Globally Acknowledged Carbon Certification System: Construction Technique and Application

Carbon accounting and disclosure are crucial components of the low-carbon transition because they enable businesses to more effectively control their carbon emissions. Presently, disparate standards, a single statistical caliber, and inadequate collecting accuracy plague the carbon accounting and data collection technologies of different nations. A sophisticated data processing model is built around richer, high-quality carbon monitoring data in order to produce more thorough and accurate corporate carbon accounting. Multimodal data sources, such as carbon satellite and power data, are also introduced. The benefits and drawbacks of three model schemes—the graph neural network (GNN), the self-supervised vision transformers model, and the Physics-Informed neural networks (PINN) model—are illustrated through practical research and tests. In order to enable it to become an internationally recognized carbon certification standard, a new international carbon certification system is finally proposed. It integrates globally recognized carbon satellites, drones, and artificial intelligence algorithm models. This means that it goes from data collection and processing to carbon measurement and disclosure, and ultimately lands in the scenario model of global green bond issuance.