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This analysis integrates regional models of power system reliability, output from atmosphere-ocean general circulation models, and results from the Interruption Cost Estimate (ICE) Calculator to project long-run costs to electric utility customers from power interruptions under different future severe weather and electricity system scenarios. We discuss the challenges when attempting to model long-run costs to utility customers including the use of imperfect metrics to measure severe weather. Despite these challenges, initial findings show that discounted cumulative customer costs, through the middle of the century, could range from $1.5-$3.4 trillion ($2015) without aggressive undergrounding of the power system and increased utility operations and maintenance (O&M) spending and $1.5-$2.5 trillion with aggressive undergrounding and increased spending. By the end of the century, cumulative customer costs could range from $1.9-$5.6 trillion (without aggressive undergrounding and increased spending) and $2.0-$3.6 trillion (with aggressive undergrounding and increased spending). We find that, in some scenarios, aggressive undergrounding of distribution lines and increased O&M spending is not always cost-effective. We conclude by identifying important topics for follow-on research, which have the potential to improve the cost estimates of this model. [ABSTRACT FROM AUTHOR]

The US energy service company (ESCO) industry has a well-established track record of delivering substantial energy and dollar savings in the public and institutional facilities sector, typically through the use of energy savings performance contracts (ESPC). The ESCO industry has the opportunity to play an important role in achieving demand-side energy efficiency under the US Environmental Protection Agency's (EPA) proposed Clean Power Plan. The EPA considered demand-side energy efficiency as a compliance strategy for proposed greenhouse gas (GHG) emissions standards under section 111 (d) of the Clean Air Act. To date, there has been little or no research in the public domain to estimate electricity or fuel savings for the entire US ESCO industry. Estimating these savings levels is a foundational step in order to determine total avoided GHG emissions from demand-side energy efficiency measures installed by US ESCOs. We find that on average, 66 % of total energy savings are in the form of electricity, but that in more comprehensive projects, almost 50 % of savings are produced from fuel resources. Overall, we estimate that active US ESCO industry projects generated about 34 TWh of electricity savings in 2012. About 15 TWh of these electricity savings were for municipal, local, and state government facilities; universities/colleges; K-12 schools; and healthcare (MUSH) facilities customers who did not rely on utility customer-funded energy efficiency programs. We extend the electricity analysis to estimate total energy savings and find that the US ESCO industry saved ∼224 million MMBtu in 2012 or ∼1 % of the total US commercial building energy consumption. [ABSTRACT FROM AUTHOR]

Abstract: This study evaluates avoided emissions potential of CO2, SO2 and NOx assuming a 10% penetration level of photovoltaics (PV) in ten selected U.S. states. We estimate avoided emissions using an hourly energy system simulation model, EnergyPLAN. Avoided emissions vary significantly across the country−mainly due to three state-specific factors: the existing resource mix of power plants (power grid fuel mix), the emission intensity of existing fossil fuel power plants and the PV capacity factor within each state. The avoided emissions per solar PV capacity (g/W)—for ten U.S. states—ranged from 670 to 1500 for CO2, 0.01–7.80 for SO2 and 0.25–2.40 for NOx. In general, avoided emissions are likely to be higher in locations with 1) higher share of coal plants; 2) higher emission of existing fossil fuel plants; and 3) higher PV capacity factor. To further illustrate the quantitative relationship between avoided emissions and the three state-specific factors, we conducted a sensitivity analysis. Finally, we estimated the change in avoided emissions in a coal-intensive state by varying the operational constraints of fossil-fuel power plants. At the 10% penetration level avoided emissions were not constrained by the ramp rate limitations, but the minimum capacity requirement significantly affected the avoided emission estimates. [Copyright &y& Elsevier]

To estimate the economic effects of weather variability in the United States, the authors define and measure weather sensitivity as the variability in economic output that is attributable to weather variability, accounting for changes in technology and changes in levels of economic inputs (i.e., capital, labor, and energy). Using 24 yr of economic data and weather observations, quantitative models of the relationship between state-level sectoral economic output and weather variability are developed for the 11 nongovernmental sectors of the U.S. economy; temperature and precipitation measures were used as proxies for all weather impacts. All 11 sectors are found to have statistically significant sensitivity to weather variability. Economic inputs were then constant and economic output was estimated in the 11 estimated sector models, varying the weather inputs only using 70 yr of historic weather observations. It was found that U.S. economic output varies by up to $$485 billion yr−1 of 2008 gross domestic product, about 3.4%%, owing to weather variability. U.S. states that are more sensitive to weather variability are identified and sectors are ranked by their degree of weather sensitivity. This work illustrates a valid approach to measuring the economic impact of weather variability, gives baseline information and methods for more detailed studies of the sensitivity of each sector to weather variability, and lays the groundwork for assessing the value of current or improved weather forecast information given the economic impacts of weather variability. [ABSTRACT FROM AUTHOR]

Abstract: Previous investigations suggest that contaminant transport from the large Kennebec/Androscoggin watershed is an important large-scale process in mid-coast Maine. To investigate this phenomenon, we determined the concentrations of Cd, Cr, Cu, Ni, Pb, Sn and Zn in the surface sediments of 47 stations in the tidal Kennebec/Androscoggin system. Most stations exhibited elevated metal concentrations. Highest levels were found in the main stem of the system. Distribution patterns lead to the conclusion that metals enter the system from the watershed and are transported to the nearshore Gulf of Maine. The coarse-grained, ebb tide dominated flow prevents the accumulation of contaminants in the estuary. This supports the hypothesis of that the Kennebec and Androscoggin watersheds are sources for contaminants observed in the nearshore Gulf of Maine. [ABSTRACT FROM AUTHOR]

Theory-based evaluation utilizes terms such as 'moderators' and 'CMO configurations' to describe how the validity of a programme theory depends on the context in which it operates. From a constructivist perspective, however, contexts are influenced by ongoing social (re)construction. The truth value of a programme theory is contingent upon this construction. This helps explain why tests of programme theories provided by theory-based evaluators are often unconvincing to programme advocates. A typology of 'magic', 'tragic and 'competing' programmes, each of which influence their contexts in different ways, is described and the implications for evaluation discussed. [ABSTRACT FROM AUTHOR]

There has been a limited amount of peer-reviewed literature on long-term trends in electricity reliability including the underlying factors that impact reliability across the United States. In this analysis, we considered up to 16 years of data from 203 U S. utilities—representing about 70% of electricity sales. Annual frequency of interruptions for an average customer—at the regional and U.S. national-level—has generally decreased over this timeframe. But we do not find that there is a statistically significant trend in the annual duration of interruptions for an average customer. We find that more explicit measures of severe weather are correlated with reliability. We are able to explain 7% and 16% of past variation in the reliability metrics system average interruption duration and frequency indices, respectively, is due to severe weather—a significant improvement over earlier studies. We find that current year spending by utilities is correlated with worse reliability and that cumulative spending over the preceding three years is correlated with better reliability. Finally, we demonstrate that using a statistical instrument to represent the annual frequency of interruptions for an average customer can greatly improve analysis of trends in the annual duration of interruptions for an average customer. • Frequency of power interruptions experienced by U.S. customers is decreasing over time. • Inconclusive trend in total annual duration of power interruptions for typical customer. • Current year utility spending is correlated with worse reliability. • Cumulative spending over preceding three years is correlated with better reliability. [ABSTRACT FROM AUTHOR]

The AZTI Marine Biotic Index (AMBI) requires less geographically-specific calibration than other benthic indices, but has not performed as well in US coastal waters as it has in the European waters for which it was originally developed. Here we examine the extent of improvement in index performance when the Ecological Group (EG) classifications on which AMBI is based are derived using local expertise. Twenty-three US benthic experts developed EG scores for each of three regions in the United States, as well as for the US as a whole. Index performance was then compared using: (1) EG scores specific to a region, (2) national EG scores, (3) national EG scores supplemented with standard international EG scores for taxa that the US experts were not able to make assignments, and (4) standard international EG scores. Performance of each scheme was evaluated by diagnosis of condition at pre-defined good/bad sites, concordance with existing local benthic indices, and independence from natural environmental gradients. The AMBI performed best when using the national EG assignments augmented with standard international EG values. The AMBI using this hybrid EG scheme performed well in differentiating apriori good and bad sites (>80% correct classification rate) and AMBI scores were both concordant and correlated ( r s = 0.4–0.7) with those of existing local indices. Nearly all of the results suggest that assigning the EG values in the framework of local biogeographic conditions produced a better-performing version of AMBI. The improved index performance, however, was tempered with apparent biases in score distribution. The AMBI, regardless of EG scheme, tended to compress ratings away from the extremes and toward the moderate condition and there was a bias with salinity, where high quality sites received increasingly poorer condition scores with decreasing salinity. [ABSTRACT FROM AUTHOR]

Although rodents are well-known reservoirs and vectors for a number of zoonoses, the functional role that peridomestic rodents serve in the amplification and transmission of foodborne pathogens is likely underappreciated. Clear links have been identified between commensal rodents and outbreaks of foodborne pathogens throughout Europe and Asia; however, comparatively little research has been devoted to studying this relationship in the United States. In particular, regional studies focused on specific rodent species and their foodborne pathogen reservoir status across the diverse agricultural landscapes of the United States are lacking. We posit that both native and invasive species of rodents associated with food-production pipelines are likely sources of seasonal outbreaks of foodborne pathogens throughout the United States. In this study, we review the evidence that identifies peridomestic rodents as reservoirs for foodborne pathogens, and we call for novel research focused on the metagenomic communities residing at the rodent-agriculture interface. Such data will likely result in the identification of new reservoirs for foodborne pathogens and species-specific demographic traits that might underlie seasonal enteric disease outbreaks. Moreover, we anticipate that a One Health metagenomic research approach will result in the discovery of new strains of zoonotic pathogens circulating in peridomestic rodents. Data resulting from such research efforts would directly inform and improve upon biosecurity efforts, ultimately serving to protect our food supply.