285 research outputs found

    Spatio-Temporal Modeling of Wildfire Risks in the U.S. Forest Sector

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    In the US forestry industry, wildfire has always been one of the leading causes of damage. This topic is of growing interest as wildfire has caused huge losses for landowners, residents and governments in recent years. While individual wildfire behavior is well studied (e.g. Butry 2009; Holmes 2010), a lot of new literature on broadscale wildfire risks (e.g. by county) is emerging (e.g. Butry et al. 2001; Prestemon et al. 2002). The papers of the latter category have provided useful suggestions for government wildfire management and policies. Although wildfire insurance for real estate owners is popular, the possibility to develop a forestry production insurance scheme accounting for wildfire risks has not yet been investigated. The purpose of our paper is to comprehensively evaluate broadscale wildfire risks in a spatio-temporal autoregressive scenario and to design an actuarially fair wildfire insurance scheme in the U.S. forest sector. Our research builds upon an extensive literature that has investigated crop insurance modeling. Wildfire risks are closely linked to environmental conditions. Weather, forestland size, aspects of human activity have been proved to be crucial causal factors for wildfire (Prestemon et al. 2002; Prestemon and Butry 2005; Mercer et al. 2007). In light of these factors, we carefully study wildfires ignited by different sources, such as by arson and lightning, and identify their underlying causes. We find that the decomposition of forestland ecosystem and socio-economic conditions have significant impacts on wildfire, as well as weather. Our models provide a good fit to data on frequency and propensity for fires to exist (e.g. R-square ranges from 0.4 to 0.8) and therefore provide important fundamental information on risks for the development of insurance contracts. A number of databases relevant to this topic are used. With the Florida wildfire frequency and loss size database, a complete survey of four measurements of annual wildfire risks is implemented. These four measurements are annual wildfire frequency, burned area, fire per acre and burned ratio at county level. In addition, the national forestry inventory and analysis (FIA) database, Regional Economic Information Systems (REIS) database and the national weather database have supplied forestland ecosystem, socioeconomic, and weather condition information respectively. With our spatio-temporal lattice models, impacts of environmental factors on wildfire and implications of wildfire management policies are assessed. Forestland size, private owners’ share of forestland, population and drought would positively contribute to wildfire risks significantly. Cold weather and high employment are found to be helpful in lessening wildfire risks. Among the forestland ecosystem, oak / pine & oak / hickory forestland would reduce wildfire risks while longleaf / slash & loblolly / shortleaf pine forestland would have a mixed impact. An interesting finding is that oak / gum / cypress forestland would reduce wildfire frequency, but would enhance wildfire propensity at the same time. Hurricanes could intensify wildfire risks in the same year, but would significantly decrease the next year’s wildfire risks. Meanwhile, cross sample validation verifies that our method can forecast wildfire risks adequately well. Since our approach does not incorporate any fixed-effect indicator or trend as in the panel data analysis (Prestemon et al. 2002), it offers a universal tool to evaluate and predict wildfire risks. Hence, given environmental information of a location, a corresponding actuarially fair insurance rate can be calculated.wildfires, forestry, weather, socio-economic, Spatio-Temporal autocorrelation, Risk and Uncertainty,

    Future Hadron Colliders: from physics perspectives to technology R&D

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    High energy hadron colliders have been instrumental to discoveries in particle physics at the energy frontier and their role as discovery machines will remain unchallenged for the foreseeable future. The full exploitation of the LHC is now the highest priority of the energy frontier collider program. This includes the high luminosity LHC project which is made possible by a successful technology-readiness program for Nb3_{3}Sn superconductor and magnet engineering based on long-term high-field magnet R&D programs. These programs open the path towards collisions with luminosity of 5×1034 cm−2s−1 and represents the foundation to consider future proton colliders of higher energies. This paper discusses physics requirements, experimental conditions, technological aspects and design challenges for the development towards proton colliders of increasing energy and luminosity

    Projecting Wildfire Emissions and Their Air Quality Impacts in the Southeastern U.S. from 2010 to Mid-century

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    Wildfires can severely impair the health of ecosystems, life forms and regional economies. In the rapidly changing U. S. Southeast, both climate and socioeconomic factors (e.g., population and income) drive wildfires, and need to be represented in wildfire inventories to assess the air quality (AQ) impacts and health risks of wildfires long-term. This motivated the development of a wildfire emissions projection methodology leveraging published models of annual areas burned (AAB) based on county-level socioeconomic and climate projections for 2011-2060. It is applied to project two sets of AAB with different climate downscaling approaches, to estimate wildfire emissions for 2010 and four mid-century years. These are compared with emissions estimated using 18-year historical mean AAB without changes in climate and socioeconomics. Competing climate and socioeconomic factors result in 7% - 32% lower projected AAB than historical values, and 13% - 62% lower fine particulate matter (PM2.5) emissions than estimated from historical AAB in the selected years, with climate driving their temporal variability. Evaluation of the emissions projection methods in air quality (AQ) simulations against those using the National Emissions Inventory (NEI), and network observations for 2010 show little difference among the methods in ozone (0.08% - 0.93%) and PM2.5 (1% - 8%). Larger, comparable biases relative to observations in all three methods for secondary species, especially in winter, are attributable to non-wildfire emissions or secondary chemical production. The projection methods predict primary wildfire PM better than the NEI, providing confidence that they can assess current wildfire AQ impacts, while enabling longer-term AQ assessments unachievable with static inventories. AQ simulations using the projected wildfire emissions, and projected emission reductions in SOx and NOx from energy and transportation (by ~80% at mid-century) show peak periods and locations of wildfire impacts on ozone and PM shifting from autumn in Midwestern locations in 2010, to warmer and drier summers east and south by mid-century, following the AAB spatiotemporal patterns. Although considerably lower than 2010 levels, summertime PM2.5 increases by 4%-5% in 2040-2060 in this emission scenario, driven by increases in OC and unspeciated other PM.Doctor of Philosoph

    Iowa State Journal of Research 52.3

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    Patanakamjorn, S., W.D. Guthrie, and W.R. Young. Proreus simulans: an earwig predator of the European corn borer, Ostrinia furnacalis. 277 Prestemon, D.R., W.J. Goudy, and R.G. Pounds. Housing attitudes in rural Iowa communities. 283 Laosuwan, P., and R.E. Atkins. Genetic effects for grain yield and components and relationships among agronomic characters in converted exotic sorghums. 291 Helgerson, O.T., and J.C. Gordon. Coalspoil performance of Arnot bristly locust and Crandon hybrid poplar. 299 Ellis, C.J. Syringeal histology. VIII. Shortbilled marsh wren, Cistothorus platensis stellaris. 307 Takle, E.S., J.M. Brown, and W.M. Davis. Characteristics of wind and wind energy in Iowa. 313 Bajuk, L.A., J.C. Gordon, and L.C. Promnitz. Greenhouse evaluation of the growth potential of Alnus glutinosa clones. 341</p

    Space-Time Modeling of Timber Prices

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    A space-time econometric model was developed for pine sawtimber timber prices of 21 geographically contiguous regions in the southern United States. The correlations between prices in neighboring regions helped predict future prices. The impulse response analysis showed that although southern pine sawtimber markets were not globally integrated, local supply and demand shocks did transmit partially to immediate neighboring regions, and could also have weaker effects in more distant regions.impulse response, market integration, space-time model, spatial correlation, Demand and Price Analysis,
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