From Smart Data to Sustainable Decisions: Intelligent Technologies in Environmental Monitoring and Resource Management

Abstract

The rapid advancement of intelligent technologies, such as machine learning, optimization algorithms, and decision support systems (DSS), presents significant opportunities for improving environmental monitoring and resource management. Such technologies are able to handle large, complicated datasets to deliver actionable insights that are able to support sustainable decision-making. Nonetheless, data quality, model reliability, model uncertainty, and stakeholder participation are important issues that should be considered carefully during the transformation of raw environmental data into informed decisions. The review describes the application of intelligent systems to different contexts of environmental decisions, such as compliance and enforcement, early warning systems, resource allocation, and conservation planning. We argue that these systems must be reliable and fair, highlighting the importance of data governance, transparency, and ethical considerations. We also discuss the practical demands of being deployed in the real world, including the interoperability of the data, adapting the model to new circumstances, and the financial and technical constraints of deploying the more sophisticated technologies. Based on case studies, we determine not only the achievements but also the drawbacks of the existing applications, providing information on how intelligent technologies could be better combined into the decision-making process. In conclusion, we present a research and policy agenda wherein we would focus on integrating the system, assessing the model continuously, and involving a wide range of stakeholders to make sure that these technologies are used in a way that enhances environmental sustainability and social equity.