Abstract

Electric vehicles are important technologies to reduce adverse environmental impacts stemming from transportation. In this paper, we investigated the environmental, social, and economic impacts of electric vehicles in Qatar, where the electricity generation is currently 100% natural gas. As electric vehicles is expected replace the internal combustion engine vehicles and each vehicle type has different supply chain for the fuel, the sustainability impacts in the upstream of fuel supply-chain can be significantly different. Hence, a supply-chain based approach is developed to capture impact shift from petroleum sector to electricity generation sector in the supply chain of electricity and petroleum. Results revealed that electric vehicles can save up to 28% of life cycle cost, 71% of Greenhouse gas emissions, 51% of photochemical oxidant formation, and 63% of human health impacts. This paper is an output of a project supported by Qatar University, grant number QUSD-CENG-2018\\2019-2. Scopu

Abstract

Multi-criteria evaluation methods are often used for sustainability assessment. One such method is the Multi-criteria Spider-gram Cumulative Surface Area (MCSA score) recently used for this purpose (Nzila et al., Multi-criteria sustainability assessment of biogas production in Kenya. Applied Energy 93:496-506, 2012). This paper presents results illustrating that the MCSA method results and rankings might be biased by the arbitrary order of the criteria. This paper also addresses the way the comparison of two alternatives can be biased by the presence of a third (possibly irrelevant) alternative. Such dependence bias is due to the use of an internal normalization operation, a problem shared by some multi-criteria analysis methods. The paper concludes with a few suggestions to avoid such biases. © 2013 Elsevier Ltd.

Abstract

Narrow trenches are a common technique for the installation of utility pipelines of small diameter. Theexcavated soil is not always appropriate as landfill and, in those cases, an appropriate soil from some-where else (ex. a borrow pit or another construction site) should be used instead (classical solution, CS).Another common solution is to use a controlled low-strength (cementitious) material (CLSM) as backfillinstead of compacted soil. However, both solutions lead to increased raw material consumption, wastegeneration, need for transportation, and CO2emissions. In an attempt to address these issues, researchersdeveloped an eco-trench (ECO) that reuses the excavated soil of narrow trenches to produce a controlledlow-strength material to be used as landfill. Although technically viable, the sustainability of this solutionversus the traditional solution has not been properly addressed. Hence, this paper aims to develop amethod for the sustainability assessment of trenches. The Sustainability Index of Trenches (SIT), based onthe MIVES decision-making method, enables the assessment and prioritisation of different types oftrenches according to sustainability criteria. Criteria, indicators, weights and value functions were spe-cifically defined based on seminars with experts in thefield of utility services and construction. A casestudy was performed in which four types of trenches (CS, CS with recycling CSþR, CLSM and ECO) wereassessed and prioritised according to SIT. ECO resulted in the most sustainable alternative with a SIT of0.80 out of 1 followed by CSþR, CS and CLSM with SITs of 0.63, 0.40 and 0.38 respectively. The sensitivityanalysis showed consistent results in different scenarios. Thesefindings demonstrate the capability andreliability of SIT as a decision-making tool for the evaluation of the sustainability of different constructionprocesses for trenches and the prioritisation of the most suitable solution for different situations Peer Reviewed Document type: Article

Abstract

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.

Abstract

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.

Abstract

native 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.