This open access book provides a theoretical framework and case studies on decision science for regional sustainability by integrating the natural and social sciences. The cases discussed include solution-oriented transdisciplinary studies on the environment, disasters, health, governance and human cooperation. Based on these case studies and comprehensive reviews of relevant works, including lessons learned from past failures for predictable surprises and successes in adaptive co-management, the book provides the reader with new perspectives on how we can co-design collaborative projects with various conflicts of interest and how we can transform our society for a sustainable future. The book makes a valuable contribution to the global research initiative Future Earth, promoting transdisciplinary studies to bridge the gap between science and society in knowledge generation processes and supporting efforts to achieve the UN’s Sustainable Development Goals (SDGs). Compared to other publications on transdisciplinary studies, this book is unique in that evolutionary biology is used as an integrator for various areas related to human decision-making, and approaches social changes as processes of adaptive learning and evolution. Given its scope, the book is highly recommended to all readers seeking an integrated overview of human decision-making in the context of social transformation.

Science, engineering, and technology permeate nearly every facet of modern life and hold the key to solving many of humanity's most pressing current and future challenges. The United States' position in the global economy is declining, in part because U.S. workers lack fundamental knowledge in these fields. To address the critical issues of U.S. competitiveness and to better prepare the workforce, A Framework for K-12 Science Education proposes a new approach to K-12 science education that will capture students' interest and provide them with the necessary foundational knowledge in the field. A Framework for K-12 Science Education outlines a broad set of expectations for students in science and engineering in grades K-12. These expectations will inform the development of new standards for K-12 science education and, subsequently, revisions to curriculum, instruction, assessment, and professional development for educators. This book identifies three dimensions that convey the core ideas and practices around which science and engineering education in these grades should be built. These three dimensions are: crosscutting concepts that unify the study of science through their common application across science and engineering; scientific and engineering practices; and disciplinary core ideas in the physical sciences, life sciences, and earth and space sciences and for engineering, technology, and the applications of science. The overarching goal is for all high school graduates to have sufficient knowledge of science and engineering to engage in public discussions on science-related issues, be careful consumers of scientific and technical information, and enter the careers of their choice. A Framework for K-12 Science Education is the first step in a process that can inform state-level decisions and achieve a research-grounded basis for improving science instruction and learning across the country. The book will guide standards developers, teachers, curriculum designers, assessment developers, state and district science administrators, and educators who teach science in informal environments.

Global urbanization promises better services, stronger economies, and more connections; it also carries risks and unforeseeable consequences. To deepen our understanding of this complex process and its importance for global sustainability, we need to build interdisciplinary knowledge around a systems approach. Urban Planet takes an integrative look at our urban environment, bringing together scholars from a diverse range of disciplines: from sociology and political science to evolutionary biology, geography, economics and engineering. It includes the perspectives of often neglected voices: architects, journalists, artists and activists. The book provides a much needed cross-scale perspective, connecting challenges and solutions on a local scale with drivers and policy frameworks on a regional and global scale. The authors argue that to overcome the major challenges we are facing, we must embark on a large-scale reinvention of how we live together, grounded in inclusiveness and sustainability.

Based upon ten case studies, Prediction explores how science-based predictions guide policy making and what this means in terms of global warming, biogenetically modifying organisms and polluting the environment with chemicals.

Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. - Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery - Includes considerations sustainability analysts must evaluate when applying big data - Features case studies illustrating the application of data science in sustainability analyses

A powerful investigation into the chances for humanity's future from the author of the bestseller The World Without Us. In his bestselling book The World Without Us, Alan Weisman considered how the Earth could heal and even refill empty niches if relieved of humanity's constant pressures. Behind that groundbreaking thought experiment was his hope that we would be inspired to find a way to add humans back to this vision of a restored, healthy planet-only in harmony, not mortal combat, with the rest of nature. But with a million more of us every 4 1/2 days on a planet that's not getting any bigger, and with our exhaust overheating the atmosphere and altering the chemistry of the oceans, prospects for a sustainable human future seem ever more in doubt. For this long awaited follow-up book, Weisman traveled to more than 20 countries to ask what experts agreed were probably the most important questions on Earth -- and also the hardest: How many humans can the planet hold without capsizing? How robust must the Earth's ecosystem be to assure our continued existence? Can we know which other species are essential to our survival? And, how might we actually arrive at a stable, optimum population, and design an economy to allow genuine prosperity without endless growth? Weisman visits an extraordinary range of the world's cultures, religions, nationalities, tribes, and political systems to learn what in their beliefs, histories, liturgies, or current circumstances might suggest that sometimes it's in their own best interest to limit their growth. The result is a landmark work of reporting: devastating, urgent, and, ultimately, deeply hopeful. By vividly detailing the burgeoning effects of our cumulative presence, Countdown reveals what may be the fastest, most acceptable, practical, and affordable way of returning our planet and our presence on it to balance. Weisman again shows that he is one of the most provocative journalists at work today, with a book whose message is so compelling that it will change how we see our lives and our destiny.

DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

Transformation of the Earth's social and ecological systems is occurring at a rate and magnitude unparalleled in human experience. Data science is a revolutionary new way to understand human-environment relationships at the heart of pressing challenges like climate change and sustainable development. However, data science faces serious shortcomings when it comes to human-environment research. There are challenges with social and environmental data, the methods that manipulate and analyze the information, and the theory underlying the data science itself; as well as significant legal, ethical and policy concerns. This timely book offers a comprehensive, balanced, and accessible account of the promise and problems of this work in terms of data, methods, theory, and policy. It demonstrates the need for data scientists to work with human-environment scholars to tackle pressing real-world problems, making it ideal for researchers and graduate students in Earth and environmental science, data science and the environmental social sciences.

Risk assessment has become a dominant public policy tool for making choices, based on limited resources, to protect public health and the environment. It has been instrumental to the mission of the U.S. Environmental Protection Agency (EPA) as well as other federal agencies in evaluating public health concerns, informing regulatory and technological decisions, prioritizing research needs and funding, and in developing approaches for cost-benefit analysis. However, risk assessment is at a crossroads. Despite advances in the field, risk assessment faces a number of significant challenges including lengthy delays in making complex decisions; lack of data leading to significant uncertainty in risk assessments; and many chemicals in the marketplace that have not been evaluated and emerging agents requiring assessment. Science and Decisions makes practical scientific and technical recommendations to address these challenges. This book is a complement to the widely used 1983 National Academies book, Risk Assessment in the Federal Government (also known as the Red Book). The earlier book established a framework for the concepts and conduct of risk assessment that has been adopted by numerous expert committees, regulatory agencies, and public health institutions. The new book embeds these concepts within a broader framework for risk-based decision-making. Together, these are essential references for those working in the regulatory and public health fields.

A practical, bipartisan call to action from the world’s leading thinkers on the environment and sustainability Sustainability has emerged as a global priority over the past several years. The 2015 Paris Agreement on climate change and the adoption of the seventeen Sustainable Development Goals through the United Nations have highlighted the need to address critical challenges such as the buildup of greenhouse gases in the atmosphere, water shortages, and air pollution. But in the United States, partisan divides, regional disputes, and deep disagreements over core principles have made it nearly impossible to chart a course toward a sustainable future. This timely new book, edited by celebrated scholar Daniel C. Esty, offers fresh thinking and forward-looking solutions from environmental thought leaders across the political spectrum. The book’s forty essays cover such subjects as ecology, environmental justice, Big Data, public health, and climate change, all with an emphasis on sustainability. The book focuses on moving toward sustainability through actionable, bipartisan approaches based on rigorous analytical research.