3.1.1. Why Do We Choose CGE Model?

Generally speaking, if we want to simulate an event that does not actually happen, and the event will lead to significant changes in the economic structure, the data-driven empirical evidence model will no longer be applicable, such as the econometric model (for example, the panel model, generalized method of moment model, time-series model), and machine learning (such as the BP neural network algorithm, genetic algorithm, and hybrid algorithm).

Therefore, when considering the simulation of 2060 carbon neutrality, we need to use the scenario analysis model, which is good at simulating counterfactual events. For example, the LEAP (long-range energy alternatives planning system) model, system dynamics, DSGE (dynamic stochastic general equilibrium) model, and the CGE model.

However, among these scenario analysis models, only the CGE model can describe the relationship between industries in detail because the CGE model is the model with the largest data demand (requiring the input-output table and other data, energy, and emission data of various departments), and it is also a model with relatively weak assumptions. The CGE model can simulate the behavior of maximizing the utility/profit of enterprises, residents, governments, and foreign manufacturers. Additionally, the model considers the mutual restriction relationship between different actors.

## 3.1.2. The Brief Introduction of CGE Model

The model is widely used to simulate various policies' macro impact [45–47]. This paper's CGE model is from the existing literature, and the exogenous parameters in the model are basically passed through several rounds of inspection [48–50], and the substitution elasticity is set based on a well-known CGE model [51,52]. The CGE model constructed in this paper includes more than 3200 endogenous variables and corresponding equations. It considers the behavior patterns of residents, enterprises, the government, and international firms. It is mainly based on a general equilibrium theory (advanced theory of game theory) and a large number of microeconomic theories (such as manufacturer behavior theory, resident consumption theory, etc). In order to couple the energy and environment block, we additionally considered the relevant theories of energy economics and environmental economics.

The applied model's name is the China Energy-Environment-Economy Analysis 2.0 (CEEEA) model, and it is a dynamic recursive model considering multi-sector and multihouseholds. The flow chart for establishing and simulating the CGE model is shown in Figure 2. It has five blocks:


tionship between energy use and CO2 emissions, and the carbon pricing strategies of the government.

5. Macroscopic closure and market-clearing block. This block is used to simulate the closure conditions and market-clearing assumptions of the whole economy. Based on the neoclassical macro-closure conditions, the model considers the clearing of commodity and factor markets and assumes that there is no factor redundancy or shortage.

**Figure 2.** Flow chart of constructing and simulating the CGE model.
