• Energy Research

Abstract 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.

Abstract 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.

In strategic energy planning, human-oriented factors are uncertain and lead to unpredictable challenges. Thus, decision-makers must contextualize the target society to address these uncertainties. More precisely, uncertainties lead to performance gaps between assumed and actual sustainability target outcomes. This study proposed a new framework that considers vital elements, including occupant motivation, preference, socioeconomic characteristics, and building features (MPSEB). To utilize this model, a thorough face-to-face survey questionnaire was administered to measure these elements. This study explored how these elements affect the patterns of residential energy consumption in a region with numerous expat communities of various ethnic and cultural backgrounds. In particular, the study investigated the patterns of energy behaviors and human-building interactions among the residents of Qatar by collecting empirical evidence and conducting a subsequent survey analysis. Machine learning approaches were employed to explore the survey data and determine the interdependencies between features, as well as the significance of the fundamental factors influencing human-building interactions. The XGBoost method was used to conduct a feature importance analysis to determine factors contributing to residential energy consumption. The results revealed the primary behavioral and socioeconomic factors that affect residential energy consumption, and confirmed the influence of human factors in Qatar while considering its diverse population. This publication was made possible by an NPRP award [ NPRP13S-0206–200272 ] from the Qatar National Research Fund (a member of Qatar Foundation ). The statements made herein are solely the responsibility of the authors. The open access publication of this article was funded by the Qatar National Library (QNL).

In strategic energy planning, human-oriented factors are uncertain and lead to unpredictable challenges. Thus, decision-makers must contextualize the target society to address these uncertainties. More precisely, uncertainties lead to performance gaps between assumed and actual sustainability target outcomes. This study proposed a new framework that considers vital elements, including occupant motivation, preference, socioeconomic characteristics, and building features (MPSEB). To utilize this model, a thorough face-to-face survey questionnaire was administered to measure these elements. This study explored how these elements affect the patterns of residential energy consumption in a region with numerous expat communities of various ethnic and cultural backgrounds. In particular, the study investigated the patterns of energy behaviors and human-building interactions among the residents of Qatar by collecting empirical evidence and conducting a subsequent survey analysis. Machine learning approaches were employed to explore the survey data and determine the interdependencies between features, as well as the significance of the fundamental factors influencing human-building interactions. The XGBoost method was used to conduct a feature importance analysis to determine factors contributing to residential energy consumption. The results revealed the primary behavioral and socioeconomic factors that affect residential energy consumption, and confirmed the influence of human factors in Qatar while considering its diverse population. This publication was made possible by an NPRP award [ NPRP13S-0206–200272 ] from the Qatar National Research Fund (a member of Qatar Foundation ). The statements made herein are solely the responsibility of the authors. The open access publication of this article was funded by the Qatar National Library (QNL).

The goal of this study is to use machine-learning (ML) techniques and empirical big data to examine the influence of the COVID-19 pandemic on electricity usage and electricity demand forecasting accuracy in buildings in Qatar over time and across sectors. Furthermore, this study statistically investigates the relationship between building electricity consumption and the number of daily infected cases in the State of Qatar. The effect of the pandemic on electricity usage was quantified during various periods of the pandemic years. Around 1 million electricity meter readings per year were considered for six different types of building usage between the years 2010 and 2021. The findings indicate that there was a gap between the actual and simulated electricity consumption during the pandemic years. Furthermore, the results show that the fluctuation in electricity consumption was correlated with the number of daily infected cases in some socioeconomic sectors. The changes in the pattern of electricity consumption during the pandemic years (2020–2021) affected the accuracy of the ML models in predicting electricity consumption in 2022.

The goal of this study is to use machine-learning (ML) techniques and empirical big data to examine the influence of the COVID-19 pandemic on electricity usage and electricity demand forecasting accuracy in buildings in Qatar over time and across sectors. Furthermore, this study statistically investigates the relationship between building electricity consumption and the number of daily infected cases in the State of Qatar. The effect of the pandemic on electricity usage was quantified during various periods of the pandemic years. Around 1 million electricity meter readings per year were considered for six different types of building usage between the years 2010 and 2021. The findings indicate that there was a gap between the actual and simulated electricity consumption during the pandemic years. Furthermore, the results show that the fluctuation in electricity consumption was correlated with the number of daily infected cases in some socioeconomic sectors. The changes in the pattern of electricity consumption during the pandemic years (2020–2021) affected the accuracy of the ML models in predicting electricity consumption in 2022.