Workstreams
Data taxonomies facilitate alignment of diverse datasets for transparency and accountability insights.
☸️ Data taxonomies facilitate alignment of diverse datasets for transparency and accountability insights.
For effective climate action orchestration (see ‘The Climate Orchestra’), handling climate data is akin to solving a multidimensional puzzle. The vast scope of climate data, with its disparate sources, formats, and collection methodologies, poses unique challenges. But it also offers a remarkable opportunity for deeper insights, if effectively harnessed. Central to this endeavor is the development of data taxonomies or schemas - structured frameworks designed to align diverse datasets and facilitate the extraction of valuable transparency and accountability insights. Data taxonomies empower cross-comparison and alignment across different data accounting systems and collection methods, such as remote sensing technologies, digital MRV approaches, and methodologies developed by the Intergovernmental Panel on Climate Change (UNEP 2023). They ensure smooth data conversion, enabling different data sets to 'speak the same language.’
The need for such harmonization extends across all levels of climate data. Whether it's national and regional governments, local/urban GHG inventories compiled by practitioners and researchers, or protocols developed by non-state actors, a common language of data is essential. However, achieving this can be complex, resource-intensive, and often results in varying data quality.
Networks are important in developing collaborative solutions, such as ICLEI, GCoM, and C40, that develop data collection and reporting standards, and share tools and protocols that foster data harmonization. Examples of such initiatives include the Global Covenant of Mayors - EU Secretariat’s efforts to create a harmonized dataset of GHG inventories for over 6,000 cities in Europe and Southern Mediterranean countries. However, the challenge doesn't end with harmonization. Integrating diverse data streams to optimize their utility requires interoperable systems—ones that allow open data exchange between different systems, types, and standards. Interoperability is crucial to maintain traceability and trackability as new datasets flood in.
Furthermore, digital approaches and technologies such as machine learning and Decentralized Ledger Technologies (DLT) can automate data harmonization, processing, and dissemination, creating interoperability among fragmented systems. [Machine Learning sentence, use ClimActor as an example?] By offering a tamper-resilient, immutable ledger, DLT ensures transparency. This harmonization and integration of diverse accounting systems are crucial for real-time data access and the creation of a reporting system that aggregates data from various sources, ensuring traceability across actors and data sources to prevent double counting.
https://www.nature.com/articles/s41597-020-00682-0/figures/4
