Date
Monday, November 13, 2023
Time
5:15 PM - 6:45 PM
Name
Leveraging Data to Equitably Prioritize Program Impact
Session ID
Poster Presentations
Track
Equity & Empowerment
Dan Streit
Description

Residents in the Bay Area experience some of the largest divides in class, wealth, and climate impact. A United Way report found that one in three California households do not earn enough to meet basic needs. For these families, climate resilience solutions that improve health and save money can be financially inaccessible. Equity-driven programs can spur more participation and help more people access solutions to make buildings healthier and more resilient. BayREN is a coalition of nine counties who partner to catalyze energy, water, and greenhouse gas reductions. Recently, BayREN implemented an equity-driven restructuring of its multifamily efficiency program. It sought to prioritize funding for projects that save energy AND generate health, resilience, and/or housing burden reduction benefits. BAMBE now offers bonus incentives for projects based on the location of the project and the installed measures. BayREN engaged Slipstream to help it develop a data-driven framework accompanied by an interactive map to determine eligibility for bonus incentives. We had to discern, within the CPUC’s regulatory limitations, how the program could generate non-energy benefits (NEBs) that benefit multifamily tenants. We then developed a strategy to identify geographic areas with high vulnerability. Creating the framework required careful consideration of numerous potential data indicators and competing approaches to leveraging that data into meaningful measures of levels of local exposure to economic, health, and resilience risks. By sharing this case study and soliciting questions and critiques from attendees, the session will explore key questions: 1. What geographic data best demonstrates tenant vulnerability related to selected NEBs? 2. How to balance data tenure and data granularity? 3. How should interconnectedness and relative importance of selected data indicators guide the calculation of eligibility indexes. 4. Should index values be adjusted based on input from community members? 5. Are graduated or threshold-based incentive eligibility rules more equitable?

Supporting Document 1