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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.

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.

Electric mobility is a trending topic around the world, and many countries are supporting electric vehicle technologies to reduce environmental impacts from transportation such as greenhouse gas emissions and air pollution in cities. While such environmental impacts are widely studied in the literature, there is not much emphasis on a comprehensive sustainability assessment of these vehicle technologies, encompassing the three pillars of sustainability as the environment, society, and economy. In this study, we presented a novel comprehensive life cycle sustainability assessment for four different support utility electric vehicle technologies, including hybrid, plug-in hybrid, and full battery electric vehicles. A hybrid multi-regional input-output based life cycle sustainability assessment model is developed to quantify fourteen sustainability indicators representing the three pillars of sustainability. As a case study, we studied the impacts for Qatar, a country where 100% of electricity generation is from natural gas and have a very unique supply-chain, mainly due to a wide range of exported products and services. The analysis results showed that all-electric vehicle types have significant potential to lower global warming potential, air pollution, and photochemical oxidant formation. A great majority (above 90%) of the emissions occurs within the region boundaries of Qatar. In the social indicators, internal combustion vehicles performed better than all other electric vehicles in terms of employment generation, compensation of employees, and taxes. The results highlighted that adoption of electric vehicle alternatives doesn't favor macro-economic indicators and they have slightly less for a life-cycle cost. The proposed assessment methodology can be useful for a comprehensive regionalized life cycle sustainability assessment of alternative vehicle technologies and developing regionalized sustainable transportation policies worldwide.

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.

Bitcoin is a virtual, decentralized currency based on Blockchain technology. Regardless of where you send Bitcoin, the greenhouse gas emissions stemming from these transactions are distributed around the world. Furthermore, with the increasing public and institutional interest in Bitcoin, the value, complexity of Bitcoin mining, Blockchain networks, as well as the energy required for Bitcoin mining have been rapidly increasing. Here we show the global distribution of greenhouse gas emissions resulting from Bitcoin mining across the globe. We also estimated the carbon footprint of Bitcoin mining per transaction, per country, and per year for the last six years. The carbon footprint estimations of Bitcoin mining are calculated with consideration of the global supply-chain of Bitcoin mining around the world. According to our systematic estimations, carbon emissions are in rapid increase and there is a significant discrepancy between the locations of Bitcoin holders and the locations of emissions. China plays a major role both in emissions due to overall global mining and as a major manufacturer/supplier of Bitcoin mining equipment for all Bitcoin mining countries.

This study demonstrates the application of design principles for tools in Engineering, Procurement, and Construction (EPC) projects for project management purposes. It advocates the use of proper data analytics and visualization that can be implemented to support effective project progress reporting as well as performance monitoring. At first, an Entity Relationship Diagram (ERD) of the collected data was developed, and then the database was retrieved into the Microsoft Power BI for analysis and visualization. The project's relevant details were visualized and analyzed in terms of the major key performance indications that help evaluate the current situation of projects and aids in future decision making for project performance and portfolio management. A real case of a construction company has been selected and examined. Analytical results support finding the story behind the data. On the other hand, the effects point out that the use of the suitable facts analytics method coupled with the right analytics method and appropriate data visualization software would result in optimum use of information for future aspiration of project success and proper project progress reporting and performance evaluation. It will help companies to transfer from traditional data storage style to big data analytics and powerful use of enterprise business data for companies' growth and success in the field of EPC projects and the construction industries as a whole. Furthermore, it can be used for quicker and extra decisive choices aiming to keep projects on music about their security performance, scheduled time, cost, and great level. Using the proposed dashboard is creating a summary of the accessible records that prints a photo of how initiatives and portfolios are performing, permitting decision-makers to take their future strategic steps aiming for the improvement of their initiatives and accordingly success in their cutting-edge and future endeavors to reap the objective of all stakeholders of this organization. In this paper, it was demonstrated that the visualization of the contractor's performance and KPI are bringing assurance on contractor performance in addition to daily operational monitoring. Furthermore, it helps managers in organizing the workload to ensure the project's completion timely and meeting the customer demand as expected.

Sustainability of food consumption requires the understanding of multi-dimensional environmental, economic and social impacts using a holistic and integrated sustainability assessment and modeling framework. This article presents a novel method on the assessment and modeling of sustainability impacts of food consumption. First, sustainability impacts of food consumption categories are quantified using high sector resolution input-output tables of U.S. economy. Later, an integrated sustainability modeling framework based on two supervised machine-learning techniques such as k-means clustering and logistics regression is presented. The proposed framework involves five steps: (1) economic input-output life cycle sustainability assessment, (2) non-dimensional normalization, (3) sustainability performance evaluation, (4) centroid-based clustering analysis, and (5) sustainability impact modeling. The findings show that the supply chains of food production sectors are accounted for major environmental impacts with higher than 80% of portions for total carbon footprints. Animal slaughtering, rendering, and processing is found as the most dominant sector in most of the environmental impact categories. The logistic model results revealed an overall model accuracy equal to 91.67%. Furthermore, among all the environmental sustainability indicators, it has found that CO and SO2 are the most significant contributors. The results also show that 13.7% of the food and beverage sectors are clustered as high, in which the bread and bakery product manufacturing is the central sector. The large value of the variance (5.24) is attributed to the large total weighted impact value of the animal (except poultry) slaughtering, rendering, and processing cluster.

This paper is an overview of using data visualized tools to provide a better insight on a large amount of data and represent it in a visualized form, this study will be applied on a large number of data related to Electric Vehicles (EV) usage in three different states in the USA which are California, New York, and Washington, the tool used for this study is Microsoft Power BI. There are three cases generated to study the number of the used EV's and specify the types, brand, and models used in each state based on the data of the registered EV's, then present how the number of EVs effect station numbers in the three states. The data has been collected from the state's governmental websites, and then it was compiled as a Microsoft Excel workbook, which formed a large database that was used as a data recourse in Microsoft Power BI. After that, it has been visualized and analyzed to end up with some rich visuals which will clarify the data for the end-user. The visualization tool generates reports and dashboards which can be used to provide the final results, where it gives a solid vision on what to improve and how to improve it. After analyzing the data using Microsoft Power BI, a clear vision has been created and recommendations have been suggested. In general, the results of the study indicated that the state of California is the highest state that uses EVs, compared to New York and Washington, respectively. Regarding the EVs brands, Tesla was the top producer of Battery Electric Vehicles (BEV). On the other hand, Toyota and Chevrolet are the top producers of Plug-in Electric Vehicles (PHEV).

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

Due to growing production and consumption worldwide, the energy demand is increasing rapidly, which puts additional burdens on the world's scarce natural resources. Therefore, there is a need for efficient use of scarce materials of the earth to meet the increasing energy demand. With this motivation, material footprints of Turkey's and UK's national energy development plans are investigated by applying a global, multiregional input-output (GMRIO) model. A spatial material footprint analysis is conducted for 10 metallic and 9 nonmetallic minerals to reveal the regional and global material dependence of Turkey and UK related to electricity production from 11 different sources. As a high-resolution GMRIO database, the EXIOBASE v.2 is extended by material extraction data that enabled us to calculate the material footprints tracing the complex global supply chains of electric power generation sectors. The Autoregressive Integrated Moving Average (ARIMA) model is also developed to forecast the material footprints of electricity production until 2050. Three energy development plans such as Business-as-Usual (BAU), Official Plan (OP), and Renewable Energy development (RED) plan are investigated to compare the materials dependence of different national energy development policies. Current research concluded that environmental policies applied for national energy development should consider the different levels of complexities of regional and global supply chains for material footprint analysis. European Commission 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.

In this research, a global multiregional input-output model is built to investigate the material footprint of electricity production from renewable and nonrenewable energy sources in Turkey and UK. Three national electricity production scenarios such as S1-Business-As-Usual, S2-Official Government Plan and S3-Go-Green Plan are analyzed to help policy makers to estimate the consequences of energy investment scenarios on resource footprint based on 19 minerals from 12 different electricity production sectors. The Autoregressive Integrated Moving Average (ARIMA) is used as a time-series forecasting technique in order analyze the scenarios until 2050. The findings showed that coal is the most material intensive electricity production resource. Under business-as-usual scenario, electricity production by coal in Turkey is expected to be responsible for 83.7% of metallic mineral and 80.3% of nonmetallic mineral consumption by 2050. For per kilowatt-hour of electricity produced in Turkey, coal, natural gas, and oil together cause 81% of the total mineral consumption. However, under business-as-usual scenario in UK, 84.6% of metallic mineral and 81.4% of nonmetallic mineral consumption will be due to electricity production from coal and natural gas combined while coal alone will constitute to about 41% of the nonmetallic mineral consumption in 2050. In addition, the nonmetallic mineral consumption by electricity production from coal and natural gas in UK will be around 95.5% by 2050 under all three scenarios. The findings of this research can help identifying the critical minerals and energy resources to propose the most optimum energy mix and eventually to reduce dependency on the critical materials.

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.

Electric vehicle technologies are attractive alternatives to traditional vehicles towards achieving sustainable transportation. The adoption of these technologies has a great potential in reducing road transportation externalities. As Qatar aims to achieve 10% electric vehicles by 2030, this research reveals the macro-level environmental, social, and economic impacts and benefits of electric vehicles in Qatar. The studied vehicle technologies are, gasoline vehicle (ICV), hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), and battery electric vehicle (BEV). In this regard, we quantified 9 macro level indicators using Multi regional input-output (MRIO)-based life cycle sustainability assessment (LCSA) framework and compared the vehicles accordingly. The results show that, electric vehicles are better options in terms of Global Warming Potential (GWP), Particulate Matter Formation (PMF), and Photochemical Ozone Formation (POF) impacts. In addition to that, the results demonstrated that electric vehicles are more cost effectives than the traditional ones, while they are worse than traditional vehicles in terms of employment, operating surplus, and Gross Domestic Product (GDP) impacts.

This research is the first empirical analysis on the macro-level social, economic and environmental impacts of food consumption categories in the United States of America. Current study assessed the direct and supply chain-related indirect social, economic and environmental footprints of 29 U.S. food consumption categories by using high resolution economic input-output tables of the U.S economy. To accomplish this goal, the supply and use tables published by the U.S. Bureau of Economic Analysis are merged with a range of social, economic and environmental metrics. To this end, we developed 14 macro level indicators. The results are presented for total impacts and per million-dollar economic output basis considering the direct and supply-chain impacts. This research is important attempt to develop the first social, economic and environmental impact database for U.S. food consumption that can be produced for other sectors. Analysis results also indicate that supply chains of food consumption categories are heavily responsible for the impacts with over 80% shares for some socioeconomic and environmental indicators such as gross operating surplus and imports. Especially, animal (except poultry) slaughtering, rendering and processing category is found as the most dominant sector in most of the socioeconomic and environmental impact categories. Scopus

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.

The main objectives of this research are to improve our understanding of energy-climate-manufacturing nexus within the context of regional and global manufacturing supply chains as well as show the significance of full coverage of entire supply chain tiers in order to prevent significant underestimations, which might lead to invalid policy conclusions. With this motivation, a multi region input–output (MRIO) sustainability assessment model is developed by using the World Input–Output Database, which is a dynamic MRIO framework on the world’s 40 largest economies covering 1440 economic sectors. The method presented in this study is the first environmentally-extended MRIO model that harmonizes energy and carbon footprint accounts for Turkish manufacturing sectors and a global trade-linked carbon and energy footprint analysis of Turkish manufacturing sectors is performed as a case study. The results are presented by distinguishing the contributions of five common supply chain phases such as upstream suppliers, onsite manufacturing, transportation, wholesale, and retail trade. The findings showed that onsite and upstream supply chains are found to have over 90% of total energy use and carbon footprint for all industrial sectors. Electricity, Gas and Water Supply sector is usually found to be as the main contributor to global climate change, and Coke, Refined Petroleum, and Nuclear Fuel sector is the main driver of energy use in upstream supply chains. Overall, the largest portion of total carbon emissions of Turkish manufacturing industries is found in Turkey’s regional boundary that ranged between 40% and 60% of total carbon emissions. In 2009, China, United States, and Rest-of-the-World’s contribution is found to be more than 50% of total energy use of Turkish manufacturing. The authors envision that a global MRIO framework can provide a vital guidance for policy makers to analyze the role of global manufacturing supply chains and prevent significant underestimations due to inclusion of limited number of tiers for sustainable supply chain management research.

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.

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.

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.

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.

Eco-efficiency assessment is of great importance for monitoring and managing environmental and economic aspects of sustainable development. The eco-efficiency indicators are required to assess and measure the impact of multiple environmental aspects per unit of economic value-added. The aggregation of multiple environmental impacts in the presence of high correlation is a critical challenge to sustainability practitioners. This study presents a weighting approach using ridge penalization-based regression to overcoming the consequence of the high correlation among the environmental aspects and hence providing accurate weighting values. The performance of the proposed approach is assessed using economic and environmental footprints of 20 food industries in the United States. The new weighting approach is expected to provide decision-makers with a quantitative management tool for monitoring and controlling core operational functions associated with the sustainable development and management.

In this research, a hybrid life cycle sustainability assessment and multi-objective decision making are jointly applied to highlight how sustainability assessment results can be used for sustainable management and further country-level policymaking, and Qatar is selected as a case study to implement the proposed method. 14 macro-level sustainability indicators are quantified for four different technologies of sport utility vehicles (SUV), including internal combustion vehicles (ICV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and battery electric vehicles (BEV), using a global multiregional input–output analysis to distinguish in between regional and global supply chain-related impacts. A compromise programming model is developed based on the sustainability assessment results to determine what should be the optimal distribution of alternative vehicles based on varying importance of different sustainability indicators and scope of the analysis. The optimal vehicle distributions are determined for two different battery charging scenarios, through the existing electricity grid and solar energy. Furthermore, the optimal distributions are also investigated when the scope of the analysis is limited to regional boundary versus the total impacts encompassing the global supply chains in addition to the regional impacts. When environmental indicators are assigned the top priority (100%), the results show that HEVs should compromise over 90% of the vehicle fleet. In a balanced weighting case, the optimal vehicle distribution consists of around 81% HEV and 19% BEV if charged through the electricity grid. The proposed method can provide important insights for developing policies to achieve sustainable and efficient policies considering various aspects including the scope of assessment and relative importance of quantified sustainability indicators.

Date of Conference: 16-19 December 2018 Conference Name: 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018 The global construction industry is predicted to grow rapidly over the next decades by developing globalization, urbanization, and infrastructure renewal. Global Construction 2020 forecasts that China, USA, India, Japan, and Canada will have the most contribution to construction development. Sustainability analyses (analysis of environmental, economic, and social) of construction sectors are highlighted by increasing trend in this industry. In this study, we analyze environmental impact, particularly carbon footprints, of five leading construction markets using a global carbon footprint accounting tool based on the World Input-Output Database (WIOD). To this end, we examine direct and indirect carbon emissions within sector itself and at national and global scales employing scope-based carbon footprint, production-consumption based, and global impact distribution analyses. According to these analyses, we identified the notable hotspots where carbon reduction is required. This way, governments are able to manage and reduce carbon footprints on parts, which are increasingly important in the construction sector.

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.

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.

Although the food industry has a significant impact on the European economy and society, its contribution to energy consumption and global climate challenge is also considerably high compared to other manufacturing industries. However, the global energy and carbon impacts of European food production are not addressed sufficiently. With this motivation, this research aims to advance the body of knowledge on carbon and energy footprint analysis of food industries in the 27 member states of the European Union and Turkey. We employed a time series multi-region input–output analysis to analyze the carbon and energy footprints of food manufacturing industries. As a global multi-region input–output database, this research used the World Input–Output Database, which provides a time-series of world input–output tables for 40 countries worldwide covering 1440 economic sectors. The results from this study indicate that Germany, France and Spain have the largest food production-related energy footprint. All European countries have upstream suppliers as the dominant contributors of their total energy consumption, except for Romania, for which onsite impacts are dominant. Furthermore, the largest share of carbon emissions related to Turkish food manufacturing is found in Turkey's geographical boundary, whereas more than 50% of the total energy footprint of Turkey's food manufacturing industry is located in various regions outside of Turkey, including the rest of the world and particularly United States and the European Union. The findings show that upstream supply chains are responsible for over 90% of carbon emissions, while direct emissions and those from the first three-layers of food manufacturing supply chains are found to be responsible for approximately 80% of total carbon emissions.

The metropolitan city of Istanbul is becoming overcrowded and the demand for clean water is steeply rising in the city. The use of analytical approaches has become more and more critical for forecasting the water supply and demand balance in the long run. In this research, Istanbul's water supply and demand data is collected for the period during 2006 and 2014. Then, using an autoregressive integrated moving average (ARIMA) model, the time series water supply and demand forecasting model is constructed for the period between 2015 and 2018. Three important sustainability metrics such as water loss to supply ratio, water loss to demand ratio, and water loss to residential demand ratio are also presented. The findings show that residential water demand is responsible for nearly 80% of total water use and the consumption categories including commercial, industrial, agriculture, outdoor, and others have a lower share in total water demand. The results also show that there is a considerable water loss in the water distribution system which requires significant investments on the water supply networks. Furthermore, the forecasting results indicated that pipeline projects will be critical in the near future due to expected increases in the total water demand of Istanbul. The authors suggest that sustainable management of water can be achieved by reducing the residential water use through the use of water efficient technologies in households and reduction in water supply loss through investments on distribution infrastructure. © 2015 by the authors.

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.

In this paper, environmental sustainability performance assessment of 27 U.S. and Canada metropoles is addressed. A four-step hierarchical fuzzy multi-criteria decision-making approach is developed. In the first step, the proposed methodology is established by determining the sustainability performance indicators (a total of 16 sustainability indicators are considered), collecting the data and contacting experts from academia, U.S. government agencies and within the industry. In the second step, experts are contacted and the entire list is finalized; sustainability performance evaluation forms are delivered; and then expert judgment results are obtained and quantified, respectively. In the third step, the proposed Multi-criteria Intuitionistic Fuzzy Decision Making model is developed and sustainability performance scores are quantified by using the collected data, multi-criteria decision making model and sustainability indicator weights obtained from expert judgment phase. In the final step, the sustainability scores and rankings of the 27 metropoles, results analysis and discussions, and statistical highlights about the research findings are provided. Results indicated that the average sustainability performance score is found to be 0.524 over scale between 0 and 1. The metropole with the greatest sustainability performance score is found to be New York with 0.703 and the poorest performing city is identified as Cleveland with 0.394. The results of the statistical analysis indicate that the greatest significant correlations are obtained with carbon dioxide (CO2) emissions per person (−0.749 – significant negative correlation with sustainability performance score) and share of workers traveling by public transport (+0.753 – significant positive correlation with sustainability performance score). Therefore, the CO2 emissions and public transport are found to have the most significant impact on the sustainability scores.

A B S T R A C T The World Business Council for Sustainable Development (WBCSD) and the World Resource Institute (WRI) set the scope-based carbon footprint accounting standards in which all possible supply-chain related indirect greenhouse gas emissions are captured. Although this carbon footprint accounting standards are widely used in regional policy making, there is little effort in analyzing the scope-based carbon footprints of nations using a multi-region input–output (MRIO) analysis in order to consider the role of global trade. This research aims to advance the body of knowledge on carbon footprint analysis of the manufacturing sectors with a holistic approach combining the WBCSD & WRI’s scope-based carbon footprint accounting standards with a time series MRIO framework. To achieve this goal, a global scope-based carbon footprint analysis of the Turkish manufacturing sectors has been conducted as a case study. We employed a time series MRIO analysis by using the World Input–Output Database on the world’s 40 largest economies covering 1440 economic sectors. The results showed that electricity, gas and water supply was the most dominant sector in the supply chains of the Turkish industrial sectors with the largest carbon footprint. On average, indirect emissions of the Turkish manufacturing industry are found to be higher than direct emissions during the period from 2000 to 2009. The results of this analysis revealed that supply chain related indirect emissions (represented by scope 3) are responsible for nearly 56.5% total carbon emissions of sectors, which highlights the crucial role of supply chains on overall carbon footprint of sectors.

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.

Waste management in construction is critical for the sustainable treatment of building-related construction and demolition (C&D) waste materials, and recycling of these wastes has been considered as one of the best strategies in minimization of C&D debris. However, recycling of C&D materials may not always be a feasible strategy for every waste type and therefore recycling and other waste treatment strategies should be supported by robust decision-making models. With the aim of assessing the net carbon, energy, and water footprints of C&D recycling and other waste management alternatives, a comprehensive economic input–output-based hybrid life-cycle assessment model is developed by tracing all of the economy-wide supply-chain impacts of three waste management strategies: recycling, landfilling, and incineration. Analysis results showed that only the recycling of construction materials provided positive environmental footprint savings in terms of carbon, energy, and water footprints. Incineration is a better option as a secondary strategy after recycling for water and energy footprint categories, whereas landfilling is found to be as slightly better strategy when carbon footprint is considered as the main focus of comparison. In terms of construction materials’ environmental footprint, nonferrous metals are found to have a significant environmental footprint reduction potential if recycled.

Purpose In the USA, several studies have been conducted to analyze the energy consumption and atmospheric emissions of Warm-mix Asphalt (WMA) pavements. However, the direct and indirect environmental, economic, and social impacts, termed as Triple-Bottom-Line (TBL), were not addressed sufficiently. Hence, the aim of this study is to develop TBLoriented sustainability assessment model to evaluate the environmental and socio-economic impacts of pavements constructed with different types of WMA mixtures and compare them to a conventional Hot-mix Asphalt (HMA). The types of WMA technologies investigated in this research include Asphamin® WMA, Evotherm™ WMA, and Sasobit® WMA. Methods Toachievethisgoal,supplyandusetablespublished by the U.S. Bureau of Economic Analysis were merged with 16 macro-level sustainability metrics. A hybrid TBL-LCA model was built to evaluate the life-cycle sustainability performance of using WMA technologies in construction of asphalt pavements. The impacts on the sustainability were calculated in terms of socio-economic (import, income, gross operating surplus, government tax, work-related injuries, and employment) and environmental (water withdrawal, energy use, carbon footprint, hazardous waste generation, toxic releases into air, and land use). A stochastic compromise programming model was then developed for finding the optimal allocation of different pavement types for the U.S. highways. Results and discussion WMAsdid not perform betterinterms of environmental impacts compared to HMA. Asphamin® WMA was found to have the highest environmental and socio-economic impacts compared to other pavement types. Material extractions and processing phase had the highest contribution to all environmental impact indicators that shows the importance of cleaner production strategies for pavement materials. Based on stochastic compromised programming results, in a balanced weighting situation, Sasobit® WMA had the highest percentage of allocation (61 %); while only socio-economic aspects matter, Asphamin® WMA had the largest share (57 %) among the asphalt pavements. The optimization results also supported the significance of an increased WMA use in the U.S. highways. Conclusions This research complemented previous LCA studies by evaluating pavements not only from environmental emissions and energy consumption standpoint, but also from socio-economicperspectives.Multi-objectiveoptimizationresults also provided important insights for decision makers whenfinding the optimum allocationof pavement alternatives based on different environmental and socio-economic priorities. Consequently, this study aimed to increase awareness of the inherent benefits of economic input–output analysis and multi-criteria decision making through application to emerging sustainable pavement practices.

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.

Due to the fact that food manufacturing is one of the major drivers of the global environmental issues, there is a strong need to focus on sustainable manufacturing toward achieving long-term sustainability goals in food production of the United States. In this regard, current study assessed the direct and indirect environmental footprint of 33 U.S. food manufacturing sectors by using the Economic Input-Output Life Cycle Assessment (EIO-LCA) model. Then, a non-parametric mathematical optimization tool, namely Data Envelopment Analysis (DEA), is utilized to benchmark the sustainability performance of food manufacturing sectors by using the results of the EIO-LCA model. Next, sustainability performance indices (SPIs), rankings, target improvements, and sensitivity of environmental impact indicators are presented. The average SPI score of U.S. food manufacturing sectors is found as 0.76. In addition, 19 out of 33 food sectors are found as inefficient where an average of 45–71% reduction is indicated for various environmental impact categories. Analysis results also indicate that supply chains of food manufacturing sectors are heavily responsible for the impacts with over 80% shares for energy, water and carbon footprint, fishery and grazing categories. Especially, animal (except poultry) slaughtering, rendering and processing sector is found as the most dominant sector in most of the impact categories (ranked as 2nd in fishery and forest land). Sensitivity analysis indicated that forest land footprint is found to be the most sensitive environmental indicator on the overall sustainability performance of food manufacturing sectors.

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.

While carbon footprint reduction potential and energy security aspects of renewable and non-renewable resources are widely considered in energy policy their effects on water resources are mostly overlooked. This research aims to develop a framework for water and carbon footprint analysis to estimate the current and future trends of water consumption and withdrawal by electricity production sectors for national energy development plans - alongside carbon emissions from various electricity sources. With this motivation the Turkish electric power industry is selected as a case study and a decision support tool is developed to determine the water consumption withdrawal and carbon emissions from energy mixes under three different scenarios namely Business-As-Usual (BAU) Official Governmental Plan (OGP) and Renewable Energy-Focused Development Plan (REFDP). The results indicate that water is used substantially even by renewable resources such as hydroelectricity and biomass which are generally considered to be more environmental friendly than other energy sources. The average water consumption of the OGP energy mix in 2030 is estimated to be about 8.1% and 9.6% less than that of the BAU and REFDP scenarios respectively. On the other hand it is found that the water withdrawal of the energy mix in 2030 under the REFDP scenario is about 46.3% and 16.9% less than that of BAU and OGP scenarios. Carbon emissions from BAU are projected to be 24% higher than OGP and 39% higher than REFDP in 2030. Carbon emissions and water usage are strongly correlated in BAU scenario as compared with OGP and REFDP thus carbon friendly energy sources will result in fewer water consumptions and withdrawals particularly under REFDP. (C) 2017 Elsevier Ltd. All rights reserved.

With the dramatic increase of traffic volume, traffic congestion has become a topic of considerable interest in the United States. Congestion has resulted in enormous economic and environmental losses, and the use of intelligent transportation systems (ITS) has been found to be an effective solution to relieve congestion in urbanized areas. The study presented in this paper aimed to advance the body of knowledge on sustainability impacts through a triple bottom line (TBL) evaluation of congestion relief in Florida. Rather than consider only the direct economic benefits as in traditional projects, this study strove to fill the gap for decision makers in the analysis of sustainability impacts from a holistic perspective. A critical approach to this research was to include both the direct and the indirect environmental, economic, and ecologic impacts associated with the chain of supply paths of ITS. To meet this goal, economic input-output tables, published by the Bureau of Economic Analysis, were linked to various TBL sustainability indicators to gain better insight into the sustainability impact of congestion relief. Study results indicated that 1.38 E+05 tons of greenhouse gas emissions (tons of carbon dioxide equivalent) and 3.00 E+04 global hectares of carbon dioxide uptake land were saved in Florida in 2010. Moreover, annual delay reduction costs savings were $420 million, of which the net fuel-based savings were $17.2 million.