MEIC is a bottom-up modeling framework for emission inventory, including unified source category system, intelligence fusion and modeling technologies of multi-source big data, big-data-based methods for dynamic emission characterization, emission factor database, dynamic technology of scale conversion with spatiotemporal integration, multi-scale high-resolution emission processing system, and cloud computing platform. The MEIC model allows the dynamic and continuous processing of the whole process from basic data of emission inventories to model-ready 3-D emission files required by climate and air quality models. It can generate multi-year, multi-spatial-scale, and multi-chemical-component emission inventories, seamlessly connected to climate and air quality models.

Source categorization >

At global scale, the MEIC model covers the emissions from fossil fuel combustion and cement process with 55 sectors together with 42 fossil fuel types, leading to about 1800 sources in total. The database covers 208 countries or territories worldwide, from 1970 to present. It is dynamically updated every year.

For China, the MEIC model covers six types of anthropogenic emission sources, including stationary combustion, industrial processes, mobile source, solvent use source, agriculture source, and waste treatment. According to the differences of pollutant generation mechanism and emission characteristics, each type of emission sources can be divided into four levels: sector/industry, fuel/product, combustion/production technology, and end-of-pipe control technology. From the first level to the fourth level, a complete system of emission source categories is established step by step, and the fourth level is taken as the basic calculation unit of emission inventory. The current version of the MEIC model for China includes more than 700 anthropogenic sources since 1990, and is dynamically updated every year.

Big-data-based methodology for dynamic emission characterization >

An algorithm of intelligence fusion is developed targeting heterogeneous big data from multiple sources. It integrates the massive global anthropogenic activity rates across different scales, which are collected and collated from more than 130 public or commercial databases. In this way, standard data cubes of activity rates are built, including 55 sectors, 42 fuel types, 208 countries or territories, over 800 sub-country administrative divisions, and more than 100,000 facilities.

Technology and dynamic-process-based methods are established for emission quantification, which resolves the quantitative relationships between emissions and technology turnover. Energy consumption, combustion/industrial/control technologies, and emission factor database are fully coupled to estimate emissions accurately. Each calculation unit is mapped to the activity database, technology turnover model, and emission factor database, which enable MEIC to update quickly by capturing the changes of activity rates and the evolution of technologies.

Technologies of dynamic emission quantification with high spatiotemporal resolution are respectively established based on big data for the non-aggregated activity rates in key sectors such as energy-intensive industries, road transport, marine navigation, and aviation. This achieves the non-aggregated characterization of activity rates and emissions for ~100,000 highly energy-consuming facilities, 2.3 billion motor vehicles, more than 140,000 air routes, and over 800,000 sea voyages worldwide, systematically improving spatial and temporal representativeness of emissions. The big-data model covers power industry, iron and steel industry, cement industry, road transport sector, aviation sector, and navigation sector, which totally account for 70% of global CO2 emissions in 2021.

For power, cement, and iron and steel industries, MEIC model uses the process-based method to develop facility-based emission inventories. For each facility, MEIC model tracks the technology evolution with its entire life cycle to dynamically represent the emission variations induced by activity rate changes, technical progress, and more stringent emission standards. The methodology of facility-level emission characterization can be found in detail at GID website (

For residential sources, the MEIC model is based on tens of thousands of household survey data covering most provinces in China, which systematically corrects the statistical errors of residential coal and biomass fuel consumption in the energy statistical yearbook, provides more accurate activity rates and technology distribution parameterization scheme for the establishment of residential emission source model, and reduces the uncertainties of residential emission inventory.

Methodology for estimation of CO2 emissions and emission factors >

The CO2 emission estimates in MEIC model are based on the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. At global scale, country-specific heating values by fuel are obtained from IEA World Conversion Factors database; fuel-specific carbon content values are the recommended values in the 2006 IPCC Guidelines for National Greenhouse Gas Inventories; fuel-specific oxidation factors are taken from the CDIAC database. For China, heating values and carbon content values are based on the Guidelines for Provincial Greenhouse Gas Inventories, and further revised by measurements of coal samples; oxidation factors are obtained from the Guidelines for Provincial Greenhouse Gas Inventories. For some emerging economies, heating values and carbon content values are compiled from local energy statistics or UNFCCC national submissions; oxidation factors are localized data collected from literature.

Multi-scale high resolution emission processing system >

Dynamic technology of scale conversion module with spatiotemporal integration is developed based on a mass of highly-resolved data, and allows seamless continuous accounting across “global, country, sub-country, enterprise, and grid” spatial scales and “annual, monthly, and daily” temporal scales. A self-developed multi-dimensional and high-resolution emission processing system is also built to generate model-ready gridded emission input for climate and air quality models. The emission processing system composes three modules for temporal allocation, spatial allocation, and speciation, respectively. The temporal module allocates annual emissions to hourly emissions based on the monthly, weekly, and daily profiles. The spatial module allocates point and area sources to 3-D grids by using spatial and vertical distributions. The speciation module is developed using the profile-assignment approach. Emissions of total NMVOC and PM are firstly split into individual species and then mapped into various chemical mechanisms that are configured in chemical transport models, such as SAPRC99, SAPRC07, CBIV, CB05, CBMZ, RADM2, RACM, GEOS-Chem, and MOZART.

Cloud computing platform >

MEIC model adopts high-speed data processing algorithm of high-dimensional sparse data and cloud computing technology to build an online technology platform that provides online calculation, visualized analyses, data download and dynamic emission data update. The platform can provide high-resolution and model-ready emission data to support mainstream atmospheric chemistry model simulations and air quality forecasting models. Users can require, customize, and access demanded data through the online platform.