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Cities of the 21st century are buffeted with challenges, leaving potentially serious consequences on the future of urban living. Smartening development in cities has reinvented hopes of melting down predicaments in the early 2000s'. However, perplexed by the intensifying complexities of smart cities, urban living in smart cities needs to be evaluated with multiple conflicting criteria. Multi-criteria-based evaluations have been an answer to this case when attempting to gauge the composite performance of multiple decision-making entities. Several multi-criteria assessment techniques exist when dealing with selection problems. Nonetheless, the vagueness associated with the methodologies accompanied by uncertainties and complexities is inevitable in multi-attribute assessments. Fuzzy-based multi-criteria models are often an answer to such uncertainties when modelling real-world problems. The study thus presents a novel fuzzy expert-based multi-criteria decision support model, where the Analytical Hierarchy Process (AHP) is combined with the Evaluation based on Distance from Average Solution (EDAS) approach under a spherical fuzzy environment to create a composite index for comprehensive performance monitoring. The case of 35 high-tech European cities was used to empirically validate the proposed novel approach and thus construct a composite index. The composite index considers the intricate facet of integrating the concept of smart cities with sustainability, urban resilience, and livability under a unified framework. The fuzzy c-means partitioning technique was then used to segment smart cities into high, medium, and low-performing classes. A comparative analysis considering several distance-based approaches under a fuzzy environment with the SF-AHP and EDAS methodology is conducted to validate the robustness and stability of the proposed novel decision support model. The results revealed London as the top-ranked smart city that promotes sustainability, resilience, and livability in its current urban development model. Dusseldorf, Zurich, Munich, Oslo, Dublin, Amsterdam, Hamburg, Rome, Moscow, and Stockholm were no exemption from addressing the tritactic goals of sustainability, urban resilience, and livability well into their urban development plan and were placed in the high-performance cluster. The proposed model is efficient to express decision makers' preferences in a larger space and modelling functional parameters including hesitancy independently in the 3-dimensional domain. The model supports decision-makers and relocation analysts to assess the performance of smart cities and set targets to improve performance to remodel urban development to a more sustainable, resilient, and livable pattern.

The endeavors toward sustainable transportation systems are a key concern for planners and decision-makers where increasing public transport attractiveness is essential. In this paper, a machine-learning-based predictive modeling approach is proposed for metro ridership prediction, considering the built environment around the stations; it is in the best interest of sustainable transport planning to ultimately contribute to the achievement of Sustainable Development Goals (UN-SDGs). A total of twelve parameters are considered as input features including time of day, day of the week, station, and nine types of land use density. Hence, a time-series database is used for model development and testing. Several machine learning (ML) models were evaluated for their predictive performance: ridge regression, lasso regression, elastic net, k-nearest neighbor, support vector regression, decision tree, random forest, extremely randomized trees, adaptive boosting, gradient boosting, extreme gradient boosting, and stacking ensemble learner. Bayesian optimization and grid search are combined with 10-fold cross-validation to tune the hyperparameters of each model. The performance of the developed models was validated based on the test dataset using five quantitative performance measures. The results demonstrated that, among the base learners, the decision tree showed the highest performance with an R2 of 87.4% on the test dataset. KNN and SVR were the second and third-best models among the base learners. Furthermore, the feature importance investigation explains the relative contribution of each type of land use density to the prediction of the metro ridership. The results showed that governmental land use density, educational facilities land use density, and mixed-use density are the three factors that play the most critical role in determining total ridership. The outcomes of this research could be of great help to the decision-making process for the best achievement of sustainable development goals in relation to sustainable transport and land use.

Sustainability has been enshrined in the official claims of mega sporting events, and scholarly debates persist on how to achieve environmental stewardship and a prolonged legacy. Revitalizing the public transportation system with alternative fuel bus technologies has speculated significant environmental and socio-economic benefits to the host nation. This paper thus attempts to quantify and analyze the environmental, social, and economic impacts of alternative fuel buses throughout their entire life cycle stages to support the thought process of sustainable mobility practices in hosting mega-events. A hybrid, multi-regional Input-Output (MRIO) based life cycle sustainability assessment (LCSA) model is developed to assess three bus types: compressed natural gas (CNG), electric buses (EBs), and diesel buses (DBs). The results are investigated in terms of a combination of various manufacturing and end-of-life scenarios by encompassing impacts embedded in the global supply chains. A scenario-based analysis is then conducted to assess the impacts and associated benefits of EB compared to alternative bus technologies in host nations, during and post-event, taking the upcoming FIFA World Cup Qatar 2022™ as a case. The results for the MRIO analysis demonstrate the impacts embedded in the global supply chains and on-site benefits of adopting alternative fuel buses may compromise each other, as impacts might be shifted towards other countries (through manufacturing and end-of-life, in exporting countries) while minimizing the operation phase impacts in the importing country, Qatar. Even though the economic benefits of EBs are higher than the CNG buses, and with almost similar social benefits, the environmental impact of the CNG buses inside the host nation is much less than the DBs. This would thus make CNG buses a more balanced alternative than the DBs and EBs. The outcome of this research stays as an eye-opener to the local organizing committee of mega-events to rethink the current sough alternatives for the use of electric transit with more balanced alternatives as proposed in the study, to achieve the mandated carbon neutrality and environmental footprint mitigation goals. This paper is an output of a project supported by the Qatar University-Marubeni funding program, grant number M-QJRC-2020-2. The authors acknowledge and appreciate Marubeni and Qatar University for the generous and continuous support for electric vehicle research at Qatar University. Dr. Burak Sen is supported by the EC H2020 Marie Skłodowska-Curie Action 2236 Co-Funded Brain Circulation Scheme 2 (CoCirculation2). Scopus

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.

Eco-efficiency analysis can provide useful information for sustainability benchmarking of products and sectors while assessing and monitoring their economic and environmental performances. The eco-efficiency is defined as a ratio between economic performance and environmental impact. With multiple environmental and economic metrics, the eco-efficiency assessment is computationally complex. One common aspect of this complexity is associated with the importance (a.k.a. Relative weights) of sustainability indicators in the presence of high multicollinearity. A novel weighting method integrating two well-established methods for reducing the multicollinearity consequence during the aggregation process is presented in the study. The proposed method's mathematical and operational procedures, called Weighted Penalized Maximum Likelihood Estimation (W-PMLE), are demonstrated for the eco-efficiency analysis of U.S food consumption. The eco-efficiency analysis results revealed that the CO2 emissions, the level of consumption of the metallic mineral, and water were the most critical to the eco-efficiency performance of U.S. consumption.

Eco-efficiency composite indicators are widely accepted as the ratio of environmental impact to created economic value. These indicators are realistic measures for assessing sustainability performance considering the economy and environment. The weights reflect the importance of indicators to the aggregated environmental impacts. Estimating the relative weight of indicators is highly subjective, and therefore the search for a single unique weighting method has been going on for years. The regression-based weights are one of the most recent trends in sustainability modeling. Since these methods are designed initially to investigate the impact of multiple variables on a response variable rather than to estimate weights, some drawbacks are associated with their potential to provide proper weights. This paper presents a novel weighting approach integrating linear mixed-effect models with Johnson's relative weights to address these drawbacks and provide meaningful relative weights for eco-efficiency composite indicators. The proposed approach's operational and computational procedures are illustrated using a real example, and the eco-efficiency of food consumption of 38 countries is estimated for the period between 1990 and 2012 using a consumption-based sustainability accounting method. The findings have shown that energy use and GHG indicators are the most critical contributor to global food consumption's environmental impacts. The country-based eco-efficiency performance in this work has shown that China, India, and Russia are located in the low eco-efficiency score class. The Spatio-temporal analysis downgraded the geographical location's significance on the trends of eco-efficiency behavior in time and space. On the other hand, it revealed the different types of emerging hot spots over the world. WOS:000653765000014 Q2

This study presents a combined application of hybrid life cycle sustainability assessment and multi-criteria decision-making, aiming to further advance an integrated sustainability assessment and decision-making for the selection of alternative-fuel taxis. First, a multiregional hybrid life cycle sustainability assessment model is built to evaluate macro-level sustainability impacts of various vehicle types: conventional gasoline vehicles, compressed natural gas vehicles, hybrid, and battery electric vehicles. Second, considering the subjective nature of the evaluation process, the interval-valued neutrosophic sets-based analytic hierarchy process is suggested to assess the results obtained from the life cycle model to determine the weight of each evaluation criterion. Then, the technique for order preference by similarity to the ideal solution is used to rank the sustainability performance. Two different charging scenarios are also tested. The results show that solar-powered BEVs are the best in the environmental impacts with the exceptions of water consumption and land use. Solar-powered BEVs are superior in human health impact, while, ICVs are the best in compensation and employment generations. The ranking results reveal that solar-powered BEVs are the best alternatives when all indicators are considered, followed by CNG vehicles. The proposed method provides a practical and life cycle-based decision-making approach to support and prioritize effective policies for more sustainable transportation.

A primary concern of conventional Portland cement concrete (PCC) is associated with the massive amount of global cement and natural coarse aggregates (NCA) consumption, which causes depletion of natural resources on the one hand and ecological problems on the other. As a result, the concept of green concrete (GC), by replacing cement with supplementary cementitious materials (SCMs) such as ground granulated blast furnace slag (GGBFS), fly ash (FA), silica fume (SF), and metakaolin (MK), or replacing NCA with recycled coarse aggregates, can play an essential role in addressing the environmental threat of PCC. Currently, there is a growing body of literature that emphasizes the importance of implementing GC in concrete applications. Therefore, this paper has conducted a systematic literature review through the peer-reviewed literature database Scopus. A total of 114 papers were reviewed that cover the following areas: (1) sustainability benefits of GC, (2) mechanical behavior of GC in terms of compressive strength, (3) durability properties of GC under several environmental exposures, (4) structural performance of GC in large-scale reinforced beams under shear and flexure, and (5) analytical investigation that compares the GC shear capacities of previously tested beams with major design codes and proposed models. Based on this review, the reader will be able to select the optimum replacement level of cement with one of the SCMs to achieve a certain concrete strength range that would suit a certain concrete application. Also, the analysis of durability performance revealed that the addition of SCMs is not recommended in concrete exposed to a higher temperature than 400 °C. Moreover, combining GGBFS with FA in a concrete mix was noticed to be superior to PCC in terms of long-term resistance to sulfate attack. The single most striking observation to emerge from the data comparison of the experimentally tested beams with the available concrete shear design equations is that the beams having up to 70% of FA as a replacement to OPC or up to 100% of RCA as a replacement to NCA were conservatively predicted by the equations of Japan Society of Civil Engineers (JSCE-1997), the American Concrete Institute (ACI 318-19), and the Canadian Standards Association (CSA-A23.3-14).

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 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.

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.

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.

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

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.

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.

Sustainable transportation and mobility are key components and central to sustainable development. This research aims to reveal the macro-level social, economic, and environmental impacts of alternative vehicle technologies in the U.S. The studied vehicle technologies are conventional gasoline, hybrid, plug-in hybrid with four different all-electric ranges, and full battery electric vehicles (BEV). In total, 19 macro level sustainability indicators are quantified for a scenario in which electric vehicles are charged through the existing U.S. power grid with no additional infrastructure, and an extreme scenario in which electric vehicles are fully charged with solar charging stations. The analysis covers all life cycle phases from the material extraction, processing, manufacturing, and operation phases to the end-of-life phases of vehicles and batteries. Results of this analysis revealed that the manufacturing phase is the most influential phase in terms of socio-economic impacts compared to other life cycle phases, whereas operation phase is the most dominant phase in the terms of environmental impacts and some of the socio-economic impacts such as human health and economic cost of emissions. Electric vehicles have less air pollution cost and human health impacts compared to conventional gasoline vehicles. The economic cost of emissions and human health impact reduction potential can be up to 45% and 35%, respectively, if electric vehicles are charged through solar charging stations. Electric vehicles have potential to generate income for low and medium skilled workers in the U.S. In addition to quantified sustainability indicators, some sustainability metrics were developed to compare relative sustainability performance alternative passenger vehicles. BEV has the lowest greenhouse gas emissions and ecological land footprint per $ of its contribution to the U.S. GDP, and has the lowest ecological footprint per unit of its energy consumption. The only sustainability metrics that does not favor the BEV is the water-energy ratio, where the conventional gasoline vehicle performed best.