In September 2024, Hurricane Helene inflicted unprecedented damage across South Carolina’s Upstate and nearby inland counties in North Carolina, causing widespread, prolonged power outages that disproportionately affected rural communities and demanded tightly coordinated action among public utilities, government agencies, and nongovernmental organizations to restore electric service. To capture time-sensitive lessons from this response, we collected a qualitative dataset on interagency coordination during power-restoration operations. This data descriptor presents a set of in-depth, semi-structured interviews conducted in 2025 with key personnel directly involved in the Upstate South Carolina restoration including 21 managers from utilities, governmental bodies, and community organizations. The interviews probe organizational adaptation, interagency alignment, and the emergence of team flow in coordinated restoration. The dataset is intended to help researchers and policymakers understand the complex, ad hoc collaboration that underpins disaster recovery and to provide qualitative context that complements quantitative analyses of infrastructure resilience.

Air pollution remains a significant environmental health risk in some regions of the United States, contributing to a wide range of health impacts, especially among socially and medically vulnerable populations. Historically, coal-fired power plants have been major sources of ambient fine particulate matter (PM2.5); although their contributions have declined substantially in recent decades. Despite well-documented geographic declines in coal-related PM2.5 exposure, less is known about persistent regional disparities and how they intersect with social determinants of health. This study examines associations between coal-related PM2.5 exposure and health across U.S. counties in 2020, using nine indicators spanning mental and physical conditions. We find that a 1% increase in coal PM2.5 exposure is associated with a 0.02% to 0.34% increase in the prevalence of several outcomes, including mental health disorders, chronic obstructive pulmonary disease (COPD), and diabetes. These associations vary markedly by region, with the strongest effects concentrated in the Southeastern United States. Additionally, high household energy burden amplifies the adverse mental and physical health impacts of coal PM2.5 exposure. Persistent regional inequities underscore the need for targeted, place-based interventions to reduce ongoing local exposure and its health consequences.

As climate-induced disasters increasingly compromise electric grid reliability, Mobile Community Microgrids (MCMs) have emerged as a promising strategy to strengthen local energy resilience. This study examines the socioeconomic and perceptual determinants of public MCM acceptance using a nationally representative survey of 1,996 U.S. residents. Hierarchical regression results indicate that the desire for improved power reliability is the strongest predictor of acceptance, followed by expectations of faster disaster response and lower energy costs. Power-outage experience is also a significant driver of support, with stronger effects among men and respondents who face frequent disruptions. Although political ideology appears influential in baseline models, its association attenuates after accounting for economic conditions. Preferences for deployment locations further diverge by outage experience: respondents with frequent outages prioritize residential and disadvantaged communities, whereas those with fewer disruptions place greater emphasis on critical infrastructure. These findings highlight the need for MCM deployment strategies that are responsive to heterogeneous community perceptions and place-based resilience priorities.

Public support for clean energy tends to follow perceived climate risk, but the relationship between objective climate hazards and that perception remains unexplored. Drawing on Yale Climate Opinion Maps, FEMA's National Risk Index, and 2024 election results across 3094 U.S. counties, we examine geographic co-variation among objective climate risk, perceived risk, social vulnerability, political context, and support for a clean energy transition. We found that support is strongly spatially clustered, closely tracking regional patterns of risk perception and political identity. The relationship between objective hazards and perceived risk reverses across the political spectrum: in liberal-leaning counties, greater hazard exposure coincides with higher perceived risk, wheras in conservative-leaning counties, it coincides with lower perceived risk. Perceived climate risk is the strongest county-level correlate of clean energy support, but this association is weaker in conservative counties than in liberal ones. Social vulnerability is positively associated with support in conservative counties, and negatively in moderate and liberal ones. The same objective risk and vulnerability thus translate into markedly different risk perceptions and energy preferences across poli contexts, and strategies that ignore this asymmetry are unlikely to build durable support.

This study examines utility-level variation in annual residential electricity consumption across Georgia and North Carolina, focusing on utility governance, electricity pricing, service-territory characteristics, and utility-run incentive programs. We construct a hand-collected inventory of 699 residential energy programs across 193 electric utilities and merge it with utility-level data, such as electricity consumption, rate, energy burden, climate, demographics and housing conditions, and EV charging-station data. The analytic sample includes 105 utilities with complete consumption and covariate data. Our results show that each additional EV program associated with approximately 310 kWh (2.5%) higher annual per-household use, even after controlling for public EV charging-station density. Cooperative utilities initially exhibit higher household consumption than municipal utilities, but this difference loses statistical significance after accounting for program offerings and demographic factors. A one-cent/kWh higher electricity rate is associated with a 227 kWh (1.8%) to 334 kWh (2.7%) reduction in annual per-household electricity consumption, which may reflect both efficiency-driven conservation and energy-limiting behaviors. Prevalence of energy burden is associated with more electricity use after controlling for income, suggesting structural inefficiency in high-burden utility territories. These findings indicate that utility incentive programs should be paired with managed charging, demand response, targeted efficiency investment, and time-of-use pricing.

Achieving substantial decarbonization in the United States requires broad public support across diverse regions, yet the geographic outline of policy endorsement remain underexplored. Addressing this gap, we analyze nationally representative survey data to assess attitudes toward renewable energy investment, carbon regulation, climate education, and fossil fuel development in eight U.S. regions. Grounded in polycentric governance theory, our approach links regional preferences to local economic dependencies and environmental priorities. Results show significant regional disparities: support for prioritizing clean energy development is highest in the Northeast at 69.1 % and lowest in the Northern Great Plains at 55.3 %. Northeastern and Northwestern residents strongly favor renewable energy, carbon regulation, and climate change education, whereas the Great Plains regions exhibit weaker backing for these policies and greater support for fossil fuel expansion. These findings highlight the political geography of the U.S. and energy transition, suggesting a one-size-fits-all federal climate strategy may overlook regional realities. This study recommends tailoring climate strategies to regional economic contexts to enhance policy durability. By incorporating regional opinion dynamics, this study advances understanding of how public attitudes can enable or constrain national climate goals.

The electric power system is essential to social and economic well-being but remains vulnerable to climate-related hazards that exacerbate power outages. This study asks: Are the duration of power outages and the extent of energy burdens (EBs) associated with public beliefs about climate change across the United States? While previous research identified media exposure, demographics, and geography as influences on climate change beliefs, the role of power outages and EBs has not been examined. Using county-level geospatial regression analysis, we find a significant positive association between power outages and climate change beliefs across the continental United States, with longer outages associated with greater beliefs. Each one-unit increase in maximum outage duration corresponds to a 5.16% increase in beliefs, accounting for EB, media exposure, political affiliation, and education. However, the interaction between power outages and EBs reveals a negative relationship with climate change beliefs in three out of the four regions, namely, the South, West, and Midwest, indicating higher energy costs can dampen the belief-enhancing effect of outages. These findings highlight that power outages and EBs are important correlates of climate change beliefs and have region-specific implications for climate action policy and public communication amid mounting climate-related disruptions.

The adoption of rooftop photovoltaic (PV) systems can create upward pressure on retail electricity rates as utilities are forced to spread their fixed costs of generation and transmission across a smaller customer base. Since high-income households are more likely to purchase PV systems, low-income households may be disproportionately impacted by these rate increases. Using a novel combination of agent-based computational economic modeling and a choice experiment of rooftop solar adoption, we show how this pecuniary externality between low- and high-income customers increases low-income electricity bills by 10% in an area with some of the highest poverty rates in the United States. Since high-income solar adoption is less sensitive to electricity bills than low-income adoption, this pecuniary externality also reduces PV adoption inequity by nearly 1 percentage point. However, the reduction in PV adoption inequity, and the bill savings it generates, are not large enough to offset the $7.8 million ($9.86 per customer) annual increase in low-income electricity bills. Low-income assistance programs will likely fail to fully internalize the pecuniary externality due to horizontal and within-income-group vertical equity concerns.

Residential rooftop photovoltaic (RPV) solar power offers a dual benefit: it reduces energy bills and helps combat climate change. Yet, the widespread adoption of RPV is influenced by multifaceted factors. This research delves into five key dimensions: socio-demographic and household characteristics, spatial location and racial diversity, solar suitability and rooftop potential, energy pricing and consumption behavior, and policy and incentives. Through an analysis of U.S. census tract data, this study covers residential solar adoption across all U.S. states, encompassing regions with high solar radiation and the top 5% of adoption areas. Regression models reveal that the most robust predictors of RPV adoption are policy incentives and the potential for energy cost savings. This result underscores an interactive relationship involving electricity expenses, solar radiation, and suitability. The interplay of income and education significantly contributes to a higher adoption rate. Surprisingly, the interaction between energy pricing and consumption inhibits solar adoption in areas with substantial energy use. Longer commutes negatively impact adoption, resulting in lower rates in rural areas. However, diverse populations in these regions lead to heightened adoption rates. These findings emphasize the necessity of scrutinizing interactive effects and suggest actionable policy recommendations to facilitate the widespread adoption of RPV technology, particularly in underserved and diverse communities.

Intermittent renewable electricity generation increases flexibility needs in energy systems. Demand response (DR) programs can effectively address these needs. However, their success depends on consumer willingness to participate. This study quantifies load-shifting potentials from air conditioners, water heaters, electric vehicles, and washing machines based on survey data from 1,402 Japanese households. Logistic regression analyses link willingness to engage in DR programs with socio-demographic and dwelling characteristics, yielding specific participation rates by appliance and household type. These participation rates are integrated with appliance-specific ownership data and annual energy consumption calculations to estimate flexibility potentials for Japan's Kanto region. The study employs three distinct quantification approaches: (1) a bottom-up activity-based model, (2) a top-down average-based estimate, and (3) a hybrid method using synthetic population data. The results indicate that water heaters consistently exhibit the highest load-shifting potential across all scenarios and time frames, both per household and regionally. By 2050, electric vehicles emerge as an equally important source of flexibility due to increased adoption rates and improvements in water heater efficiency. While the top-down estimation method effectively captures overall population-level flexibility potentials, the hybrid approach is critical for accurately representing household-level variations. These findings facilitate the development of targeted DR programs and contribute to more realistic flexibility modeling within energy systems analysis. Future research should further explore behavioral uncertainties and address additional barriers to the implementation of residential demand response.

Media coverage plays a crucial role in shaping public understanding of energy challenges and climate policy; robustly monitoring the way that energy challenges are depicted in news media is therefore important for understanding progress on meeting climate change goals. This study employs corpus linguistics methodology to analyze global media representations of the energy trilemma - the balance between accessibility, security, and environmental sustainability - in coverage of UN Climate Change Conferences (COP21-COP27, 2015–2022). Analysis of 18,578 news articles (11.6 million words) from 12 countries reveals significant cross-national variation in trilemma coverage. Using a novel quantitative measure validated against human ratings, we found that sustainability dominated media discourse compared to security and accessibility. This ranking was observed in each nation, though there was also variation between countries and over time in the extent of coverage of each aspect of the trilemma. Notably, media coverage patterns diverged from objective energy policy indicators. The study highlights the advantages of such corpus linguistic analysis of news articles over both traditional qualitative analyses of language and quantitative, decontextualized language-analysis tools.

Rooftop solar adoption has increased considerably in recent years thanks to a combination of lower panel costs and generous incentive programs. This paper estimates the increase in residential rooftop solar adoption associated with three types of solar incentive programs and isolates the effect of these programs in both high and low-income census tracts. We utilize a dataset of census tract-level rooftop solar adoption compiled using a machine learning-based image classification tool that identifies solar photovoltaic panels from satellite images. This allows us to study areas of the country that have lower solar adoption rates and incomes than areas previously studied. We find evidence that programs designed specifically to encourage adoption in low-income areas are associated with a smaller gap between low- and high-income solar adoption. However, property-tax benefits and net metering, which are more prevalent across the U.S., are associated with an increase in the gap between low- and high-income solar adoption.

Recent research underscores the importance of ensuring that net-zero pathways are perceived as legitimate and socially acceptable, as public attitudes can trigger significant backlash. This article investigates the narratives surrounding industrial decarbonization in the UK within Twitter’s ‘digital town square.’ Intermediary agents play a crucial role in shaping this discourse by fostering debate, offering specialized expertise, and promoting specific technological narratives. Our contribution is twofold: first, we systematically analyze tweets from intermediary agents to identify key technological and economic narratives; second, we employ natural language processing to demonstrate a striking consistency between these narratives and the perspectives of incumbent industries and central government. This alignment suggests that the technological selection of certain decarbonization strategies by both industry and government may undermine the social acceptability of industrial decarbonization technologies and associated infrastructure.

The adoption of electric vehicles (EVs) is transforming the landscape of energy consumption. While the technical and economic dimensions of EV adoption are increasingly well understood, the aspect of justice in demand flexibility remains underexplored. This study examines the complex relationship between flexibility in EV charging behaviors and the influence of socio-psychological and justice factors. We explore a range of demographic and social-psychological variables including charging anxiety, environmental concerns, perceived cost-saving perception, perceived privacy, and trust in utility providers. Our results reveal that these variables positively influence the changes in charging habits, including time-shifting and load-reduction. This study also uncovers disparities in charging behavior adjustments across various demographics groups. For instance, White respondents are more likely to charge their EVs during off-peak hours than their non-White counterparts and homeowners show a greater intention to reduce EV charging load during peak hours compared to renters. Additionally, high-income individuals exhibit a stronger willingness to shift charging times to off-peak, with White respondents within the high-income group being the most likely to reduce the amount of charging load during peak hours. Conversely, low-income White respondents are less inclined to make such adjustments. These disparities are likely tied to socioeconomic status, as more vulnerable groups often face greater constraints in adjusting their schedules. Therefore, it is imperative that policies prioritize flexibility justice by addressing the specific needs and behaviors of vulnerable groups, aiming to mitigate the additional burdens resulting from their limited flexibility.

The intricate relationship between energy burden (EB) and indoor environmental quality (IEQ) is vital for human well-being within the built environment. While previous studies have focused on vulnerable groups, individuals with health concerns have received limited attention. This interdisciplinary study delves into the disparities and cumulative impacts of EB, the built environment, and social demographics, with a particular focus on assistance-dependent populations (ADPs). Based on 2,588 online respondents in the U.K. during the COVID-19 pandemic, our research unveils significant relationships between EB, heating insecurity, and perceived thermal discomfort. ADPs reported an average EB of 5.5% and poorer housing quality than their counterparts, with inadequate temperatures emerging as a primary concern. The correlation analysis highlights a strong connection between the perception of thermal discomfort and energy-saving behaviors. We also explored the interactions of EB, homeownership, and assistance-dependent status to uncover concentrated disadvantages in housing issues and identified vulnerable groups. Notably, irrespective of their EB, ADPs face more challenges than non-ADPs, highlighting the greater predictive significance of assistance-dependent status over EB. Moreover, our findings suggest that assistance-dependent renters constitute the most vulnerable group. Considering that ADPs already contend with preexisting physical illnesses, the revelation that they are more prone to experiencing higher EB and residing in inferior conditions is of utmost importance. It underscores the urgency of mitigating these additional health risks and ensuring the availability of a healthy and safe living environment for vulnerable demographics, thereby advancing the goal of equity within the built environment for overall well-being.

Digital divide and energy insecurity are pervasive issues among underserved communities, issues that become prounoued during the COVID-19 lockdowns. These disparities underscore the critical need to address them promptly to narrow socio-economic gaps. Our study, based on an online survey of 2,588 respondents in the United Kingdom, explores how concentrated socio-economic disadvantage exacerbates insecurities relating to energy and internet access. Our findings reveal that marginalized groups including low-income households, women, renters, ethnic minorities, and individuals with lower educational attainment are disproportionately affected. Our research extends beyond financial implications to explore the broader social and psychological effects such as trust in utility and internet providers. The study also demonstrates how heightened burdens from energy and internet costs adversely affect the quality of indoor environments, underscoring the interconnected nature of these challenges. Based on these insights, we advocate for policy interventions that adopt comprehensive social justice frameworks to tackle these intersecting inequalities effectively.

Collective privacy loss becomes a colossal problem, an emergency for personal freedoms and democracy. But, are we prepared to handle personal data as scarce resource and collectively share data under the doctrine: as little as possible, as much as necessary? We hypothesize a significant privacy recovery if a population of individuals, the data collective, coordinates to share minimum data for running online services with the required quality. Here, we show how to automate and scale-up complex collective arrangements for privacy recovery using decentralized artificial intelligence. For this, we compare for the first time attitudinal, intrinsic, rewarded, and coordinated data sharing in a rigorous living-lab experiment of high realism involving >27,000 real data disclosures. Using causal inference and cluster analysis, we differentiate criteria predicting privacy and five key data-sharing behaviors. Strikingly, data-sharing coordination proves to be a win-win for all: remarkable privacy recovery for people with evident costs reduction for service providers.

Energy justice advocates for the equitable and accessible provision of energy services, mainly focusing on marginalized communities. Adopting machine learning in analyzing energy-related data can unintentionally reinforce social inequalities. This perspective highlights the stages in the machine learning process where biases may emerge, from data collection and model development to deployment. Each phase presents distinct challenges and consequences, ultimately influencing the fairness and accuracy of machine learning models. The ramifications of machine learning bias within the energy sector are profound, encompassing issues such as inequalities, the perpetuation of negative feedback loops, privacy concerns regarding, and economic impacts arising from energy burden and energy poverty. Recognizing and rectifying these biases is imperative for leveraging technology to advance society rather than perpetuating existing injustices. Addressing biases at the intersection of energy justice and machine learning requires a comprehensive approach, acknowledging the interconnectedness of social, economic, and technological factors.

The integration of a decentralized home energy management system (HEMS) marks a pivotal advancement in the pursuit of enhanced energy efficiency and sustainability within modern households. While numerous studies focus on developing efficient and innovative programs from technical perspectives, the willingness of individuals to adopt these systems is equally crucial for achieving widespread adoption and ultimately creating a more energy-efficient society. Based on a large-scale online survey with 1,196 participants in California in 2021, we investigated the intentions related to the adoption of decentralized HEMS, particularly considering demand flexibility encompassing both air-conditioning (AC) and electric vehicles (EV) control, specifically focusing on socio-demographic disparities. Our analysis found greater openness for allowing HEMS to control AC usage compared to scheduling EV charging, possibly due to immediate comfort needs or trust in AC predictability. Low-income households showed less flexibility in adjusting both AC and EVs, while high-income households were less likely to decrease EV charging. Furthermore, homeowners exhibited greater flexibility compared to renters. Disparities between different racial backgrounds in EV charging time-shifting were more pronounced than in AC aspects. Our findings indicated that vulnerable populations may lack the flexibility and resources necessary to shift their energy consumption patterns effectively, potentially amplifying energy-related disparities and exacerbating their energy burden. Policy recommendations highlight the need for multifaceted approaches in addressing demand flexibility and energy management, especially with emerging technologies like EVs, to ensure equitable strategies for promoting sustainable energy practices across diverse communities.