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Over time, what resulted from these assessments was that we selected the following sources to provide commercial electricity: hydroelectric, coal, nuclear, natural gas, and oil. (Oil is by far the smallest source.) Note that each of these current sources meet ALL of the above six essential criteria — and if they don’t (like oil recently becoming more expensive), then they get replaced, by other conventional sources that do. As a result, today, and a hundred years from now, these sources can provide ALL of the electrical needs of our society — and continue to meet all six criteria. So what’s the problem? A new criteria has been recently added to the list of criteria: environmental impact — and the current number one environmental impact consideration is greenhouse gas emissions (e.g. CO2). So why has this joined the Big Six? It is a direct result of the current debate on global warming. In response to intense political pressure, governments have acquiesced to these forces to make emissions an additional criterion. Having government step in and mandate that utility companies change the principles that have been the foundation of our electrical supply system for a hundred years is disconcerting, transforming such a successful system based on a position that is not yet scientifically resolved. Furthermore, this new criteria for electrical supply sources now has taken priority over all the other six. It has, as of late, become the ONLY benchmark of importance — the other six have essentially been put aside, and are now given only lip service. In this unraveling of sensibility there is one final incredible insult to science: alternative sources of commercial electricity that claim to meet this new super-criteria (to make a consequential impact on CO2 reduction) don’t even have to prove that they actually do it! Let's look at the environmental poster child: wind power, and examine each of the six time-tested criteria, then the new one...

Algae are an attractive source of biomass energy since they do not compete with food crops and have higher energy yields per area than terrestrial crops. In spite of these advantages, algae cultivation has not yet been compared with conventional crops from a life cycle perspective. In this work, the impacts associated with algae production were determined using a stochastic life cycle model and compared with switchgrass, canola, and corn farming. The results indicate that these conventional crops have lower environmental impacts than algae in energy use, greenhouse gas emissions, and water regardless of cultivation location. Only in total land use and eutrophication potential do algae perform favorably. The large environmental footprint of algae cultivation is driven predominantly by upstream impacts, such as the demand for CO2 and fertilizer. To reduce these impacts, flue gas and, to a greater extent, wastewater could be used to offset most of the environmental burdens associated with algae. To demonstrate the benefits of algae production coupled with wastewater treatment, the model was expanded to include three different municipal wastewater effluents as sources of nitrogen and phosphorus. Each provided a significant reduction in the burdens of algae cultivation, and the use of source-separated urine was found to make algae more environmentally beneficial than the terrestrial crops.

This project will examine how people make decisions about appliance purchases and the effect that these choices have on energy demand. Currently, approximately half of U.S. greenhouse gas emissions are attributable to residential appliance use. However, consumers can reduce their long-run energy needs by replacing old appliances with ones that are more energy efficient. It is not surprising, then, that efficiency standards have been the cornerstone of U.S. energy conservation efforts to date. Unfortunately, the effect of these standards on appliance purchase behavior is not well understood. There are two primary reasons why. Current datasets lack crucial information, and even with appropriate data it is difficult to accurately model the dynamic aspect of appliance purchase behavior. This project addresses both of these issues.