There exists considerable evidence that manufacturing costs and consumer prices of residential appliances have decreased in real terms over the last several decades. This phenomenon is generally attributable to manufacturing efficiency gained with cumulative experience producing a certain good, and is modeled by an empirical experience curve. The technical analyses conducted in support of U.S. energy conservation standards for residential appliances and commercial equipment have, until recently, assumed that manufacturing costs and retail prices remain constant during the projected 30-year analysis period. This assumption does not reflect real market price dynamics. Using price data from the Bureau of Labor Statistics, we present U.S. experience curves for room air conditioners, clothes dryers, central air conditioners, furnaces, and refrigerators and freezers. These experience curves were incorporated into recent energy conservation standards analyses for these products. Including experience curves increases the national consumer net present value of potential standard levels. In some cases a potential standard level exhibits a net benefit when considering experience, whereas without experience it exhibits a net cost. These results imply that past energy conservation standards analyses without experience curves may have undervalued the economic benefits of potential standard levels, possibly resulting in less stringent standards and reduced energy savings than was economically justified.

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Daily transportation mode choice is largely habitual, but transitions between life events may disrupt travel habits and can shift choices between alternative transportation modes. Although much is known about general mode switches following life event transitions, less is understood about differences that may exist between subpopulations, especially from a long-term perspective. Understanding these differences will help planners and policymakers introduce more targeted policy interventions to promote sustainable transportation modes and inform longer-term predictions. Extending beyond existing literature, we use data collected from a retrospective survey to investigate the effects of life course events on mode use situated within different long-term life trajectory contexts. We apply a machine-learning method called joint social sequence clustering to define five distinct and interpretable cohorts based on trajectory patterns in family and career domains over their life courses. We use these patterns as an innovative contextual system to investigate (1) the heterogeneous effects of life events on travel mode use and (2) further differentiation between gender and generation groups in these life event effects. We find that events occurring relatively early in life are more strongly associated with changes in mode-use behavior, and that mode use can also be affected by the relative order of events. This timing and order effect can have lasting impacts on mode use aggregated over entire life cycles: members of our "Have-it-alls" cohort-who finish their education, start working, partner up, and have children early in life-ramp up car use at each event, resulting in the highest rate of car use occurring the earliest among all the cohorts. Women drive more when having children primarily when their family formation and career formation are intertwined early in life, and younger generations rely relatively more on car use during familial events when their careers have a later start.

Energy researchers need data on residential appliances to make effective recommendations for reducing energy consumption. For some products, however, traditional data sources do not have sufficient detail. Online surveys can provide a less expensive alternative for data collection, but the accuracy of these surveys is still unclear. Here, we compare the results of Amazon Mechanical Turk (AMT) online surveys of refrigerators, freezers, televisions, and ceiling fans to the nationwide Residential Energy Consumption Survey (RECS) deployed by the U.S. Energy Information Administration (EIA). To account for differences in demographic distributions between the online survey results and the general population, we weighted the results using standard cell weighting and raking techniques, as well as a combination of these, termed "hybrid." The weighted results gave a distribution of product ownership that was reasonably close to RECS, albeit with small, statistically significant differences in some cases. The cell weighting method provided a slightly better agreement with RECS than the other two approaches. We recommend AMT online surveys as an efficient and cost-effective way of gathering in-home use data on appliances that are not adequately covered by existing data sources.2

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