No abstract

Residential adoption of solar photovoltaics (PV) is spreading rapidly, supported by policy initiatives at the federal, state, and local levels. Potential adopters navigate increasingly complex decision-making landscapes in their path to adoption. Much is known about the individual-level drivers of solar PV diffusion that steer adopters through this process, but relatively little is known about the evolution of these drivers as solar PV markets mature. By understanding the evolution of emerging solar PV markets over time, stakeholders in the diffusion of solar PV can increase policy effectiveness and reduce costs. This analysis uses survey data to compare two adjacent markets across a range of relevant characteristics, then models changes in the importance of local vs cosmopolitan information sources by combining theory relating market maturity to adopter behavior with event-history techniques. In younger markets, earlier, innovative adoptions that are tied to a preference for cosmopolitan information sources are more prevalent than expected, suggesting a frustrated demand for solar PV that segues into adoptions fueled by local information preferences contemporary with similar adoptions in older markets. The analysis concludes with policy recommendations to leverage changing consumer information preferences as markets mature.

a b s t r a c tTo better identify how to reduce peak demand charges for a university campus, we investigated the relationship between individual building peak demand and the campus peak energy use by evaluating the pattern of energy use across time and day. To facilitate this evaluation, we developed a pivot table analysis tool that enables ready cross-building comparisons in a visually intuitive display. We used a university campus as an example to facilitate potential peak demand charge savings based on analysis of which buildings contribute to peak energy demand, and understanding the factors contributing to that building-dependent energy demand.

In the aftermath of shock events, policy responses tend to be crafted under significant time constraints and high levels of uncertainty. The extent to which individuals comply with different policy designs can further influence how effective the policy responses are and how equitably their impacts are distributed in the population. Tools which allow policymakers to model different crisis trajectories, policy responses, and behavioral scenarios ex ante can provide crucial timely support in the decision-making process. Set in the context of COVID-19 shelter in place policies, in this paper we present the COVID-19 Policy Evaluation (CoPE) tool, which is an agent-based modeling framework that enables researchers and policymakers to anticipate the relative impacts of policy decisions. Specifically, this framework illuminates the extent to which policy design features and behavioral responsiveness influence the efficacy and equity of policy responses to shock events. We show that while an early policy response can be highly effective, the impact of the timing is moderated by other aspects of policy design such as duration and targeting of the policy, as well as societal aspects such as trust and compliance among the population. More importantly, we show that even policies that are more effective overall can have disproportionate impacts on vulnerable populations. By disaggregating the impact of different policy design elements on different population groups, we provide an additional tool for policymakers to use in the design of targeted strategies for disproportionately affected populations.