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• Develop design guidelines for feedback systems (bills, meters, etc.) directly linked to social/cognitive/human factors findings (presented above)–a compendium of “best practices.” • Conduct research to better define the basis for behavior change in energy consuming activities–what is the reward system? What is the role of negative reinforcement? Can the concept of household or personal carbon allowances find a realistic implementation? • Conduct preference and rating studies using survey methods to understand consumer perceptions of risks and benefits associated with various change scenarios (this can be used to quickly evaluate potential tradeoffs, such as time-based shut down of “always on” devices versus centrally-driven price increases for power usage at unnecessary times–e.g., all those home computers trickle-charging all night long. • How can social or behavioral constraints be built into the energy consumption system that will “nudge” people to make desirable energy choices? There are a many possibilities here, some of which may be device-specific human factors design questions such as grid-friendly appliances, and others that may be longer-term life choice and land-use planning questions (where to live). • Conduct longitudinal assessments of persistence across different types of feedback, including automation that would enable consumers to pre-set price response behavior (with an opt-out feature) that would enable in-home uses respond to price signals from the utility. Such an assessment would track the various dimensions of persistence, such as the physical, behavioral, and economic. To summarize, there is clearly a potential for reducing GHG emissions through end-use energy conservation and efficiency by means of providing smart grid/meter feedback to households. Although a variety of questions remain, as described in this section, the time seems right to take the next step in smart grid/metering demonstration programs by extending them to wider geographic regions and much larger sample sizes. Given the implementation plans that various utilities have for deploying these technologies, detailed plans for behavioral studies need to be started now. A.5 References Abrahamse W, L Steg, C Vlek, and T Rothengatter. 2005. “A Review of Intervention Studies Aimed at Household Energy Conservation.” Journal of Environmental Psychology 25(3):273-291. Abrahamse W, L Steg, C Vlek, and T Rothengatter. 2007. “The Effect of Tailored Information, Goal Setting, and Tailored Feedback on Household Energy Use, Energy-Related Behaviors, and Behavioral Antecedents.” Journal of Environmental Psychology 27:265-276. Becker LJ. 1978. “Joint Effect of Feedback and Goal Setting on Performance: A Field Study of Residential Energy Conservation.” Journal of Applied Psychology 63(4):428-433. Benders RMJ, R Kok, HC Moll, G Wiersma, and KJ Noorman. 2006. “New Approaches for Household Energy Conservation – In Search of Personal Household Energy Budgets and Energy Reduction Options.” Energy Policy 34(18):3612-3622. Brandon G and A Lewis. 1999. “Reducing Household Energy Consumption: A Qualitative and Quantitative Field Study.” Journal of Environmental Psychology 19:75-85. A.6PDF Image | The Smart Grid: An Estimation of the Energy and CO2 Benefits
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