SEARCH · Search NASA

Results for “social responsibility”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Social-ecological interactions in a disaster context: Puerto Rican farmer households’ food security after Hurricane Maria

Islands are uniquely vulnerable to extreme weather events and food insecurity, and have additional response challenges due to their limited landmasses and economies, isolation, colonial legacies, and high dependence of food imports. Domestic farmers have a key role in producing food for island communities like Puerto Rico, which can safeguard food security when food importation may be challenging. Nevertheless, in the context of disaster, farmers themselves may be vulnerable to food insecurity and unable to contribute to domestic markets. This paper examines Puerto Rican farmers households’ food security in the aftermath of 2017’s Hurricane Maria using a social-ecological lens. Survey data from 405 farmers gathered eight months after Maria, coupled with biophysical data from the hurricane’s impacts (winds, rains, and landslides), were analyzed. Overall, 69% of farmers experienced at least one month of food insecurity in the aftermath of Hurricane Maria, and 38% reported persistent food insecurity (three months or more). A multinomial logistic regression suggests that biophysical impacts, but especially social factors, such as age and constraint access to external sources of support, are linked with persistent food insecurity. This suggests that the biophysical impacts of the hurricane interact with existing infrastructure and social resources to affect farmer vulnerability and the food environment in different ways. Thus, strengthening adaptive capacity in multiple domains can help farmers and vulnerable populations better navigate the disruptions faced during disasters to alleviate food insecurity.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Using Facebook to Recruit Urban Participants for Smartphone-Based Travel Surveys

Social media has become an integral part of everyday life for many individuals, serving as a platform to express opinions, share memories and lifestyles, follow news, and adapt to social trends and norms. The wealth of user information and analytics on these platforms has facilitated the development and sale of tailored products and services, benefiting advertisers and researchers seeking survey participants. Social media advertising has demonstrated its effectiveness in reaching hard-to-reach populations. However, transport researchers have yet to capitalise on this potential fully. This paper presents our experience using social media to recruit participants for two smartphone travel surveys conducted in Australia. We demonstrate that social media recruitment and smartphone-based travel surveys are highly effective, adaptable, and can be rapidly deployed in response to research opportunities, such as during the early phase of the COVID-19 pandemic when traditional methods may be less suitable. This approach also holds great potential for travel surveys targeting the general population. This paper shares several lessons from this experiment, including our administrative approach and detailed technical instructions to utilise open-source software tools for conducting smartphone travel surveys like ours. This approach significantly reduces study costs compared to most commercial solutions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Community Resilience Indicator Analysis: Commonly Used Indicators from Peer-Reviewed Research (Updated for Research Published 2003-2021)

In 2017, FEMA’s National Integration Center (NIC) Technical Assistance (TA) Branch identified a need to establish a data-driven basis for prioritizing locations for TA investment and guiding local emergency management planning. To achieve this goal, FEMA tasked Argonne National Laboratory (Argonne) with identifying commonly used indicators of community resilience across the landscape of published peer-reviewed research. FEMA and Argonne completed the first Community Resilience Indicator Analysis (CRIA) in 2018 and repeated the process in 2022. The CRIA process begins with a literature review and cataloguing of published peer-reviewed assessment methodologies on social vulnerability and community resilience. The literature review findings are then filtered by inclusion criteria established by the CRIA research team to ensure the methodologies are: (1) Quantitative, (2) Data and methodology are publicly available, (3) Calculated at the county level or lower, (4) Examine generalized hazard risk (rather than a singular hazard), and (5) Focused on pre-disaster community conditions. After this, the research team identifies the commonly used indicators across these methodologies and selects the best data source for each indicator. Finally, the research team bins the data for visual display, conducts a correlation analysis and creates a composite index, the FEMA Community Resilience Index (FEMA CRI). In 2018, the CRIA identified eight resilience and vulnerability assessment methodologies and 20 commonly used indicators (indicators used in three or more of the eight methodologies). The FEMA CRI in 2018 was created from these 20 indicators and was produced for at the county level. The 2022 CRIA updated the literature review to expand the list of methodologies examined and followed the same process, resulting in an analysis of 14 methodologies published between 2003 and 2021 and 22 indicators identified as commonly used (indicators used in five or more of the 14 methodologies). In 2022, the research team produced the FEMA CRI at the county and the census tract levels. To make the CRIA data more accessible and more actionable, each individual indicator and the FEMA CRI is binned and included in FEMA’s Resilience Analysis and Planning Tool (RAPT). RAPT enables emergency managers and community partners to quickly visualize relative differences in potential resilience by county, tribe and census tract. By reviewing the data for each of these 22 indicators individually, emergency managers can gain insights for targeted outreach strategies, planning, mitigation investments and response and recovery operations. Communities, regional governments and others can use this data to better understand potential challenges to resilience. As the social science field of examining and validating indicators of resilience evolves, FEMA will update RAPT to provide emergency managers and community partners with additional data and tools to inform planning, mitigation, response and recovery. It is important to understand that the role of the emergency manager is not to change or to “improve” the data, but to plan appropriately for the community characteristics reflected in the data. These datasets are community characteristics that researchers have identified as important considerations for resilience. For example, people with disabilities may have greater challenges to be resilient to disasters. If a community has a high population of people with disabilities, the emergency manager(s) may need to create tailored preparedness outreach programs and strategies to ensure those residents have support if evacuation is necessary. Rather than label these indicators as an absolute measure of resilience, FEMA considers “potential challenges to resilience” a better frame to understand these indicators. Everyone is vulnerable to disasters. While scholars theorize that certain characteristics may make an individual or a household more socially vulnerable, the data does not reflect measures that individuals and/or communities have taken to address potential challenges, such as emergency management planning and outreach or household preparedness measures. To aid emergency managers in understanding how to use these indicators, calling them potential challenges to resilience supports a more positive and strategic application of the data in all phases of emergency management.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Emerging Energy Market Analysis Initiative, Methodological Framework

Planning and operations of the electric power sector are undergoing radical changes. Climate change mitigation efforts have forced rapid changes to the technology mix. Technologies like wind and solar have experienced rapid growth, while investment in fossil sources has peaked or is declining. These foundational changes are forcing changes to energy systems. Demand-side adoption of electrified technologies, including electric vehicles, is changing load profiles and opening up new avenues for consumer participation in the power systems. The implications of an evolving power system pertain to more than environmental and technical dimensions. Changes to the generation mix and its consequent upstream and downstream impacts such as fuel production have significant and highly concentrated consequences on economies and employment. Shifts towards distributed (or decentralized) generating assets offer the potential to reshape economic and employment opportunities associated with the energy sector across space and socioeconomic groups. The Emerging Energy Market Analysis (EMA) initiative aims to identify sustainable, regionally acceptable, and high-value energy solutions that are secure and equitable. Unlike short-term, least-cost choices that can narrowly account for traditional options, EMA’s focus on emerging energy markets recognizes that new or adapted practices and technologies can alter the frontier of solutions and advance a community’s social, economic, and natural pathways. Such change requires a more comprehensive analysis of societal input, resources, capabilities, and infrastructure. These considerations lay the foundation for community decision-making models that are responsive to community values as well as the history and drivers. The result is a community-based decision and engagement model that will be valuable to decisionmakers and developers of advanced and emerging energy solutions, seeking a social license to operate prior to project development.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

Adoption of perennial energy crops in the US Midwest: Causal and heterogeneous determinants

The success of a bioenergy policy relies largely on the wide adoption of perennial energy crops at the farm-scale. This study uses survey data to examine potential adoption decisions by farmers in the US Midwest and the causal effects of various direct and indirect influencing factors, especially heterogeneous preferences of farmers. A Bayesian network (BN) model is developed to delineate the causal-relationship between farmers adoption decisions and the influencing factors. Here, we find a dominating role of economic factors and a non-negligible impact of non-economic factors, such as the perceived environmental benefits and the extent of familiarity with perennial energy crops. To examine the effect of heterogeneity in farmer preferences, we classify the surveyed farmers into four categories based on their attitudes toward the economic, social, and environmental dimensions of perennial energy crops. We identify statistically significant between-group differences in the responses of the four types of farmers to the various influencing factors. Our findings contribute to disentangling the complicated motivations that will influence perennial energy crop adoption decisions and provide implications for more targeted policy development that need to consider the heterogeneous drivers of farmer decisions about land use.

09 BIOMASS FUELS↗

DOE OSTI2021

Data for Adoption of Perennial Energy Crops in the U.S. Midwest, Causal and Heterogeneous Determinants

The success of a bioenergy policy relies largely on the wide adoption of perennial energy crops at the farm scale. This study uses survey data to examine potential adoption decisions by farmers in the U.S. Midwest and the causal effects of various direct and indirect influencing factors, especially heterogeneous preferences of farmers. A Bayesian network (BN) model is developed to delineate the causal relationship between farmers adoption decisions and the influencing factors. We find a dominating role of economic factors and a non-negligible impact of non-economic factors, such as the perceived environmental benefits and the extent of familiarity with perennial energy crops. To examine the effect of heterogeneity in farmer preferences, we classify the surveyed farmers into four categories based on their attitudes toward the economic, social, and environmental dimensions of perennial energy crops. We identified statistically significant between-group differences in the responses of the four types of farmers to the various influencing factors. Our findings contribute to disentangling the complicated motivations that will influence perennial energy crop adoption decisions and provide implications for more targeted policy development that need to consider the heterogeneous drivers of farmer decisions about land use.

Modeling↗

DOE OSTI2022

State of Common Grid Services Definitions

This document is prepared as part of the Department of Energy’s Grid Modernization Laboratory Consortium (GMLC) 2.5.2 project, whose goal is to develop and socialize a common set of grid service definitions relevant to grid-related interactions with distributed energy resources (DER: responsive generation, storage, and loads), and to advance the concept and requirements of the Energy Services Interface (ESI) to the point of launching related interface standards and guides that can be implemented in communication protocols and business process definitions. The notion of “grid services” is integral to the definition of an ESI because a key principle of the ESI is that it permits coordination between grid operators and DER facilities in a way that is service-oriented, with an understanding of performance expectations. This document reviews the current state of grid service definitions, including those actively used in the market today as well as new services that have been proposed for future implementation. The document describes grid services used in transmission as well as distribution systems. In defining grid services, this document also distinguishes between two fundamental concepts: an “operational objective” and a “grid service,” which describes a generator’s or customer’s expected physical performance in delivering power to or consuming power from the grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Common Grid Services: Terms and Definitions Report

This document is prepared as part of the Department of Energy’s Grid Modernization Laboratory Consortium (GMLC) 2.5.2 project, whose goal is to develop and socialize a common set of grid service definitions relevant to grid-related interactions with distributed energy resources (DER: responsive generation, storage, and loads) and to advance the concept and requirements of the Energy Services Interface (ESI) to the point of launching related interface standards and guides that can be implemented in communication protocols and business process definitions. The notion of grid services is associated with the definition of an ESI because a key principle of the ESI is that it permits coordination between grid operators and DER facilities in a way that is service-oriented with an understanding of performance expectations. The project investigated the current state of North American grid service definitions from various market operators and utilities, as well as the Federal Energy Regulatory Commission and the North American Electric Reliability Corporation, actively used today (Liu et al. 2022). This document builds on that work to propose terms and definitions for a set of grid service types that address operational objectives commonly found in power system operations. These grid service types derive from existing grid services used in bulk electric market operations, recognizing that each operational authority uses somewhat different names with variations on service performance parameters. Finding commonality at the wholesale or bulk system operation level will hopefully engender progress in seeking agreement on grid services for DER engagement at the distribution level in the emerging retail marketplace.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

AMIA KDDM Working Group Collaborative Workshop: Enriching Electronic Health Records with Social Determinants of Health to Improve Outcomes and Health Equity

Prior research has demonstrated that social determinants of health (SDoH) are major drivers of health outcomes and contributors to widespread health inequities. It was estimated that, in the United States, SDoH could be responsible for up to 40% of all preventable deaths, significantly higher than the 10-15% for which better medical care is responsible. Public health interventions that target SDoH are instrumental for improving health outcomes and reducing long-standing health inequities. Currently, most mainstream EHR vendors have implemented SDoH screeners in their EHR systems. However, the utility of the screeners is low, rendering patient-level SDoH still widely unavailable in the structured fields. SDoH are sometimes mentioned in free-text clinical notes (e.g., social context section) where natural language processing (NLP) can be applied to extract relevant information. Contextual-level SDoH can be identified from multiple data sources, many of which are publicly available and spatiotemporally linked to EHR data. As such, there is an opportunity for the KDDM research community to create innovative solutions to draw meaningful insights by creating and using rich data with SDoH to improve health outcomes while reducing disparities. In this workshop organized by AMIA Knowledge Discovery and Data Mining Working Group (AMIA KDDM WG), we will invite world-leading experts from academia, national laboratories, and life science industry with varied backgrounds in biomedical informatics, epidemiology, data science, machine learning, natural language processing, and pediatric cardiology to discuss the best practice of capturing, standardizing, and using SDoH information in various applications aiming at improving outcomes and health equity.

He, Zhe↗

DOE OSTI2025

Artificial Intelligence for Smart Transportation

There are more than 7,000 public transit agencies in the U.S. (and many more private agencies), and together, they are responsible for serving 60 billion passenger miles each year. A well-functioning transit system fosters the growth and expansion of businesses, distributes social and economic benefits, and links the capabilities of community members, thereby enhancing what they can accomplish as a society. Since affordable public transit services are the backbones of many communities, this work investigates ways in which Artificial Intelligence (AI) can improve efficiency and increase utilization from the perspective of transit agencies. This book chapter discusses the primary requirements, objectives, and challenges related to the design of AI-driven smart transportation systems. We focus on three major topics. First, we discuss data sources and data. Second, we provide an overview of how AI can aid decision-making with a focus on transportation. Lastly, we discuss computational problems in the transportation domain and AI approaches to these problems.

Wilbur, Michael↗

DOE OSTI2020

CpG ODN D35 improves the response to abbreviated low-dose pentavalent antimonial treatment in non-human primate model of cutaneous leishmaniasis

Cutaneous leishmaniasis (CL) affects the lives of 0.7–1 million people every year causing lesions that take months to heal. These lesions can result in disfiguring scars with psychological, social and economic consequences. Antimonials are the first line of therapy for CL, however the treatment is lengthy and linked to significant toxicities; further, its efficacy is variable and resistant parasites are emerging. Shorter or lower dose antimonial treatment regimens, which would decrease the risk of adverse events and improve patient compliance, have shown reduced efficacy and further increase the risk emergence of antimonial-resistant strains. The progression of lesions in CL is partly determined by the immune response it elicits, and previous studies showed that administration of immunomodulatory type D CpG ODNs, magnifies the immune response to Leishmania and reduces lesion severity in nonhuman primates (NHP) challenged with Leishmania major or Leishmania amazonensis. Here we explored whether the addition of a single dose of immunomodulating CpG ODN D35 augments the efficacy of a short-course, low-dose pentavalent antimonial treatment regimen. Results show that macaques treated with D35 plus 5mg/kg sodium stibogluconate (SbV ) for 10 days had smaller lesions and reduced time to re-epithelization after infection with Leishmania major. No toxicities were evident during the studies, even at doses of D35 10 times higher than those used in treatment. Critically, pentavalent antimonial treatment did not modify the ability of D35 to induce type I IFNs. The findings support the efficacy of D35 as adjuvant therapy for shorter, low dose pentavalent antimonial treatment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

MACCS Theory Manual

This report describes the models of the MACCS computer code as presented in MACCS Version 3.10.0. The purpose of MACCS is to simulate the impact of severe accidents at nuclear power plants on the surrounding environment. MACCS has been developed by Sandia National Laboratories for the U.S. Nuclear Regulatory Commission. From a given release of radioactive material into the atmosphere, MACCS estimates the extent and magnitude of radiological contamination, offsite doses, protective actions, socioeconomic impacts and costs, and health effects. Since the weather at the time of an accident is not predictable, MACCS supports various sampling options to run a representative set of simulations to evaluate weather variability. MACCS simulates atmospheric transport with a straight-line Gaussian plume segment model. From the estimated air and ground concentrations, MACCS models dose projections through several dose exposure pathways. These exposures can be offset by protective actions during the emergency response and long-term recovery of the accident. MACCS users directly specify the evacuation and sheltering area, while other protective actions (e.g., relocation, farmland restrictions, decontamination) are based on user-specified dose or concentration limits. While protective actions help reduce dose accumulation, they also cause social and economic impacts. MACCS models the extent of displaced individuals and land contamination, and the cost of offsite property damage, economic disruptions, and various accident expenditures caused by protective actions. Finally, from the dose accumulation, MACCS estimates early and stochastic health effects according to dose-response models. The purpose of consequence analyses is to be able to understand and estimate the impact of nuclear accidents. Consequence analysis is an essential tool to inform determinations of adequate protection of the public, to understand nuclear power hazards, to measure the value of regulations, and to help us appreciate the importance of nuclear safety. As such, MACCS has a variety of regulatory uses including environmental analyses (10 CFR 51.53, 52.47), regulatory cost-benefit analyses, backfit analyses (10 CFR 50.109), consequence analysis studies such as SOARCA (NUREG-1935), Level 3 PRA studies, and risk-informing of emergency planning (10 CFR 50 App. E and 50.47). This report updates the previous MACCS theory manual (NUREG/CR-4691 Vol. 2; Chanin, Sprung, Ritchie, & Jow, 1990) and accompanies the MACCS User's Guide (SAND-2021-1588) that describes the use and input requirements of the graphical user interface of MACCS known as WinMACCS. The MACCS User's Guide is also a reference guide that describes data input file formats, describes various software components in the MACCS code suite, and provides a set of example tutorials for running WinMACCS. Also, soon to be published is a MACCS input parameter guidance report (NUREG/CR-7270) that provides technical bases for commonly used MACCS input values. This page left blank

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Carbondale Community Geothermal Coalition – Replicable Models for Decarbonizing Mixed-Use Rural Communities

The Carbondale Community Geothermal Coalition (CCGC) set out to prove that a fifth-generation geothermal district heating & cooling (5G GDHC) network can be technically feasible, cost-effective, and socially equitable for a small, mixed-use rural community. During Budget Period 1 (Planning & Design) the team: ● Completed a 449 ft thermal-response test confirming excellent ground conductivity (k = 1.69 Btu ft -1 hr -1 °F -1 ) and a benign 56.3 °F undisturbed temperature. Page 1 Final Technical Report for Award Number: DE-EE0010663 ● Built a fully calibrated digital twin (URBANopt → Modelica) for 240 k ft 2 of existing + future buildings. ● Designed a 75-borehole ambient-temperature loop (ATL) augmented by 75 solar-thermal panels, supplying 44 % of district heating and 100 % cooling in Stage-1. ● Produced retrofit bid-ready drawings for the Third Street Center and Second Street Townhomes. ● Reached > 200 community stakeholders and exceeded equity targets (46 % Hispanic/Latinx participation). ● Drafted a multi-tiered workforce plan aligned with Colorado Mountain College (Hispanic Serving Institution) and IGSHPA certifications.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Machine Intelligence to Detect, Characterise, and Defend against Influence Operations in the Information Environment

Social media has enabled a new era of manipulation in the information and cognitive domains. Deceptive content—misleading, falsified, and fabricated—is routinely created and spread in the modern social media environment with the intent to create confusion and widen political and social divides, and exploit the societal conflict exacerbated by these divides in the real-world (aka physical domain). Such disinformation campaigns demonstrate a threat to the integrity of economic, political, cultural, public health, and national security institutions around the world. In this work we overview our artificial intelligence (AI) capabilities to detect, describe, and defend against information operations on Twitter as an example social platform to understand the influence of misleading and falsified content diffusion and better enable those charged with defending against such manipulation to enable responsive parties to counter it. We first present novel linguistically-informed deep learning (DL) models for misinformation and disinformation detection, and present an in-depth linguistic analysis of psycho-linguistic markers across broad deception categories. We then demonstrate how our models perform in the multilingual and multimodal setting and categorize falsified and misleading content based on the intent to deceive. We also provide a large-scale analysis to describe user behavior and spread patterns while engaging with deceptive content and report novel findings about the immediate diffusion of deceptive content by characterizing the vulnerable sub-populations and their demographics, and explicitly measuring speed and scale of deception spread to uncover who shares deceptive content, how quickly, how much, and how evenly. In addition, we measure audience reactions to misinformation and disinformation at scale, distinguishing the reactions of users identified as bots versus humans. Finally, we take advantage of deep translation and generation models to create unique solutions for real-time defense against digital deception and discuss how to apply causal inference to prescribe and intervene into strategic communications jointly across information, cognitive, and physical domains.

artificial intelligence, deep learning, neural lan↗

DOE OSTI2023

Towards understanding the robust strengthening of ENSO and more frequent extreme El Niño events in CMIP6 global warming simulations

The El Niño Southern Oscillation (ENSO) has profound impacts on weather patterns across the globe, yet there is no consensus on its response to global warming. Several modelling studies find a stronger ENSO in global warming scenarios, while other studies suggest ENSO weakening. Using a broad range of models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and four types of warming experiments, here we show that the majority of the models predict a stronger ENSO by century-end in Shared Social Pathway (SSP) experiments, and in idealized 1pctCO 2 and abrupt 4xCO 2 experiments. Several models, however, do predict no change or ENSO weakening, especially in the idealized experiments. Critically, the strongest forcing (abrupt-4xCO 2 ) does not induce the strongest ENSO response, while differences between the models are much greater than those between warming scenarios. For the long-term response (over 1000 years) the models disagree even on the sign of change. Furthermore, changes in ENSO sea surface temperature (SST) variability are only modestly correlated with the tropical Pacific mean state change. The highest correlation for ENSO SST amplitude is found with the mean zonal SST gradient in the SSP5-8.5 experiment (R = –0.58). In contrast, changes in ENSO rainfall variability correlate well with changes in the mean state, as well as with changes in ENSO SST variability. When evaluating the Bjerknes Stability Index for a subset of models, we find that it is not a reliable predictor of ENSO strengthening, as this index tends to predict greater stability with warming. We argue that the enhanced ENSO stability is offset by increases in atmospheric noise or/and potential nonlinear effects. However, a robust inter-model mechanism that can explain a stronger ENSO simulated with global warming is still lacking. Therefore, caution should be exercised when considering ENSO changes based on a single model or warming scenario.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Screening Tool for Equitable Adoption and Deployment of Solar (STEADy Solar)

The Screening Tool for Equitable Adoption and DeploYment of Solar (STEADy Solar) is a database and mapping tool designed to promoting clean energy investments for low-income communities across the United States. The tool indicates locations that may be eligible for the Investment Tax Credit bonus adders defined in the 2022 Inflation Reduction Act (IRA) and combines this information with demographics, social vulnerability, solar technical potential, solar economics (modeled net present value), and building counts by use-type. It can be used by states, municipalities, community-based organizations, developers, and researchers to identify sites where solar projects may be economical and where federal incentives may be available to support equitable adoption of solar. Specific values include: Areas eligible for the Energy Communities Tax Credit Bonus Program (including brownfield site counts) Areas eligible for the Low Income Communities Bonus Credit Program (including Tribal Lands, and covered affordable housing project counts) Areas categorized as disadvantaged by Justice40 Commercial and Residential Solar economics characterized by the Net Present Value and Simple Payback Period Total Population, Race, and Ethnicity Median Household Income, Poverty rate, Household Tenure Social Vulnerability Count of buildings, developable rooftop solar capacity (in kWdc) and estimated annual generation potential (in kWh) on four building types: Government General Services, Government Emergency Response, Grade Schools, and Colleges/Universities. The linked report describes the STEADy dataset metadata and presents high level insights from the data. The downloadable and formatted excel dataset makes it easy for users to gain insights for their locations. Supporting .csv and shapefiles provide users with the full data to run their own analyses on equitable solar siting.

14 SOLAR ENERGY↗

DOE OSTI2022

Movement with meaning: integrating information into meta-ecology

Fluxes of matter, energy and information over space and time contribute to ecosystems' functioning and stability. The meta-ecosystem framework addresses the dynamics of ecosystems linked by these fluxes but, to date, has focused solely on energy and matter. Here, we synthesize existing knowledge of information's effects on local and connected ecosystems and demonstrate how new hypotheses emerge from the integration of ecological information into meta-ecosystem theory. First, we begin by defining information and reviewing how it flows among ecosystems to affect connectivity, local ecosystem function and meta-ecosystem dynamics. We focus on the role of semiotic information: that which can reduce an individual's – or a group's – uncertainty about the state of the world. Semiotic information elicits behavioral, developmental and life history responses from organisms, potentially leading to fitness consequences. Organisms' responses to information can ripple through trophic interactions to influence ecosystem processes, their local and regional dynamics, and the spatiotemporal flows of energy and matter, therefore information should affect meta-ecosystem dynamics such as stability and productivity. While specific subdisciplines of ecology currently consider different types of information (e.g. social and cultural information, natural and artificial light or sound, body condition, genotype and phenotype), many ecological models currently account for neither the spatio–temporal distribution of information nor its perception by organisms. We identify the empirical, theoretical and philosophical challenges in developing a robust information meta-ecology and offer ways to overcome them. Finally, we present new hypotheses for how accounting for realistic information perception and responses by organisms could impact processes such as home range formation and spatial insurance, and thus our understanding of ecological dynamics across spatial and temporal scales. Accounting for information will be essential to understanding how dynamics such as fitness, organismal movement and trophic interactions influence meta-ecosystem functioning, and predicting how ecosystem processes are affected by anthropogenic pressures.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A Method for Measuring Coupled Individual and Social Vulnerability to Environmental Hazards

Although models of social vulnerability to environmental hazards are commonly developed to support policy interventions in emergencies and disasters, their utility is hindered by a lack of contextual information on individuals exposed to and affected by hazards. We develop a novel approach to model social vulnerability that couples individuals and their varying forms of protective capacity with the social fabric of the communities in which they reside. The backbone of our model is the Public-Use Microdata Sample (PUMS), a product of the U.S. Census Bureau that preserves a representative sample of completed responses to the American Community Survey (ACS). The PUMS enables us to understand the full range of individual protective capacities against a hazard in an exposed area, which we term individual vulnerability profiles (IVPs). In this case, we examine IVPs in the Coney Island-Brighton Beach section of New York City, which suffered severe impacts during Hurricane Sandy in 2012. To manage the large number of unique IVPs in Coney Island-Brighton Beach, we perform a segmentation analysis to generalize them into thematic cohort vulnerability profiles (CVPs) representing a typology of vulnerable people in Coney Island-Brighton Beach during Sandy. From synthetic populations of CVPs, we then estimate how individuals in varying housing types were coexposed to Sandy at the census tract level by classifying these areas into community social vulnerability profiles (SVPs). Our results provide a topology of social vulnerability that simultaneously links individual, community, and population-wide concerns, enabling a more holistic understanding of resources and interventions beneficial to human security during events like Sandy than is attainable with area-level metrics.

54 ENVIRONMENTAL SCIENCES↗