Lost in Siting: The Hidden Carbon Cost of Inequitable Residential Solar Installations

TL;DR

Facing ambitious decarbonization targets, the paper questions whether current rooftop PV deployment, though carbon-reducing, is energy- and carbon-efficient given inequitable siting. The authors fuse Project Sunroof, ACS5, Ember, and MEDSL data to quantify solar energy generation potential, carbon offset potential, and realized installations across ZIP codes and demographics, revealing that high-offset areas often have few installations. They introduce the SunSight toolkit to simulate multi-objective siting strategies—energy, carbon, and equity—to inform future deployments. Results show that carbon- and equity-aware strategies can increase carbon reductions by up to $39.8\%$ while preserving substantial energy gains (Round Robin achieving 94.6% of Status Quo energy). This work provides a practical framework for equitable, climate-effective solar siting and makes its data and tools publicly available.

Abstract

The declining cost of solar photovoltaics (PV) combined with strong federal and state-level incentives have resulted in a high number of residential solar PV installations in the US. However, these installations are concentrated in particular regions, such as California, and demographics, such as high-income Asian neighborhoods. This inequitable distribution creates an illusion that further increasing residential solar installations will become increasingly challenging. Furthermore, while the inequity in solar installations has received attention, no prior comprehensive work has been done on understanding whether our current trajectory of residential solar adoption is energy- and carbon-efficient. In this paper, we reveal the hidden energy and carbon cost of the inequitable distribution of existing installations. Using US-based data on carbon offset potential, the amount of avoided carbon emissions from using rooftop PV instead of electric grid energy, and the number of existing solar installations, we surprisingly observe that locations and demographics with a higher carbon offset potential have fewer existing installations. For instance, neighborhoods with relatively higher black population have 7.4% higher carbon offset potential than average but 36.7% fewer installations; lower-income neighborhoods have 14.7% higher potential and 47% fewer installations. We propose several equity- and carbon-aware solar siting strategies. In evaluating these strategies, we develop Sunsight, a toolkit that combines simulation/visualization tools and our relevant datasets, which we are releasing publicly. Our projections show that a multi-objective siting strategy can address two problems at once; namely, it can improve societal outcomes in terms of distributional equity and simultaneously improve the carbon-efficiency (i.e., climate impact) of current installation trends by up to 39.8%.

Figures (8)

• Figure 1: The estimated potential carbon offset (defined in \ref{['sec:prob']}) of building all viable rooftop solar panels plotted against the number of current rooftop installs for each ZIP code covered by Google's Project Sunroof. ZIP codes with > 250 existing installs or potential carbon offset > 800,000 metric tons are not shown for legibility, but are used to fit the trend lines. The ZIP codes are split along the quartiles of their carbon offset and each group is fit by a quadratic function to minimize LSE. Pearson correlation coefficients (PCC) for the first (blue, 0-25 percentile) to fourth quartile are 0.23, 0.12, 0.07, and -0.03 respectively.
• Figure 2: Solar energy generation potential (y-axis) and carbon offset potential (x-axis) per 400W panel. Each circle represents a single ZIP code, and the circle's color represents the magnitude of realized potential in that ZIP code.
• Figure 4: Inequity and inefficiency analysis using (a) the realized potential (existing installs / possible installs) for a given group compared to the national average and (b) the carbon offset potential per panel for a given group compared to the national average. A value of 1 means that the given group has the same statistics as the national average. Each group on the x-axis accounts for all the states. Green bars with a cross pattern represent the states with above median values, and blue bars with circles represent those with below median values. State-level statistics are computed by aggregating ZIP code-level data for all groups except political inclination, which is already at the state level.
• Figure 5: State-level granularity maps of relevant features.
• Figure 6: Projections of the additional (yearly) energy generation achieved using different panel siting strategies.