Climate Data Management Systems: Systematic Review Of Analytical Tools For Informing Policy Decisions

Abstract

This study systematically reviews the current advancements in Climate Data Management Systems (CDMS), focusing on the integration of artificial intelligence (AI), geospatial analysis, and emerging technologies such as blockchain. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, a comprehensive review of 50 high-quality peer-reviewed studies was conducted to identify the latest tools, techniques, and challenges in climate data management. The findings indicate that AI-powered tools, particularly machine learning algorithms, have significantly enhanced the accuracy and efficiency of climate forecasting, allowing for real-time monitoring and anomaly detection. Additionally, the application of geospatial technologies has improved the resolution and scope of environmental data analysis, enabling more precise assessments of land use changes, deforestation, and urban heat islands. However, challenges related to data integration, interoperability, and scalability remain significant, particularly in developing regions where financial and technological constraints hinder the effective deployment of CDMS. Furthermore, while blockchain shows potential in securing data integrity and enhancing transparency, its practical adoption is still in the early stages. This review highlights the critical need for collaborative efforts, technological innovation, and tailored strategies to optimize CDMS, ultimately supporting global climate resilience and sustainable policy-making. The synthesis of these 50 studies provides a comprehensive understanding of the current state of CDMS and outlines future research directions to address existing gaps in climate data management.