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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Income Verification Strategies for Income-Based Solar Programs

The Inflation Reduction Act has created substantial new programs that support adoption of solar power by low-income households, including the $7 billion Solar For All program and the Low-Income Communities Bonus Credit Program, which increases the investment tax credit for certain types of deployment. In addition, a growing number of states are using solar programs to reduce energy burdens and create energy justice opportunities for low-income households and disadvantaged communities. Verifying the income of participating customers is an important component of these programs. Program managers are seeking strategies to verify a large number of subscribing customers in an accurate, timely, and cost-efficient manner. To help inform program managers, Berkeley Lab investigated how a number of energy and non-energy programs manage income verification. The most common approach is to require proof through tax documents, pay stubs, or other formal income documentation, which can pose an impediment to enrolling eligible customers and create a paperwork burden for administrators. In order to reduce the burden for both the applicant and the program manager, some programs use alternative methods. We identify three common alternative verification methods: -Categorical eligibility: Customers enrolled in other, similar income-verified assistance programs are automatically eligible for enrollment in other income-qualified programs. -Geographic eligibility: Eligibility is based on the customer’s location within a specified area, typically a low-income or disadvantaged community or census tract, and; -“Self-attestation”: The participant claims eligibility with or without further documentation. We describe these options, their pros and cons, give examples of how they are used, and explore how some low-income programs address administrative issues, audits, or other quality control measures. Finally, we explore the risk of mistaken verifications (finding a participant eligible when they are not) in the different strategies. While this memo was initiated by a request relating to income-based community solar programs, the methods are applicable to any program with income eligibility requirements in the energy or non-energy sector. Funding was provided for this research by the Solar Energy Technologies Office of the US Department of Energy, through the National Community Solar Partnership.

14 SOLAR ENERGY↗
DOE OSTI2023

Mobility Gaps between Low-Income and Not Low-Income Households: A Case Study in New York State

Understanding the travel challenges faced by low-income residents has always been and continues to be one of the most important transportation equity topics. This study aims to explore the mobility gaps between low-income households (HHs) and not low-income HHs, and how the gaps vary within different socio-demographic population groups in New York State (NYS). The latest National Household Travel Survey data was used as the primary data source for the analysis. The study first employed the K-prototype clustering algorithm to categorize the HHs in NYS based on their socio-demographic attributes. Five population groups were identified based on nine different household (HH) features such as HH size, vehicle ownership, and elderly status of its members. Then, the mobility differences, measured by trip frequency, trip distance, travel time, and person miles traveled, were examined among the five population groups. Results suggest that the individuals in low-income HHs consistently took fewer trips and made shorter trips compared to their not low-income counterparts in NYS. The travel distance gaps were most obvious among white HHs with more vehicles than drivers. In addition, while the population from low-income HHs made shorter trips on average (2.7 mi shorter per trip), they experienced longer travel time than those from not low-income HHs (1.8 min longer per trip). These key findings provide a deeper understanding of the travel behavior disparities between low-income and not low-income households. The findings could also support policymakers and transportation planners in addressing the critical needs of residents in low-income households in NYS and provide inputs for designing a more equitable transportation system.

Liu, Yuandong↗
DOE OSTI2018

Income Trends of Residential PV Adopters: An analysis of household-level income estimates [PowerPoint presentation]

The residential photovoltaic (PV) market has expanded rapidly over the past decade, but questions exist about how equitably that growth has occurred across income groups. Prior studies have investigated this question but are often limited by narrow geographic study regions, now-dated analysis timeframes, or coarse estimates of PV-adopter incomes. At the same time, a spate of new programs and initiatives, as well as innovations in business models and product design, have emerged in recent years with the aim of making solar more accessible and affordable to broader segments of the population. Yet, many of those efforts are proceeding without robust underlying information about the income characteristics of recent residential PV adopters. This work aims to establish basic factual information about income trends among U.S. residential solar adopters, with some emphasis on low- and moderate-income (LMI) households. The analysis is unique in its relatively extensive coverage of the U.S. solar market, relying on Berkeley Lab’s Tracking the Sun dataset, which contains project-level data for the vast majority of all residential PV systems in the country (a subset of which are ultimately included in the analysis sample). This analysis is also unique in its use of household-level income estimates that provide a more-precise characterization of PV-adopter incomes than in most prior studies.

14 SOLAR ENERGY↗
DOE OSTI2020

On the Impact of Income, Age, and Travel Distance on the Value of Time

The value of time (VOT) is a fundamental component used in transportation modeling, policy analysis, and economic appraisal. Decades of research and practice have empirically estimated the VOT across many factors (e.g., mode, purpose, time, comfort, etc.), yet little is known about its underlying form. Although it is well established that VOT can vary, it is still unclear whether patterns exist in this variation. The objective of this paper is not to merely estimate the VOT, but to model the VOT across multiple continuous and interacting variables. The purpose is to reveal its functional form with respect to mode, age, gender, purpose, income, and time of day to provide a generalizable understanding for future research and practice. Such an understanding can help develop simpler models and reduce the need for bespoke estimations for every conceivable variable perturbation. This research utilized a household travel survey containing 14,159 reported trips with imputed travel time and costs for the alternative mode choices. The average overall estimated VOT is 40.32 $/h, with results showing VOT varying log-linearly with income and trip distance, but following a Gaussian function (normal curve) with age. Overall, the results show that travel distance dominates VOT variation, which increases exponentially at a rate that is 3.61 times higher per mile of distance than per $10,000 of income, and that VOT by age peaks at age 54. This basic understanding of how the VOT varies sets the foundation for answering the subsequent question for why it might vary.

Engineering↗
DOE OSTI2025

Demographic Microsimulator for Integrated Urban Systems: Adapting Panel Survey of Income Dynamics to Capture the Continuum of Life

Agent-based models (ABMs) in transportation modeling simulate activity and travel decisions at the disaggregate level of households and individuals. To do this, ABMs require detailed and realistic information on agents’ socioeconomic and demographic characteristics. Various synthetic population generators have been proposed to address this need. However, most of those currently in practice are cross-sectional in nature and do not account for the dynamics within households and individuals as they progress through life events over time. This is a major shortcoming, as literature has shown that transportation decisions are affected by the transition between and co-occurrence of life cycle events. While some demographic evolution simulators have been proposed to address this issue, they are developed using cross-sectional data and capture only a small set of life cycle events and their interdependence. Addressing these drawbacks, we propose a demographic microsimulator (DEMOS) that captures the “continuum of life” by considering a range of household- and individual-level life cycle events. DEMOS is developed using the Panel Survey of Income Dynamics, one of the world’s longest-running longitudinal surveys. The DEMOS submodels consider key life cycle events that are influenced by agents’ demographic variables. DEMOS is applied to evolve the population of the San Francisco Bay Area over a 9-year horizon. Results demonstrate how DEMOS generates life trajectories and how DEMOS outputs match the observed demographic trends. DEMOS is expected to enable longitudinal analysis in the context of ABMs and expand ABMs analyses relating to dynamic processes such as household-level vehicle transactions.

Demographic evolution↗
DOE OSTI2021

The Solar Influencer Next Door: Predicting Low-Income Solar Referrals and Leads

Increasing the adoption of solar among low-to-moderate income (LMI) households remains an important policy goal because of its promise to simultaneously reduce energy burden and support the just distribution of benefits of renewable energy. However, scaling LMI solar remains challenging due to affordability and access issues. Most existing LMI adoption has occurred under public-funded programs, highlighting the importance of increasing the cost-effectiveness of these programs at scale. We develop a new household-level data set on LMI solar lead acquisition, referrals, and adoption to understand the processes through which LMI solar uptake has occurred in California. Then, we develop models to predict two sub-mechanisms in the solar adoption process: whether an otherwise qualified lead becomes "lost" i.e. non-responsive to outreach and, for existing clients, whether they refer solar to others. For the program analyzed, participants received their solar system at no cost, which deemphasizes economic drivers of solar adoption and could differ from other program experiences. Both models substantially improved the accuracy of prediction relative to a baseline. Overall, we find that peer effects and solar economics are important to predicting referrals, and household demographic factors in lead loss prediction. Finally, we find that referrals are both the highest quality and largest source of LMI solar leads, providing a promising mechanism to expand LMI programs further.

customer acquisition costs↗
DOE OSTI2023

Increasing the Reach of Low-Income Energy Programmes through Behaviourally Informed Peer Referral

Subsidized energy assistance programmes are a popular policy tool for promoting energy justice, but, like other social benefits programmes, are often undersubscribed. To improve uptake, some programmes have turned to social influence strategies, such as asking programme participants to refer their peers. Here, through a field experiment with California's low-income solar programme (N = 7,676), we show that referral behaviour depends on how existing participants are approached. Adding behavioural science strategies to a referral reward increases peer referral rates, referral quality and ultimately solar adoption. Compared with only reminding existing adopters of a potential US$200 reward for referrals that result in adoption, adding an appeal to reciprocity through a non-contingent US$1 gift - and further combining this gift with a simplified referral process - leads to 2.6-5.2 times as many solar contracts. These results highlight the potential of behaviourally informed peer referral programmes to accelerate equitable access to clean energy.

California↗
DOE OSTI2022

Residential Solar-Adopter Income and Demographic Trends: November 2022 Update [Slides]

The report describes income, demographic, and other socio-economic trends among U.S. residential rooftop solar adopters. The report is based on address-level data for roughly 2.8 million residential rooftop solar systems installed through 2021, representing 86% of all U.S. systems. With its unique size, geographic scope, and level of detail, this report is intended to serve as a foundational reference document for policy-makers, industry stakeholders, and researchers. Key findings include the following: -Median solar adopter income was about $\$110$k/year in 2021, compared to a U.S. median of about $\$63$k/year for all households and $\$79$k/year for all owner-occupied households -The degree of income skew varies significantly across all states, but all states exhibit some positive income skew, with median solar-adopter incomes ranging from 131-168% of the respective county-median income for all households -Notwithstanding the fact that solar adopter incomes skew high, a substantial share of adopters could be considered low-to-moderate income (LMI), with 22% of all 2021 adopters earning less than 80% of area median income, and an additional 21% between 80% and 120% of area median income. -Solar-adopter incomes are declining over time, with median incomes dropping from $\$129$k in 2010 to $\$110$k in 2021, as adoption becomes more proportionately distributed across the population and has started to broaden into low- and middle-income states since 2016. -Solar-adopter incomes are consistently higher for systems paired with battery storage, for host-owned systems, and for systems installed on single-family homes; higher income adopters also consistently install larger systems. -Solar adopters tend to live in Census Tracts not identified as “disadvantaged communities” (using the U.S. Department of Energy’s interim definitions developed March 2022), making up 11% of adopters compared to 18% of U.S. households. -Compared to the broader population, solar adopters tend to: identify as Non-Hispanic White, be primarily English-speaking, have higher education levels, be middle-aged, work in business and finance-related occupations, and live in higher-value homes In conjunction with the report, Berkeley Lab has published an updated accompanying set of online data visualizations that allow users to further explore the underlying data. Berkeley Lab is also offering related analytical support to states, local agencies, and other organizations on issues related to solar adoption among low-to-moderate income households; requests for analytical support may be submitted through this online form.

13 HYDRO ENERGY↗
DOE OSTI2023

Residential Solar-Adopter Income and Demographic Trends: 2023 Update [Slides]

The report describes income, demographic, and other socio-economic trends among U.S. residential rooftop solar adopters. The report is based on address-level data for roughly 3.4 million residential rooftop solar systems installed through 2022, representing 86% of all U.S. systems. With its unique size, geographic scope, and level of detail, this report is intended to serve as a foundational reference document for policy-makers, industry stakeholders, and researchers. Key findings include the following: (1) Median solar adopter income was about $\$117$k/year in 2022, compared to a U.S. median of about $\$69$k/year for all households and $\$86$k/year for all owner-occupied households; (2) The degree of income skew varies significantly across all states, but all exhibit some positive income skew relative to all households in the state, with median solar-adopter incomes ranging from 108-180% of the respective state-median income for all households; (3) Roughly 45% of solar adopters in 2022 had incomes below 120% of their area median income (AMI), a threshold sometimes used to define “low-and-moderate income” (or LMI), while 23% were below 80% of AMI, often used to define “low-income”; (4) Solar adoption continues to shift toward less affluent households, with the median current income of solar adopters dropping from $\$140$k for households that installed systems in 2010 to $\$117$k in 2022; (5) PV systems installed in 2022 by households earning less than $50k had a median size of 6.1 kW, 34% were third-party owned, and 5% included battery storage, compared to corresponding values of 7.6%, 17%, and 15% for households earning more than 200 dollars k; and (6) Compared to all households in their respective state, solar adopters tend to be negligibly more rural; have higher home values; and are more likely to be college educated, identify as non-Hispanic white, live outside a disadvantaged community (DAC), be middle-aged, work in a business or financial occupation, and own a single-family home In conjunction with the report, Berkeley Lab has published an updated accompanying set of online data visualizations that allow users to further explore the underlying data. Berkeley Lab is also offering related analytical support to states, local agencies, and other organizations on issues related to solar adoption among low-to-moderate income households.

14 SOLAR ENERGY↗
DOE OSTI2024

Residential Solar-Adopter Income and Demographic Trends: 2024 Update [Slides]

The report describes income, demographic, and other socio-economic trends among U.S. residential rooftop solar adopters. The report is based on address-level data for roughly 4.1 million residential rooftop solar systems installed through 2023, representing 87% of all U.S. systems. With its unique size, geographic scope, and level of detail, this report is intended to serve as a foundational reference document for policy-makers, industry stakeholders, and researchers. Key findings include the following: -The median income of households that installed solar in 2023 was about $\$$115k/year, compared to a U.S. median of $\$$75k/year for all households and $\$$94k/year for all U.S. owner-occupied households. -Compared to owner-occupied households in the same state, 2023 solar-adopter incomes were 7% higher in the median case, and in 10 states, median solar-adopter incomes were below the corresponding median income for all owner-occupied households. -Roughly 49% of solar adopters in 2023 had incomes below 120% of their area median income (AMI), a threshold sometimes used to define “low-and-moderate income” (or LMI), while 26% were below 80% of AMI, often used to define “low-income”. -Solar adoption continues to shift toward less affluent households over time, with the median present-day income of solar adopters dropping from $\$$141k for households that installed systems in 2010 to $\$$115k in 2023. -PV systems installed in 2023 by households earning less than $\$$50k had a median size of 6.4 kW, 33% were third-party owned, and 6% included battery storage, compared to corresponding values of 8.0 kW, 18%, and 14% for households earning more than $\$$200k. -Compared to all households in their respective state, solar adopters in 2023 were slightly more likely to be college educated and to live in rural areas; had higher home values; and were more likely to live outside a disadvantaged community (DAC), be middle-aged, identify as non-Hispanic white, work in a business or financial occupation, and own a single-family home. In conjunction with the report, Berkeley Lab has published an updated accompanying set of online data visualizations that allow users to further explore the underlying data. Berkeley Lab is also offering related analytical support to states, local agencies, and other organizations on issues related to solar adoption among low-to-moderate income households; requests for analytical support may be submitted through this online form.

14 SOLAR ENERGY↗
DOE OSTI2024

A consistent dataset for the net income distribution for 190 countries and aggregated to 32 geographical regions from 1958 to 2015

Abstract. Data on income distributions within and across countries are becoming increasingly important for informing analysis of income inequality and understanding the distributional consequences of climate change. While datasets on income distribution collected from household surveys are available for multiple countries, these datasets often do not represent the same concept of inequality (or income concept) and therefore make comparisons across countries, over time and across datasets difficult. Here, we present a consistent dataset of income distributions across 190 countries from 1958 to 2015 measured in terms of net income. We complement the observed values in this dataset with values imputed from a summary measure of the income distribution, specifically the Gini coefficient. For the imputation, we use a recently developed nonparametric principal-component-based approach that shows an excellent fit to data on income distributions compared to other approaches. We also present another version of this dataset aggregated from the country level to 32 geographical regions. Our dataset is developed for the purpose of calibrating models such as integrated human–Earth system models with detailed data on income distributions. This dataset will enable more robust analysis of income distribution at multiple scales. The latest version of our data are available on Zenodo: https://doi.org/10.5281/zenodo.7093997 (Narayan et al., 2022b).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Long-term decarbonization impacts on residential energy security across income groups and US states

Abstract The impact of a transition to a net-zero economy on the residential energy sector across diverse income groups in the US remains uncertain. Here, we employ an integrated human-Earth system model, incorporating an expanded set of ten income groups in the residential energy sector, to examine the distributional impacts of long-term decarbonization scenarios on residential energy security at the state level through 2050. We use multiple metrics of energy security, including energy burden, energy satiation gap, and the distribution of energy service across income groups. Our findings show that the net-zero decarbonization scenarios affect residential energy security differently across income groups, with low-to-mid-income groups experiencing larger negative impacts on the dimensions studied here. Comparatively, climate change impact on residential energy security is minor through 2050 based on our model outcomes. Specifically, the net-zero decarbonization scenarios lead to increased energy burden across all income groups and states in 2050, where the lowest (highest) income group in each state shows an average of 0.6 (0.2) percentage point increase in energy burden, relative to the business-as-usual in 2050. The distribution of energy service consumption across income groups is also slightly more skewed under these scenarios. As incomes grow across all deciles in the future, residential energy security generally improves through 2050. Targeted interventions could mitigate the disproportionate impacts that some groups could incur under a transition to a net-zero economy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Residential energy demand, emissions, and expenditures at regional and income-decile level for alternative futures

Income and its distribution profile are important determinants of residential energy demand and carry direct implications for human well-being and climate. We explore the sensitivity of residential energy systems to income growth and distribution across shared socioeconomic pathway-representative concentration pathways scenarios using a global, integrated, multisector dynamics model, Global Change Analysis Model, which tracks national/regional household energy services and fuel choice by income decile. Nation/region energy use patterns across deciles tend to converge over time with aggregate income growth, as higher-income consumers approach satiation levels in floorspace and energy services. However, in some regions, existing within-region inequalities in energy consumption persist over time due to slow income growth in lower income groups. Due to continued differences in fuel types, lower income groups will have higher exposure to household air pollution, despite lower contributions to greenhouse gas emissions. We also find that the share of income dedicated to energy is higher for lower deciles, with strong regional differences.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Low-Income Energy Affordability: Conclusions From A Literature Review

This paper examines the persistent problem of high energy burdens among low-income households, based on a review of more than 180 publications that pointed to several promising opportunities to address energy affordability including inclusive solar programs, leveraged health care benefits, and behavioral economics. using an equity and affordability lens. Even after decades of weatherization and bill-payment programs, low-income households, on average, continue to spend a higher share of their income on electricity and natural gas bills than any other income group. Energy burden for low-income households is not declining, and it remains persistently high, particularly in the South, in rural America, among minority households, and those with children and elderly residents. On a per household basis, utility companies spend less on energy-efficiency programs for low-income households than for other income groups. In addition, government and utility programs that promote rooftop solar power, electric vehicles, and home energy storage are largely inaccessible to low-income households. Our review identifies promising opportunities to address energy affordability including inclusive solar programs, leveraged health care benefits, behavioral economics, data analytics, advanced information and communication technologies, and grid resiliency. Scalable approaches require linking implementing agencies, programs and policies to tackle the complex web of causes and impacts on low-income households with high energy burdens.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Non-parametric projections of the net-income distribution for all U.S. states for the Shared Socioeconomic Pathways

Abstract Income distributions are a growing area of interest in the examination of equity impacts brought on by climate change and its responses. Such impacts are especially important at subnational levels, but projections of income distributions at these levels are scarce. Here, we project U.S. state-level income distributions for the Shared Socioeconomic Pathways (SSPs). We apply a non-parametric approach, specifically a recently developed principal components algorithm to generate net income distributions for deciles across 50 U.S. states and the District of Columbia. We produce these projections to 2100 for three SSP scenarios in combination with varying projections of GDP per capita to represent a wide range of possible futures and uncertainties. In the generation of these scenarios, we also generated tax adjusted historical deciles by U.S. states, which we used for validating model performance. Our method thus produces income distributions by decile for each state, reflecting the variability in state income, population, and tax regimes. Our net income projections by decile can be used in both emissions- and impact-related research to understand distributional effects at various income levels and identify economically vulnerable populations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Low-Income Energy Affordability Data - LEAD Tool - 2018 Update

The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, and energy characteristics. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts. The file below, "1. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "2. Data Dictionary 2018". The Low-Income Energy Affordability Data comes primarily from the 2018 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2018 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit: https://www.energy.gov/eere/slsc/low-income-energy-affordability-data-lead-tool For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), visit: https://betterbuildingsinitiative.energy.gov/accelerators/clean-energy-low-income-communities

affordability↗