4 edition of Linking uncertain energy and environmental models found in the catalog.
Linking uncertain energy and environmental models
Bibliography: p. 31.
|Statement||David Leinweber, Ray Squitieri.|
|Series||[Rand report] - The Rand Corporation ;, R-2609/1-DOE/RC|
|Contributions||Squitieri, Ray L., 1950- joint author., United States. Dept. of Energy.|
|LC Classifications||AS36 .R3 R-2609/1, HD9502.U52 .R3 R-2609/1|
|The Physical Object|
|Pagination||xvi, 31 p. :|
|Number of Pages||31|
|LC Control Number||80027597|
Uncertainty in Environmental Decision-Making: Issues, Challenges and Future Directions H.R. Maiera and J.C. Ascough IIb a School of Civil and Environmental Engineering, The University of Adelaide, Adelaide SA , Australia, [email protected] b USDA-ARS, Agricultural Systems Research Unit, Centre Avenue, Bldg. D, Suite , Fort Collins, CO . Abstract: This paper provides an introduction to alternative models of uncertain commodity prices. A model of commodity price movements is the engine around which any valuation methodology for commodity production projects is built, whether discounted cash flow (DCF) models or the recently developed modern asset pricing (MAP) by:
Huang X, Zhao T, Kudratova S () Uncertain mean-variance and mean-semivariance models for optimal project selection and scheduling. Knowl Based Syst –11 CrossRef Google Scholar Huang X, Zhao T () Project selection and adjustment based on uncertain by: Decision Modeling and Behavior in Complex and Uncertain Environments. Editors providing the reader with a variety of perspectives both theoretic and applied to meet the challenges of devising models to understand the decision-making process. Book Title Decision Modeling and Behavior in Complex and Uncertain Environments Editors.
A single-source guide to harnessing the power of 3D visualization tools for analysis and representation of landscapes. Current technology allows designers to model environmental phenomena and space in new and exciting ways that go beyond the two-dimensional plane. Figure 1. Renewable energies, emissions, and health. This book chapter aims to establish a recent literature review on studies linking the establishment of renewable energy systems with their effect on health, distinguishing between the studies that are focused on the link between climate change and health with a global perspective, and those studies that link the effects of Author: María del P. Pablo-Romero, Rocío Román, Antonio Sánchez-Braza, Rocío Yñiguez.
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Get this from a library. Linking uncertain energy and environmental models: executive summary: prepared for the U.S. Department of Energy. [David Leinweber; Ray L Squitieri; United States.
Department of Energy.]. Get this from a library. Linking uncertain energy and environmental models: prepared for the U.S. Department. [David Leinweber; Ray Squitieri; United States.
Department of Energy.]. Linking uncertain energy and environmental models: Executive summary: prepared for the U.S. Department of Energy ([Rand report] - The Rand Corporation) [David Leinweber] on *FREE* shipping on qualifying offers. Uncertainty in the predictions of science when applied to the environment is an issue of great current relevance in relation to the impacts of climate change, protecting against natural and man-made disasters, pollutant transport and sustainable resource by: Suggested Citation:"Develop Environmental Options for the Energy System."National Research Council.
Linking Science and Technology to Society's Environmental gton, DC: The National Academies Press. doi: / TYPES OF ENVIRONMENTAL MODELS R. Letcher and A. Jakeman Centre for Resource and Environmental Studies, The Australian National University, Australia Keywords: environmental models, environmental systems, modeling, Complex Nonlinear Interactions, subsurface water systems, hydrological systems Contents 1.
Introduction Size: KB. Environmental Modelling: an Uncertain Future. introduces students, scientists and decision makers to the different concepts and techniques of uncertainty estimation in environmental prediction; and techniques for decision making under uncertainty.
This book will be relevant to environmental modellers, practitioners and decision makers. Environmental Modelling: An Uncertain Future. - CRC Press Book Uncertainty in the predictions of science when applied to the environment is an issue of great current relevance in relation to the impacts of climate change, protecting against natural and man-made disasters, pollutant transport and sustainable resource management.
environmental data and models to support all phases and aspects of decision making, in particular supporting group and participatory decision making processes.
Intelligent Environmental Decision Support Systems can include qualitative, quantitative, mathematical, statistical. Environmental Modelling – An Uncertain Future. Uncertainty Estimation for Environmental Models. Menu and widgets.
Environmental Modelling – An Uncertain Future. Contents; Pseudo-code for the Mersene Twister and links to various implementations can be found via the entry in the Wikipaedia.
[Show full abstract] perceived environmental uncertainty, organization structure, and boundary spanning activity were investigated.
In a field study comprising 12 Author: Jiyao Chen. Written in a clear and accessible style by leading quantitative researchers and energy risk management practioners in the liberalising energy markets.
Relates key modelling difficulties to practical strategies for risk management in the face of Author: Risk Books.
The book is carefully structured into three parts which focus on i) policy decisions that have been underpinned by energy-system models, ii) specific aspects of supply and end-use sector modeling, including technology learning and behavior and iii) how additional insights can be gained from linking energy-system models with other models.
pertains to environmental models. These type of models include (but are not limited to) petroleum and geothermal reservoir models, groundwater models and surface water hydraulic and hydrologic models. As well as presenting theory, an equally important role of this book is to draw some important conclusions from the theory.
economy-energy-environment models is a large step in the necessary direction for making consistent economy and environmental projections.
These integrated CGE-models have been used for forecasting purposes and numerous analyses of energy and environmental policies during the last two decades. Two models are estimated, one based on a closed-ended question framed around Germany's target of 35% renewable energy in electricity provision byand the other on an open-ended format that captures changes in WTP over time.
In Environmental Modelling: An Uncertain Future. Keith Beven offers a guide to uncertainty for both modelers and users of models. Beven begins with a general introduction to the modeling process, covering parameters, variables and boundary conditions, problems of scale and incommensurability, model spaces and model ensembles, as well as the.
Visit to get more information about this book, to buy it in print, or to download it as a free PDF. The general desire on the part of societies to improve their standard of living has resulted in greater consumption of water, food, mineral, and energy services and expansion of our.
Uncertainty analysis in building energy assessment has become an active research field because a number of factors influencing energy use in buildings are inherently uncertain. A Simple Model of Electric Power Network 48 Power flow equations. An Introductory Guide to Uncertainty Analysis in Environmental and Health Risk Assessment J.
S. Hammonds F. O. Hoffman DOE United States Department of Energy EPA United States Environmental Protection Agency models applied to assess human health and environmental risk.
Uncertainty analyses involve the propagationFile Size: KB. Ted Gayer discusses how behavioral economics is helping to explain how both policymakers and the general population assess risk, particularly as it relates to energy efficiency and environmental Author: Ted Gayer.SETSTOCH is a tool for linking Algebraic Modeling Languages with Specialized Stochastic Programming Solvers.
Its main role is to retrieve from the modeling language a dynamically ordered core model (baseline scenario) that is then sent automatically to the Stochastic Solver.
The user is thus able to take full advantage of specific features supplied by the by: 2.