The macroeconomic determinants of volatility in precious metals markets

This paper models the monthly price volatilities of four precious metals (gold, silver, platinum and palladium prices) and investigates the macroeconomic determinants (business cycle, monetary environment and financial market sentiment) of these volatilities. Gold volatility is shown to be explained by monetary variables, but this is not true for silver. Overall, there is limited evidence that the same macroeconomic factors jointly influence the volatility processes of the four precious metal price series, although there is evidence of volatility feedback between the precious metals. These results are consistent with the view that precious metals are too distinct to be considered a single asset class, or represented by a single index. This finding is of importance for portfolio managers and investors.     

The seasonal cycles and photochemistry of C2–C5 alkanes at Mace Head

Continuous in-situ measurements of NMHCs at Mace Head, Ireland during two full annual cycles from January 2005 to January 2007 were used to investigate NMHC emission sources and transport including dilution and photochemical oxidation. The Mace Head research station is ideally located to sample a wide range of air masses including polluted European transport, clean North Atlantic and Arctic air masses and the ultra-clean, Southern Atlantic air masses. The variety in air mass sampling is used to investigate interaction of emissions, transport, dilution and photochemistry. Variability of long-lived hydrocarbon ratios is used to assess and estimate typical transport times from emission source to the Mace Head receptor. Seasonality in the ratios of isomeric alkane pairs (for butane and pentanes) are used to assess the effects of atmospheric transport and photochemical ageing. Finally, the natural logarithms of NMHC ratios are used to assess photochemical oxidation.     

Modeling the Hydrology of Climate Change in California’s Sierra Nevada for Subwatershed Scale Adaptation1

Young, Charles A., Marisa I. Escobar‐Arias, Martha Fernandes, Brian Joyce, Michael Kiparsky, Jeffrey F. Mount, Vishal K. Mehta, David Purkey, Joshua H. Viers, and David Yates, 2009. Modeling the Hydrology of Climate Change in California’s Sierra Nevada for Subwatershed Scale Adaptation. Journal of the American Water Resources Association (JAWRA) 45(6):1409‐1423. Abstract: The rainfall‐runoff model presented in this study represents the hydrology of 15 major watersheds of the Sierra Nevada in California as the backbone of a planning tool for water resources analysis including climate change studies. Our model implementation documents potential changes in hydrologic metrics such as snowpack and the initiation of snowmelt at a finer resolution than previous studies, in accordance with the needs of watershed‐level planning decisions. Calibration was performed with a sequence of steps focusing sequentially on parameters of land cover, snow accumulation and melt, and water capacity and hydraulic conductivity of soil horizons. An assessment of the calibrated streamflows using goodness of fit statistics indicate that the model robustly represents major features of weekly average flows of the historical 1980‐2001 time series. Runs of the model for climate warming scenarios with fixed increases of 2°C, 4°C, and 6°C for the spatial domain were used to analyze changes in snow accumulation and runoff timing. The results indicated a reduction in snowmelt volume that was largest in the 1,750‐2,750 m elevation range. In addition, the runoff center of mass shifted to earlier dates and this shift was non‐uniformly distributed throughout the Sierra Nevada. Because the hydrologic model presented here is nested within a water resources planning system, future research can focus on the management and adaptation of the water resources system in the context of climate change.     

Adaptive Management of Stream Channel Maintenance at Bridge Crossings in the Northern Tier Region,Pennsylvania1

Abstract: An adaptive management framework is applied to the problem of identifying mitigation measures for sediment deposition near bridge crossings in small streams in the Northern Tier region of northern Pennsylvania. The presence of the rigid bridge infrastructure introduces a challenge for applying adaptive management practices, because the integrity of the bridge structure itself has to be maintained regardless of the mitigation practices used in the stream channel near the bridge. In an effort to overcome the unacceptable risk that field‐scale adaptive management experiments present to rigid bridge infrastructure, an adaptive management approach for laboratory‐scale experimentation of mitigation methods at bridge crossings in the Northern Tier region is presented as a way to decrease the level of uncertainty about channel response to mitigation measures and increase the rate of learning about the effectiveness of these measures. Four cycles of adaptive management experiments are discussed to demonstrate that this approach results in fast and efficient learning about channel response to mitigation methods for the given conditions. The value of monitoring and of assessment of monitored data in the overall efficiency of the adaptive management approach is highlighted. Assessment of what was learned in the adaptive management experiment cycles presented here leads to new directions to continually improve management policies and practices in stream channels at bridge crossings in the Northern Tier region. The adaptive management process, rather than continuing with a normally risk‐averse management approach, results in opportunities for learning new information about a system’s response.     

Modeling effects of traffic and landscape characteristics on ambient nitrogen dioxide levels in Connecticut

An integrated exposure model was developed that estimates nitrogen dioxide (NO(2)) concentration at residences using geographic information systems (GIS) and variables derived within residential buffers representing traffic volume and landscape characteristics including land use, population density and elevation. Multiple measurements of NO(2) taken outside of 985 residences in Connecticut were used to develop the model. A second set of 120 outdoor NO(2) measurements as well as cross-validation were used to validate the model. The model suggests that approximately 67% of the variation in NO(2) levels can be explained by: traffic and land use primarily within 2 km of a residence; population density; elevation; and time of year. Potential benefits of this model for health effects research include improved spatial estimations of traffic-related pollutant exposure and reduced need for extensive pollutant measurements. The model, which could be calibrated and applied in areas other than Connecticut, has importance as a tool for exposure estimation in epidemiological studies of traffic-related air pollution.     

The drivers of risk to water security in Shanghai

Big cities are often said to have big water problems, and Shanghai is no exception. In this paper, we examine and compare the influence of the major factors that give rise to the risk of water insecurity in Shanghai. There is an extensive and diverse literature on these issues, dealt with in isolation, and here, we provide a synthesis of the literature, together with our own assessments and calculations, to assess what are the risks to Shanghai’s water supply and what is our degree of confidence in this assessment. We describe the systems that supply water to the city, and past and future changes in the systems, including changes in the glaciers that supply some water to the river, changes in climate, changes in land use, the construction of dams, and water diversions. We show how, at the same time as Shanghai is increasing its dependence on the Yangtze river, water diversions and sea level rise are increasing the risk that this water will be too saline to consume at certain times of the year. This analysis suggests that most of the major drivers of the risk to water security in Shanghai are within the power of environmental managers to control.     

Nitrogen critical loads and management alternatives for N-impacted ecosystems in California

Empirical critical loads for N deposition effects and maps showing areas projected to be in exceedance of the critical load (CL) are given for seven major vegetation types in California. Thirty-five percent of the land area for these vegetation types (99,639 km²) is estimated to be in excess of the N CL. Low CL values (3–8 kg N ha^?1 yr^?1) were determined for mixed conifer forests, chaparral and oak woodlands due to highly N-sensitive biota (lichens) and N-poor or low biomass vegetation in the case of coastal sage scrub (CSS), annual grassland, and desert scrub vegetation. At these N deposition critical loads the latter three ecosystem types are at risk of major vegetation type change because N enrichment favors invasion by exotic annual grasses. Fifty-four and forty-four percent of the area for CSS and grasslands are in exceedance of the CL for invasive grasses, while 53 and 41% of the chaparral and oak woodland areas are in exceedance of the CL for impacts on epiphytic lichen communities. Approximately 30% of the desert (based on invasive grasses and increased fire risk) and mixed conifer forest (based on lichen community changes) areas are in exceedance of the CL. These ecosystems are generally located further from emissions sources than many grasslands or CSS areas. By comparison, only 3–15% of the forested and chaparral land areas are estimated to be in exceedance of the NO₃^? leaching CL. The CL for incipient N saturation in mixed conifer forest catchments was 17 kg N ha^?1 yr^?1. In 10% of the CL exceedance areas for all seven vegetation types combined, the CL is exceeded by at least 10 kg N ha^?1 yr^?1, and in 27% of the exceedance areas the CL is exceeded by at least 5 kg N ha^?1 yr^?1. Management strategies for mitigating the effects of excess N are based on reducing N emissions and reducing site N capital through approaches such as biomass removal and prescribed fire or control of invasive grasses by mowing, selective herbicides, weeding or domestic animal grazing. Ultimately, decreases in N deposition are needed for long-term ecosystem protection and sustainability, and this is the only strategy that will protect epiphytic lichen communities.     

A performance evaluation of the National Air Quality Forecast Capability for the summer of 2007

This paper provides a performance evaluation of the real-time, CONUS-scale National Air Quality Forecast Capability (NAQFC) that supported, in part, its transition into operational status. This evaluation focuses primarily on discrete forecasts for the maximum 8-h O₃ concentrations covering the 4-month period, June through September, 2007, using measurements obtained from EPA's AIRNow network. Results indicate that the 2007 NAQFC performed as well or better than previous configurations, despite the expansion of the forecast domain into the western half of the nation that is dominated by complex terrain. The mean, domain-wide, season-long correlation was 0.70. When examined over time, the domain-wide correlations exhibit a fairly consistent nature, with values exceeding 0.60 (0.70) over 90% (55%) of the days. The NAQFC systematically over-predicted the 8-h O₃ concentrations, continuing a trend established by earlier NAQFC configurations, though to a lesser degree. The summer-long mean forecast value of 53.2 ppb was 4.2 ppb higher than the observed value, resulting in a domain-wide Normalized Mean Bias (NMB) of 8.7%. Most of the over-prediction is associated with observed concentrations less than 50 ppb. In fact the model tends to under-predict when concentrations exceed 70 ppb. As with the bias, the error associated with the latest configuration was also lower. The summer-long Root Mean Square Error of 13.0 ppb (Normalized Mean Error (NME) = 20.4%) represented marked improvements over earlier forecasts. Examination of the spatial distribution of both the NMB and NME reveals that the NAQFC was generally within 25% for the NME and 25% for the NMB over a majority of the domain. Several areas of poorer performance, where the NMB and NME often exceed 25% and in some cases 50%, were noted. These areas include southern California, where the NAQFC tended to under-predict concentrations (especially on weekends) and the southeast Atlantic and Gulf coasts regions, where the model over-predicted. Subsequent analysis revealed that the incorrect temporal allocation of precursor emissions was likely the source of the under-prediction in southern California, while inaccurate simulation of PBL heights likely contributed to the over-prediction in the coastal regions.     

Electricity from Fossil Fuels without CO2 Emissions: Assessing the Costs of Carbon Dioxide Capture and Sequestration in U.S. Electricity Markets

ABSTRACT

The decoupling of fossil-fueled electricity production from atmospheric CO₂ emissions via CO₂ capture and sequestration (CCS) is increasingly regarded as an important means of mitigating climate change at a reasonable cost. Engineering analyses of CO₂ mitigation typically compare the cost of electricity for a base generation technology to that for a similar plant with CO₂ capture and then compute the carbon emissions mitigated per unit of cost. It can be hard to interpret mitigation cost estimates from this plant-level approach when a consistent base technology cannot be identified. In addition, neither engineering analyses nor general equilibrium models can capture the economics of plant dispatch. A realistic assessment of the costs of carbon sequestration as an emissions abatement strategy in the electric sector therefore requires a systems-level analysis. We discuss various frameworks for computing mitigation costs and introduce a simplified model of electric sector planning. Results from a “bottom-up” engineering-economic analysis for a representative U.S. North American Electric Reliability Council (NERC) region illustrate how the penetration of CCS technologies and the dispatch of generating units vary with the price of carbon emissions and thereby determine the relationship between mitigation cost and emissions reduction.     

Natural emissions for regional modeling of background ozone and particulate matter and impacts on emissions control strategies

Natural emissions adopted in current regional air quality modeling are updated to better describe natural background ozone and PM concentrations for North America. The revised natural emissions include organosulfur from the ocean, NO from lightning, sea salt, biogenic secondary organic aerosol (SOA) precursors, and pre-industrial levels of background methane. The model algorithm for SOA formation was also revised. Natural background ozone concentrations increase by up to 4 ppb in annual average over the southeastern US and Gulf of Mexico due to added NO from lightning while the revised biogenic emissions produced less ozone in the central and western US. Natural PM_2.5 concentrations generally increased with the revised natural emissions. Future year (2018) simulations were conducted for several anthropogenic emission reduction scenarios to assess the impact of the revised natural emissions on anthropogenic emission control strategies. Overall, the revised natural emissions did not significantly alter the ozone responses to the emissions reductions in 2018. With revised natural emissions, ozone concentrations were slightly less sensitive to reducing NOx in the southeastern US than with the current natural emissions due to higher NO from lightning. The revised natural emissions have little impact on modeled PM_2.5 responses to anthropogenic emission reductions. However, there are substantial uncertainties in current representations of natural sources in air quality models and we recommend that further study is needed to refine these representations.