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Many regulations issued by the U.S. Environmental Protection Agency (EPA) are based on the results of computer models. Models help EPA explain environmental phenomena in settings where direct observations are limited or unavailable, and anticipate the effects of agency policies on the environment, human health and the economy. Given the critical role played by models, the EPA asked the National Research Council to assess scientific issues related to the agency's selection and use of models in its decisions. The book recommends a series of guidelines and principles for improving agency models and decision-making processes. The centerpiece of the book's recommended vision is a life-cycle approach to model evaluation which includes peer review, corroboration of results, and other activities. This will enhance the agency's ability to respond to requirements from a 2001 law on information quality and improve policy development and implementation.
Human well-being is inextricably linked to the condition of the natural environment. Environmental management decisions often aim to maintain ecosystems in a healthy and resilient condition while providing the ecosystem goods and services that humans want and need. Models, methods, frameworks, and metrics are needed to characterize and forecast the potential benefits from remediation, restoration, and revitalization that improve human health and well-being through the delivery of ecosystem services. However, ecosystems are complex, and layering on social and economic considerations can make environmental decision-making seem intractable. Dynamics of socio-ecological systems are complicated, making models a pivotal tool for identifying and quantifying relationships, assessing historical patterns, and forecasting alternative decision scenarios. The goal of this Research Topic is to leverage modeling approaches to provide science-based evidence, metrics, and frameworks and methods for quantifying how restored ecosystem goods and services lead to benefits for public health, community well-being, and economic vitality. Modeling approaches may range in complexity from conceptual models to statistical models to dynamic process models, empirically-derived to mechanistic to participatory. Research will evaluate connections between ecosystem condition, ecosystem services, and human health and well-being, and may include covarying socio-economic or biophysical factors that modify relationships between ecosystem health and perceived or realized benefits. Applications or case studies will demonstrate how to integrate community priorities with nature-based solutions to enhance benefits of environmental remediation, ecological restoration, community revitalization, and climate resilience decisions.
Syukuro Manabe is perhaps the leading pioneer of modern climate modeling. Beyond Global Warming is his compelling firsthand account of how the scientific community came to understand the human causes of climate change, and how numerical models using the world's most powerful computers have been instrumental to these vital discoveries. Joined here by atmospheric scientist Anthony Broccoli, Manabe shows how climate models have been used as virtual laboratories for examining the complex planetary interactions of atmosphere, ocean, and land. Manabe and Broccoli use these studies as the basis for a broader discussion of human-induced global warming--and what the future may hold for a warming planet. They tell the stories of early trailblazers such as Svante Arrhenius, the legendary Swedish scientist who created the first climate model of Earth more than a century ago, and provide rare insights into Manabe's own groundbreaking work over the past five decades. Expertly walking readers through key breakthroughs, they explain why increasing atmospheric carbon dioxide has caused temperatures to rise in the troposphere yet fall in the stratosphere, why the warming of the planet's surface differs by hemisphere, why drought is becoming more frequent in arid regions despite the global increase in precipitation, and much more.
Providing a reliable and resilient supply of electric power to communities across the United States has always posed a complex challenge. Utilities must support daily operations to serve a diverse array of customers across a heterogeneous landscape while simultaneously investing in infrastructure to meet future needs, all while juggling an enormous array of competing priorities influenced by costs, capabilities, environmental and social impacts, regulatory requirements, and consumer preferences. A rapid pace of change in technologies, policies and priorities, and consumer needs and behaviors has further compounded this challenge in recent years. The National Academies of Sciences, Engineering, and Medicine convened a workshop on February 3, 2020 to explore strategies for incorporating new technologies, planning and operating strategies, business models, and architectures in the U.S. electric power system. Speakers and participants from industry, government, and academia discussed available models for long-term transmission and distribution planning, as well as the broader context of how these models are used and future opportunities and needs. This publication summarizes the presentations and discussions from the workshop.
Forecasting the future with advanced data models and visualizations. To envision and create the futures we want, society needs an appropriate understanding of the likely impact of alternative actions. Data models and visualizations offer a way to understand and intelligently manage complex, interlinked systems in science and technology, education, and policymaking. Atlas of Forecasts, from the creator of Atlas of Science and Atlas of Knowledge, shows how we can use data to predict, communicate, and ultimately attain desirable futures. Using advanced data visualizations to introduce different types of computational models, Atlas of Forecasts demonstrates how models can inform effective decision-making in education, science, technology, and policymaking. The models and maps presented aim to help anyone understand key processes and outcomes of complex systems dynamics, including which human skills are needed in an artificial intelligence-empowered economy; what progress in science and technology is likely to be made; and how policymakers can future-proof regions or nations. This Atlas offers a driver's seat-perspective for a test-drive of the future.
Risk analytics is developing rapidly, and analysts in the field need material that is theoretically sound as well as practical and straightforward. A one-stop resource for quantitative risk analysis, Practical Spreadsheet Risk Modeling for Management dispenses with the use of complex mathematics, concentrating on how powerful techniques and methods can be used correctly within a spreadsheet-based environment. Highlights Covers important topics for modern risk analysis, such as frequency-severity modeling and modeling of expert opinion Keeps mathematics to a minimum while covering fairly advanced topics through the use of powerful software tools Contains an unusually diverse selection of topics, including explicit treatment of frequency-severity modeling, copulas, parameter and model uncertainty, volatility modeling in time series, Markov chains, Bayesian modeling, stochastic dominance, and extended treatment of modeling expert opinion End-of-chapter exercises span eight application areas illustrating the broad application of risk analysis tools with the use of data from real-world examples and case studies This book is written for anyone interested in conducting applied risk analysis in business, engineering, environmental planning, public policy, medicine, or virtually any field amenable to spreadsheet modeling. The authors provide practical case studies along with detailed instruction and illustration of the features of ModelRisk®, the most advanced risk modeling spreadsheet software currently available. If you intend to use spreadsheets for decision-supporting analysis, rather than merely as placeholders for numbers, then this is the resource for you.
Models are fundamental for estimating the possible costs and effectiveness of different policies for reducing greenhouse gas (GHG) emissions. There is a wide array of models to perform such analysis, differing in the level of technological detail, treatment of technological progress, spatial and sector details, and representation of the interaction of the energy sector to the overall economy and environment. These differences impact model results, including cost estimates. More fundamentally, these models differ as to how they represent fundamental processes that have a large impact on policy analysis-such as how different models represent technological learning and cost reductions that come through increasing production volumes, or how different models represent baseline conditions. Reliable estimates of the costs and potential impacts on the United States economy of various emissions reduction and other mitigation strategies are critical to the development of the federal climate change research and development portfolio. At the request of the U.S. Department of Energy (DOE), the National Academies organized a workshop, summarized in this volume, to consider some of these types of modeling issues.
This book offers practical advice on managing enterprise modeling (EM) projects and facilitating participatory EM sessions. Modeling activities often involve groups of people, and models are created in a participatory way. Ensuring that this is done efficiently requires dedicated individuals who know how to organize modeling projects and sessions, how to manage discussions during these sessions, and what aspects influence the success and efficiency of modeling in practice. The book also includes a summary of the theoretical background to EM, although participatory modeling can also be used in conjunction with other methods that are not made for EM, such as those made for goal-oriented requirements engineering and information systems analysis. The first four chapters present an overview of enterprise modeling from various viewpoints (including methods, processes and organizational challenges), providing a background for those that need to refresh their basic knowledge. The next six chapters form the core of the book and detail the roles and competences needed in an EM project, typical stakeholder behaviors and how to handle them, tools and methods for managing participatory modeling and facilitation, and how to train modeling experts for these social aspects of modeling. Lastly, a concluding chapter presents a summary and an outlook on current research in participatory EM. This book is intended for anybody who wants to learn more about how to facilitate participatory modeling in practice and how to set up and carry out EM projects. It does not require any in-depth knowledge about specific EM methods and tools, and can be used by students and lecturers for courses on participatory modeling, and by practitioners wanting to extend their knowledge of social and organizational topics to become an experienced facilitator and EM project manager.