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At least 307 records · Page 17

Equity implications of net-zero emissions: A multi-model analysis of energy expenditures across income classes under economy-wide deep decarbonization policies

With companies, states, and countries targeting net-zero emissions around midcentury, there are questions about how these targets alter household welfare and finances, including distributional effects across income groups. This paper examines the distributional dimensions of technology transitions and net-zero policies with a focus on welfare impacts across household incomes. The analysis uses a model intercomparison with a range of energy-economy models using harmonized policy scenarios reaching economy-wide, net-zero CO 2 emissions across the United States in 2050. Here we employ a novel linking approach that connects output from detailed energy system models with survey microdata on energy expenditures across income classes to provide distributional analysis of net-zero policies. Although there are differences in model structure and input assumptions, we find broad agreement in qualitative trends in policy incidence and energy burdens across income groups. Models generally agree that direct energy expenditures for many households will likely decline over time with reference and net-zero policies. However, there is variation in the extent of changes relative to current levels, energy burdens relative to reference levels, and electricity expenditures. Policy design, primarily how climate policy revenues are used, has first-order impacts on distributional outcomes. Net-zero policy costs, in both absolute and relative terms, are unevenly distributed across households, and relative increases in energy expenditures are higher for lowest-income households. However, we also find that recycled revenues from climate policies have countervailing effects when rebated on a per-capita basis, offsetting higher energy burdens and potentially even leading to net progressive outcomes. Model results also show carbon Laffer curves, where revenues from net-zero policies increase but then decline with higher stringencies, which can diminish the progressive effects of climate policies. We also illustrate how using annual income deciles for distributional analysis instead of expenditure deciles can overstate the progressivity of emissions policies by overweighting revenue impacts on the lowest-income deciles.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Community vulnerability is the key determinant of diverse energy burdens in the United States

Low-income households generally experience a high energy burden; however, the factors influencing energy burdens are beyond socio-economics. This study explores the relationships between the multidimensionality of community vulnerability factors and energy burden across multiple geospatial levels in the United States. Our study found the distribution of energy burden in 2020 showed a great deal of variety, ranging from a minimum of 2.93 % to a maximum of 30.45 % across 3142 counties. The results of non-spatial and spatial regressions showed that the vulnerability ranks of socioeconomic, household composition and disability, minority and language, household type and transportation, and COVID mortality rate are significant predictors of energy burdens at the national level. However, at the regional level, only socioeconomic, minority and language significantly influence energy burdens. Minority and language negatively impact energy burdens except for the South East-Central region. Additionally, our analyses highlight the need to consider community vulnerability indicators' spatial homogeneity and heterogeneity. At the national level, only the epidemiological factors index is a spatially homogeneous predictor; on the regional and state level, the spatially homogeneous predictors such as socioeconomic status, household composition and disability, and household type and transportation vary by region. Such a region-sensitive relationship between energy burden and the predictors indicates spatial heterogeneity. Here this study suggests policy recommendations through the lens of the multidimensionality of community vulnerability factors. Implementing flexible national energy policies while making particular energy assistance policies for the vulnerable population at the regional or state levels is essential.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

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↗

What Makes You Hold on to That Old Car? Joint Insights From Machine Learning and Multinomial Logit on Vehicle-Level Transaction Decisions

What makes you hold on to that old car? While the vast majority of household vehicles are still powered by conventional internal combustion engines, the progress of adopting emerging vehicle technologies will critically depend on how soon the existing vehicles are transacted out of the household fleet. Leveraging a nationally representative longitudinal data set, the Panel Study of Income Dynamics, this study examines how household decisions to dispose of or replace a given vehicle are: 1) influenced by the vehicle’s attributes, 2) mediated by households’ concurrent socio-demographic and economic attributes, and 3) triggered by key life cycle events. Coupled with a newly developed machine learning interpretation tool, TreeExplainer, we demonstrate an innovative use of machine learning models to augment traditional logit modeling to both generate behavioral insights and improve model performance. We find the two gradient-boosting-based methods, CatBoost and LightGBM, are the best performing machine learning models for this problem. The multinomial logistic model can achieve similar performance levels after its model specification is informed by TreeExplainer. Both machine learning and multinomial logit models suggest that while older vehicles are more likely to be disposed of or replaced than newer ones, such probability decreases as the vehicles serve the family longer. Pickup trucks and sport utility vehicles are less likely to be disposed of or replaced than cars, and leased vehicles are more likely to be transacted than owned vehicles. We find that married families, families with higher education levels, homeowners, and older families tend to keep their vehicles longer. Life events such as childbirth, residential relocation, and change of household composition and income are found to increase vehicle disposal and/or replacement. We provide additional insights on the timing of vehicle replacement or disposal, in particular, the presence of children and childbirth events are more strongly associated with vehicle replacement among younger parents.

33 ADVANCED PROPULSION SYSTEMS↗

Empirical Estimation of the Energy Impacts of Projects Installed through Residential Property Assessed Clean Energy Financing Programs in California

We examine the energy use impacts of energy efficiency and solar PV projects financed by residential property assessed clean energy (R-PACE) programs in California. We leverage household-level interval meter data to apply normalized metered energy consumption (NMEC) methods at significant scale—more than 25,000 electric meters and more than 15,000 gas meters. We develop a comparison group to account for non-project-related changes in usage. The projects include homes that replaced existing HVAC equipment with higher-efficiency units and homes that installed central heating or air conditioning equipment for the first time. We have limited information on pre-project household equipment stock so we develop a method to infer new installations. We find that projects that installed energy efficiency technologies reduce electricity consumption by approximately 3% and gas consumption by approximately 3.5% on average. When we remove homes that installed new cooling and heating equipment for the first time, savings rise to approximately 5% for electricity and approximately 6% for gas. Given the California climate and the results of an existing study of similar California projects, these results are in line with expectations. Solar PV projects produce electricity that offsets approximately 69% of household electricity consumption on average. We estimate that California R-PACE projects installed through the end of 2019 produce annual reductions in grid-tied electricity consumption of 506 GWh (equivalent to the electricity consumption of approximately 74,000 California households) and gas consumption reductions of 2 million therms (equivalent to the gas consumption of approximately 4700 California households) in a typical weather year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

COVID-19 impacts on residential occupancy schedules and activities in U.S. Homes in 2020 using ATUS

Many aspects of the daily lives of those living in the United States were substantially impacted by the COVID-19 pandemic in the year 2020. A broad diversity of measures was implemented to curb the spread of the virus, many of which included adjustments to where and how people worked, went to school, and otherwise conducted their daily lives compared to pre-pandemic times. This has impacted how residential buildings are used, how much time people spend in their homes, and as a result, how much energy these buildings consume. The main objective of this study is to analyze, at a national scale, the differences in the occupancy schedules and activities conducted in homes in the U.S., as compared to pre-pandemic. 15 years of American Time Use Survey and Current Population Survey data, from 2006 to 2020, was used in this study to analyze the occupancy schedules for both pandemic (2020) and pre-pandemic (2006–2019) times. These impacts were also analyzed with respect to variables including, weekday/weekend, month of the year, age of the occupants, household income, and household size. The impact of the pandemic on occupant schedules were most substantial in the initial months, whereas as the months progressed, these occupancy profiles slowly changed. Across 2020, people spent, on average, 8 % more time (1.9 h) in their home on weekdays, and 3–6 % (1.2 h) on weekend days. The percentage of time spent for different activities and locations within homes were also studied. For 1-member households, their time spent at home decreased whereas for 2-, 3-, and 4- member households, they spent more time at home. Overall, people spent around 45% more time doing office- and work-related activities at home compared to pre-pandemic, which is likely due to increased remote working and schooling. This research helps to improve the understanding of the occupancy presence and absence profiles in U.S. residential buildings due to the pandemic and provides new insights as to modified profiles for researchers, building designers, and policy makers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A comparison between residential relocation timing of Sydney and Chicago residents: A Bayesian survival analysis

We report that understanding households' behaviour in residential relocation timing is of great importance in the field of transport engineering and economics. This research aims to develop a residential relocation model by considering the potential dynamic impacts of other households' decisions and variables, including economic and demographic attributes, housing features, intra-household decision-making structures, travel mode choice, and other life-course attributes. A multivariate parametric survival model with both fixed and time-varying covariates is developed. To the best of the authors' knowledge, this study is the first paper in the literature of residential relocation timing to propose the use of a Bayesian model in contrast to the widely used classic frequentist approach and have conducted a discussion on its advantages. An emerging residential relocation dataset collected for two cities in Australia and the USA (Sydney and Chicago cities) has been used, which covers residence, vehicle ownership, occupation, education, economic and demographic attributes of respondents. A comprehensive comparison between the results of two cities and a comparison between two Bayesian and frequentist approaches are made. This study confirms the impact of life-course variables, intra-household decision-making behaviours, and sociodemographic attributes on home mobility. According to the model outputs, the accelerating or decelerating impact of explanatory variables on the relocation timing has been almost the same in the two cities. The Bayesian model was confirmed to have some advantages over the frequentist model, including being straightforward to interpret, availability of making inferences on the results, and ease of handling complex models, and optimisation convergence complexities.

99 GENERAL AND MISCELLANEOUS↗

Assessing thermal comfort and participation in residential demand flexibility programs

Residential space-conditioning-based demand flexibility (DF) has become an increasingly sought-after method for demand-side load management to enhance grid reliability and facilitate integration of renewable energy generation. However, predicting the effectiveness and flexibility of residential DF resources is challenging due to the variability in household energy use behaviors. Current estimates show that only 50 % of projected savings from DF resources are actualized due to regulatory, technological, and social barriers. From a household perspective, concerns over thermal comfort during space conditioning-based DF events significantly impact participation decisions. Currently, there is a very limited understanding of how thermal comfort during space-conditioning-based DF events in real-world settings impacts household energy use behaviors and, consequently, the success of DF programs in achieving targeted savings. This paper proposes a method to comprehensively assess the thermal comfort implications of DF strategies and presents results of their impacts on DF event participation decisions and demand savings. Here, the proposed method was applied to a heat pump DF field study in Cordova, Alaska. The study’s key findings are: 1) DF event setpoint offsets that maintain indoor operative temperatures between 18 to 22 °C (65 to 71°F) may be preferred in Cordova, Alaska; 2) Household-level thermal comfort is more sensitive to the duration of the DF event than to the degree of temperature offset from baseline conditions; 3) The delayed impact of changes in indoor operative temperature in response to setpoint offsets, both during and after a DF event, influences occupants’ thermal comfort perceptions and willingness to persistently participate in events. The findings from application of the proposed method can help inform future larger-scale occupant-centric DF programs as it can capture information not readily available through utility and device-level energy use data. Thus, it can supplement these sources and help program administrators develop occupant-centric DF strategies, enabling more accurate predictions of participation rates and savings estimates for space-conditioning-based DF programs.

Demand side management↗

2015 Madison County, Indiana, In the Moment Travel Study

The 2015 In the Moment Travel Study—a pilot study—captured the travel behavior and characteristics of residents in Madison County, Indiana. The Madison County Council of Governments sponsored the study, which was administered by Resource Systems Group and conducted from February to March 2015. It used an activity sampling or "random moments" sampling approach via a smartphone application to capture travel behavior and characteristics from the survey participants. This approach included brief smartphone interactions, e.g., a few minutes per interaction, conducted multiple times a day over multiple days, which was considered less burdensome than traditional household travel diary surveys, which often require 20-30 minutes in one sitting. This proof-of-concept study included households that also participated in the 2014 Heartland in Motion household travel diary survey. Because of this, an assessment of the accuracy and completeness of the collected smartphone application data and comparisons between the "random moments" sample method and traditional household travel surveys can conceivably be drawn.

1Hz data↗

2014-2015 Puget Sound Regional Travel Study

The 2014-2015 Puget Sound Regional Travel Study collected information about household and individual travel patterns for residents throughout a four-county region in Washington State. Study results were used to update the region's travel and land-use models and to calibrate local traffic and travel models. The study also helped the Puget Sound Regional Council (PSRC) and its regional partners develop plans that accommodate the diverse travel needs and preferences of residents. The Resource Systems Group administered the study on behalf of PSRC. Global positioning system (GPS)-equipped smartphones were used to provide data pertaining to the daily travel of 547 individual participants. Because the region's university students may have been underrepresented in the initial 2014 household travel study, the PSRC added a college-population travel survey in fall 2014. In spring 2015, a second household data collection effort was conducted to increase the frequency of data collection and to collect GPS data as well as a sample of longitudinal data from households that completed the 2014 survey.

1Hz data↗

1994 Cleveland Area Home Interview Travel Survey

The Greater Cleveland Regional Transit Authority and the Northeast Ohio Areawide Coordinating Agency conducted a home-interview travel survey to update information on current travel patterns and trip characteristics in the northeast Ohio area. The survey was administered during the spring of 1994 to 1,651 households, and 17,101 trips were recorded. The survey included 1,056 households from Cuyahoga County. Participants were assigned a travel day (Monday through Friday only) and mailed a household survey form and travel diaries for each household member five years old or older.

1Hz data↗

1996 Dallas-Fort Worth Travel Survey

The Dallas-Fort Worth Travel Survey encompassed households within the Consolidated Metropolitan Statistical Area of Dallas-Fort Worth. This includes all or part of Collin, Dallas, Denton, Ellis, Johnson, Kaufman, Parker, Rockwall, and Tarrant counties. The survey was conducted by the Applied Management & Planning in agreement with the North Central Texas Council of Governments (NCTCOG). Two main objectives drove the study. The main one was to update the existing data for NCTCOG's regional travel. Secondly, the survey aimed to provide new data elements to upgrade existing models. A total of 9,398 households was recruited for the study. Of these, 3,996 (42.5%) provided complete information. The survey revealed households' travel behavior preferences through a collection of information about household characteristics and travel using a unique "travel as an activity" approach. Respondents were asked to account for all of their time, including both trips and activities, during weekdays from February 19, 1996, to June 30, 1996.

1Hz data↗

2002 Treasure Valley Transportation Survey

The 2002 Treasure Valley Transportation Survey was conducted in Ada and Canyon counties in southwest Idaho, under contract with the Community Planning Association of Southwest Idaho. The full study was conducted during September 2002 and October 2002 and entailed the collection of activity and travel information for all household members, regardless of age, during an assigned 24-hour period (Tuesday, Wednesday, or Thursday). In addition to providing basic demographic information about each household and its members, the survey documented specific travel characteristics and trips made, including the number of occupants, trip purpose, time of day, and questions specific to mode use. Travel days for the survey were spread across the pilot study (August 8, 2002) and the full study (September 3, 2002-October 31, 2002). In total, 3,488 households were recruited to participate in the study. Of these, 2,582 completed travel diaries (fully completed and passed edit check procedures), and the information was retrieved from all household members.

1Hz data↗

2000 The Twin Cities Metropolitan Area Travel Behavior Inventory

The 2000 Twin Cities Metropolitan Area Travel Behavior Inventory, conducted by Twin Cities Metropolitan Council—Saint Paul, was the first major travel survey in the region since 1990. The 2000 inventory includes a home interview survey, an establishment survey, a transit survey, external station traffic counts, and an external station origin/destination survey. The study area under the household inventory survey included the seven counties of the regional Metropolitan Planning Organization and thirteen adjacent counties. Demographic, socioeconomic, and travel data were gathered for 6,386 households. Of these, 6,219 households completed the travel logs, which includes household members older than five years old.

1Hz data↗

1995 San Diego Region Travel Behavior Survey

The 1995 San Diego Region Travel Behavior Survey was conducted January-June of 1995 under the auspices of the San Diego Association of Governments. The survey was an essential element in the regional study of transportation activity and travel patterns. The survey was designed to examine the relationship between characteristics of households and travel behavior, as well as to provide information state and local decision makers require when considering future regional transportation needs and investments. In total, 2,375 households were recruited to participate in the study. Of these, 2,062 households completed travel diaries, and the information was retrieved from all household members older than age five. 2,049 were successfully geocoded to California state plane coordinates for home addresses. A total of 17,060 trips were profiled in the course of this survey during the assigned 24-hour weekday.

1Hz data↗

1965 Bay Area Transportation Study Commission Travel Survey

The Bay Area Transportation Study (BATS) Commission collected data from 30,686 Bay Area households using a face-to-face, in-home survey for a regional travel study to better understand their transportation needs and preferences. These households represented about 2.1% of the 1,387,000 Bay Area households in 1965. All household members around five years old were asked to record their travel for one assigned travel day. Data from this survey were used in efforts to develop travel demand models from the mid-1960s to the late 1970s.

1Hz data↗

1998 Dallas-Fort Worth Travel Survey

The 1998 Dallas-Fort Worth House Travel Survey accomplished two main goals: updating existing data for the North Central Texas Council of Governments regional travel demand models and providing new data to permit the models to be upgraded. The survey encompassed households within the Consolidated Metropolitan Statistical Area of Dallas-Fort Worth. A total of 4,641 households provided complete information. The survey revealed households' travel behavior preference with the collection of information about household characteristics and travel using a unique "travel as an activity" approach.

1Hz data↗

1999 Dallas-Fort Worth Travel Survey

The 1999 Dallas-Fort Worth House Travel Survey accomplished two main goals: updating the existing data for the North Central Texas Council of Governments regional travel demand models and providing new data to permit the models to be upgraded. The survey encompassed households within the Consolidated Metropolitan Statistical Area of Dallas-Fort Worth. A total of 4,641 households provided complete information. The survey revealed households' travel behavior preference with the collection of information about household characteristics and travel using a unique "travel as an activity" approach.

1Hz data↗