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| data science in environment and sustainability: Data Science Applied to Sustainability Analysis Jennifer Dunn, Prasanna Balaprakash, 2021-05-11 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 |
| data science in environment and sustainability: Data, Statistics, and Useful Numbers for Environmental Sustainability Benoit Cushman-Roisin, Bruna Tanaka Cremonini, 2021-05-19 Data, Statistics, and Useful Numbers for Environmental Sustainability: Bringing the Numbers to Life is an accessible reference for researchers working in environmental and sustainability fields who need to communicate the latest data and statistics to reinforce their own research or message. The book compiles the most-needed numbers into one resource and covers a variety of relevant topics, including materials, energy, environment, city planning, electronics, and waste. This handbook is clearly indexed and full of comprehensive tables, making it easy to find answers. Researchers in environmental and sustainability-related fields will find it an invaluable resource. Collects and presents important environmental data in one accessible resource Provides key information needed for effectively communicating environmental and sustainability issues Offers a clear index Includes detailed tables throughout for ease of access |
| data science in environment and sustainability: Computational Intelligent Data Analysis for Sustainable Development Ting Yu, Nitesh Chawla, Simeon Simoff, 2016-04-19 Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development present |
| data science in environment and sustainability: Big Data Analytics for Internet of Things Tausifa Jan Saleem, Mohammad Ahsan Chishti, 2021-04-20 BIG DATA ANALYTICS FOR INTERNET OF THINGS Discover the latest developments in IoT Big Data with a new resource from established and emerging leaders in the field Big Data Analytics for Internet of Things delivers a comprehensive overview of all aspects of big data analytics in Internet of Things (IoT) systems. The book includes discussions of the enabling technologies of IoT data analytics, types of IoT data analytics, challenges in IoT data analytics, demand for IoT data analytics, computing platforms, analytical tools, privacy, and security. The distinguished editors have included resources that address key techniques in the analysis of IoT data. The book demonstrates how to select the appropriate techniques to unearth valuable insights from IoT data and offers novel designs for IoT systems. With an abiding focus on practical strategies with concrete applications for data analysts and IoT professionals, Big Data Analytics for Internet of Things also offers readers: A thorough introduction to the Internet of Things, including IoT architectures, enabling technologies, and applications An exploration of the intersection between the Internet of Things and Big Data, including IoT as a source of Big Data, the unique characteristics of IoT data, etc. A discussion of the IoT data analytics, including the data analytical requirements of IoT data and the types of IoT analytics, including predictive, descriptive, and prescriptive analytics A treatment of machine learning techniques for IoT data analytics Perfect for professionals, industry practitioners, and researchers engaged in big data analytics related to IoT systems, Big Data Analytics for Internet of Things will also earn a place in the libraries of IoT designers and manufacturers interested in facilitating the efficient implementation of data analytics strategies. |
| data science in environment and sustainability: Health, Safety, and Environmental Data Analysis Anthony J. Joseph, 2020-07-09 Professionals in environmental health and safety (EHS) management use statistics every day in making decisions. This book was created to provide the quantitative tools and techniques necessary to make important EHS assessments. Readers need not be statistically or mathematically inclined to make the most of this book-mathematical derivations are kept to a minimum and subjects are approached in a simple and factual manner, complemented with plenty of real-world examples. Chapters 1-3 cover knowledge of basic statistical concepts such as presentation of data, measurements of location and dispersion, and elementary probability and distributions. Data gathering and analysis topics including sampling methods, sampling theory, testing, and interference as well as skills for critically evaluating published numerical material is presented in Chapters 4-6. Chapters 7-11 discuss information generation topics-regression and correlation analysis, time series, linear programming, network and Gnatt charting, and decision analysis-tools that can be used to convert data into meaningful information. Chapter 12 features six examples of projects made successful through statistical approaches being applied. Readers can use these approaches to solve their own unique problems. Whether you are a EHS professional, manager, or student, Health, Safety, and Environmental Data Analysis: A Business Approach will help you communicate statistical data effectively. |
| data science in environment and sustainability: Introduction to Environmental Data Science William W. Hsieh, 2023-03-31 A comprehensive guide to machine learning and statistics for students and researchers of environmental data science. |
| data science in environment and sustainability: Introduction to Environmental Data Science Jerry Davis, 2023-03-13 Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models; statistics and modelling ranging from exploratory to modelling, considering confirmatory statistics and extending to machine learning models; time series analysis, focusing especially on carbon and micrometeorological flux; and communication. Introduction to Environmental Data Science is an ideal textbook to teach undergraduate to graduate level students in environmental science, environmental studies, geography, earth science, and biology, but can also serve as a reference for environmental professionals working in consulting, NGOs, and government agencies at the local, state, federal, and international levels. Features • Gives thorough consideration of the needs for environmental research in both spatial and temporal domains. • Features examples of applications involving field-collected data ranging from individual observations to data logging. • Includes examples also of applications involving government and NGO sources, ranging from satellite imagery to environmental data collected by regulators such as EPA. • Contains class-tested exercises in all chapters other than case studies. Solutions manual available for instructors. • All examples and exercises make use of a GitHub package for functions and especially data. |
| data science in environment and sustainability: Environmental Statistics and Data Analysis Wayne R. Ott, 2018-12-13 This easy-to-understand introduction emphasizes the areas of probability theory and statistics that are important in environmental monitoring, data analysis, research, environmental field surveys, and environmental decision making. It communicates basic statistical theory with very little abstract mathematical notation, but without omitting importa |
| data science in environment and sustainability: Machine Learning Methods in the Environmental Sciences William W. Hsieh, 2009-07-30 A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences. |
| data science in environment and sustainability: Environmental Data Analysis Zhihua Zhang, 2016-11-21 Most environmental data involve a large degree of complexity and uncertainty. Environmental Data Analysis is created to provide modern quantitative tools and techniques designed specifically to meet the needs of environmental sciences and related fields. This book has an impressive coverage of the scope. Main techniques described in this book are models for linear and nonlinear environmental systems, statistical & numerical methods, data envelopment analysis, risk assessments and life cycle assessments. These state-of-the-art techniques have attracted significant attention over the past decades in environmental monitoring, modeling and decision making. Environmental Data Analysis explains carefully various data analysis procedures and techniques in a clear, concise, and straightforward language and is written in a self-contained way that is accessible to researchers and advanced students in science and engineering. This is an excellent reference for scientists and engineers who wish to analyze, interpret and model data from various sources, and is also an ideal graduate-level textbook for courses in environmental sciences and related fields. Contents: Preface Time series analysis Chaos and dynamical systems Approximation Interpolation Statistical methods Numerical methods Optimization Data envelopment analysis Risk assessments Life cycle assessments Index |
| data science in environment and sustainability: Artificial Intelligence Methods in the Environmental Sciences Sue Ellen Haupt, Antonello Pasini, Caren Marzban, 2008-11-28 How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods. |
| data science in environment and sustainability: Artificial Intelligence and Data Science in Environmental Sensing Mohsen Asadnia, Amir Razmjou, Amin Beheshti, 2022-02-09 Artificial Intelligence and Data Science in Environmental Sensing provides state-of-the-art information on the inexpensive mass-produced sensors that are used as inputs to artificial intelligence systems. The book discusses the advances of AI and Machine Learning technologies in material design for environmental areas. It is an excellent resource for researchers and professionals who work in the field of data processing, artificial intelligence sensors and environmental applications. - Presents tools, connections and proactive solutions to take sustainability programs to the next level - Offers a practical guide for making students proficient in modern electronic data analysis and graphics - Provides knowledge and background to develop specific platforms related to environmental sensing, including control water, air and soil quality, water and wastewater treatment, desalination, pollution mitigation/control, and resource management and recovery |
| data science in environment and sustainability: Smart Sustainable Cities of the Future Simon Elias Bibri, 2018-02-24 This book is intended to help explore the field of smart sustainable cities in its complexity, heterogeneity, and breadth, the many faces of a topical subject of major importance for the future that encompasses so much of modern urban life in an increasingly computerized and urbanized world. Indeed, sustainable urban development is currently at the center of debate in light of several ICT visions becoming achievable and deployable computing paradigms, and shaping the way cities will evolve in the future and thus tackle complex challenges. This book integrates computer science, data science, complexity science, sustainability science, system thinking, and urban planning and design. As such, it contains innovative computer–based and data–analytic research on smart sustainable cities as complex and dynamic systems. It provides applied theoretical contributions fostering a better understanding of such systems and the synergistic relationships between the underlying physical and informational landscapes. It offers contributions pertaining to the ongoing development of computer–based and data science technologies for the processing, analysis, management, modeling, and simulation of big and context data and the associated applicability to urban systems that will advance different aspects of sustainability. This book seeks to explicitly bring together the smart city and sustainable city endeavors, and to focus on big data analytics and context-aware computing specifically. In doing so, it amalgamates the design concepts and planning principles of sustainable urban forms with the novel applications of ICT of ubiquitous computing to primarily advance sustainability. Its strength lies in combining big data and context–aware technologies and their novel applications for the sheer purpose of harnessing and leveraging the disruptive and synergetic effects of ICT on forms of city planning that are required for future forms of sustainable development. This is because the effects of such technologies reinforce one another as to their efforts for transforming urban life in a sustainable way by integrating data–centric and context–aware solutions for enhancing urban systems and facilitating coordination among urban domains. This timely and comprehensive book is aimed at a wide audience across science, academia industry, and policymaking. It provides the necessary material to inform relevant research communities of the state–of–the–art research and the latest development in the area of smart sustainable urban development, as well as a valuable reference for planners, designers, strategists, and ICT experts who are working towards the development and implementation of smart sustainable cities based on big data analytics and context–aware computing. |
| data science in environment and sustainability: Sustainability Science Bert de Vries, 2013 This textbook surveys key issues of sustainability - energy, nature, agro-food, resources, economics - for advanced undergraduate and graduate level courses. |
| data science in environment and sustainability: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science in environment and sustainability: Big Data Science and Analytics for Smart Sustainable Urbanism Simon Elias Bibri, 2019-05-30 We are living at the dawn of what has been termed ‘the fourth paradigm of science,’ a scientific revolution that is marked by both the emergence of big data science and analytics, and by the increasing adoption of the underlying technologies in scientific and scholarly research practices. Everything about science development or knowledge production is fundamentally changing thanks to the ever-increasing deluge of data. This is the primary fuel of the new age, which powerful computational processes or analytics algorithms are using to generate valuable knowledge for enhanced decision-making, and deep insights pertaining to a wide variety of practical uses and applications. This book addresses the complex interplay of the scientific, technological, and social dimensions of the city, and what it entails in terms of the systemic implications for smart sustainable urbanism. In concrete terms, it explores the interdisciplinary and transdisciplinary field of smart sustainable urbanism and the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics. This new era of science and technology embodies an unprecedentedly transformative and constitutive power—manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are studied and understood, and in how they are planned, designed, operated, managed, and governed in the face of urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach based on a computational understanding of city systems and processes that reduces urban life to logical and algorithmic rules and procedures, while also harnessing urban big data to provide a more holistic and integrated view or synoptic intelligence of the city. This is increasingly being directed towards improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development. This timely and multifaceted book is aimed at a broad readership. As such, it will appeal to urban scientists, data scientists, urbanists, planners, engineers, designers, policymakers, philosophers of science, and futurists, as well as all readers interested in an overview of the pivotal role of big data science and analytics in advancing every academic discipline and social practice concerned with data–intensive science and its application, particularly in relation to sustainability. |
| data science in environment and sustainability: Environmental Technology and Sustainability Basanta Kumara Behera, Ram Prasad, 2020-04-05 Environmental Technology and Sustainability: Physical, Chemical and Biological Technologies for Clean Environmental Management provides a dependable source of information on the fundamental scientific evidence involved in environmental protection and sustainable development. The book provides the basic natural sciences that underpin the understanding, development and application of environment technologies that support a clean inhabitable world that includes environmental technologies and sustainable, renewable energy systems. It considers the science and technology for environmental benefits, including the development of both smarter, cleaner technologies for environmental protection, conservation, and more. - Provides methods and processes for CO2 Sequestration - Focuses on technologies for reducing greenhouse gases and for biofuel production - Outlines issues surrounding contaminated water and provides solutions for water management - Describes problems facing air pollution, including sources and mitigation - Includes contaminated soil management |
| data science in environment and sustainability: Environmental Science and Sustainability Sherman, Daniel J., Montgomery, David R., 2020-01-13 Environmental Science and Sustainability helps students discover their role in the environment and the impact of their choices. Authors David Montgomery and Daniel Sherman bring scientific and environmental policy expertise to a modern treatment of environmental science; in addition to teaching climate change, sustainability, and resilience, they reveal how our personal decisions affect our planet and our lives. |
| data science in environment and sustainability: Smart Cities Negin Minaei, 2022-03-27 In the age of global climate change, society will require cities that are environmentally self-sufficient, able to withstand various environmental problems and recover quickly. It is interesting to note that many smart solutions for cities are leading to an unsustainable future, including further electrification, an increased dependence on the Internet, Internet of Things, Big Data, and Artificial Intelligence, and basically any technology that leads us to consume more electricity. This book examines critical topics in Smart Cities such as true sustainability and the resilience required for all cities. It explores sustainability issues in agriculture and the role of agri-technology for a sustainable future, including a city’s ability to locally produce food for its residents. Features: Discusses safety, security, data management, and privacy issues in Smart Cities Examines the various emerging forms of transportation infrastructure and new vehicle technology Considers how energy efficiency can be achieved through behavioral change through specific building operations Smart Cities: Critical Debates on Big Data, Urban Development and Social Environmental Sustainability brings awareness to professionals working in the fields of environmental, civil, and transportation engineering, urban planners, and political leaders about different environmental aspects of Smart Cities and refocuses attention on critical urban infrastructure that will be necessary to respond to future challenges including climate change, food insecurity, natural hazards, energy production, and resilience. |
| data science in environment and sustainability: The Atlas of AI Kate Crawford, 2021-04-06 The hidden costs of artificial intelligence, from natural resources and labor to privacy and freedom What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? In this book Kate Crawford reveals how this planetary network is fueling a shift toward undemocratic governance and increased inequality. Drawing on more than a decade of research, award-winning science, and technology, Crawford reveals how AI is a technology of extraction: from the energy and minerals needed to build and sustain its infrastructure, to the exploited workers behind automated services, to the data AI collects from us. Rather than taking a narrow focus on code and algorithms, Crawford offers us a political and a material perspective on what it takes to make artificial intelligence and where it goes wrong. While technical systems present a veneer of objectivity, they are always systems of power. This is an urgent account of what is at stake as technology companies use artificial intelligence to reshape the world. |
| data science in environment and sustainability: Global Environmental Sustainability Choy Yee Keong, 2020-10-31 Global Environmental Sustainability: Case Studies and Analysis of the United Nations' Journey toward Sustainable Development presents an integrated, interdisciplinary analysis of sustainable development, addressing global environmental problems in the contemporary world. It critically examines current actions being taken on global and local scales, particularly in relation to the UN's efforts to promote sustainable development. This approach is supported by empirical analysis, drawing upon a host of interweaving insights spanning economics, politics, ecology, environmental philosophy, and ethics, among others. As a result, it offers a comprehensive and well-balanced assessment of the overall perspective of sustainable development supported by in-depth content analysis, theoretical evaluation, empirical and actual case studies premised on solid data, and actual field work. Also, the book marks a milestone in placing the Covid-19 pandemic into a perspective for understanding the universality of human collective environmental behavior and action.By utilizing in-depth analysis, both quantitative and qualitative, and challenging the status quo of what is expected in the global approach to sustainable development, Global Environmental Sustainability provides the theory and methodology of empirical sustainable development which is especially germane to our advanced society today, which is deeply entrenched in a crisis of environmental morality. More particularly, it serves as a salient source of moral reconstitution of society grounded in empirical reality to liberate man's excessive spirit of individualism and self-aggrandizement to the detriment of the environment. Epistemologically, the book furnishes a remarkable tour de force with a new level of analytical insight to help researchers, practitioners, and policymakers in sustainability and environmental science, as well as the many other disciplines involved in sustainable development, to better understand sustainability from a new perspective and provides a methodological direction to pursue solutions going forward. - Provides a systematic exposition of sustainable development in all its complexity, with all the chapters complementing each other in an integral way - Presents extensive empirical evidence of various environmental problems across the world including China, the United States, Canada, Southeast Asia, South America and Africa, and the extent to which the United Nations has succeeded in driving toward global environmental sustainability - Provides a cogent examination of the treatment of our global commons by some of the world's most powerful leaders - Includes data from field studies and in-depth interviews with indigenous people in Borneo's rainforests of the Malaysian state of Sarawak most affected by environmental change |
| data science in environment and sustainability: Introduction to Environmental Data Science Jerry D. Davis, 2023 Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models; statistics & modelling ranging from exploratory to modelling, considering confirmatory statistics and extending to machine learning models; time series analysis, focusing especially on carbon and micrometeorological flux; and communication. Introduction to Environmental Data Science. It is an ideal textbook to teach undergraduate to graduate level students in environmental science, environmental studies, geography, earth science, and biology, but can also serve as a reference for environmental professionals working in consulting, NGOs, and government agencies at the local, state, federal, and international levels-- |
| data science in environment and sustainability: The Impact of the COVID-19 Pandemic on Green Societies Chinmay Chakraborty, Swapnila Roy, Susmita Sharma, Tien Anh Tran, 2021-03-22 This book covers the sustainability issues of a green environment towards economics and society in terms of alteration in industrial pollution levels, effect of reduced carbon emissions, changes in water bodies characteristics with respect to heavy metal contamination, monitoring of associated impact with respect to ecology and biodiversity, impact of reduced noise levels and air quality influences on human health, handling and management of biomedical waste. According to WHO, 80% of people living in urban areas are exposed to air exceeding safe limits. The advent of sustainability‟ in development science has led planners to apply evolving notions of sustainability‟ to the contemporary debate over how cities and regions should be revitalized, redeveloped, and reformed. Market allocation of resources, sustained levels of growth and consumption, an assumption that natural resources are unlimited and a belief that economic growth will „trickle down‟ to the poor have been its hallmarks. The recent advance technology helps to promote green and clean modern societies continuously. The Internet of things will be playing an important role in the upcoming years in environment protection and sustainable development. There is a focus on paradigm shift in the sustainable development for the green environment during the period of isolation of COVID-19. This is the moment for the mobilization against the climate crisis. The sudden fall in pollutants and subsequent blue skies signifies a dramatic shift for India and also other affected countries during this period. Fighting climate change requires a collaborative approach between all spheres of society unlike the former. It must heavily redirect resources towards local, sustainable activities, including education, health, sustainable agriculture and circular management of resources. The impact of COVID-19 pandemic which has resulted in the dramatic change in the different aspects of the environment. The global lockdown has led to a rejuvenation of nature, ecosystems, biodiversity. Even urban environments are discovering a degree of peace and serenity, which led to decrease in greenhouse gas emission. |
| data science in environment and sustainability: The Principles of Green and Sustainability Science Adenike A. Akinsemolu, 2020-03-28 This book uses the concept of sustainability in science to address problems afflicting the environment, and to devise measures for improving economies, societies, behaviors, and people. The book pursues a scientific approach, and uses scientific evidence as the basis for achieving sustainability. The key topics addressed include: unemployment, health and disease, unsustainable production, our common future, renewable energies, waste management, environmental ethics, and harmful anthropogenic activities. Whereas past literature has mainly examined sustainability as an environmental issue, this book expands the conversation into various sciences, including mathematics, biology, agriculture, computer science, engineering, and physics, and shows how sustainability could be achieved by uniting these fields. It offers a wealth of information across various disciplines, making it not only an intriguing read but also informative and insightful. |
| data science in environment and sustainability: Statistical Methods for Climate Scientists Timothy DelSole, Michael Tippett, 2022-02-24 An accessible introduction to statistical methods for students in the climate sciences. |
| data science in environment and sustainability: Environmental Engineering for the 21st Century National Academies of Sciences, Engineering, and Medicine, National Academy of Engineering, Division on Engineering and Physical Sciences, Division on Earth and Life Studies, Water Science and Technology Board, Ocean Studies Board, NAE Office of Programs, Board on Life Sciences, Board on Environmental Studies and Toxicology, Board on Earth Sciences and Resources, Board on Energy and Environmental Systems, Board on Chemical Sciences and Technology, Board on Atmospheric Sciences and Climate, Board on Agriculture and Natural Resources, Committee on the Grand Challenges and Opportunites in Environmental Engineering for the Twenty-First Century, 2019-03-08 Environmental engineers support the well-being of people and the planet in areas where the two intersect. Over the decades the field has improved countless lives through innovative systems for delivering water, treating waste, and preventing and remediating pollution in air, water, and soil. These achievements are a testament to the multidisciplinary, pragmatic, systems-oriented approach that characterizes environmental engineering. Environmental Engineering for the 21st Century: Addressing Grand Challenges outlines the crucial role for environmental engineers in this period of dramatic growth and change. The report identifies five pressing challenges of the 21st century that environmental engineers are uniquely poised to help advance: sustainably supply food, water, and energy; curb climate change and adapt to its impacts; design a future without pollution and waste; create efficient, healthy, resilient cities; and foster informed decisions and actions. |
| data science in environment and sustainability: Environmental Science for a Changing World (Canadian Edition) Karen Ing, Susan Karr, Marnie Brankreun, 2013-09-02 Environmental Science for a Changing World captivates students with real-world stories while exploring the science concepts in context. Engaging stories plus vivid photos and infographics make the content relevant and visually enticing. The result is a text that emphasizes environmental, scientific, and information literacies in a way that engages students. |
| data science in environment and sustainability: Sustainability Perspectives: Science, Policy and Practice Peter A. Khaiter, Marina G. Erechtchoukova, 2019-10-15 The book presents methodological and applied aspects of sustainability and sustainable management from different countries and regions around the globe. It discusses approaches to sustainability assessment, demonstrates how ideas of sustainability and sustainable management are incorporated into public policies and private actions at local and national levels. Authors focus on promoting greater sustainability in natural resource management, energy production and storage, housing design, industrial reorganization, coastal planning, land use, and business strategy, including sustainability indicators, environmental damages, and theoretical frameworks. Chapters reflect environmental, economic and social issues in sustainable development, challenges encountered, and lessons learned as well as solutions proposed. |
| data science in environment and sustainability: Drawdown Paul Hawken, 2017-04-18 • New York Times bestseller • The 100 most substantive solutions to reverse global warming, based on meticulous research by leading scientists and policymakers around the world “At this point in time, the Drawdown book is exactly what is needed; a credible, conservative solution-by-solution narrative that we can do it. Reading it is an effective inoculation against the widespread perception of doom that humanity cannot and will not solve the climate crisis. Reported by-effects include increased determination and a sense of grounded hope.” —Per Espen Stoknes, Author, What We Think About When We Try Not To Think About Global Warming “There’s been no real way for ordinary people to get an understanding of what they can do and what impact it can have. There remains no single, comprehensive, reliable compendium of carbon-reduction solutions across sectors. At least until now. . . . The public is hungry for this kind of practical wisdom.” —David Roberts, Vox “This is the ideal environmental sciences textbook—only it is too interesting and inspiring to be called a textbook.” —Peter Kareiva, Director of the Institute of the Environment and Sustainability, UCLA In the face of widespread fear and apathy, an international coalition of researchers, professionals, and scientists have come together to offer a set of realistic and bold solutions to climate change. One hundred techniques and practices are described here—some are well known; some you may have never heard of. They range from clean energy to educating girls in lower-income countries to land use practices that pull carbon out of the air. The solutions exist, are economically viable, and communities throughout the world are currently enacting them with skill and determination. If deployed collectively on a global scale over the next thirty years, they represent a credible path forward, not just to slow the earth’s warming but to reach drawdown, that point in time when greenhouse gases in the atmosphere peak and begin to decline. These measures promise cascading benefits to human health, security, prosperity, and well-being—giving us every reason to see this planetary crisis as an opportunity to create a just and livable world. |
| data science in environment and sustainability: OECD Insights Sustainable Development Linking Economy, Society, Environment Strange Tracey, Bayley Anne, 2008-12-02 A succinct examination of the concept of sustainable development: what it means; how it is impacted by globalisation, production and consumption; how it can be measured; and what can be done to promote it. |
| data science in environment and sustainability: Sustainable Development Through Data Analytics and Innovation Jorge Marx Gómez, Lawal O. Yesufu, 2022-09-26 Sustainable development is based on the idea that societies should advance without compromising their future development requirements. This book explores how the application of data analytics and digital technologies can ensure that development changes are executed on the basis of factual data and information. It addresses how innovations that rely on digital technologies can support sustainable development across all sectors and all social, economic, and environmental aspects and help us achieve the Sustainable Development Goals (SDGs). The book also highlights techniques, processes, models, tools, and practices used to achieve sustainable development through data analysis. The various topics covered in this book are critically evaluated, not only theoretically, but also from an application perspective. It will be of interest to researchers and students, especially those in the fields of applied data analytics, business intelligence and knowledge management. |
| data science in environment and sustainability: Artificial Intelligence and The Environment Cindy Mason, 2020-07-07 This volume reports 16 AI projects on engineering sustainability using AI, Machine Learning, Signal Processing, Databases and other Technologies (Hybrid AI). Sixty scientists contribute to the volume on ‘Boots on the Ground’ topics including fire fighting, forestry sustainability, flood prediction, algae bloom prediction, water pollution prediction, sewage treatment, recycling and resource consumption. There are also ‘Data, Data Everywhere’ topics including biodiversity cataloguing, plant physiology and climate modeling, forest ecosystem modelling, satellite data aggregation and viewing and weather forecasting. The contributions of each team of scientists, AI researchers and engineers has been assembled with a set of helpful questions and answers called “Classroom Connection” at the end of each chapter. The existence of this book serves to document the AI projects in existence and some of the people who have been actively working to create sustainability using AI. Inside you’ll find many examples of hybrid AI - systems so complex, they need every AI trick in the book to solve them, and then some. The book is presented at the 2019 U.N. Climate Summit in Madrid Spain.--Publisher's description. |
| data science in environment and sustainability: Advances and New Trends in Environmental Informatics Andreas Kamilaris, Volker Wohlgemuth, Kostas Karatzas, Ioannis N. Athanasiadis, 2021-12-18 This book is an outcome of the 34th International Conference EnviroInfo 2020, hosted virtually in Nicosia, Cyprus by the Research Centre on Interactive Media, Smart Systems and Emerging Technologies (RISE). It presents a selection of papers that describe innovative scientific approaches and ongoing research in environmental informatics and the emerging field of environmental sustainability, promoted and facilitated by the use of information and communication technologies (ICT). The respective articles cover a broad range of scientific aspects including advances in core environmental informatics-related technologies such as earth observation, environmental modelling, big data and machine learning, robotics, smart agriculture and food solutions, renewable energy-based solutions, optimization of infrastructures, sustainable industrial processes, and citizen science, as well as applications of ICT solutions intended to support societal transformation processes toward the more sustainable management of resource use, transportation and energy supplies. Given its scope, the book is essential reading for scientists, experts and students in these fields of research. Chapter “Developing a Configuration System for a Simulation Game in the Domain of Urban CO2 Emissions Reduction” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. |
| data science in environment and sustainability: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science in environment and sustainability: Sustainability and Environmental Decision Making Euston Quah, Renate Schubert, 2021-06-07 The primary aim of this reference volume is to provide an accessible and comprehensive review of current methods used to address resource evaluation and environmental as well as climate issues, and in a manner easily understood by decision-makers and the non-economists interested in environmental policy matters. Theoretical insight and empirical observations from various countries will be presented and recommendations on sustainable environmental decision-making will be given. Natural resource managers, environmental and climate decision-makers, government policy makers, and economics scholars will all find this volume to be an essential reference. |
| data science in environment and sustainability: Sustainability Tom Theis, Jonathan Tomkin, 2018-01-23 With Sustainability: A Comprehensive Foundation, first and second-year college students are introduced to this expanding new field, comprehensively exploring the essential concepts from every branch of knowldege - including engineering and the applied arts, natural and social sciences, and the humanities. As sustainability is a multi-disciplinary area of study, the text is the product of multiple authors drawn from the diverse faculty of the University of Illinois: each chapter is written by a recognized expert in the field. |
| data science in environment and sustainability: Social Science Theory for Environmental Sustainability Marc J. Stern, 2018-06-22 Social-ecological challenges call for a far better integration of the social sciences into conservation training and practice. Environmental problems are, first and foremost, people problems. Without better understandings of the people involved, solutions are often hard to come by, regardless of expertise in biology, ecology, or other traditional conservation sciences. This novel book provides an accessible survey of a broad range of theories widely applicable to environmental problems that students and practitioners can apply to their work. It serves as a simple reference guide to illuminate the value and utility of social science theories for the practice of environmental conservation. As part of the Techniques in Ecology and Conservation Series, it will be a vital resource for conservation scientists, students, and practitioners to better navigate the social complexities of applying their work to real-world problem-solving. |
| data science in environment and sustainability: GIS, Applied Computing and Data Science for Water Management Noamen Rebai, |
| data science in environment and sustainability: Agro-Environmental Sustainability in MENA Regions Mohamed Abu-hashim, Faiza Khebour Allouche, Abdelazim Negm, 2021-10-11 This book focuses on the status quo and the latest information on the water-soil-agriculture nexus in the MENA countries. It presents several case studies and applications from e.g. Morocco, Algeria, Tunisia, Egypt and Jordan, while also sharing and discussing the latest findings. The content includes a range of agriculture-related topics that focus on: water resources management, impacts of climate change, and wastewater treatment for reuse in agriculture sectors; in addition, sustainable approaches to agricultural-based industry, organic crop production, crop water requirements, and soil environment are discussed in an updated and comprehensive review. In turn, the book discusses the applications of GIS and remote sensing as a new technology for better agriculture management, as well as its use in Egypt as a representative country. In closing, it considers the implementation of an environmental information system in data-scarce MENA countries from the standpoint of the water-food nexus, and addresses the question of climate justice in the MENA region. Exploring various dimensions of MENA country-based case studies on achieving sustainable agriculture, the book offers an invaluable source of topical information for agricultural sustainability-related stakeholders in the region, researchers and graduate students alike. |
| data science in environment and sustainability: Advances in the Leading Paradigms of Urbanism and their Amalgamation Simon Elias Bibri, 2020-06-20 This book explores the recent advances in the leading paradigms of urbanism, namely compact cities, eco-cities, and data–driven smart cities, and the evolving approach to their amalgamation under the umbrella term of smart sustainable cities. It addresses these advances by investigating how and to what extent the strategies of compact cities and eco-cities and their merger have been enhanced and strengthened through new planning and development practices, and are being supported and leveraged by the applied solutions pertaining to data-driven smart cities. The ultimate goal is to advance sustainability and harness its synergistic effects on multiple scales. This entails developing and implementing more effective approaches to the balanced integration of the three dimensions of sustainability, as well as to producing combined effects of the strategies and solutions of the prevailing approaches to urbanism that are greater than the sum of their separate effects in terms of the tripartite value of sustainability. Sustainable urban development is today seen as one of the keys towards unlocking the quest for a sustainable world. And the big data revolution is set to erupt in cities throughout the world, heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities and the spaces we live in thanks to the IoT. Big data and the IoT technologies are seen as powerful forces that have tremendous potential for advancing urban sustainability. Indeed, they are instigating a massive change in the way sustainable cities can tackle the kind of special conundrums, wicked problems, and significant challenges they inherently embody as complex systems. They offer a multitudinous array of innovative solutions and sophisticated approaches informed by groundbreaking research and data–driven science. As such, they are becoming essential to the functioning of sustainable cities. Besides, yet knowing to what extent we are making progress towards sustainable cities is problematic, adding to the fragmented, conflicting picture that arises of change on the ground in the face of the escalating rate and scale of urbanization and in the light of emerging ICT and its novel applications. In a nutshell, new circumstances require new responses. This timely and multifaceted book is intended for a wide readership. As such, it will appeal to researchers, academics, urban scientists, urbanists, planners, designers, policy-makers, and futurists, as well as all readers interested in sustainable cities and their ongoing and future data-driven transformation. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
