Page 34 - Research on Financial Development Mechanism and Path of Forestry Carbon Sequestration in Developing Countries under Double Carbon Targets

Research on Financial Development Mechanism and Path of Forestry Carbon
Sequestration in Developing Countries under Double Carbon Targets

assessment method has the advantages of clear principle, clear intermediate process
and strong structural integrity. Once the model is established, the time and manpower
required will be much less. However, the method assumes that there is no difference be-
tween different products in the same department at all, because the difference of depart-
ment merging may seriously affect the accounting result, i.e. agglomeration deviation,
especially when accounting the carbon footprint of micro-objects. At the same time,
due to the long preparation period of the input-output table, the actual research is usual-
ly based on the data of previous years, which results in systematic errors in the analysis
results.
In order to carry out detailed and comprehensive carbon footprint analysis, a Hybri-
dLCA method can be constructed by combining LCA with IOA, so as to simultaneous-
ly solve the truncation error problem of LCA method and the agglomeration deviation
problem of IOA method, with both the accuracy of LCA and the integrity of IOA. In
this context, the carbon footprint of industrial industries, individual enterprises, large
product groups, even households, governments, individuals or specific social organiza-
tions can be obtained by the calculation model of input-output analysis. However, the
carbon footprint calculation method based on HybridLCA not only needs to specify
the resource input in the production process of products or services, but also needs to
match the production process with the departments in the input-output table, so it still
faces high uncertainty.
Compared with the above methods, the LCA method is relatively advanced in de-
velopment, has certain advantages in universality, systematization and quantification,
and expands the product system in time and space, which has more practical signifi-
cance. However, this method still has several disadvantages. First, the truncation error
problem. In the LCA evaluation process, the appropriate system boundary must be
determined to minimize the truncation error; Second, when LCA describes complex ob-
jects, it is difficult to observe and make statistics due to various changes in the objects,
which results in the data quality being difficult to control. This requires controlling
the quality of the data sources and developing a data cleaning algorithm accordingly.
Thirdly, there is a lack of distinguishing the spatio-temporal attributes. Localized da-
tabases can reflect the regional differences in space, while the timeliness of data needs
to be guaranteed in time, such as establishing dynamic data lists. Fourthly, there are
still subjective judging steps in the LCA method, which weakens the objectivity of the
evaluation, and puts forward the requirements for systematically developing more spe-

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