The book presents contributions on statistical models and methods applied, for both data science and SDGs, in one place. Measuring and controlling data of SDGs, data driven measurement of progress needs to be distributed to stakeholders. In this situation, the techniques used in data science, specially, in the big data analytics, play an important role rather than the traditional data gathering and manipulation techniques. This book fills this space through its twenty contributions. The contributions have been selected from those presented during the 7th International Conference on Data Science and Sustainable Development Goals organized by the Department of Statistics, University of Rajshahi, Bangladesh; and cover topics mainly on SDGs, bioinformatics, public health, medical informatics, environmental statistics, data science and machine learning. The contents of the volume would be useful to policymakers, researchers, government entities, civil society, and nonprofit organizations for monitoring and accelerating the progress of SDGs.

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

The book presents contributions on statistical models and methods applied, for both data science and SDGs, in one place. Measuring and controlling data of SDGs, data driven measurement of progress needs to be distributed to stakeholders. In this situation, the techniques used in data science, specially, in the big data analytics, play an important role rather than the traditional data gathering and manipulation techniques. This book fills this space through its twenty contributions. The contributions have been selected from those presented during the 7th International Conference on Data Science and Sustainable Development Goals organized by the Department of Statistics, University of Rajshahi, Bangladesh; and cover topics mainly on SDGs, bioinformatics, public health, medical informatics, environmental statistics, data science and machine learning. The contents of the volume would be useful to policymakers, researchers, government entities, civil society, and nonprofit organizations for monitoring and accelerating the progress of SDGs.

This book explores the interdisciplinary and transdisciplinary fields of energy systems, occupant behavior, thermal comfort, air quality and economic modelling across levels of building, communities and cities, through various data analytical approaches. It highlights the complex interplay of heating/cooling, ventilation and power systems in different processes, such as design, renovation and operation, for buildings, communities and cities. Methods from classical statistics, machine learning and artificial intelligence are applied into analyses for different building/urban components and systems. Knowledge from this book assists to accelerate sustainability of the society, which would contribute to a prospective improvement through data analysis in the liveability of both built and urban environment. This book targets a broad readership with specific experience and knowledge in data analysis, energy system, built environment and urban planning. As such, it appeals to researchers, graduate students, data scientists, engineers, consultants, urban scientists, investors and policymakers, with interests in energy flexibility, building/city resilience and climate neutrality.

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

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.

This volume gathers papers presented at the LISA 2020 Sustainability Symposium in Kumasi, Ghana, May 2–6, 2022. They focus on sustainable methods and practices of using statistics and data science to address real-world problems. From utilizing social media for statistical collaboration to predicting obesity among rural women, and from analyzing inflation in Nigeria using machine learning to teaching data science in Africa, this book explores the intersection of data, statistics, and sustainability. With practical applications, code snippets, and case studies, this book offers valuable insights for researchers, policymakers, and data enthusiasts alike. The LISA 2020 Global Network aims to enhance statistical and data science capability in developing countries through the creation of a network of collaboration laboratories (also known as “stat labs”). These stat labs are intended to serve as engines for development by training the next generation of collaborative statisticians and data scientists, providing research infrastructure for researchers, data producers, and decision-makers, and enabling evidence-based decision-making that has a positive impact on society. The research conducted at LISA 2020 focuses on practical methods and applications for sustainable growth of statistical capacity in developing nations.