Electric air source heat pumps (ASHPs) appear to be a key technology for decarbonizing space heating in existing residential buildings, yet their current market share in much of North America remains low. To explore how the potential future ASHP market may differ from the present one, we use a subset of data from the Canadian Home Heating Survey (n = 461) to provide a comprehensive characterization of three market segments of British Columbian homeowners: Pioneers (heat pump owners), Potential Early Mainstream buyers (homeowners currently willing to purchase an ASHP), and Late Mainstream buyers (homeowners currently unwilling to purchase an ASHP). We assess variable associations with market segments according to the Attitude-Behavior-Context theory, which posits that pro-environmental behavior is shaped by attitudinal, contextual, and socio-demographic factors. We also compare how market segmentation changes before and after respondents receive technical information on different home heating systems. Relative to Pioneers and the Potential Early Mainstream (PEM), we find that the Late Mainstream (LM) are generally lower income, lower educated, less environmentally- and technologically-oriented in their lifestyles, less open to change, less familiar with heat pumps and home energy efficiency, more negative in their perceptions about heat pumps, and less aware and supportive of policies aimed at reducing residential emissions. We also find that after respondents read technical information about home heating systems, approximately 10% of heat pump non-owners shift from the LM to the PEM; however, within the PEM, there is little growth in high willingness to adopt.

. 2015. The role of seasonality and non-lethal carry-over effects on density-dependent dispersal. Ecosphere 6(12):272. http://dx.doi.org/10.1890/ES15-00257.1Abstract. Understanding dispersal is critical for predicting a wide range of ecological dynamics.Variation in intraspecific density is widely regarded as a major factor influencing dispersal rates but it is not clear why dispersal is positively related to density in some systems and negatively related to density in other systems. Using seasonal populations of Drosophila melanogaster, we experimentally show that dispersal rates are both positively related to breeding density at the time of dispersal and negatively related to density at the beginning of the previous non-breeding season. This suggests that flies use density at the time of dispersal as a cue for habitat quality but are also negatively influenced by the delayed, non-lethal effects of density in the previous season. A parameterized model indicates that a carry-over effect not only causes a decrease in the proportion of individuals that disperse, but also a decrease in population size caused by lower per capita breeding output. Our results demonstrate how density can have contrasting effects on dispersal and population size depending on when density is measured in the annual cycle and that non-lethal effects on individuals can have important, but previously unrecognized, consequences for both the movement rates and long-term dynamics of seasonal populations.

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A proliferation of energy models has been developed across disciplines to explore energy and greenhouse gas (GHG) emissions-reduction strategies in cities. Hybrid models are especially useful because they incorporate more dynamics to simulate realistic results informed by relevant high-level policy decisions and building-level factors. Spatial and aspatial energy models, however, are not often linked, which overlooks the spatial impact of energy and emissions policies in urban environments. A new method is presented that links these types of models to understand how building stocks change over time in response to policies. This approach integrates outputs from an aspatial economic model, CIMS, with buildings in a spatially explicit urban building energy model (UBEM), UMI. The energyeconomy model is parameterised against the UBEM using identified baseline condition and proposed future policy interventions. Building stock replacement and retrofit change are downscaled and disaggregated to individual buildings based on existing stock age and a probability-based Markov chain model (MCM). This integration enables simulations of cross-scale policy interventions that are sensitive to both economically and mechanically driven factors. An application of this approach shows how it can be used to evaluate how different policies interact with and influence building energy demand and GHG emissions. PRACTICE RELEVANCE The results are integrated as a series spatially explicit energy modeling procedure (UMI) at the neighborhood scale. This process enables local assessments of efficacy of the proposed city scale and even regional policies in municipalities with various energy and GHG emission agendas. In the presented case study (of the Sunset neighborhood of Vancouver, BC, Canada) this method can quantify the elasticity of emission reductions from various urban form changes (e.g. infill, transportation-oriented development, etc.), new building code (i.e. BC Energy Step Code), active transportation and retrofit strategies from 2020 to 2050.