By Sania Murtuza, Vibha Venkataraman, Tai Vu, & Yao Sun, Ph.D. (New Jersey Institute of Technology)


Guided by the theoretical and practical frameworks from the literature, the research team led efforts to specify neighborhoods in New Jersey using zip codes for this research project on low-income communities’ access to solar energy. To identify these neighborhoods, the team explored both geographic and demographic factors which consist of several thousand residents.

Utilizing resources like the New Jersey Community Solar Project Finder and demographic databases, the team identified low and moderate-income (LMI) areas within such cities as Newark, Trenton, and Jersey City, particularly targeting the poorest zip codes. The team collected data on LMI households, owner-occupied housing rates, the installation of renewable energy sources, and pipeline data from sources like the U.S. Census and New Jersey Clean Energy.

The preliminary dataset highlights demographics such as race, gender, education level, marital status, and health status alongside information on solar panel installations and renewable energy initiatives. This comprehensive approach involved cross-referencing data from various sources to provide a proper understanding of the relationship between poverty, demographics, and renewable energy access in the selected areas, with a goal of generating a clearer picture of the socioeconomic and environmental landscape in those low-income communities.

The research team is also working on creating an interactive map with Tableau to depict the regions where solar energy is most prominent in New Jersey, compared to the communities where it is less prominent. Using data from the NJ Clean Energy Program and the preliminary demographic data, the team will start with counties in Trenton, Jersey City, and Newark to build a map that would allow the viewers to get information on each of them regarding the distribution of solar panels.

In addition, the project team conducted comprehensive research to identify participant engagement tools to recruit research subjects based on zip code and homeownership status. The team considered and compared built-in screeners, technology alignment, and participant pools across different recruitment tools and have scheduled meetings with the technicians to hopefully gather further in-depth information of the tools’ various features and capabilities.