• Energy Research

Electric Vehicles (EVs) are promoted as a viable near-term vehicle technology to reduce dependence on fossil fuels and resulting greenhouse gas (GHG) emissions associated with conventional vehicles (CVs). In spite of the benefits of EVs, several obstacles need to be overcome before EVs will be widely adopted. A major barrier is that consumers tend to resist new technologies that are considered alien or unproved, thus, policy decisions that consider their critical concerns will have a higher level of success. This research identifies potential socio-technical barriers to consumer adoption of EVs and determines if sustainability issues influence consumer decision to purchase an EV. This study provides valuable insights into preferences and perceptions of technology enthusiasts; individuals highly connected to technology development and better equipped to sort out the many differences between EVs and CVs. This group of individuals will likely be early adopters of EVs only if they perceive them to be superior in performance compared to CVs. These results can guide policymakers in crafting energy and transportation policy. It can also provide guidance to EV engineers' decision in incorporating consumer preference into EV engineering design.

Mass penetration of electric vehicles into the market will have a number of impacts and benefits, including the ability to substantially reduce greenhouse gas emissions from the transportation sector. Therefore, it is expected that in coming years this technology will progressively penetrate the market. This research presents an analysis of factors that influence electric vehicle adoption by modeling the conditions under which an individual, particularly one with an engineering or technical background, is more or less likely to adopt an electric vehicle. This model is developed by considering demographic determinants as well as behavioral and attitudinal measures that affect individual adoption of the technology. The methodology involves applying logistic regression to provide a good fit and predict the response given explanatory variables. Analyzing these outcomes generates empirical findings that better inform electric vehicle technology and policy development. This study takes into account preferences of potential customers and analyzes how individuals with engineering and technology background differ in electric vehicle adoption considerations compared to the general population. Therefore, this research provides both engineers and policy makers with critical information for developing future electric vehicle technology. The model results show that several factors including willingness to pay for new appealing technology, distance driven, perceptions of electric vehicles as good for the environment, perception of EV speed are statistically significant in influencing willingness to purchase an electric vehicle.

Plug-in electric vehicles (PEVs) have immense potential for reducing greenhouse gas emissions and dependence on fossil fuels, and for smart grid applications. Although a great deal of research is focused on technological limitations that affect PEV battery performance targets, a major and arguably equal concern is the constraint imposed by the finite availability of elements or resources used in the manufacture of PEV batteries. Availability of resources, such as lithium, for batteries is critical to the future of PEVs and is, therefore, a topic that needs attention. This study addresses the issues related to lithium availability and sustainability, particularly supply and demand related to PEVs and the impact on future PEV growth. In this paper, a detailed review of the research on lithium availability for PEV batteries is presented, key challenges are pinpointed and future impacts on PEV technology are outlined.

With the increase in awareness about the wide range of issues and adverse effects associated with the use of conventional energy sources came an increase in project management research related to sustainability and sustainable development. Part of that research is devoted to the development of sustainable project typologies that classify projects based on a variety of external factors that can significantly impact these projects. This research focuses on developing a sustainable project typology that classifies sustainable projects based on the external institutional influences. The typology explores the influence of the coercive, normative, and mimetic institutional isomorphisms on the expected level of change, level of uncertainty, project team skills and experience levels, and the level of technology information exchange in sustainable projects. Two case studies are presented to demonstrate the use of the typology to classify sustainable projects based on the external institutional influences.

A robust project selection process is critical for the selection of sustainable projects that meet the needs of an organization or community. There are multiple factors or criteria that can be considered in the selection of the appropriate sustainable project, but it can be challenging to find sufficient depth of expert opinion to perform a strong evaluation of these criteria. Several researchers have turned to the sustainable project literature as a source of expert opinion to evaluate the criteria used in sustainable project selection and rank them based on importance using different multi-criteria decision-making (MCDM) methodologies. However, using the literature as a source of expert opinion poses a different set of challenges and may not accurately represent the actual opinions of sustainable project subject matter experts (SMEs) and practitioners. In this study, the fuzzy analytic hierarchy process (FAHP) methodology is used to determine the importance of project cost, project maturity, skill and experience, uncertainty, and technology information transfer as selection criteria using collected opinions from academic sustainable project experts and practitioners. The results are then compared with previous research that used the literature to rank these five criteria based on importance when selecting between multiple sustainable project alternatives. The results show that project cost is still considered the major driver of decision making in sustainable project selection by both the literature and practice. However, unlike the literature-as-experts approach, SMEs prioritize skill and experience and technology information transfer over project maturity and uncertainty. Project managers and decision makers can use these findings to best prioritize the types of challenges that may occur depending on inputs for the FAHP analysis.

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

The project selection process is a crucial step in sustainable development. Effective sustainable development depends on the ability to select the appropriate sustainable project to implement to ensure that the desired goals are met. Some of the most common characteristics or criteria used in evaluating sustainable projects include novelty, uncertainty, skill and experience, technology information transfer, and project cost. Prioritizing these criteria based on relative importance helps project managers and decision makers identify elements that require additional attention, better allocate resources, as well as improve the selection process when evaluating different sustainable project alternatives. The aim of this research is to use the fuzzy analytic hierarchy process (FAHP) methodology in which fuzzy numbers are utilized to realistically represent human judgment to rank the different project criteria based on relative importance and impact on sustainable projects. The results from the FAHP show that the most important criterion to consider in sustainable project selection is project cost, followed by novelty and uncertainty as the second and third most important criteria, respectively. The two least important criteria out of the total of five examined in this research were the skill and experience and technology information transfer, respectively. These results will help project managers and decision makers identify selection criteria with higher weights of importance. Given that the selection criteria chosen for this research are not limited to the evaluation of a specific type of sustainable projects or a specific location, they can be used to evaluate different types of sustainable projects in different environments and locations.