According to the specific measurement methods, it can be divided into input-output model, econometric model, CGE model, CERI-AIM model, Logistic model, MARKAL model, life cycle model, CARBON model and decision tree model.
The input-output model is mainly used to measure the implicit carbon emissions. For the carbon emissions of industries or products, in addition to the direct emissions from direct energy consumption, there are also indirect emissions from indirect energy consumption through intermediate inputs. The input-output model reflects the input relationship between industries through the consumption coefficient of the industry, so as to achieve the purpose of measuring the implied carbon emissions.
Econometric models generally predict the development and changes of economic variables through the past statistical relationship between economic variables. The Long-Term Energy Alternative Planning Model (LEAP) is a representative of this type of model. The model predicts long-term energy usage from past energy usage patterns, thereby estimating greenhouse gas emissions. The limitation of the econometric model in measuring carbon emissions is mainly manifested in that the model only reflects the behavioral characteristics of the economic system in the corresponding time period in the past, and economic entities cannot directly respond to policies efficiently or accurately, so it is not suitable for analyzing larger policy changes.
The CGE model is derived from Walras’ general equilibrium theory. In research in the field of climate change, the advantage of the CGE model is that it establishes a quantitative relationship between various factors, so that we can examine the impact of disturbances from a certain factor on the entire system, which can be used to estimate greenhouse gas emissions and analyze the impact of emission reduction policies. The Chinese economic CGE model constructed by the Chinese Academy of Social Sciences belongs to this category. Compared with the econometric model, the CGE model has a clear microeconomic structure and the connection between macro and micro variables, and the model is no longer a “black box”; compared with the mixed model, because it incorporates policy variables into the overall economic system, no matter how the policy changes impact, it can be reflected in the entire economic system, which has a better effect on policy evaluation. However, the data required by the CGE model is quite complex, and its dynamic model adopts a recursive mechanism, which is reasonable in short-term prediction, but insufficient in long-term prediction.
The ERI-AIM model is a relatively complete policy evaluation model that integrates three types of models: emissions, climate, and impacts. By establishing an energy system model system and calculation method suitable for national conditions, it can forecast the future energy demand, carbon dioxide emission trends and their impact on the macro economy. The main functions and objectives of the model are: to evaluate the effect and impact of introducing carbon tax policies in various technical emission reduction countermeasures; to evaluate the possibility and comprehensive effect of combining carbon tax with other countermeasures. The model consists of three modules. The first module is the energy service quantity calculation module. It can calculate the social energy demand, and calculate the energy service demand by combining with external modules that determine economic and social variables. The second module is an energy efficiency calculation module that calculates changes in energy efficiency. It takes secondary energy supply as one side and energy service demand as the side to form a “Reference Energy System” (RES), which is the part that fully describes the technical information of energy equipment. The third module is a module for selection of various service technologies that determine energy efficiency. It evaluates the quality of service equipment according to economic accounting standards, and selects the best equipment for various service needs at various stages.
Logistic Model: In a natural socio-economy, there are many biomass and economic quantities that are monotonic growth functions x(t) at time t. Its growth rate gradually increases from slow to fast in the early stage, and then gradually slows down from fast growth in the later stage, and finally tends to a finite value K, which is usually called the saturation value. Displayed on the graph, its scatter is similar to a flattened s-shaped curve, called the S-growth curve (Logistic curve), and the corresponding function is called the s-growth model, which is used to express the relationship between carbon emissions and time, so as to measure carbon emissions.
MARKAL model: This model is a dynamic linear programming model. Based on the reference energy system, various energy extraction, processing, conversion and distribution links and end-use energy links in the energy system are described in detail. And for each link, not only existing technologies, but also various advanced technologies that may appear in the future can be considered. The optimization goal of the model is to meet the demand of various useful energy sources, and the total energy supply cost discounted by the energy system during the planning period is the lowest. The constraints of the model mainly include energy carrier balance, , power base load equation, power peak load equation, low temperature heat peak load equation, capacity transfer equation, demand equation, equation describing the relationship between conversion technology and processing technology capacity and activity, available resource accumulation equation, emission calculation equation, user-defined equation, etc. Chen Wenying and Wu Zongxin of Tsinghua University established China’s MARKAL model based on the idea of this model, and obtained macro indicators of energy consumption and carbon dioxide emissions.
Lifecycle Model: life cycle analysis/assessment (LCA), known as the “environmental management tool of the 1990s”, is a method for subsequent evaluation of environmental issues related to the process of a product “from the cradle to the grave” (Yu Xiujuan, 2003). The LCA requires a detailed study of energy requirements, raw material use and waste to the environment from activities over its life cycle. Including raw material recycling, extraction, transportation, manufacturing/processing, distribution, utilization/reuse/maintenance and subsequent waste disposal. The main purpose is to conduct scientific and systematic quantitative research on the environmental consequences or potential environmental impacts of a product, process or production activity (Ma Zhonghai, 2002).
According to the definition of life cycle assessment, in theory, every activity process will generate CO2 gas. As the research is undertaken starting from the resource development of the activity, different departments and processes will be involved. It is necessary to track all the processes that energy and raw materials go through in this process to form a full energy chain, and conduct a comprehensive quantitative and qualitative analysis of the gas emissions of each link in the chain. Therefore, when using this method to study the greenhouse gases emitted by each activity process, the research object is different from the conventional carbon source classification method, and the activity chain is used as the classification unit.
The CARBON model was developed by Professor Xu Deying from the Institute of Forest Ecology and Environment, Chinese Academy of Forestry. Based on this model, he calculated the carbon balance of China’s forests. Under the IPCC algorithm framework, the CARBON model takes into account the regional distribution and forest structure changes in the implementation process, and China’s forests are divided into 5 areas. Each district is further divided into 5 age groups: young forest, middle-aged forest, near-aged forest, mature forest and over-mature forest. According to the existing forest census data, determine the forest area of different age classes, the standing stock volume of different age classes, the average annual growth rate, the annual harvesting area, the area of conversion of forest land to other types and other types to forest land, and the wood density. , the ratio of stem and biomass, carbon content in soil and other variables, wood consumption structure, etc., to calculate the carbon uptake and release of my country’s forests.