Justice 40

A model to support equitable deployment of energy interventions, by determining the least-cost portfolio of place-based energy investments to reduce energy insecurity of vulnerable populations.

Button to the J-40 tool

The J-40 tool interface

Person sitting at a table looking at an electricity bill

Background and Motivation

There is an historic relationship between socioeconomic status and energy insecurity in the United States. Studies have shown that low-income households and communities of color are more likely to live in energy inefficient homes – with poor building envelope insulation and inefficient appliances – that require more energy consumption to achieve minimum levels of comfort. In the US, nearly 28 million households are energy burdened as measured by expenditures exceeding 6% of household income on energy.

Existing energy infrastructure planning methods tend to ignore socio-demographic attributes of different locations. A tool that can assess place-based energy interventions is necessary to ensure equitable implementation of energy infrastructure. 


The goal of this tool is to offer decision makers an optimal portfolio of policy interventions that explicitly mitigates energy insecurity of a user-defined population by reducing its energy burden.

  • Energy burden is defined as the percentage of gross household income spent on energy costs (e.g. heating, cooling, electricity)
  • Energy insecurity is defined as the inability of a household to meet its basic heating, cooling, and energy needs due to an energy burden above a certain threshold (normally 6%).
Energy meter

Energy Burden as a Metric of Energy Insecurity in the U.S.

Map showing energy burden across US
Figure source: ACEEE 2020

About the Model

The model captures the combined effect of a set of energy infrastructure interventions - weatherization, rooftop solar, community solar, and community wind - on the energy burden of different sociodemographic groups. For each census tract, the model chooses the least cost combination of interventions to address the energy burden, considering the specific population demographics and techno-economic potentials of energy technologies. This model is formalized as linear optimization, with technology deployment constraints.

Chart showing structure of the model

Use the Model with Python

Button to the Python Project    Button to upload input files for the Python Project

Contribute to the Tool

Button to the repository

Project Team
Berkeley Lab
Berkeley Lab
Benjamin Sigrin headshot
Benjamin Sigrin
Jenny Heeter headshot
Jenny Heeter
Anjuli Jain Figueroa headshot
Anjuli Jain Figueroa
Department of Energy
Michael Reiner headshot
Michael Reiner
Department of Energy
Deborah Sunter headshot
Deborah Sunter
Department of Energy
Tony Reames headshot
Tony Reames
Department of Energy