounts, are merged with a range of environmental, economic, and social metrics to develop a comprehensive sustainability assessment framework for the U.S. construction industry. After determining these sustainability assessment metrics, the direct and indirect sustainability impacts of U.S construction sectors have been analyzed from a triple bottom-line perspective. When analyzing the total sustainability impacts by each construction sector, “Residential Permanent Single and Multi-Family Structures" and "Other Non-residential Structures" are found to have the highest environmental, economic, and social impacts in comparison with other construction sectors. The analysis results also show that indirect suppliers of construction sectors have the largest sustainability impacts compared with on-site activities. For example, for all U.S. construction sectors, on-site construction processes are found to be responsible for less than 5 % of total water consumption, whereas about 95 % of total water use can be attributed to indirect suppliers. In addition, Scope 3 emissions are responsible for the highest carbon emissions compared with Scopes 1 and 2. Therefore, using narrowly defined system boundaries by ignoring supply chain-related impacts can result in underestimation of triple bottom-line sustainability impacts of the U.S. construction industry. Life cycle assessment (LCA) studies that consider all dimensions of sustainability impacts of civil infrastructures are still limited, and the current research is an important attempt to analyze the triple bottom-line sustainability impacts of the U.S. construction sectors in a holistic way. We believe that this comprehensive sustainability assessment model will complement previous LCA studies on resource consumption of U.S. construction sectors by evaluating them not only from environmental standpoint, but also from economic and social perspectives.

The current waste management literature lacks a comprehensive LCA of the recycling of construction materials that considers both process and supply chain-related impacts as a whole. Furthermore, an optimization-based decision support framework has not been also addressed in any work, which provides a quantifiable understanding about the potential savings and implications associated with recycling of construction materials from a life cycle perspective. The aim of this research is to present a multi-criteria optimization model, which is developed to propose economically-sound and environmentally-benign construction waste management strategies for a LEED-certified university building. First, an economic input-output-based hybrid life cycle assessment model is built to quantify the total environmental impacts of various waste management options: recycling, conventional landfilling and incineration. After quantifying the net environmental pressures associated with these waste treatment alternatives, a compromise programming model is utilized to determine the optimal recycling strategy considering environmental and economic impacts, simultaneously. The analysis results show that recycling of ferrous and non-ferrous metals significantly contributed to reductions in the total carbon footprint of waste management. On the other hand, recycling of asphalt and concrete increased the overall carbon footprint due to high fuel consumption and emissions during the crushing process. Based on the multi-criteria optimization results, 100% recycling of ferrous and non-ferrous metals, cardboard, plastic and glass is suggested to maximize the environmental and economic savings, simultaneously. We believe that the results of this research will facilitate better decision making in treating construction and debris waste for LEED-certified green buildings by combining the results of environmental LCA with multi-objective optimization modeling.

An ecologically-based hybrid life cycle assessment model is used to evaluate the resource consumption and atmospheric emissions of continuously reinforced concrete and a hot-mix asphalt pavements. The cumulative mass and ecological resource consumption values are lower for continuously reinforced concrete, but the median values of cumulative energy and industrial energy consumption were lower for hot-mix asphalt. In addition, the use of reclaimed asphalt pavement results in a higher sensitivity for the ecological resource consumption of hot-mix asphalt compared to that of fly ash when use on the natural capital utilization of continuously reinforced concrete pavement. The cumulative and industrial exergy consumption values are significantly reduced with increases in reclaimed asphalt pavement and fly ash, and the use of low fuel transportation modes.

Warm-mix asphalt (WMA) has received considerable attention in the past few years as a potential solution for the reduction of energy consumption and emissions during production and construction of asphalt mixtures. However, many concerns and questions are still unanswered regarding its environmental benefits. Although several studies have been conducted to quantify the atmospheric emissions of WMA pavements, the direct and indirect role of ecological resource consumption was generally excluded in these studies. In this study, a thermodynamic based hybrid life cycle assessment model was developed to evaluate the environmental impacts of different types of WMA pavements and compare it to that of a conventional hot mix asphalt (HMA) one. The impacts on the ecosystem were calculated in terms of cumulative mass, energy, industrial exergy, and ecological exergy. Monte Carlo simulation was also conducted to analyze the variability of critical input parameters. The results of this study showed that although the mixing phase is important, it should not be the only phase to evaluate the decreased amount of atmospheric emissions of WMA pavements.

Algae cofiring scenarios in a 360 MW coal power plant were studied utilizing an ecologically based hybrid life cycle assessment methodology. The impacts on the ecological system were calculated in terms of cumulative mass, energy, industrial exergy, and ecological exergy. The environmental performance metrics, including efficiency, loading, and renewability ratios were also quantified to assess the sustainability of cofiring scenarios from a holistic perspective. The analysis results revealed that cumulative mass and ecological exergy consumption were higher for algae cofiring compared to single coal firing due to high material and energy inputs for the algae cultivation. On the contrary, total energy and industrial exergy utilization were reduced with an increasing share of algae cofiring where algae is dried with solar energy. Additionally, natural gas dried algae cofiring scenarios had a lower renewability ratio in comparison with single coal firing. The results of this study are vital for the policy makers to decide on more environmentally friendly algae cofiring options by considering the potential impacts on ecological system.

A multi-criteria optimization model has been developed to propose economically sound and environmentally benign construction waste management strategies for LEED© certified green buildings. First, an input-output based hybrid life cycle assessment model has been developed to quantify the net environmental impacts of different waste management options such as recycling, conventional landfilling and incineration. After quantifying the net environmental burdens associated with these waste management alternatives, a compromised programming model has been utilized to determine the optimum recycling strategy considering environmental and economic impacts, simultaneously.

The aim of this study is to analyze the environmental performance of 15 passenger transportation modes using a combined application of data envelopment analysis (DEA) and hybrid life cycle assessment (LCA). The proposed approach can offer vital guidance for decision makers regarding the relative environmental efficiency of transportation modes, and enhance the interpretation of LCA results.

Sustainability assessment tools are critical in the process of achieving sustainable development. Eco-efficiency has emerged as a practical concept which combines environmental and economic performance indicators to measure the sustainability performance of different product alternatives. In this paper, an analytical tool that can be used to assess the eco-efficiency of construction materials is developed. This tool evaluates the eco-efficiency of construction materials using data envelopment analysis; a linear programming based mathematical approach. Life cycle assessment and life cycle cost are utilized to derive the eco-efficiency ratios, and data envelopment analysis is used to rank material alternatives. Developed mathematical models are assessed by selecting the most eco-efficiency exterior wall finish for a school building. Through this study, our goal is to show that DEA-based eco-efficiency assessment model could be used to evaluate alternative construction materials and offer vital guidance for decision makers during material selection.