Search

Abstract Background Health outcomes are strongly impacted by social determinants of health, including social risk factors and patient demographics, due to structural inequities and discrimination. Primary care is viewed as a potential medical setting to assess and address individual health-related social needs and to collect detailed patient demographics to assess and advance health equity, but limited literature evaluates such processes. Methods We conducted an analysis of cross-sectional survey data collected from n = 507 Maryland Primary Care Program (MDPCP) practices through Care Transformation Requirements (CTR) reporting in 2022. Descriptive statistics were used to summarize practice responses on social needs screening and demographic data collection. A stepwise regression analysis was conducted to determine factors predicting screening of all vs. a targeted subset of beneficiaries for unmet social needs. Results Almost all practices (99%) reported conducting some form of social needs screening and demographic data collection. Practices reported variation in what screening tools or demographic questions were employed, frequency of screening, and how information was used. More than 75% of practices reported prioritizing transportation, food insecurity, housing instability, financial resource strain, and social isolation. Conclusions Within the MDPCP program there was widespread implementation of social needs screenings and demographic data collection. However, there was room for additional supports in addressing some challenging social needs and increasing detailed demographics. Further research is needed to understand any adjustments to clinical care in response to identified social needs or application of data for uses such as assessing progress towards health equity and the subsequent impact on clinical care and health outcomes.

BackgroundSocial needs screening can help modify care delivery to meet patient needs and address non-medical barriers to optimal health. However, there is a need to understand how factors that exist at multiple levels of the healthcare ecosystem influence the collection of these data in primary care settings.MethodsWe conducted 20 semi-structured interviews involving healthcare providers and primary care clinic staff who represented 16 primary care practices. Interviews focused on barriers and facilitators to awareness of and assistance for patients' social needs in primary care settings in Maryland. The interviews were coded to abstract themes highlighting barriers and facilitators to conducting social needs screening. The themes were organized through an inductive approach using the socio-ecological model delineating individual-, clinic-, and system-level barriers and facilitators to identifying and addressing patients' social needs.ResultsWe identified several individual barriers to awareness, including patient stigma about verbalizing social needs, provider frustration at eliciting needs they were unable to address, and provider unfamiliarity with community-based resources to address social needs. Clinic-level barriers to awareness included limited appointment times and connecting patients to appropriate community-based organizations. System-level barriers to awareness included navigating documentation challenges on the electronic health record.ConclusionsOvercoming barriers to effective screening for social needs in primary care requires not only practice- and provider-level process change but also an alignment of community resources and advocacy of policies to redistribute community assets to address social needs.

Abstract The mitochondrial enzyme dihydroorotate dehydrogenase (DHODH) catalyzes one of the rate‐limiting steps in de novo pyrimidine biosynthesis, a pathway that provides essential metabolic precursors for nucleic acids, glycoproteins, and phospholipids. DHODH inhibitors (DHODHi) are clinically used for autoimmune diseases and are emerging as a novel class of anticancer agents, especially in acute myeloid leukemia (AML) where pyrimidine starvation was recently shown to reverse the characteristic differentiation block in AML cells. Herein, we show that DHODH blockade rapidly shuts down protein translation in leukemic stem cells (LSCs) and has potent and selective activity against multiple AML subtypes. Moreover, we find that ablation of CDK5, a gene that is recurrently deleted in AML and related disorders, increases the sensitivity of AML cells to DHODHi. Our studies provide important molecular insights and identify a potential biomarker for an emerging strategy to target AML.

The dendritic cell receptor Clec9A facilitates processing of dead cell-derived antigens for cross-presentation and the induction of effective CD8+ T cell immune responses. Here, we show that this process is regulated by E3 ubiquitin ligase RNF41 and define a new ubiquitin-mediated mechanism for regulation of Clec9A, reflecting the unique properties of Clec9A as a receptor specialized for delivery of antigens for cross-presentation. We reveal RNF41 is a negative regulator of Clec9A and the cross-presentation of dead cell-derived antigens by mouse dendritic cells. Intriguingly, RNF41 regulates the downstream fate of Clec9A by directly binding and ubiquitinating the extracellular domains of Clec9A. At steady-state, RNF41 ubiquitination of Clec9A facilitates interactions with ER-associated proteins and degradation machinery to control Clec9A levels. However, Clec9A interactions are altered following dead cell uptake to favor antigen presentation. These findings provide important insights into antigen cross-presentation and have implications for development of approaches to modulate immune responses.

Abstract Constraint-based optimization, such as flux balance analysis (FBA), has become a standard systems-biology computational method to study cellular metabolisms that are assumed to be in a steady state of optimal growth. The methods are based on optimization while assuming (i) equilibrium of a linear system of ordinary differential equations, and (ii) deterministic data. However, the steady-state assumption is biologically imperfect, and several key stoichiometric coefficients are experimentally inferred from situations of inherent variation. We propose an approach that explicitly acknowledges heterogeneity and conducts a robust analysis of metabolic pathways (RAMP). The basic assumption of steady state is relaxed, and we model the innate heterogeneity of cells probabilistically. Our mathematical study of the stochastic problem shows that FBA is a limiting case of our RAMP method. Moreover, RAMP has the properties that: A) metabolic states are (Lipschitz) continuous with regards to the probabilistic modeling parameters, B) convergent metabolic states are solutions to the deterministic FBA paradigm as the stochastic elements dissipate, and C) RAMP can identify biologically tolerable diversity of a metabolic network in an optimized culture. We benchmark RAMP against traditional FBA on genome-scale metabolic reconstructed models of E. coli, calculating essential genes and comparing with experimental flux data.

Culinary Shakespeare: Staging Food and Drink in Early Modern England Edited by David B. Goldstein and Amy L. Tigner Pittsburgh: Duquesne University Press, 2016 Early modern food studies has grown [...]

AbstractCarbon Dioxide Removal (CDR) is an emerging activity with extremely limited deployment to date, but which is mathematically required to achieve net (rather than true) zero or negative anthropogenic contribution to climate change. The required scale of CDR, however, depends on decisions about what activities will be allowed to emit greenhouse gases – the “residual emissions” that must be compensated via CDR. Simultaneously, CDR’s availability is limited by resource needs and feasibility, much like conventional depletable resources. Governance and institutions, especially related to how CDR is allocated and paid for, will fundamentally shape CDR efforts, including by structurally incentivizing particular approaches and monitoring, reporting, and verification (MRV) objectives. We argue that the emerging tendency toward market-based, unconstrained, and for-profit CDR presents fundamental and predictable risks for climate and justice goals. Such a model incentivizes growth in profitable compensatory removal applications, effectively allocating limited resources based on ability to pay rather than public good, while also increasing the amount of CDR required to meet global climate targets. “Luxury” removals that could otherwise be mitigated not only displace, but actively disincentivize deployment for compensatory removals in high priority but low wealth applications, and for drawdown. Meeting these needs would likely become a socialized cost. Markets also establish unit-level property rights that require specific kinds of MRV that are misaligned with climate outcomes and face incentives for poor quality verification. We describe the need, development context, function, and resource limitations of CDR, then characterize the major challenges with the emerging unconstrained, for-profit governance model. We argue that instead implementing CDR as a centrally planned sector, with publicly deliberated and adaptable volumetric targets integrated with other climate action, could enable more just and effective outcomes.

This issue formally launches Environmental Research: Energy , a new open-access interdisciplinary journal focused on energy systems as we grapple with the challenges and opportunities of decarbonization and advancing global social justice. As a society-owned journal in IOP’s Environmental Research portfolio, Environmental Research: Energy joins a publishing tradition focused on accessibility, fairness, and excellence, with a crucial topical focus on energy systems. Supported by an interdisciplinary and global editorial board, Environmental Research: Energy welcomes and is committed to creating conditions conducive to multi- and interdisciplinary research, particularly given the deep connections across society, technology, and culture that characterize energy systems. Our inaugural issue highlights the journal’s interest in both supply and demand side views of energy systems both adapting to and mitigating climate change across the world, and upcoming focus issues broaden and deepen our emphasis on multidisciplinary investigations into these crucial global issues. Thank you for your support!

Hydrogen is an energy carrier with potential applications in decarbonizing difficult-to-electrify energy and industrial systems. The environmental profile of hydrogen varies substantially with its inputs. Water consumption is a particular issue of interest as decisions are made about capital and other investments that will affect the scale and scope of hydrogen use. This study focuses on electrolytic hydrogen due to its path to greenhouse gas neutrality and irreducible water demand (though other pathways might be more water intensive). Specifically, it evaluates life cycle consumptive freshwater intensity of electrolytic hydrogen in the United States at volumes associated with 12 scenarios for a deeply decarbonized 2050 US energy system from two modeling efforts for which both electricity fuel mix and electrolytic hydrogen production were projected (America's Zero Carbon Action Plan and Net Zero America), in addition to volumes for a stylized energy storage project (500 MW hydrogen-fired turbine). Freshwater requirements for hydrogen could be large. Under a central estimate for 2050 US electrolytic hydrogen production, electrolytic freshwater demand for process and feedstock inputs alone (i.e., excluding water for electricity) would be about 7.5% of total 2014 US freshwater consumption for energy (1 billion cubic meters/year, 109 m3/y; [0.2%, 15%] across scenarios, for 2050 electrolytic hydrogen production of [0.3, 18] exajoules, EJ). Including water associated with production of input electricity doubles this central estimate to 15% (2 × 109 m3/y; [1%, 23%] across scenarios). Turbines using electrolytic hydrogen are estimated to be about as freshwater intensive as a coal or nuclear plant, assuming decarbonized, low-water electricity inputs. Although a decarbonized energy system is projected to require less water for resource capture and electricity conversion than the current, fossil-dominated energy system, additional conversion processes supporting decarbonization, like electrolysis, could offset water savings.

Water, energy, and food are all essential components of human societies. Collectively, their respective resource systems are interconnected in what is called the “nexus”. There is growing consensus that a holistic understanding of the interdependencies and trade-offs between these sectors and other related systems is critical to solving many of the global challenges they present. While nexus research has grown exponentially since 2011, there is no unified, overarching approach, and the implementation of concepts remains hampered by the lack of clear case studies. Here, we present the results of a collaborative thought exercise involving 75 scientists and summarize them into 10 key recommendations covering: the most critical nexus issues of today, emerging themes, and where future efforts should be directed. We conclude that a nexus community of practice to promote open communication among researchers, to maintain and share standardized datasets, and to develop applied case studies will facilitate transparent comparisons of models and encourage the adoption of nexus approaches in practice.

Abstract Hydroelectric power has unusual technical characteristics that could become more valuable as the penetration of variable generation renewables grows, but its use for electricity generation is constrained by complex physical, safety, and socioenvironmental considerations. Hydroelectricity can therefore be difficult to represent in national‐scale energy models, and is frequently presumed to be either overly flexible or inflexible. While a few grid models address this complexity via detailed hydraulic process models, more simplified optimization and dispatch models could benefit from the use of empirical parameter values. In this study, we combine a new data set comprising 7.8 million flow‐hours of data from 2011 to 2016 at 158 dams across the United States with monthly and hourly generation data from the U.S. Energy Information Administration (EIA) to elucidate such empirical constraints for the continental United States. We introduce an approach for estimating power generation from hourly water discharge, then present regionally resolved interannual seasonal and diurnal generation patterns; frequency analyses of ramp rates; minimum and maximum generation rates; daily reversals; and load duration curves, all available interactively with a new data visualization tool. We suggest that due largely to hydropower's role as power generation that also serves non‐energy purposes, it acts as a predictable variable generator with constrained dispatchability, more like a supply side analog to demand response resources than like a battery. This observation is particularly relevant for high penetration renewable energy scenarios, given hydroelectric generators' expected value for grid stabilization and load balancing.

Climate change is driving urgent investments in decarbonization. One core decarbonization strategy is to electrify energy services that currently directly use fossil fuels, because electricity can be generated from zero greenhouse gas energy resources. Shifting fossil-based services to electricity, however, requires a major expansion of electricity supply and increases dependence on electricity for critical services. Home heating is a particular challenge, especially in very cold climates. Unserved heating loads can be fatal. Electrified heating is expected to drive peak loads (and thus overall grid size) due to high coincident and nondeferrable loads. This study shows that highly efficient housing presents an opportunity to simultaneously protect people and structurally reduce peak load, reducing the need for electricity supply infrastructure while increasing people’s resilience to weather extremes. This study uses seven building efficiency scenarios from the National Renewable Energy Laboratory’s End Use Saving Shapes to investigate the impact of residential building efficiency on grid size in 2050, using the example of Pierre, South Dakota as a very cold weather location that might also experience substantial new housing demand due to climate-induced human mobility. We find that the deepest efficiency electrification scenario we investigate reduces peak demand by about half relative to low-efficiency electrification. Costs of about $3900/kilowatt (kW) peak load reduction are competitive with the cost of new decarbonized supplies capable of meeting peak load, though building efficiency costs are usually privatized while supply expansion costs are distributed across ratepayers. Decarbonization scenarios suggest the US grid might need to expand by a factor of 5–8 in the next 25 years: extremely rapid growth will be needed regardless, but targets might not be reachable with inefficient end users. Residential building efficiency presents an urgent opportunity to reduce peak demand and provide safer and more resilient housing.

Here we reply to a comment by Dr. Robert Kennedy from the US Department of Energy (DOE) on our paper ‘US power sector carbon capture and storage under the Inflation Reduction Act could be costly with limited or negative abatement potential,’ which found that the 45Q carbon oxide sequestration tax credit incentivizes behaviors under profit-maximizing conditions that could increase rather than decrease GHG emissions in the power sector relative to a counterfactual without the tax credit. Our reply addresses claims that regulatory structures would prevent the negative outcomes we model in the original paper; that our cost and performance assumptions are inaccurate; and that other technologies will see growth. We show that the comment provides no evidence that our original analysis is incorrect and that information that has emerged since the March 2023 publication of our paper supports our assumptions, including public statements by carbon capture project proponents and new information about expected project costs. We agree with Dr. Kennedy that the incentives we highlight where 45Q might incentivize higher emissions at high cost are shocking; we disagree that we have incorrectly characterized these incentives. We share a belief that many of these loopholes can be closed, and disagree that they have already been closed.

Electric vehicle (EV) charging infrastructure buildout is a major greenhouse gas (GHG) mitigation strategy among governments and municipalities. In the United States, where petroleum-based transportation is the largest single source of GHG emissions, the Infrastructure Investment and Jobs Act of 2021 will support building a national network of 500 000 EV charging units. While the climate benefits of driving electric are well established, the potential embodied climate impacts of building out the charging infrastructure are relatively unexplored. Furthermore, ‘charging infrastructure’ tends to be conceptualized in terms of plugs and stations, leaving out the electrical and communications systems that will be required to support decarbonized and efficient charging. In this study, we present an EV charging system (EVCS) model that describes the material and operational components required for charging and forecasts the scale-up of these components based on EV market share scenarios out to 2050. We develop a methodology for measuring GHG emissions embodied in the buildout of EVCS and incurred during operation of the EVCS, including vehicle recharging, and we demonstrate this model using a case study of Georgia (USA). We find that cumulative GHG emissions from EVCS buildout and use are negligible, at less than 1% of cumulative emissions from personal light duty vehicle travel (including EV recharging and conventional combustion vehicle driving). If an accelerated EVCS buildout were to stimulate a faster transition of the vehicle fleet, the emissions reduction of electrification will far outweigh emissions embodied in EVCS components, even assuming relatively high carbon inputs prior to decarbonization.

Large energy infrastructure is often socially and environmentally disruptive, even as it provides services that people have come to depend on. Residents of areas affected by energy development often note both negative and positive impacts. This reflects the multicategory nature of socioenvironmental outcomes and emphasizes the importance of careful, community-oriented decision making about major infrastructural transitions for processes like decarbonization. Quantitative tools like life cycle assessment (LCA) seek to collect and report comprehensive impact data, but even when successful, their value for decision support is limited by a lack of mechanisms to systematically engage with values-driven tradeoffs across noncommensurable categories. Sensitivity analyses designed to help decision makers and interested parties make sense of data are common in LCA and similar tools, but values are rarely explicitly addressed. This lack of attention to values—arguably the most meaningful set of decision inputs in such tools—can lead to overreliance on single issue (e.g. climate change impact) or proxy (e.g. monetized cost) outputs that reduce the value of holistic evaluations. This research presents results from preregistered hypotheses for a survey of residents of energy-producing communities in the United States (US) and Australia, with the goal of with the goal of uncovering energy transition-relevant priorities by collecting empirical, quantitative data on people’s priorities for outcomes aligned with LCA. The survey was designed to identify diverse value systems, with the goal of making it easier for users to identify and consider value conflicts, potentially highlighting needs for further data collection, system redesign, or additional engagement. Notably, results reveal remarkably consistent priority patterns across communities and subgroups, suggesting that the common LCA practice of equal prioritization might be masking decision-relevant information. Although this effort was designed specifically to support research on energy transitions, future work could easily be extended more broadly.

The United States’ (US) largest-ever investment in expected climate mitigation, through 2022’s Inflation Reduction Act (IRA), relies heavily on subsidies. One major subsidy, the 45Q tax credit for carbon oxide sequestration, incentivizes emitters to maximize production and sequestration of carbon oxides, not abatement. Under IRA’s 45Q changes, carbon capture and storage (CCS) is expected to be profitable for coal- and natural gas-based electricity generator owners, particularly regulated utilities that earn a guaranteed rate of return on capital expenditures, despite being costlier than zero-carbon resources like wind or solar. This analysis explores investment decisions driven by profitability rather than system cost minimization, particularly where investments enhance existing assets with an incumbent workforce, existing supplier relationships, and internal knowledge-base. This analysis introduces a model and investigates six scenarios for lifespan extension and capacity factor changes to show that US CCS fossil power sector retrofits could demand $0.4–$3.6 trillion in 45Q tax credits to alter greenhouse gas emissions by −24% ($0.4 trillion) to +82% ($3.6 trillion) versus business-as-usual for affected generators. Particularly given long lead times, limited experience, and the potential for CCS projects to crowd or defer more effective alternatives, regulators should be extremely cautious about power sector CCS proposals.

The environmental profile of electricity is changing rapidly, motivating a need for provider- and time-specific estimates for accurate environmental assessment. This work shows that defensible, provider- and time-specific emissions projections can be derived from two major factors: committed emissions from existing power plants and policy restrictions on future system characteristics. This letter introduces a bottom–up, power plant-based model that projects utility-specific annual average greenhouse gas (GHG) intensity of electricity in the U.S. state of California for 2018–2050, believed to be the first openly available GHG emissions model with utility-specific projections. California is a useful case study for testing in part because of its strict regulatory GHG targets and the complexity of its electricity system, including limited asset ownership by utilities and substantial reliance on imported electricity. This plant-based approach to emissions projections bounds uncertainty in a way that less infrastructurally grounded approaches cannot. For example, emissions from unspecified sources of power can be estimated based on available plants. Based on historical power plant lifetimes, existing policy, and default model assumptions, the CO _2 intensity of Californian electricity is projected to drop from 175 kg CO _2 /MWh (sales + losses, 2020) to 95 kg CO _2 /MWh by 2030, operationally decarbonizing by 2047. Upstream methane leakage increases GHG intensity of natural gas-fired power plants by about 30%, assuming a 100 year time horizon and national average estimates for leakage (which likely underestimate leakage for California). Although drivers like market conditions also affect future outcomes, California’s current policy targets do not appear to require early retirement for utility generation assets, though up to about two gigawatts of extant in-state merchant capacity might be affected. Under current policy, new generating assets must either comply with the 100% clean electricity standard by 2045 or stop selling in California before the end of their expected useful life.

Mail surveys remain a popular method of eliciting attitudinal information, but declining response rates motivate inquiry into new, lower cost methods of contacting potential respondents. This work presents methodological findings from a medium-sized (~12,000 addresses) mail survey testing a United States Postal Service direct mail product called Every Door Direct Mail as a low-cost approach to anonymous mail survey distribution. The results suggest that under certain conditions, Every Door Direct Mail can be a useful approach for mail survey distribution, with response rates similar to those observed with analogous first-class mailing approaches but lower cost per response. As a tool for postal carrier-route saturation mailing that does not use names or addresses, Every Door Direct Mail is potentially useful for researchers who work in small, specific geographies or value or require anonymity. The results from this work suggest good performance on demographics and socially undesirable answers for Every Door Direct Mail relative to addressed mailings. The major disadvantages include an inability to conduct household-level probability sampling, an inability to customize nonresponse follow-up, and minimum mailing sizes associated with the postal carrier route saturation requirement. Every Door Direct Mail is unlikely to become a major tool for survey researchers, but it could be useful in niche applications. This study introduces Every Door Direct Mail to the survey methodology literature and presents empirical data intended to help researchers considering using Every Door Direct Mail.

Although natural gas is often viewed as a commodity fuel with limited variability due to standardization for pipeline transportation, life cycle impacts of natural gas vary substantially. Greenhouse gas (GHG) intensity is one of the most policy-relevant environmental characteristics of natural gas, particularly as decarbonization efforts proceed. Given that natural gas is mostly methane, a powerful GHG, methane emissions from the natural gas system contribute substantially to the GHG intensity of natural gas. Research has established that methane emissions from natural gas systems are climatically relevant and higher than long understood, in part due to variation in production-stage emissions by basin. This work combines recent estimates of basin-level US production-stage methane emissions, data on US natural gas production, consumption, and trade, and a spatial evaluation of pipeline connections between production basins and consumer states to generate first-order estimates of the production-stage methane emissions intensity of natural gas consumed in the United States. Although natural gas is a commodity product, the environmental footprint of a given unit of natural gas varies based on its origin and infrastructural needs. We find that production-stage methane emissions intensity of delivered natural gas by state varies from 0.9% to 3.6% (mass methane emitted from natural gas production sites per mass methane withdrawn). These production-stage emissions add 16%–65% (global warming potential (GWP)-100; 38%–157%, GWP-20) to combustion carbon dioxide emissions. Other sources of life cycle methane emissions downstream of production can be similar in magnitude. Natural gas consumed in Arizona, Kansas, and New Mexico has the highest estimated production-stage methane emissions intensity, largely due to reliance on high-emission basins. Limitations include emissions-related data gaps and sensitivity to allocation approaches, but results demonstrate decision-relevant variability in the GHG impact of natural gas.

Decarbonization is an urgent global policy priority, with increasing movement towards zero-carbon targets in the United States and elsewhere. Given the joint decarbonization strategies of electrifying fossil fuel-based energy uses and decarbonizing the electricity supply, understanding how electricity emissions might change over time is of particular value in evaluating policy sequencing strategies. For example, is the electricity system likely to decarbonize quickly enough to motivate electrification even on relatively carbon-intensive systems? Although electricity sector decarbonization has been widely studied, limited research has focused on evaluating emissions factors at the utility level, which is where the impact of electrification strategies is operationalized. Given the existing fleet of electricity generators, ownership structures, and generator lifespans, committed emissions can be modeled at the utility level. Generator lifespans are modeled using capacity-weighted mean age-on-retirement for similar units over the last two decades, a simple empirical outcome variable reflecting the length of time the unit might reasonably be expected to operate. By also evaluating generators in wholesale power markets and designing scenarios for new-build generation, first-order annual average emissions factors can be projected forward on a multidecadal time scale at the utility level. This letter presents a new model of utility-specific annual average emissions projections (greenhouse gases and air pollutants) through 2050 for the United States, using a 2019 base year to define existing asset characteristics. Enabling the creation and evaluation of scenario-based projections for dynamic environmental intensity metrics in a decarbonizing electricity sector can inform life cycle and other environmental assessment studies that evaluate impact over time, in addition to highlighting particular opportunities and risks associated with the timing and location of long-lived capital investments as the fossil fuel electricity generator fleet turns over. Model results can also be used to contextualize utilities’ decarbonization commitments and timelines against their asset bases.

Digitally enabled technologies are increasingly cyber-physical systems (CPSs). They are networked in nature and made up of geographically dispersed components that manage and control data received from humans, equipment, and the environment. Researchers evaluating such technologies are thus challenged to include CPS subsystems and dynamics that might not be obvious components of a product system. Although analysts might assume CPS have negligible or purely beneficial impact on environmental outcomes, such assumptions require justification. As the physical environmental impacts of digital processes (e.g. cryptocurrency mining) gain attention, the need for explicit attention to CPS in environmental assessment becomes more salient. This review investigates how the peer-reviewed environmental assessment literature treats environmental implications of CPS, with a focus on journal articles published in English between 2010 and 2020. We identify nine CPS subsystems and dynamics addressed in this literature: energy system, digital equipment, non-digital equipment, automation and management, network infrastructure, direct costs, social and health effects, feedbacks, and cybersecurity. Based on these categories, we develop a ‘cyber-consciousness score’ reflecting the extent to which the 115 studies that met our evaluation criteria address CPS, then summarize analytical methods and modeling techniques drawn from reviewed literature to facilitate routine inclusion of CPS in environmental assessment. We find that, given challenges in establishing system boundaries, limited standardization of how to evaluate CPS dynamics, and failure to recognize the role of CPS in a product system under evaluation, the extant environmental assessment literature in peer-reviewed journals largely ignores CPS subsystems and dynamics when evaluating digital or digitally-enabled technologies.

This paper explores how watershed organizations in Appalachia have persisted in addressing water quality issues in areas with a history of coal mining. We identified two watershed groups that have taken responsibility for restoring local creeks that were previously highly degraded and sporadically managed. These watershed groups represent cases of self-organized commons governance in resource-rich, economically poor Appalachian communities. We describe the extent and characteristics of links between watershed group volunteers and watershed residents who are not group members. Through surveys, participant observation, and key-informant consultation, we found that neighbors – group members as well as non-group-members – supported the group's function through informal neighboring acts. Past research has shown that local commons governance institutions benefit from being nested in supportive external structures. We found that the persistence and success of community watershed organizations depends on the informal participation of local residents, affirming the necessity of looking beyond formal, organized groups to understand the resources, expertise, and information needed to address complex water pollution at the watershed level. Our findings augment the concept of nestedness in commons governance to include that of a formal organization acting as a neighbor that exchanges informal neighboring acts with local residents. In this way, we extend the concept of neighboring to include interactions between individuals and a group operating in the same geographic area.

Climate change will require societal-scale infrastructural changes. Balancing priorities for water, energy, and climate will demand that approaches to water and energy management deviate from historical practice. Infrastructure designed to mitigate environmental harm, particularly related to climate change, is likely to become increasingly prevalent. Understanding the implications of such infrastructure for environmental quality is thus of interest. Environmental life cycle assessment (LCA) is a common sustainability assessment tool that aims to quantify the total, multicriteria environmental impact caused by a functional unit. Notably, however, LCA quantifies impacts in the form of environmental “costs” of delivering the functional unit. In the case of mitigation infrastructures, LCA results can be confusing because they are generally reported as the harmful impacts of performing mitigation rather than as net impacts that incorporate benefits of successful mitigation. This paper argues for defining mitigation LCA as a subtype of LCA to facilitate better understanding of results and consistency across studies. Our recommendations are informed by existing LCA literature on mitigation infrastructure, focused particularly on stormwater and carbon management. We specifically recommend that analysts: (1) use a performance-based functional unit; (2) be attentive to burden shifting; and (3) assess and define uncertainty, especially related to mitigation performance.

Renewable natural gas (RNG) is a fuel comprised of essentially pure methane, usually derived from climate-neutral (e.g. biogenic or captured) carbon dioxide (CO _2 ). RNG is proposed as a climate friendly direct substitute for fossil natural gas (FNG), with the goal of enabling diverse natural gas users to continue operating without substantial infrastructure overhauls. The assumption that such substitution is climate friendly relies on a major condition that is unlikely to be met: namely, that RNG is manufactured from waste methane that would otherwise have been emitted to the atmosphere. In practice, capturable waste methane is extremely limited and is more likely to be diverted from a flare than from direct atmospheric release in a climate-conscious policy context, which means that RNG systems need to be more destructively efficient than a flare to provide climate benefits versus the likely alternative management strategy. Assuming demand levels consistent with the goal of using existing FNG infrastructure, RNG is likely to be derived from methane that is either intentionally produced or diverted from a flare, so essentially any methane leakage is climate additional. Further, in a decarbonizing system, RNG will likely compete with lower-emissions resources than FNG and thus provides fewer net emissions benefits over time. Anticipated leakage is climatically significant: literature estimates for methane leakage from biogas production and upgrading facilities suggest that leakage is in the 2%–4% range (mass basis), up to as much as 15%. Policy makers should consider that under reasonable leakage and demand assumptions, RNG could be climate intensive.

Water consumption from electricity systems can be large, and it varies greatly by region. As electricity systems change, understanding the implications for water demand is important, given differential water availability. This letter presents regional water consumption and consumptive intensities for the United States electric grid by region using a 2014 base year, based on the 26 regions in the Environmental Protection Agency’s Emissions & Generation Resource Integrated Database. Estimates encompass operational (i.e. not embodied in fixed assets) water consumption from fuel extraction through conversion, calculated as the sum of induced water consumption for processes upstream of the point of generation (PoG) and water consumed at the PoG. Absolute water consumption and consumptive intensity is driven by thermal power plant cooling requirements. Regional consumption intensities vary by roughly a factor of 20. This variability is largely attributed to water consumption upstream of the PoG, particularly evaporation from reservoirs associated with hydroelectricity. Solar and wind generation, which are expected to continue to grow rapidly, consume very little water and could drive lower water consumption over time. As the electricity grid continues to change in response to policy, economic, and climatic drivers, understanding potential impacts on local water resources can inform changes.

Digitally-aided reviews of large bodies of text-based information, such as academic literature, are growing in capability but are not yet common in environmental fields. Environmental sciences and studies can benefit from application of digital tools to create comprehensive, replicable, interdisciplinary reviews that provide rapid, up-to-date, and policy-relevant reports of existing work. This work reviews the potential for applications of computational text mining and analysis tools originating in the humanities to environmental science and policy questions. Two process-oriented case studies of digitally-aided environmental literature reviews and meta-analyses illustrate potential benefits and limitations. A medium-sized, medium-resolution review (∼8000 journal abstracts and titles) focuses on topic modeling as a rapid way to identify thematic changes over time. A small, high-resolution review (∼300 full text journal articles) combines collocation and network analysis with manual coding to synthesize and question empirical field work. We note that even small digitally-aided analyses are close to the upper limit of what can be done manually. Established computational methods developed in humanities disciplines and refined by humanities and social science scholars to interrogate large bodies of textual data are applicable and useful in environmental sciences but have not yet been widely applied. Two case studies provide evidence that digital tools can enhance insight. Two major conclusions emerge. First, digital tools enable scholars to engage large literatures rapidly and, in some cases, more comprehensively than is possible manually. Digital tools can confirm manually identified patterns or identify additional patterns visible only at a large scale. Second, digital tools allow for more replicable and transparent conclusions to be drawn from literature reviews and meta-analyses. The methodological subfields of digital humanities and computational social sciences will likely continue to create innovative tools for analyzing large bodies of text, providing opportunities for interdisciplinary collaboration with the environmental fields.

The Maryland Primary Care Program is a statewide advanced primary care program that works directly with practices to transform healthcare delivery by managing chronic disease, preventing unnecessary hospital utilization, and integrating with the public health system. The Maryland Primary Care Program has demonstrated how linking the public health system to primary care practices, paired with strategic financial and resource investments in primary care, can enable the delivery of high-value care and reduce acute hospital utilization. Such a system is especially prudent when responding to crises. Throughout the COVID-19 pandemic, the Maryland Primary Care Program was able to capitalize on existing infrastructure to quickly engage primary care in a robust pandemic response. Successes of this relationship included early and consistent communication channels, as well as coordinated resource distribution. In particular, this partnership allowed primary care providers, the most trusted source of healthcare in patients' lives, to directly provide patients with health information and vaccines. Now comprising more than 500 practices, this vaccine program uses data-driven reports to facilitate intentional vaccine outreach. The program has enabled a more equitable vaccine distribution system, resulting in over 400,000 vaccines administered in Maryland counties. The effectiveness of Maryland's integrated response indicates that partnerships between public health and primary care will result in an effective response in future times of crisis.