Compressed Natural Gas Vehicles: Financially Viable Option?

Natural gas vehicles are being developed because of increasing concerns about energy dependence, air quality and emissions, and, more recently, climate change. The major advantage of natural gas vehicles is their lower fuel cost. Several economic and technical factors such as limited range and availability of relevant infrastructure prevent widespread adoption of natural gas vehicles. A model for the financial analysis of the possibility of compressed natural gas (CNG) vehicles being competitive with gasoline-powered vehicles is offered. The model evaluates the extent to which commuters find adoption of CNG vehicles to be economically viable in the United States. The results indicate that the percentage of commuters who would adopt CNG vehicles is small, even if fueling infrastructure were fully developed and CNG vehicles were widely available for purchase. A larger number of vehicle miles traveled and increased gasoline prices encourage commuters to adopt CNG vehicles, while higher fuel economy and purchase price differentials result in lower adoption rates. In some cases, which vary in accordance with the values of the model’s parameters, commuters purchase a CNG vehicle as their second car and keep a gasoline-powered car as their first

Analysis of the Electric Vehicles Adoption over the United States

Increasing the use of electric vehicles (EVs) has been suggested as a possible method to decrease fuel consumption and greenhouse gas (GHG) emissions in an effort to mitigate the causes of climate change. In this study, the relationship between the market share of electric vehicles and the presence of government incentives, and other influential socio-economic factors were examined. The methodology of this study is based on a cross-sectional/time-series (panel) analysis. The developed model is an aggregated binomial logit share model that estimates the modal split between EV and conventional vehicles for different U.S. states from 2003 to 2011. The results demonstrated that electricity prices were negatively associated with EV use while urban roads and government incentives were positively correlated with states’ electric vehicle market share. Sensitivity analysis suggested that of these factors, electricity price affects electric vehicle adoption rate the most. Moreover, the time trend model analysis found that the electric vehicle adoption has been increasing over time, which is consistent with theories about diffusion of new technology

The weight-loss experience : qualitative exploration

BACKGROUND: Long-term weight management consists of weight-loss, weight-loss maintenance, and weight-gain stages. Qualitative insights into weight management are now appearing in the literature however research appears to be biased towards explorations of weight-loss maintenance. The qualitative understanding of weight loss, which begets weight-loss maintenance and might establish the experiences and behaviours necessary for successful long-term weight management, is comparatively under-investigated. The aim of this study was to investigate the weight-loss experiences of a sample of participants not aligned to clinical intervention research, in order to understand the weight-loss experiences of a naturalistic sample. METHODS: Participants (n=8) with weight-loss (n=4) and weight-maintenance experiences (n=4) were interviewed using a semi-structured interview to understand the weight-loss experience. Interview data was analysed thematically using Framework Analysis and was underpinned by realist meta-theory. RESULTS: Weight loss was experienced as an enduring challenge, where factors that assisted weight loss were developed and experienced dichotomously to factors that hindered it. Participants described barriers to (dichotomous thinking, environments, social pressures and weight centeredness) and facilitators of (mindfulness, knowledge, exercise, readiness to change, structure, self-monitoring and social support) their weight-loss goals in rich detail, highlighting that weight loss was a complex experience. CONCLUSIONS: Weight loss was a difficult task, with physical, social, behavioural and environmental that appeared to assist and inhibit weight-loss efforts concurrently. Health professionals might need to better understand the day-to-day challenges of dieters in order to provide more effective, tailored treatments. Future research should look to investigate the psycho-social consequences of weight-loss dieting, in particular self-imposed social exclusion and spousal sabotage and flexible approaches to treatment

Diagnostic accuracy of a clinical diagnosis of idiopathic pulmonary fibrosis: An international case-cohort study

We conducted an international study of idiopathic pulmonary fibrosis (IPF) diagnosis among a large group of physicians and compared their diagnostic performance to a panel of IPF experts. A total of 1141 respiratory physicians and 34 IPF experts participated. Participants evaluated 60 cases of interstitial lung disease (ILD) without interdisciplinary consultation. Diagnostic agreement was measured using the weighted kappa coefficient (\u3baw). Prognostic discrimination between IPF and other ILDs was used to validate diagnostic accuracy for first-choice diagnoses of IPF and were compared using the Cindex. A total of 404 physicians completed the study. Agreement for IPF diagnosis was higher among expert physicians (\u3baw=0.65, IQR 0.53-0.72, p20 years of experience (C-index=0.72, IQR 0.0-0.73, p=0.229) and non-university hospital physicians with more than 20 years of experience, attending weekly MDT meetings (C-index=0.72, IQR 0.70-0.72, p=0.052), did not differ significantly (p=0.229 and p=0.052 respectively) from the expert panel (C-index=0.74 IQR 0.72-0.75). Experienced respiratory physicians at university-based institutions diagnose IPF with similar prognostic accuracy to IPF experts. Regular MDT meeting attendance improves the prognostic accuracy of experienced non-university practitioners to levels achieved by IPF experts

Investigating factors affecting electric vehicles adoption: an aggregated panel data analysis over U.S. states

Increasing the usage of electric vehicles has been proposed as a policy to decrease aggregate fuel consumption and greenhouse gas (GHG) emissions in an effort to mitigate the causes of climate change. In order to increase the attraction of electric vehicles for consumers, governments have employed a number of incentives. In this study, the relationship between shares of electric vehicle and the presence of government incentives as well as other influential socio-economic factors were examined. The methodology of this study is based on a cross-sectional/time-series (panel) analysis. The developed model is an aggregated binomial logit share model that estimates the modal split between EV and conventional vehicles for different U.S. states from 2003 to 2011. The model was estimated using different panel data methods and the results were compared. The results demonstrated that electricity prices were negatively associated with EV use while, urban roads and government incentives were positively correlated with states’ electric vehicle market share. Sensitivity analysis suggested that of these factors, electricity price affects electric vehicle adoption rate the most. According to the sensitivity analysis of electric vehicle adoption rate, state of Vermont has the most sensitivity with respect to electricity price and New Jersey is the most sensitive state with respect to urban roads and incentives. Moreover, the time trend model analysis found that the electric vehicle adoption has been increasing over time, which is consistent with diffusion of new technology theory

Transportation Economics and Energy

The overall objective of this research is to study the impacts of technology improvement including fuel efficiency increment, extending the use of natural gas vehicle and electric vehicles on key parameters of transportation. In the first chapter, a simple economic analysis is used in order to demonstrate the adoption rate of natural gas vehicles as an alternative fuel vehicle. The effect of different factors on adoption rate of commuters is calculated in sensitivity analysis. In second chapter the VMT is modeled and forecasted under influence of CNG vehicles in different scenarios. The VMT modeling is based on the time series data for Washington State. In order to investigate the effect of population growth on VMT, the per capita model is also developed. In third chapter the effect of fuel efficiency improvement on fuel tax revenue and greenhouse emission is examined. The model is developed based on time series data of Washington State. The rebound effect resulted from fuel efficiency improvement is estimated and is considered in fuel consumption forecasting. The reduction in fuel tax revenue and greenhouse gas (GHG) emissions as two outcomes of lower fuel consumption are computed. In addition, the proper fuel tax rate to restitute the revenue is suggested. In the fourth chapter effective factors on electric vehicles (EV) adoption is discussed. The constructed model is aggregated binomial logit share model that estimates the modal split between EV and conventional vehicles for different states over time. Various factors are incorporated in the utility function as explanatory variables in order to quantify their effect on EV adoption choices. The explanatory variables include income, VMT, electricity price, gasoline price, urban area and number of EV stations