Explore who has rooftop solar with storage, electric vehicles, heat pumps and induction stoves today, who wants them, how much subsidy it takes to move them, and how far 2035 scenarios land from state benchmarks. Every number comes from two working papers built on the same survey.
The gap is widest where adoption is lowest: heat pumps and induction stoves. Survey-weighted estimates (De Roche et al., Figs. 1–2). Solar + storage and induction current adoption are upper bounds because of self-report over-statement.
Each respondent saw a personalized cost comparison, then named the smallest subsidy that would get them to switch. Slide along the survey's ladder for this technology.
Cumulative demand curve with 95% CIs (SI Tables 17–20). Tags mark the Low and High support subsidies used in the 2035 scenarios.
Current adopters plus interested respondents who would adopt at that subsidy, post-stratified to each tract (SI Tables 21–24). PV, heat pump and induction models cover homeowners.
Scenarios stack additive pieces: stated adoption at a subsidy tier, then modeled gains from more people knowing an adopter, longer EV range, and Title 24 electrification of new homes (7.5% of the 2035 stock).
Values read from Min et al. Fig. 2 and rounded; the paper's text gives High-support totals of about 25% (solar + storage), 30% (EVs), 24% (heat pumps) and 20% (induction). Non-EV subsidy and peer effects are modeled for homeowners only, so totals may be conservative.
Homeownership is 62% outside DACs and 43% inside them, and most subsidies here require owning the home. More support raises adoption in both groups but rarely closes the gap.
Totals with 100% new-build compliance, read from Min et al. Fig. 3 and rounded. Numbers above each pair are the gap in percentage points.
Beliefs about cost, health and safety predict interest more strongly than income or ideology. Once beliefs are in the model, ideology's effect mostly disappears, which suggests it works through those beliefs.
Coefficients in percentage points with 95% CIs (SI Tables 13–16, interest column). Beliefs run −1 (gas better) to +1 (electric better); climate worry and outage impact are per step on a 4-point scale; income per $10,000. Race effects are relative to White non-Hispanic.
Electrified end uses (EVs, heat pumps, induction) cluster in the same tracts. Solar + storage follows a different map, so some areas will be load-heavy and others generation-heavy.
Global Moran's I, all significant (p<0.05). Higher means high-adoption tracts sit next to each other.
Pearson's r between technologies' tract adoption rates.
Random effects from the mixed model (Min et al. Fig. 4D), read from the figure and rounded. Adoption follows cooling demand, not heating: hot Palm Springs (CZ 15) leads, cold Blue Canyon (CZ 16) trails, even though colder zones would cut more gas.
Tract-level estimates for every scenario are published by the authors in the CA adoption GitHub repository. This page summarizes them; it does not redraw the tract map.