In a new study that used artificial intelligence to interpret a decade’s worth of satellite images, Stanford University researchers find that low-income communities were far less motivated than high-income communities by the kinds of tax incentives that state and local governments often offer. Even when lower-income communities initially installed some solar, their adoption rate plateaued at comparatively low levels.

“This is the first study to examine the adoption of solar power across the United States in such a granular way over a long period of years,” says Ram Rajagopal, one of the paper’s senior co-authors.

“Our conclusion is a little surprising. Low-income communities didn’t accelerate their adoption of solar power,” he added. “They either didn’t start at all or they reached saturation at a very low level. By contrast, think about smartphones, which were initially so expensive that they were only for very high-income people or super tech nerds. But in less than 10 years, they became cheaper and are now owned by hundreds of millions of people.”

The study, published in today’s print issue of Joule, employed a machine-learning model – named DeepSolar++ by the researchers – that analyzed satellite images to identify where solar panels are and when they were installed in more than 400 counties across the United States. The researchers compiled images from 2006 through 2017 and then combined that data with information about each community’s demographics as well as local financial incentives for solar power.

In 2018, the Stanford team published an initial study that analyzed the number of solar installations at a single point in time. That study confirmed that solar arrays were much less common in low-income communities, but it didn’t offer much insight about the trend over time. Were those communities starting later but then catching up, as had been the case with smartphones, big-screen televisions, and other new technologies?

“If you want to accelerate the adoption of solar in lower-income communities, performance-based incentives are the way to go,” says Ram.

Here, the researchers observed correlations, not causation. They are unsure why performance-based incentives seem effective among lower-income communities. Possibly, Ram says, the less common type of incentive may motivate the owners of apartment buildings.
 

Read full article from Stanford News, “Tax rebates for solar power ineffective for low-income Americans, but a different incentive works.”