Peer Effects in the Diffusion of Solar Photovoltaic Panels

Bibliographic Details

• Authors: Bryan Bollinger, Kenneth Gillingham
• Journal: Marketing Science (INFORMS)
• Year: 2012
• DOI: 10.1287/mksc.1120.0727
• Affiliations: Yale University (Gillingham)

Methodology

• Dataset: California Solar Initiative rebate program records
• Period: January 2001 to December 2011
• Sample: 85,046 requested residential installations
• Analysis: Spatial econometrics measuring adoption probability as a function of existing installations at zip code and street level
• Controls for demographic, housing, and economic variables

Key Quantitative Findings

Zip Code Level

• Per-installation effect: Each additional installation in a zip code increases the probability of new adoption by 0.78 percentage points
• Per-ten effect: 10 additional installations raise adoption probability by 7.8%
• Installed base elasticity: A 10% increase in installed base corresponds to a 54% increase in adoption rate
• Increasing returns: The effect grows stronger as more installations accumulate (positive feedback loop)

Street Level

• Street-level effect reported as stronger than zip code level
• One source reports approximately 9% increase in street adoption rate per 1% increase in installed solar on the same street

Demographic Patterns

• Higher adoption among homeowners aged 45-65
• Larger households and longer commutes associated with greater exposure to installations
• Visibility of existing panels is a key mechanism

Mechanisms Identified

1. Information transfer: Neighbors report cost savings, reducing uncertainty
2. Social comparison: Some adopters motivated by not wanting to be “one-upped”
3. Visibility: Seeing panels on nearby roofs normalizes the technology
4. Marketing efficiency: Authors note targeting areas with existing installations is “a promising strategy” for reducing customer acquisition costs

Relevance to Demonstration Neighborhood Question

This paper provides the strongest quantitative evidence that demonstration effects work for energy technology adoption at the neighborhood scale. Key implications:

1. Concentration matters: The increasing-returns finding means a dense cluster of passive buildings would generate disproportionately more adoption than scattered installations
2. Street-level effects are strongest: A demonstration neighborhood (visible, walkable) should produce stronger peer effects than dispersed projects
3. The mechanism is proven for solar PV: Whether the same effect transfers to passive thermal buildings (which are less visually distinctive) is an open question
4. Corroborates Blackburn 2020: The wiki’s existing Blackburn reference (demos double local adoption) is consistent with Bollinger & Gillingham’s finding of strong neighborhood-scale peer effects

Limitations

• Study covers solar PV panels, not passive building design
• Solar panels are highly visible; passive thermal features are not
• California market conditions may not generalize
• Study period predates current passive house movement
• The paper addresses marketing implications more than policy design