Jun Zhao

Global Risks and Energy System Resilience offers an in-depth analysis of how global risks intersect with the robustness of modern energy systems. The book delivers a detailed exploration of the geopolitical, economic, and financial dynamics that influence energy security, presenting a clear framework for understanding and addressing these complex challenges. Through a logical approach of indicator measurement-impact assessment-scenario prediction, the book guides readers from the current landscape of risks and resilience, through the measurement and evaluation of these factors, to a nuanced understanding of their evolution across time and space. Going further, the book examines the theoretical transmission mechanisms by which various risks affect energy system resilience, supported by both literature and econometric modeling. It highlights the role of digital transformation in shaping resilience, identifies key influencing factors, and forecasts future developments in the field. This resource is indispensable for researchers, policymakers, and professionals seeking to develop effective strategies for energy security and risk mitigation, bridging theory and practice to navigate today's rapidly changing energy environment.

This book systematically discusses the workability, mechanical properties, water stability, and material design methods of ultra-high ductility magnesium phosphate cement-based composites (UHDMC) with PVA fibers, PE fibers, or hybrid fibers. The developed UHDMC possesses strain-hardening behavior, multi-cracking, and ultra-high ultimate tensile strain, which can be used in many engineering projects, such as rapid repair and strengthening of airport runways, roads, bridges, and military installations. Meanwhile, this book covers the development of sprayable UHDMC, which further expands the application scope of UHDMC. This book is expected to provide some technical guidance for the development and design of UHDMC. The book is intended for graduate students who are interested in the ductility improvement of magnesium phosphate cement-based composites, researchers investigating the mechanical properties (strength, toughness, etc.), water stability, microstructure, and sprayabilityof magnesium phosphate cement-based composites, and engineers working on rapid repair of concrete structures.

This book systematically discusses the workability, mechanical properties, water stability, and material design methods of ultra-high ductility magnesium phosphate cement-based composites (UHDMC) with PVA fibers, PE fibers, or hybrid fibers. The developed UHDMC possesses strain-hardening behavior, multi-cracking, and ultra-high ultimate tensile strain, which can be used in many engineering projects, such as rapid repair and strengthening of airport runways, roads, bridges, and military installations. Meanwhile, this book covers the development of sprayable UHDMC, which further expands the application scope of UHDMC. This book is expected to provide some technical guidance for the development and design of UHDMC. The book is intended for graduate students who are interested in the ductility improvement of magnesium phosphate cement-based composites, researchers investigating the mechanical properties (strength, toughness, etc.), water stability, microstructure, and sprayabilityof magnesium phosphate cement-based composites, and engineers working on rapid repair of concrete structures.

Energy Poverty in China: Evaluation and Alleviation offers a thorough, methodological approach to energy poverty, which is a major obstacle in terms of the green or low-carbon transition of energy systems in China. The authors follow the framework of measurement, potential impact, and alleviation paths, supporting the design of effective energy and environmental policies for a globalized nation, especially considering the low-carbon transition of energy structures and energy poverty alleviation in the context of carbon neutrality. The book begins by providing an overview of energy poverty, explaining the concept, summarizing the current situation globally and in China, and introducing the approach of the authors. The second chapter looks to build a comprehensive index system for the calculation of energy poverty in China. This is followed by three chapters that apply a range of methods to empirically assess the economic, environmental, and social effects of energy poverty. Individual chapters then explore specific alleviation paths, including digital economy, technological innovation, low-carbon energy transition, inclusive finance, and coal-to-gas/electricity policy. This book is of interest to all those with an interest in energy poverty, energy and environmental policy, energy economics, carbon emission reduction, green growth, energy transition and renewable energy, including researchers, scientists, advanced students, engineers, R&D professionals, and policy makers or other experts at governmental or non-governmental organizations.

This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals withinthe machine learning and data analysis and mining communities.

This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals withinthe machine learning and data analysis and mining communities.