Evaluating the economic return to public wind energy research and development in the United States

The U.S. government has invested in wind energy research since 1976. Building on a literature that has sought to develop and apply methods for retrospective benefit-to-cost evaluation for federal research programs, this study provides a quantitative analysis of the economic social return on these historical wind energy research investments. Importantly, the study applies multiple innovative methods and varies important input parameters to test the sensitivity of the results. The analysis considers public wind research expenditures and U.S. wind power deployment over the period 1976–2017, while also accounting for the full useful lifetime of wind projects built over this period. Assessed benefits include energy cost savings and health benefits due to reductions in air pollution. Overall, this analysis demonstrates sizable, positive economic returns on past wind energy research. Under the core analysis and with a 3% real discount rate, the net benefits from historical federal wind energy research investments are found to equal $31.4 billion, leading to an 18 to 1 benefit-to-cost ratio and an internal rate of return of 15.4%. Avoided carbon dioxide emissions are not valued in monetary terms, but are estimated at 1510 million metric tons. Alternative methods and input assumptions yield benefit-to-cost ratios that fall within a relatively narrow range from 7-to-1 to 21-to-1, reinforcing in broad terms the general finding of a sizable positive return on investment. Unsurprisingly, results are sensitive to the chosen discount rate, with higher discount rates leading to lower benefit-to-cost ratios, and lower discount rates yielding higher benefit-to-cost ratios

How Does Wind Project Performance Change with Age in the United States?

Wind-plant performance declines with age, and the rate of decline varies between regions. The rate of performance decline is important when determining wind-plant financial viability and expected lifetime generation. We determine the rate of age-related performance decline in the United States wind fleet by evaluating generation records from 917 plants. We find the rate of performance decline to be 0.53%/year for older vintages of plants and 0.17%/year for newer vintages of plants on an energy basis for the first 10 years of operation, which is on the lower end of prior estimates in Europe. Unique to the United States, we find a significant drop in performance by 3.6% after 10 years, as plants lose eligibility for the production tax credit. Certain plant characteristics, such as the ratio of blade length to nameplate capacity, influence the rate of performance decline. These results indicate that the performance decline rate can be partially managed and influenced by policy

Manual vs. automated CTA: Psychosocial Adaptation in Young Adolescents with Spina Bifida

Compared to the manually-derived model, the enumerated CTA model was 20% more parsimonious, 3.6% more accurate and 30% more efficient, and was more consistent with a priori hypotheses

Development of the State Optimism Measure

Background Optimism, or positive expectations about the future, is associated with better health. It is commonly assessed as a trait, but it may change over time and circumstance. Accordingly, we developed a measure of state optimism. Methods An initial 29-item pool was generated based on literature reviews and expert consultations. It was administered to three samples: sample 1 was a general healthy population (n = 136), sample 2 was people with cardiac disease (n = 96), and sample 3 was persons recovering from problematic substance use (n = 265). Exploratory factor analysis and item-level descriptive statistics were used to select items to form a unidimensional State Optimism Measure (SOM). Confirmatory factor analysis (CFA) was performed to test fit. Results The selected seven SOM items demonstrated acceptable to high factor loadings on a single dominant factor (loadings: 0.64–0.93). There was high internal reliability across samples (Cronbach\u27s alphas: 0.92–0.96), and strong convergent validity correlations in hypothesized directions. The SOM\u27s correlations with other optimism measures indicate preliminary construct validity. CFA statistics indicated acceptable fit of the SOM model. Conclusions We developed a psychometrically-sound measure of state optimism that can be used in various settings. Predictive and criterion validity will be tested in future studies

Mapping the genetic architecture of gene expression in human liver

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process. © 2008 Schadt et al

Regulating Clothing Outwork: A Sceptic's View

By applying the strategies of international anti-sweatshop campaigns to the Australian context, recent regulations governing home-based clothing production hold retailers responsible for policing the wages and employment conditions of clothing outworkers who manufacture clothing on their behalf. This paper argues that the new approach oversimplifies the regulatory challenge by assuming (1) that Australian clothing production is organised in a hierarchical ‘buyer-led’ linear structure in which core retail firms have the capacity to control their suppliers’ behaviour; (2) that firms act as unitary moral agents; and (3) that interventions imported from other times and places are applicable to the contemporary Australian context. After considering some alternative regulatory approaches, the paper concludes that the new regulatory strategy effectively privatises responsibility for labour market conditions – a development that cries out for further debate

Self-Organization, Layered Structure, and Aggregation Enhance Persistence of a Synthetic Biofilm Consortium

Microbial consortia constitute a majority of the earth’s biomass, but little is known about how these cooperating communities persist despite competition among community members. Theory suggests that non-random spatial structures contribute to the persistence of mixed communities; when particular structures form, they may provide associated community members with a growth advantage over unassociated members. If true, this has implications for the rise and persistence of multi-cellular organisms. However, this theory is difficult to study because we rarely observe initial instances of non-random physical structure in natural populations. Using two engineered strains of Escherichia coli that constitute a synthetic symbiotic microbial consortium, we fortuitously observed such spatial self-organization. This consortium forms a biofilm and, after several days, adopts a defined layered structure that is associated with two unexpected, measurable growth advantages. First, the consortium cannot successfully colonize a new, downstream environment until it selforganizes in the initial environment; in other words, the structure enhances the ability of the consortium to survive environmental disruptions. Second, when the layered structure forms in downstream environments the consortium accumulates significantly more biomass than it did in the initial environment; in other words, the structure enhances the global productivity of the consortium. We also observed that the layered structure only assembles in downstream environments that are colonized by aggregates from a previous, structured community. These results demonstrate roles for self-organization and aggregation in persistence of multi-cellular communities, and also illustrate a role for the techniques of synthetic biology in elucidating fundamental biological principles

Do self-reported intentions predict clinicians behaviour: a systematic review.

Background: Implementation research is the scientific study of methods to promote the systematic uptake of clinical research findings into routine clinical practice. Several interventions have been shown to be effective in changing health care professionals' behaviour, but heterogeneity within interventions, targeted behaviours, and study settings make generalisation difficult. Therefore, it is necessary to identify the 'active ingredients' in professional behaviour change strategies. Theories of human behaviour that feature an individual's "intention" to do something as the most immediate predictor of their behaviour have proved to be useful in non-clinical populations. As clinical practice is a form of human behaviour such theories may offer a basis for developing a scientific rationale for the choice of intervention to use in the implementation of new practice. The aim of this review was to explore the relationship between intention and behaviour in clinicians and how this compares to the intention-behaviour relationship in studies of non-clinicians. Methods: We searched: PsycINFO, MEDLINE, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, Science/Social science citation index, Current contents (social & behavioural med/clinical med), ISI conference proceedings, and Index to Theses. The reference lists of all included papers were checked manually. Studies were eligible for inclusion if they had: examined a clinical behaviour within a clinical context, included measures of both intention and behaviour, measured behaviour after intention, and explored this relationship quantitatively. All titles and abstracts retrieved by electronic searching were screened independently by two reviewers, with disagreements resolved by discussion. Discussion: Ten studies were found that examined the relationship between intention and clinical behaviours in 1623 health professionals. The proportion of variance in behaviour explained by intention was of a similar magnitude to that found in the literature relating to non-health professionals. This was more consistently the case for studies in which intention-behaviour correspondence was good and behaviour was self-reported. Though firm conclusions are limited by a smaller literature, our findings are consistent with that of the non-health professional literature. This review, viewed in the context of the larger populations of studies, provides encouragement for the contention that there is a predictable relationship between the intentions of a health professional and their subsequent behaviour. However, there remain significant methodological challenges

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure