|
|
|
Sort Order |
|
|
|
Items / Page
|
|
|
|
|
|
|
| Srl | Item |
| 1 |
ID:
125825
|
|
|
|
|
| Publication |
2013.
|
| Summary/Abstract |
Critics have speculated that the limited success of energy conservation programs among low-income consumers may partly be due to recipients having insufficient literacy to understand the outreach materials. Indeed, we found outreach materials for low-income consumers to require relatively high levels of reading comprehension. We therefore improved the Flesch-Kincaid readability statistics for two outreach brochures, by using shorter words and shorter sentences to describe their content. We examined the effect of that simplification on low-income consumers' responses. Participants from low-income communities in the greater Pittsburgh area, who varied in literacy, were randomly assigned to either original communications about energy conservation programs or our simplified versions. Our findings suggest that lowering readability statistics successfully simplified only the more straightforward brochure in our set of two, likely because its content lent itself better to simplification. Findings for this brochure showed that simplification improved understanding of its content among both low-literacy and high-literacy recipients, without adversely affecting their evaluation of the materials, or their intention to enroll in the advertised programs. We discuss strategies for improving communication materials that aim to reach out to low-income populations.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2 |
ID:
125730
|
|
|
|
|
| Publication |
2013.
|
| Summary/Abstract |
In-home displays, dynamic pricing, and automated devices aim to reduce residential electricity use-overall and during peak hours. We present a meta-analysis of 32 studies of the impacts of these interventions, conducted in the US or Canada. We find that methodological problems are common in the design of these studies, leading to artificially inflated results relative to what one would expect if the interventions were implemented in the general population. Particular problems include having volunteer participants who may have been especially motivated to reduce their electricity use, letting participants choose their preferred intervention, and having high attrition rates. Using estimates of bias from medical clinical trials as a guide, we recalculate impact estimates to adjust for bias, resulting in values that are often less than half of those reported in the reviewed studies. We estimate that in-home displays were the most effective intervention for reducing overall electricity use (~4% using reported data; ~3% after adjusting for bias), while dynamic pricing significantly reduced peak demand (~11% reported; ~6% adjusted), especially when used in conjunction with home automation (~25% reported; ~14% adjusted). We conclude with recommendations that can improve pilot studies and the soundness of decisions based on their results.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|