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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.

The demand for Liquefied Natural Gas (LNG) in the global markets has changed significantly. As a result, industries have been forced to consider investing significantly in supply chains to achieve an efficient distribution of LNG for cost efficiency and carbon footprint reduction. To minimize the contribution of LNG maritime transportation to global climate change, there is a need to quantify the carbon footprints systematically. In this research, we developed a novel and practical model for estimating the carbon footprint for LNG maritime transport. Using the MATLAB program, an uncertainty-based carbon footprint accounting framework is created. The Monte Carlo simulation model is built to conduct a carbon footprint analysis while the main input parameters were changed within a reliable range. Later, a multivariate sensitivity analysis is performed using the Risk Solver software to estimate the most significant parameters on the net carbon footprints. The sensitivity analysis results showed that that steam process day and steaming fuel consumption are found to be the most sensitive parameters for the overall carbon footprint for both Laden and Ballast trips. Furthermore, it was found that the Q-Max vessel produces more carbon emissions when compared to the Q-Flex, although both are traveling the same distance and are using the same fuel type. The type of fuel is also significantly affecting the emission values due to the relevant carbon content in the fuel. Like the case of the two conventional vessels, the one that is running with the only LNG is found to have fewer emissions when compared to the one run with dual-mode.

Food trade restrictions pose a serious risk for countries that are heavily reliant on food imports, potentially leading to food crises, inequality, and geopolitical conflicts on a global scale. However, such restrictions may also have transformative effects in promoting food supply chain resilience, security, and self-sufficiency. In this study, a novel econometric analysis is presented, utilizing a data-driven analytical model to investigate the impact of a food embargo on the industry, using Qatar as a case study. A structured and automated food trade database is created using Microsoft Management Server Studio and data visualization software is integrated for automated data discovery. By using a global, trade-based sustainability assessment model, which combines the multi-region input-output (MRIO) analysis with transportation mode-based (sea, road, and air) emissions, the carbon footprint of the dairy food production sector could be estimated. The study shows that the trade embargo on Qatar’s food industry can lead to significant reductions in the annual import of food products, promoting self-sufficiency, and reducing the net carbon emissions of the dairy food sector by nearly 40%. This reduction is not only achieved through food supply chain changes, such as transportation modes, but also by restrictions pushing the country to increase domestic production. Overall, the study demonstrates that a trade embargo, with the support of a well-designed national food security strategy, trade/import diversification, and the use of different modes of transportation for food products, can improve the resilience of global supply chains, self-sufficiency, and environmental sustainability. This research received no external funding.

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

Predicting metro ridership is an essential requirement for efficient metro operation and management. The dependence of metro ridership on the land use densities entails a need for an accurate predictive model. To this end, the current study is aimed to develop a novel machine learning (ML) based model to predict the metro station ridership utilizing the land use densities near metro stations. The ridership data was obtained from Qatar Rail, and the land use data were obtained from the Ministry of Municipality and Environment in Qatar. The land use densities in the catchment area of 800 m around the metro stations have been considered in this study. The non-linear relationship between the metro ridership and land use densities has been captured through different ensemble ML models including random forests, extremely randomized trees, and gradient tree boosting. Results showed that the ML models, once meticulously optimized and trained are capable of producing an accurate prediction for metro ridership. Among the ML models, gradient tree boosting showed the highest prediction capability. The authors concluded that the proposed prediction model can be utilized by both urban and transport planners in their processes to plan the land use around metro stations, predict the transit demand from those plans, and ultimately achieve the optimal use of the transit system i.e., Transit-Oriented Developments.

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.

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.

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.

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 is the crux of urban renaissance. Imbibed with utopian technological planning, understanding the convergence between smart development and sustainable practices for city development is necessary. Smart cities have succeeded in bringing high standards of living to its residents. This article evaluates the long-term sustainability performance of 35 leading European smart cities over time from 2015 till 2020 to understand on how these cities address sustainability to make the concept of smart sustainable cities more actionable. A novel Double-Frontier Slack Based Measure Data Envelopment Analysis (DFSBM-DEA) model considering undesirable factors in the technology set is proposed for the assessment. An integrated relative sustainability performance assessment model considering both the optimistic and pessimistic viewpoint simultaneously, in terms of interval efficiency is used to determine the most efficient smart city under 6 various dimensions of sustainable development. These key dimensions include; Energy and Environmental Resource, Governance and Institution, Economic dynamism, Social cohesion and solidarity, Climate Change and, Safety and Security. A productivity progress assessment from a double frontier perspective using a modified Malmquist-DEA model is then used to capture the response of each smart city in terms of their productivity growth towards achieving sustainable development. Results show Dublin (ranked 1st) as the most smart and sustainable European city under all the proposed dimensions of sustainable development from the double-frontier perspective. Along with Dublin lies Oslo, Zurich and Amsterdam as the cities with high aggregate sustainability performance. The results also revealed significant difference in the productivity progress values from the optimistic and pessimistic viewpoint, thus exemplifying the significance for the proposed aggregate productivity progress measurement model. The findings of the present study contribute to knowledge and practice for smart city modellers, decision makers and urban planners, by aiding methodological clarity in assessing sustainable capacity of cities from a double frontier perspective and, in particular, by drawing attention to underlying assumptions about the role of sustainability in smart city development. This research stands as a breakthrough in the field of relative sustainability assessment using non-parametric approaches and a benchmark for global smart cities to shape their development in light of sustainability. Scopus

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).

Recently, multi-objective evolutionary algorithms (MOEAs) have been extensively used to solve multi-objective optimization problems (MOPs) since they have the ability to approximate a set of non-dominated solutions in reasonable CPU times. In this paper, we consider the bi-objective quadratic assignment problem (bQAP), which is a variant of the classical QAP, which has been extensively investigated to solve several real-life problems. The bQAP can be defined as having many input flows with the same distances between the facilities, causing multiple cost functions that must be optimized simultaneously. In this study, we propose a memetic algorithm with effective local search and mutation operators to solve the bQAP. Local search is based on swap neighborhood structure whereas the mutation operator is based on ruin and recreate procedure. The experimental results show that our bi-objective memetic algorithm (BOMA) substantially outperforms all the island-based variants of the PASMOQAP algorithm proposed very recently in the literature.

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

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.