# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 2 Analysis of the Current Situation and Limitations of Bottom-Up Monitoring Methods
### Emission Inventory Method  
The emission inventory method is a methodology proposed by the IPCC for the estimation of GHG emissions. As outlined in the emission inventory list, the functional relationship between activity data and emission factors is established for each fire type, thereby enabling the estimation of emissions [2]. The primary equation utilized in this model is as follows:  
\[CO_{2 biomass}=\sum_{i=1}^{n}BA_{i}\times F_{i}\times CF_{i}\times EF_{i} \tag{3}\]  
where \(BA_{i}\) represents the burned area for the \(i\)-th type of biomass burning, \(F_{i}\) is the fuel biomass for the iii-th type, \(CF_{i}\) is the combustion factor, and \(EF_{i}\) is the emission factor.  
Tsinghua University has developed the Multi-resolution Emission Inventory for China (MEIC), a multi-scale emission inventory model for China. A unified source classification and grading system and an emission factor database have been constructed, and real-time dynamic calculation and online downloading of emission inventories have been realized. However, the scale of concern in the inventory is unbalanced, with a paucity of research at the mesoscale. The NCAR developed the FINN (Fire Inventory from NCARv2.5) model fire emission inventory [15], which estimates high-resolution global open biomass burning emissions and provides high-resolution input parameters for global and regional atmospheric chemistry models and air quality simulations [16]. The European Commission has developed the Emissions Database for Global Atmospheric Research (EDGAR) carbon emissions inventory, which is based on the IPCC algorithm and provides a global database for anthropogenic greenhouse gas emissions. However, it lacks natural source emissions from biomass burning. Subsequently, [PERSON] and [PERSON] employed the emission inventory method to estimate methane emissions from biomass burning in China and constructed a high spatio-temporal resolution emission inventory dataset, thereby furnishing scientific and effective data support for effective monitoring and the control of methane [17]. Furthermore, the research team utilized the emission inventory method to calculate CO\({}_{2}\) emissions from open biomass burning in Northeast China over an extended period and developed a high-resolution monthly biomass burning emission inventory for the region spanning from 2001 to 2017.  
The construction of emission inventories is contingent upon the data obtained from biomass burning activities and the emission factors for the various types of fires in question. The data are typically acquired through statistical, testing, or experimental monitoring, which are inherently subjective, uncertain, and subject to a certain lag. The inaccuracies associated with data, such as fire points, biomass, and emission factors, which are inherent to this method, are propagated throughout the emission inventory calculation process. This introduces an inherent uncertainty to the emission inventory and compromises the precision of emission estimates.