The GID model aims to dynamically track the emissions of greenhouse gases and air pollutants from global major anthropogenic sources at facility level, through the development and integration of new emission accounting methods. At present, the quantification and dynamic tracking of facility-level carbon emissions since 1970 from global thermal power, iron and steel, and cement industries have been achieved by the GID model.

A facility-level dynamic emission accounting method is used by the GID model based on big data. It utilizes the massive spatio-temporal dynamic information of activity rates included in big data records to build a facility-level model, improving the accuracy of emission accountings at high spatial and temporal resolution. The data used in the model are all obtained from public or commercial databases, including global energy statistics, enterprise/facility-level information, online continuous emission monitoring systems, power load monitoring, and satellite remote sensing data. On this basis, an intelligent fusion technology is constructed to perform data fusion, data mining, fuzzy matching, and spatio-temporal transfer for massive heterogeneous data from multiple sources. It tracks the dynamic changes of facility-level capacity, technology, fuel consumption, load curve, and geographical location of global thermal power, iron and steel, and cement industries since 1970. Finally, constrained by activity rates at country level, a dynamic big-data-based emission accounting model is constructed for the facility-level activity rates of each industry, to estimate facility-by-facility carbon emissions. In this way, the dynamic accountings of carbon emissions from more than 100,000 facilities since 1970 are achieved for global thermal power, iron and steel, and cement industries, and based on the accountings, the spatial, temporal, and structural emission changes are tracked as well.