Theses and Dissertations - Department of Civil, Construction & Environmental Engineering

Permanent URI for this collection

https://ir.ua.edu/handle/123456789/89

Browse

Browsing Theses and Dissertations - Department of Civil, Construction & Environmental Engineering by Subject "Economics"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Economic analysis of energy retrofits for buildings
    (University of Alabama Libraries, 2011) Estes, Heather Michelle; Moynihan, Gary P.; University of Alabama Tuscaloosa
    Research was conducted regarding improving the primary method the Alabama Industrial Assessment Center (AIAC) uses to make recommendations to companies regarding whether implementing an energy saving retrofit is economically sustainable. Their current decision-making criterion is based around the payback period method. An Excel-based tool was developed that is able to use the information obtained by the AIAC in their facility assessment reports to make a more informed decision regarding the economic systainability of the retrofit using the time value of money technique of annual cost and inflation. While the majority of the time the research confirmed the recommendation made using the payback period method, some of the time there was disagreement between the two methods. Further testing is warranted in order to validate any direct correlation between the payback period method and the cost-benefit ratio values obtained using this tool for economic analysis of energy retrofits for buildings. Because payback is a measure of liquidity and not profitability, the assumption can be made that the new tool is more accurate for companies to make a more informed decision regarding implementing the retrofit, but only more research could confirm this hypothesis.
  • Thumbnail Image
    Item
    A macro-level analysis of safety data using geospatial techniques and spatial econometric methods and models
    (University of Alabama Libraries, 2017) Zephaniah, Samwel Oyier; Jones, Steven L.; University of Alabama Tuscaloosa
    Motor vehicle accidents are a source of many preventable injuries and deaths, worldwide. Several statistical and econometric models have been developed to predict and explain crash events. Research indicate that 93% of traffic accidents are due to human error. The objective of this research is twofold – first, to develop a macro level safety planning framework by identifying socioeconomic factors that influence crash frequencies and second, to characterize traffic congestion attributed to a crash events. To this effect, a Geographically Weighted Poisson Regression (GWPR) model, a suite of Spatial Econometric models and a Mixed Logit model were estimated. Data used included crash records from 2009 to 2013 in Alabama comprising 647,477 crash events. These included 4,814 crashes on Interstate 65 and 21,818 crashes related to Driving Under the Influence (DUI). Other data comprised socioeconomic data from US census, weather data, traffic data, spatial data from ESRI and crowd sourced speed data. Results indicate that DUI crash rates and frequencies at postal code level are predominantly influenced by rate of employment, income, population density, level of education, household size and housing characteristics. In addition, level of congestion attributed to a crash depends on factors including traffic volume, speed, weather, time of the event, severity of the crash, presence of physical barrier separating opposing traffic lanes, work zone, percent of heavy trucks and whether the crash occurred in an urban area or rural area. These results are unequivocal regarding the importance of geographic variation and heterogeneity in driver behavior and the general road safety.