Installation and Manufacturing of Photovoltaics: an Assessment Using California and New York

Renewable energy studies are becoming increasingly important as world energy demand rises and current energy sources are increasingly questioned. Solar photovoltaics (PV) are the focus of this study as a renewable industry still in its infancy. This research examines the geography of solar panel installation and manufacturing from 2007 to 2010 in California and New York. California is the larger of the two markets and has implemented more policy support; programs that appear to have increased the pace of installations, reduce the size of the subsidy, and help lower total costs. Similar trends are observable in New York. US based companies are still making solar panels, but foreign competitors, most notably from China and Mexico, are capturing an increasing share of the market

On-Road Remote Sensing of Motor Vehicle Emissions: Associations between Exhaust Pollutant Levels and Vehicle Parameters for Arizona, California, Colorado, Illinois, Texas, and Utah

On-road remote sensing has the ability to operate in real-time, and under real world conditions, making it an ideal candidate for detecting gross polluters on major freeways and thoroughfares. In this study, remote sensing was employed to detect carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxide (NO). On-road remote sensing data taken from measurements performed in six states, (Arizona, California, Colorado, Illinois, Texas, and Utah) were cleaned and analyzed. Data mining and exploration were first undertaken in order to search for relationships among variables such as make, year, engine type, vehicle weight, and location. Descriptive statistics were obtained for the three pollutants of interest. The data were found to have non-normal distributions. Applied transformations were ineffective, and nonparametric tests were applied. Due to the extremely large sample size of the dataset (508,617 records), nonparametric tests resulted in "p" values that demonstrated "significance." The general linear model was selected due to its ability to handle data with non-normal distributions. The general linear model was run on each pollutant with output producing descriptive statistics, profile plots, between-subjects effects, and estimated marginal means. Due to insufficient data within certain cells, results were not obtained for gross vehicle weight and engine type. The "year" variable was not directly analyzed in the GLM because "year" was employed in a weighted least squares transformation. "Year" was found to be a source of heteroscedasticity; and therefore, the basis of a least-squares transformation. Grouped-years were analyzed using medians, and the results were displayed graphically. Based on the GLM results and descriptives, Japanese vehicles typically had the lowest CO, HC, and NO emissions, while American vehicles ranked high for the three. Illinois, ranked lowest for CO, while Texas ranked highest. Illinois and Colorado were lowest for HC emissions, while Utah and California were highest. For NO, Colorado ranked highest with Texas and Arizona, lowest