Data-Driven Environmental Risk Management and Sustainability Analytics (Second Edition)

Abstract

Environmental risk management (ERM) and sustainability analytics have undergone a paradigm shift from reactive, compliance-based frameworks to advanced, predictive, and data-driven methodologies. This second edition of "Data-Driven Environmental Risk Management and Sustainability Analytics" critically explores the integration of contemporary technologies such as machine learning (ML), artificial intelligence (AI), blockchain, Internet of Things (IoT), quantum computing, and cloud computing within ERM frameworks. The manuscript reviews the evolution of ERM strategies, emphasizing the transformative role of predictive analytics, real-time monitoring, and multi-stakeholder collaboration in addressing global environmental challenges including climate change, biodiversity loss, and resource depletion. Through empirical case studies on coastal flooding and urban water resource management, the research demonstrates the practical effectiveness of advanced analytics in mitigating environmental risks and enhancing resilience. Furthermore, the manuscript highlights key policy frameworks and governance models promoting transparency, data security, and sustainable development practices globally. The study concludes with actionable recommendations and identifies research gaps concerning data integration, quantum computing applications, and the ethical dimensions of emerging technologies in sustainability analytics. This edition aims to provide policymakers, researchers, practitioners, and industry professionals with actionable insights into designing and implementing robust, data-driven environmental risk management strategies aligned with sustainable development objectives.