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Global interest in LNG products and supply chains is growing, and demand continues to rise. As a clean energy source, LNG can nevertheless emit air pollutants, albeit at a lower level than transitional energy sources. An LNG plant capable of producing up to 126 MMTA was successfully developed and simulated in this study. A hybrid life cycle assessment model was developed to examine the social and human health impacts of the LNG supply chain’s environmental air emission formation. The Multiregional Input–Output (MRIO) database, the Aspen HYSYS model, and the LNG Maritime Transportation Emission Quantification Tool are the key sources of information for this extensive novel study. We began our research by grouping environmental emissions sources according to the participation of each stage in the supply chain. The MDEA Sweetening plant, LNG loading (export terminal), and LNG transportation stages were discovered to have the maximum air emissions. The midpoint air emissions data estimated each stage’s CO2-eq, NOx-eq, and PM2.5-eq emissions per unit LNG generated. According to the midpoint analysis results, the LNG loading terminal has the most considerable normalized CO2-eq and NOx-eq emission contribution across all LNG supply chain stages. Furthermore, the most incredible intensity value for normalized PM2.5-eq was recorded in the SRU and TGTU units. Following the midpoint results, the social human health impact findings were calculated using ReCiPe 2016 characterization factors to quantify the daily loss of life associated with the LNG process chain. SRU and TGTU units have the most significant social human health impact, followed by LNG loading (export terminal) with about 7409.0 and 1203.9 (DALY/million Ton LNG produced annually), respectively. Natural gas extraction and NGL recovery and fractionation units are the lowest for social human health consequences.

Electric mobility is at the forefront of innovation. Cutting down greenhouse gases when low-carbon electricity sources are maintained has answered the concerns of skeptics when switching to electric mobility. This paper presents a life-cycle-based comparative study between the electric and conventional gasoline vehicles with respect to their environmental performance, taking the case of Qatar. A well-to-wheel life cycle assessment is used to understand the carbon footprint associated with the use of alternative mobility when powered by non-renewable energy sources such as natural gas for electricity production. A survey was also conducted to evaluate the economic and practical feasibility of the use of electric vehicles in Qatar. The analysis showed that electric vehicles (EVs) have passed conventional gasoline vehicles with a minimum difference between them of 12,000 gCO2eq/100 km traveled. This difference can roughly accommodate two additional subcompact electric vehicles on the roads of Qatar. Even though Qatar is producing all of its electricity from natural gas, EVs are still producing much less carbon footprint into the atmosphere with the results showing that almost identical alternatives produce triple the amount of GHG emissions. The results of the survey showed that, despite promising results shown in switching to carbon-neutral mobility solutions, a lack of willingness prevails within the State of Qatar to incline towards electric mobility among users. This implies that Qatar has to spend a lot of time and resources to achieve its ambitious goal to decarbonize mobility on roads with 10% electric vehicles by 2030. This research highlights the need for more practical incentives and generous subsidies by the government of Qatar on e-mobility solutions to switch the transportation system into an eco-friendly one.

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

Air pollution, which causes over seven million deaths per year, is the most significant and specifically related to health impacts. Nearly 90% of the urban population worldwide is exposed to pollution not meeting the World Health Organization guidelines for air quality. Many atmospheric carbon oxides, nitrogen oxides, and particulate matter emitting sources, such as inefficient energy and polluting transportation, directly impact health. Natural gas maritime transport from various parts of the world (carbon supplied to consuming areas) has become more critical. Natural gas liquefaction offers a cleaner and more efficient transportation option and also increases its storage capacity. It is expected that natural gas will reduce the human health impact compared with other traditional fuels consumed. This research establishes a life cycle assessment model of air emission and social human health impact related to LNG maritime transport to investigate the impact of each type of fuel used for the numerous maritime carriers. In order to build a model for air emissions and social human health impact assessments based on hypotheses on various unknown criteria, a calculation model is used. The results revealed Conventional-2 fuel type has the lowest human health impact for annual mode calculations, followed by Conventional-1, Q-Max, and finally Q-Flex. The analysis method for the per year demonstrated discrepancies in the relative human health impact due to the variation of the annual LNG demand by each destination and not only per the trip needs. The results show the importance of using a relatively cleaner fuel type such as Conventional-2 in reducing the health impact of LNG maritime transportation. Moreover, it shows differences in the air emissions as well as the human health impact based on the destination’s location and annual LNG demand.

Purpose The purpose of this paper is to focus on tracing GHG emissions across the supply chain industries associated with the US residential, commercial and industrial building stock and provides optimized GHG reduction policy plans for sustainable development. Design/methodology/approach A two-step hierarchical approach is developed. First, Economic Input-Output-based Life Cycle Assessment (EIO-LCA) is utilized to quantify the GHG emissions associated with the US residential, commercial and industrial building stock. Second, a mixed integer linear programming (MILP) based optimization framework is developed to identify the optimal GHG emissions’ reduction (percent) for each industry across the supply chain network of the US economy. Findings The results indicated that “ready-mix concrete manufacturing”, “electric power generation, transmission and distribution” and “lighting fixture manufacturing” sectors were found to be the main culprits in the GHG emissions’ stock. Additionally, the majorly responsible industries in the supply chains of each building construction categories were also highlighted as the hot-spots in the supply chains with respect to the GHG emission reduction (percent) requirements. Practical implications The decision making in terms of construction-related expenses and energy use options have considerable impacts across the supply chains. Therefore, regulations and actions should be re-organized around the systematic understanding considering the principles of “circular economy” within the context of sustainable development. Originality/value Although the literature is abundant with works that address quantifying environmental impacts of building structures, environmental life cycle impact-based optimization methods are scarce. This paper successfully fills this gap by integrating EIO-LCA and MILP frameworks to identify the most pollutant industries in the supply chains of building structures.

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.

This research presents an integrated sustainability assessment framework applied to electric vehicle technologies in the United States of America. Two methods; principal component analysis and life cycle assessment are jointly used to present a novel integrated framework for eco-efficiency analysis of battery electric vehicles for each state in the USA. Three electricity production scenarios; 1) marginal electricity mix; 2) average electricity mix; and 3) 100% solar energy are investigated. Three environmental (water withdrawal, energy consumption and carbon emission) and one economic indicator as life cycle costing are merged to obtain the eco-efficiency scores of each state. The scenarios are compared by applying ANOVA and Tukey/HSD test regarding their environmental and economic values. Then, a comparison is done based on the eco-efficiency values of states in each scenario, separately. The results showed that the maximum eco-efficiency scores are obtained in three states such as Indiana, Texas and New Mexico based on marginal electricity scenario, average electricity mix scenario and solar energy scenario, respectively. The findings also revealed that 100% solar charging scenario is the most environmentally friendly option because of the environmental impacts in terms of water, energy and carbon footprints. The researchers concluded that the proposed integrated framework for eco-efficiency of electric vehicle technologies has a strong application potential for policy making in sustainability performance assessment where multiple sustainability indicators' are aimed to be integrated into the decision making process, especially to deal with the multi-collinearity associated with environmental life cycle impact data.

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.

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.

In this paper, the effect of weighting strategies on sustainability performance assessment is addressed. Eco-efficiency is used as the main metric for sustainability performance evaluation. An integrated input-output life cycle assessment (LCA) and multi criteria decision making (MCDM) approach is employed. The US manufacturing sectors’ LCA results are used in conjunction with the proposed MCDM framework to perform the eco-efficiency evaluation of 276 US manufacturing sectors. Five environmental impact categories are considered as the negative factors, namely: greenhouse gas emissions, energy use, water withdrawal, hazardous waste generation and toxic releases into air and the economic output of each manufacturing sector is considered to be the positive output. To study the overall impact of different weighting strategies; twenty weighting scenarios are designed. Five pairs of weights considered for the overall economic versus environmental impacts along with four specific weighting strategies based on Harvard, SAB, EPP and Equal weighting for each pair. According to the results of the statistical analysis, it is concluded that the weighing strategies applied to the overall environmental impacts and economic outputs cause statistically significant differences in the eco-efficiency scores.

The concept of tracing the ecologically-based life cycle impacts of agricultural and food industries (AFIs) has become a topic of interest worldwide due to their critical association with the climate change, water and land footprint, and food security. In this study, an in-depth analysis of ecological resource consumption, atmospheric emissions, land and water footprints of 54 agricultural and food industries in the U.S. were examined extensively. Initially, the supply-chain linked ecological life cycle assessment was performed with Ecologically-based Life Cycle Assessment (Eco-LCA) tool. Then, the results of life cycle inventory were used to assess the mid and end-point impacts by using the ReCiPe approach. Thirdly, ecological performance assessment was performed using well-known metrics, including loading and renewability ratios and eco-efficiency analysis. As a novel comprehensive approach, the integrated framework that consists of the Eco-LCA, ReCiPe and linear programming-based ecological performance assessment is of importance to have an overall understanding about the extent of impacts related to agricultural and food production activities across the U.S. Results indicated that grain farming, dairy food, and animal production-related sectors were found to have the greatest shares in both environmental and ecological impact categories as well as endpoint impacts on human health, ecosystem and resources. In terms of climate change, animal (except poultry) slaughtering, rendering, and processing (ASRP), cattle ranching and farming (CRF), fertilizer manufacturing (FM), grain farming (GF), fluid milk and butter manufacturing (FMBM) were found to be the top five dominant industries in climate change impacts accounting for about 60% share of the total impact.

This paper addresses the ecological resource consumption extents of the US manufacturing industries with a specific focus on renewable and non-renewable resource indicators from the national economic viewpoint. A hierarchical methodology was employed to quantify renewable and non-renewable resource life cycle inventory associated with the nation’s manufacturing sectors and to evaluate the ecological sustainability performance. Therefore, first, ecological life cycle inventory of renewable and non-renewable resource consumption of 53 national manufacturing sectors was quantified with the ecologically-based life cycle assessment framework, and then, ecological sustainability performance assessment was performed based on well-known metrics such as loading ratio (LR), renewability ratio (RR) and non-renewable based eco-efficiency (NREE). Results indicated that nonferrous metal and nonmetallic mineral product manufacturing sectors were the drivers of non-renewable resource consumption, which caused these industries, have the least nonrenewable eco-efficiency (NREE) scores, renewability ratios (RRs) and the highest environmental loading ratios (LRs). Ecological life cycle inventory results indicated that nonferrous metal production and processing non-renewable resource consumption shares ranged between 46% and 55% in the entire supply chain network. Additionally, nonmetallic mineral product manufacturing had usage share of various non-renewable resources between 23% and 74% of the supply chains’ total usage. Besides, food, tobacco and apparel manufacturing were found to have the highest RRs where the average NREE was found to be 0.4. Furthermore, sensitivity analysis of non-renewable resource indicators to NREE scores indicated that the average sensitivity ratios ranged between 5.1% and 22.4%, where ‘Talc and pyrophyllite’ was found to have the highest sensitivity.

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.

In this study, the relationship between the U.S. manufacturing and transportation industries was studied from economic and environmental life cycle sustainability perspective. The main objectives were 1) to quantify the life cycle impacts of national freight transportation activities that were triggered by the U.S. manufacturing industries and supply chains, a.k.a. manufacturing transportation nexus, and 2) assess the transportation-focused sustainability performance of manufacturing sectors based on eco-efficiency. Three environmental impact categories were focused, namely: greenhouse gas (GHG) emissions, energy use, and water withdrawals along with the economic outputs. To achieve the goals, a novel integrated methodology that consists of Economic Input–Output Life-Cycle Assessment (EIO-LCA) and Principal Component Analysis (PCA) was utilized. The scope of the study consists of 276 U.S manufacturing sectors' economic and environmental impacts associated with four transportation modes including air, rail, truck, and water transportation. Based on EIO-LCA results, food manufacturing sector was found to be responsible for the highest environmental impacts and economic output with a share of over 20% for GHG emissions, energy use, and water withdrawals and about 12% for economic output. Motor vehicle manufacturing and motor vehicle body, trailer and parts manufacturing were found to have the second and third largest share of environmental impacts and economic output, respectively. From the result of the eco-efficiency analysis, ordinance and accessory manufacturing (0.719) was found to have the highest and iron and steel mills manufacturing and agricultural chemical manufacturing (0.130) were found to have the least eco-efficiency scores. It was also critical to address that a significant negative correlation was observed between the eco-efficiency and the ton-km transportation trends.

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.

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.

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.

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.