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
|Author||: Ting Yu,Nitesh Chawla,Simeon Simoff|
|Publisher||: CRC Press|
|Release Date||: 2016-04-19|
|ISBN 10||: 1439895953|
|Pages||: 440 pages|
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
|Author||: Simon Elias Bibri|
|Release Date||: 2019-06-19|
|ISBN 10||: 9783030173111|
|Pages||: 337 pages|
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.
Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network analytics. As the study of big data becomes more popular, there is an urgent demand for studies on high-level computational intelligence and computing services for analyzing this significant area of information science. Big Data Analytics for Sustainable Computing is a collection of innovative research that focuses on new computing and system development issues in emerging sustainable applications. Featuring coverage on a wide range of topics such as data filtering, knowledge engineering, and cognitive analytics, this publication is ideally designed for data scientists, IT specialists, computer science practitioners, computer engineers, academicians, professionals, and students seeking current research on emerging analytical techniques and data processing software.
Since the United Nations Conference on Environment and Development in Rio de Janeiro in 1992, the issues of sustainability at the international, national and regional level have become a top priority for national governments, business leaders and NGOs. Sustainability Analysis: An Interdisciplinary Approach is the result of collective reflection by an international group of academics from Canada, France, Norway, Russia, Sweden, Switzerland, and the UK. It was inspired by the interdisciplinary discussions started in St Petersburg, Russia at the conference Globalisation, New Economy, and the Environment: Business and Society Challenges for Sustainable Development, organized by the editors of this volume under the auspices of the International Society for Ecological Economics in 2005. This book explores the major actors, paradigms and ideologies in sustainable development, employing novel approaches such as linguistic and discourse analysis as well as simulation games and the psychology of ecological consciousness to provide an important contribution to the environmental policy field.
|Author||: Eftihia G. Nathanail,Ioannis D. Karakikes|
|Release Date||: 2018-12-11|
|ISBN 10||: 3030023052|
|Pages||: 868 pages|
This book aims at showing how big data sources and data analytics can play an important role in sustainable mobility. It is especially intended to provide academicians, researchers, practitioners and decision makers with a snapshot of methods that can be effectively used to improve urban mobility. The different chapters, which report on contributions presented at the 4th Conference on Sustainable Urban Mobility, held on May 24-25, 2018, in Skiathos Island, Greece, cover different thematic areas, such as social networks and traveler behavior, applications of big data technologies in transportation and analytics, transport infrastructure and traffic management, transportation modeling, vehicle emissions and environmental impacts, public transport and demand responsive systems, intermodal interchanges, smart city logistics systems, data security and associated legal aspects. They show in particular how to apply big data in improving urban mobility, discuss important challenges in developing and implementing analytics methods and provide the reader with an up-to-date review of the most representative research on data management techniques for enabling sustainable urban mobility
Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes critical opportunity areas for mining optimization Brings experience and learning in digital transformation from adjacent sectors
|Author||: Jingzheng Ren,Sara Toniolo|
|Release Date||: 2019-11-19|
|ISBN 10||: 012818356X|
|Pages||: 362 pages|
Life Cycle Sustainability Assessment for Decision-Making: Methodologies and Case Studies gives readers a comprehensive introduction to life cycle sustainability assessment (LCSA) methodology for sustainability measurement of industrial systems, proposing an efficiency methodology for stakeholders and decision-makers. Featuring the latest methods and case studies, the book will assist researchers in environmental sciences and energy to develop the best methods for LCA, as well as aiding those practitioners who are responsible for making decisions for promoting sustainable development. The past, current status and future of LCSA, Life Cycle Assessment method (LCA), Life Cycle Costing (LCC), Social Life Cycle Assessment (SLCA), the methodology of LCSA, typical LCSA case studies, limitations of LCSA, and life cycle aggregated sustainability index methods are all covered in this multidisciplinary book. Includes models for assessing sustainability in environmental, energy engineering and economic scenarios Features case studies that help define the advantages and obstacles of real world applications Presents a complete view, from theory to practice, of a life cycle approach by exploring the methods and tools of sustainability assessment, analysis and design of sustainability assessment
|Author||: Lawrence Susskind,Bruno Verdini,Jessica Gordon,Yasmin Zaerpoor|
|Release Date||: 2020-10-20|
|ISBN 10||: 1785271326|
|Pages||: 250 pages|
'Environmental Problem-Solving' presents short excerpts from carefully selected readings, expert commentaries on those readings, assignments, and the best MIT student responses to the assignments and exam questions with excellent student response. The book presents four main models of environmental policy-making: competing theories of environmental ethics; tools for environmental assessment and environmental decision-making; and techniques for public engagement and group decision-making. The book covers the material presented in the semester-long course required of all students enrolled in MIT's Environmental Policy and Planning Specialization.
This three volume set presents a multidisciplinary examination of the global life support systems on which we depend by providing a selection of articles on sustainable development issues written by international experts. Volume 1 focuses on the earth and atmospheric sciences, mathematical, biological and medical sciences, social sciences and humanities, physical sciences, engineering and technology resources. Volume 2 covers chemical sciences, energy science and water engineering, as well as the main issues related to environmental sciences and ecological resources. Volume 3 offers a comprehensive view of food and agricultural engineering resources, the management of human and natural resources, economic and institutional resources, information technology and systems management, as well as a regional overview of sustainable development issues. Each article includes a bibliography, a glossary and a guide to further information available as part of the on-line Encyclopedia version (http://www.eolss.net).
This book brings together diverse voices from across the field of sustainable human computer interaction (SHCI) to discuss what it means for digital technology to support sustainability and how humans and technology can work together optimally for a more sustainable future. Contemporary digital technologies are hailed by tech companies, governments and academics as leading-edge solutions to the challenges of environmental sustainability; smarter homes, more persuasive technologies, and a robust Internet of Things hold the promise for creating a greener world. Yet, deployments of interactive technologies for such purposes often lead to a paradox: they algorithmically "optimize" heating and lighting of houses without regard to the dynamics of daily life in the home; they can collect and display data that allow us to reflect on energy and emissions, yet the same information can cause us to raise our expectations for comfort and convenience; they might allow us to share best practice for sustainable living through social networking and online communities, yet these same systems further our participation in consumerism and contribute to an ever-greater volume of electronic waste.By acknowledging these paradoxes, this book represents a significant critical inquiry into digital technology’s longer-term impact on ideals of sustainability. Written by an interdisciplinary team of contributors this book will be of great interest to students and scholars of human computer interaction and environmental studies.
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.