Socio-economic Impacts of Climate Change on Rural United States

Directly or indirectly, positively or negatively, climate change will affect all sectors and regions of the United States. The impacts, however, will not be homogenous across regions, sectors, population groups or time. The literature specifically related to how climate change will affect rural communities, their resilience, and adaptive capacity in the United States (U.S.) is scarce. This article bridges this knowledge gap through an extensive review of the current state of knowledge to make inferences about the rural communities vulnerability to climate change based on Intergovernmental Panel on Climate Change (IPCC) scenarios. Our analysis shows that rural communities tend to be more vulnerable than their urban counterparts due to factors such as demography, occupations, earnings, literacy, poverty incidence, and dependency on government funds. Climate change impacts on rural communities differs across regions and economic sectors; some will likely benefit while others lose. Rural communities engaged in agricultural and forest related activities in the Northeast might benefit, while those in the Southwest and Southeast could face additional water stress and increased energy cost respectively. Developing adaptation and mitigation policy options geared towards reducing climatic vulnerability of rural communities is warranted. A set of regional and local studies is needed to delineate climate change impacts across rural and urban communities, and to develop appropriate policies to mitigate these impacts. Integrating research across disciplines, strengthening research-policy linkages, integrating ecosystem services while undertaking resource valuation, and expanding alternative energy sources, might also enhance coping capacity of rural communities in face of future climate change

Socio-economic Impacts of Climate Change on Rural United States

Directly or indirectly, positively or negatively, climate change will affect all sectors and regions of the United States. The impacts, however, will not be homogenous across regions, sectors, population groups or time. The literature specifically related to how climate change will affect rural communities, their resilience, and adaptive capacity in the United States (U.S.) is scarce. This article bridges this knowledge gap through an extensive review of the current state of knowledge to make inferences about the rural communities vulnerability to climate change based on Intergovernmental Panel on Climate Change (IPCC) scenarios. Our analysis shows that rural communities tend to be more vulnerable than their urban counterparts due to factors such as demography, occupations, earnings, literacy, poverty incidence, and dependency on government funds. Climate change impacts on rural communities differs across regions and economic sectors; some will likely benefit while others lose. Rural communities engaged in agricultural and forest related activities in the Northeast might benefit, while those in the Southwest and Southeast could face additional water stress and increased energy cost respectively. Developing adaptation and mitigation policy options geared towards reducing climatic vulnerability of rural communities is warranted. A set of regional and local studies is needed to delineate climate change impacts across rural and urban communities, and to develop appropriate policies to mitigate these impacts. Integrating research across disciplines, strengthening research-policy linkages, integrating ecosystem services while undertaking resource valuation, and expanding alternative energy sources, might also enhance coping capacity of rural communities in face of future climate change

ECONOMICALLY OPTIMAL WILDFIRE INTERVENTION REGIMES

Wildfires in the United States result in total damages and costs that are likely to exceed billions of dollars annually. Land managers and policy makers propose higher rates of prescribed burning and other kinds of vegetation management to reduce amounts of wildfire and the risks of catastrophic losses. A wildfire public welfare maximization function, using a wildfire production function estimated using a time series model of a panel of Florida counties, is employed to simulate the publicly optimal level of prescribed burning in an example county in Florida (Volusia). Evaluation of the production function reveals that prescribed fire is not associated with reduced catastrophic wildfire risks in Volusia County Florida, indicating a short-run elasticity of -0.16 and a long-run elasticity of wildfire with respect to prescribed fire of -0.07. Stochastic dominance is used to evaluate the optimal amount of prescribed fire most likely to maximize a measure of public welfare. Results of that analysis reveal that the optimal amount of annual prescribed fire is about 3 percent (9,000 acres/year) of the total forest area, which is very close to the actual average amount of prescribed burning (12,700 acres/year) between 1994-99.Resource /Energy Economics and Policy,

Addiction Treatment and Stable Housing among a Cohort of Injection Drug Users

Background: Unstable housing and homelessness is prevalent among injection drug users (IDU). We sought to examine whether accessing addiction treatment was associated with attaining stable housing in a prospective cohort of IDU in Vancouver, Canada. Methods: We used data collected via the Vancouver Injection Drug User Study (VIDUS) between December 2005 and April 2010. Attaining stable housing was defined as two consecutive ‘‘stable housing’ ’ designations (i.e., living in an apartment or house) during the follow-up period. We assessed exposure to addiction treatment in the interview prior to the attainment of stable housing among participants who were homeless or living in single room occupancy (SRO) hotels at baseline. Bivariate and multivariate associations between the baseline and time-updated characteristics and attaining stable housing were examined using Cox proportional hazard regression models. Principal Findings: Of the 992 IDU eligible for this analysis, 495 (49.9%) reported being homeless, 497 (50.1%) resided in SRO hotels, and 380 (38.3%) were enrolled in addiction treatment at the baseline interview. Only 211 (21.3%) attained stable housing during the follow-up period and of this group, 69 (32.7%) had addiction treatment exposure prior to achieving stable housing. Addiction treatment was inversely associated with attaining stable housing in a multivariate model (adjusted hazard ratio [AHR] = 0.71; 95 % CI: 0.52–0.96). Being in a partnered relationship was positively associated with the primary outcom

Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project

The Numerical INJection Analysis (NINJA) project is a collaborative effort between members of the numerical relativity and gravitational-wave data analysis communities. The purpose of NINJA is to study the sensitivity of existing gravitational-wave search algorithms using numerically generated waveforms and to foster closer collaboration between the numerical relativity and data analysis communities. We describe the results of the first NINJA analysis which focused on gravitational waveforms from binary black hole coalescence. Ten numerical relativity groups contributed numerical data which were used to generate a set of gravitational-wave signals. These signals were injected into a simulated data set, designed to mimic the response of the Initial LIGO and Virgo gravitational-wave detectors. Nine groups analysed this data using search and parameter-estimation pipelines. Matched filter algorithms, un-modelled-burst searches and Bayesian parameter-estimation and model-selection algorithms were applied to the data. We report the efficiency of these search methods in detecting the numerical waveforms and measuring their parameters. We describe preliminary comparisons between the different search methods and suggest improvements for future NINJA analyses.Comment: 56 pages, 25 figures; various clarifications; accepted to CQ

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM