Mathematics

1. Konstantina , Ragazou
2. Nikolaos , Sariannidis
3. Alexandros , Garefalakis

Triple Bottom Line and Multiple Criteria Decision Making Analysis

This book offers a comprehensive compilation of research on the evaluation of the most environmentally and socially responsible financial institutions using a multiple criteria decision-making (MCDM) approach, with a focus on the principles of the Triple Bottom Line (TBL) model. Contributed by highly esteemed scholars in the field of MCDM/A, the book serves as a distinctive global reference guide that focuses on the examination, quantification, and administration of sustainability in relation to the TBL within a multidimensional framework for decision analysis. It presents the implementation of a set of MCDA techniques addressing challenges in the accounting systems of financial institutions and facilitating the identification of the optimal sustainable option within this domain. It highlights recent advancements in decision-making methodologies, exemplified through practical applications in the financial industry, particularly focusing on sustainability-related subjects. As such, scholars and policymakers will find this book to be a valuable resource.

1. Alex , Wernke
2. Eva , Junge
3. Julian , Bleh

The Psychology of Collective Climate Action

How do we find the courage to act together against the climate crisis? This book weaves together real-life findings and examples from the social-ecological movement with psychological research to show how motivation for collective climate action can be built.

Statistical Modeling Using Bayesian Latent Gaussian Models

This book focuses on the statistical modeling of geophysical and environmental data using Bayesian latent Gaussian models. The structure of these models is described in a thorough introductory chapter, which explains how to construct prior densities for the model parameters, how to infer the parameters using Bayesian computation, and how to use the models to make predictions. The remaining six chapters focus on the application of Bayesian latent Gaussian models to real examples in glaciology, hydrology, engineering seismology, seismology, meteorology and climatology. These examples include: spatial predictions of surface mass balance; the estimation of Antarcticäs contribution to sea-level rise; the estimation of rating curves for the projection of water level to discharge; ground motion models for strong motion; spatial modeling of earthquake magnitudes; weather forecasting based on numerical model forecasts; and extreme value analysis of precipitation on a high-dimensional grid. The book is aimed at graduate students and experts in statistics, geophysics, environmental sciences, engineering, and related fields.

The Role of GIS in COVID-19 Management and Control

Edited by medical geographer and GIS Professor Dr. Esra Ozdenerol, this book highlights key GIS capabilities and lessons learned during the COVID-19 response and can help communities prepare for the next crisis. 12 global projects are discussed with a multidisciplinary approach, illustrating the role of GIS in crisis management.

SIMPLE-G

This is an open access book.Crafted for both the economist and the curious mind, this book introduces a novel approach to blending economic and biophysical sciences to enable multi-scale analysis of a range of sustainability challenges confronting the world¿s land and water resources at both local and global scales. It focuses specifically on the interface between the environment and food systems, utilizing economic theory to structure the overall framework. However, within the SIMPLE-G framework, there is ample room to incorporate fine-scale biophysical knowledge from agronomy, climate science, ecology, geography, hydrology, as well as a range of socioeconomic considerations. This enables multi-scale analyses incorporating grid cells that can vary in size from 250 meters to sub-regional scales. This, in turn, allows for investigation of global change drivers¿ impacts on local sustainability, as well as feedbacks from local sustainability policies to regional and global outcomes.The book opens with a foreword by a prominent sustainability scientist, Prof. Navin Ramankutty, and proceeds in five parts covering, respectively: (1) introduction and overview, (2) basic economic theory underpinning SIMPLE-G, (3) the SIMPLE-G framework, including structure, data, parameters, computer implementation, and validation, (4) eight diverse applications of the SIMPLE-G framework, covering a range of geographies and sustainability challenges, and (5) a forward-looking chapter on future directions. The book provides step-by-step guidance on building and utilizing gridded models with real-world case studies demonstrating practical applications which will facilitate its use by academics, practitioners, and students conducting research on climate impacts, land-use, water resource management, food security, poverty, equity, nutritional outcomes, and overall sustainability.

1. Gregor P. , Henze
2. T. Agami , Reddy

Applied Data Analysis and Modeling for Energy Engineers and Scientists

Now in a thoroughly revised and expanded second edition, this classroom-tested text demonstrates and illustrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability, statistics, experimental design, regression, optimization, parameter estimation, inverse modeling, risk analysis, decision-making, and sustainability assessment methods to energy processes and systems. It provides a formal structure that offers a broad and integrative perspective to enhance knowledge, skills, and confidence to work in applied data analysis and modeling problems. This new edition also reflects recent trends and advances in statistical modeling as applied to energy and building processes and systems. It includes numerous examples from recently published technical papers to nurture and stimulate a more research-focused mindset. How the traditional stochastic data modeling methods complement data analytic algorithmic approaches such as machine learning and data mining is also discussed. The important societal issue related to the sustainability of energy systems is presented, and a formal structure is proposed meant to classify the various assessment methods found in the literature. Applied Data Analysis and Modeling for Energy Engineers and Scientists is designed for senior-level undergraduate and graduate instruction in energy engineering and mathematical modeling, for continuing education professional courses, and as a self-study reference book for working professionals. In order for readers to have exposure and proficiency with performing hands-on analysis, the open-source Python and R programming languages have been adopted in the form of Jupyter notebooks and R markdown files, and numerous data sets and sample computer code reflective of real-world problems are available online.