Project Abstract

The power of experience curves in technology (known as Wright’s Law, Swanson’s Law, or “learning by doing”) has made clean energy technologies less expensive than fossil fuel-generated energy, driving exponential growth in clean energy deployment globally. Can this power of learning also be harnessed for the technology of low carbon building to make Passive House construction less costly than traditional methods? This project, a partnership between Invest NYC SDG, Passive House Accelerator, and Source 2050 will (1) study how “experience curves” apply to Passive House design and construction, and (2) create a global project database to accelerate those experience curves.

Project Description & Overview

The Invest NYC SDG initiative is committed to creating a sustainable, inclusive, and resilient built environment in NYC. Passive House is a proven technology for dramatically reducing the greenhouse gas emissions of buildings and providing climate resilience to building occupants, making Passive House (1) a key tool for achieving the goals of Local Law 97, (2) a centerpiece of NYSERDA’s building decarbonization work, and (3) a rapidly growing building-based climate solution in NYC, NYS, and nationally.

At Passive House Accelerator events and industry conferences, it is common to hear project teams report rapid, project-based learning such that each Passive House project they complete becomes more efficient and less costly than the last. Do these anecdotes translate to quantifiable and significant experience curves that can be harnessed to drive costs down and accelerate market uptake? Invest NYC SDG will partner with Passive House Accelerator and Source 2050 to empower Capstone students to:

• Answer the research question, “do technology experience curves apply to Passive House, and if so, at what learning rate?”
• Increase this learning rate by building a visualization-rich High Performance Construction Project Database that shares replicable details from hundreds of projects, documents cost and performance, and shares lessons-learned with thousands of practitioners, owners, manufacturers, and policymakers in NYC, NYS, and nationally.
• Design an optimal “case study” design for each project listing, determining which data points transmit “best practice” most effectively, and how best to share that information in visual and written formats.

Datasets

• PHI Passive House Database: This online database lists 5,000 PHI Passive House buildings internationally, with basic project data; it is a good foundation for more robust case studies with data visualizations, higher quality project images, and information. Cost data is not published, so will need to be gathered from project teams.
• Phius Certified Projects Database: This online database lists 350 Phius Passive House buildings in North America, with basic project data; it is a good foundation for more robust case studies with data visualizations, higher quality project images, and information. Cost data is not published, so will need to be gathered from project teams.
• NYSERDA Buildings of Excellence Datasets: NYSERDA publishes cost, performance, and project data for its 42 Buildings of Excellence projects.
• Pennsylvania Housing Finance Agency LIHTC applicant data: Three years of cost, square footage, and certification type data for 268 proposed affordable housing projects (74 of which were Passive House).
• Passive House Accelerator Project Gallery: The Accelerator’s project gallery features 200 project entries, some with very data-rich descriptions and others with considerably less data published; it is a good foundation for more robust case studies.
• Massachusetts Clean Energy Center Passive House project data: MassCEC is tracking performance, cost, and project data for the growing number of Passive House multifamily projects that are underway thanks to state policies that incentivize Passive House development.
• Source 2050 Vendor Project Profiles: Source 2050 is asking all vendors to provide case studies, testimonials, project profiles, and similar materials as they are onboarded; these will provide unique perspectives from the trades on how these products and details performed in the context of specific project types.

Competencies

Data analytics, visualization, data mining and processing, outreach/interview skills, database management, and web integration

Learning Outcomes & Deliverables

• Data analytics report on experience curves in Passive House and the relevant learning rate.
• High Performance Construction Project Database to document data findings and share Passive House project lessons learned, published to the Passive House Accelerator website, with data visualization.
• Development of project detail packages from the Database to make available to trades through Source 2050 as complete project solutions.

Students

Rohan Kalyani, Emma Thomsen, and Weiya Yuan