Abstract

Accurate and reliable greenhouse gas (GHG) inventory reporting is essential for tracking mitigation progress, informing climate policy, and supporting national commitments under international frameworks such as the Paris Agreement. However, traditional GHG inventory systems often struggle with fragmented datasets, inconsistent reporting standards, limited temporal resolution, and uncertainties arising from disparate measurement techniques. This study introduces an innovative data integration method designed to enhance the accuracy, completeness, and reliability of GHG inventories by harmonizing multisource data and leveraging advanced analytical techniques. The proposed framework integrates satellite observations, ground-based measurements, sectoral activity data, remote sensing products, emission factors, and real-time sensor networks through a unified architecture supported by machine-learning algorithms and geospatial analytics. The method employs robust data fusion techniques such as Bayesian inference, ensemble learning, and spatiotemporal interpolation to reconcile inconsistencies, fill data gaps, and quantify uncertainty across sources. A structured validation protocol is developed to align integrated datasets with internationally recognized reporting standards, including IPCC Tier 2 and Tier 3 methodologies, ensuring methodological transparency and cross-sector comparability. Application of the integrated system to national emission datasets demonstrates significant improvements in estimating emissions from agriculture, energy, transportation, waste, and industrial processes. Models reveal enhanced detection of emission anomalies, better estimation of fugitive emissions, and improved granularity in monitoring temporal changes, particularly in sectors with historically underreported emissions. Results show that the innovative integration method reduces uncertainty margins, increases reporting consistency, and offers higher-resolution insights into emission drivers. Sensitivity analysis confirms that incorporating satellite-derived metrics and continuous sensor data substantially strengthens the robustness of national GHG inventories. The study highlights the transformative potential of integrated data systems in supporting adaptive climate governance, enabling early identification of emission trends, and improving transparency for verification processes. Overall, this research underscores the importance of advanced data integration for next-generation GHG inventory systems, providing a scalable and interoperable framework suitable for national and subnational applications. The method supports more informed climate decision-making and enhances global efforts toward achieving emissions reduction targets.