A Calculation Model of Carbon Emissions Based on Multi-Scenario Simulation Analysis of Electricity Consumption

In order to reach the peak of carbon emission in China by 2030 and to meet the low-carbon conversion of energy and the growing demand for electricity, this study aims to propose a more accurate and scientific method to calculate the carbon emissions of the entire power industry chain. This paper analyzes the historical actual operation data of the energy and power industry from 2000 to 2020, and originally proposes a carbon emission calculation model based on a multi-scenario simulation analysis of electricity consumption. This paper is an original study from the perspective of the whole industry chain of electricity production, transmission, and consumption. Firstly, a carbon emission model of the power system is established based on the carbon emission composition and transmission mechanism of the whole power industrial chain, which consists of calculation models for carbon emissions from overall electricity demand and carbon emissions from electricity network losses. Secondly, the concept of carbon emission coefficient is proposed, and the key parameters of the carbon emission coefficient of the power system are obtained through the econometric model. On this basis, the carbon emission coefficient is obtained by regression fitting of multiple key parameters according to historical data. Finally, electricity consumption per unit output value (ECPUOV) and per capita electricity consumption (PCEC) are used to predict electricity consumption in the next 15 years. This paper also makes a quantitative analysis of the relationship between CO2 emissions from the power system and electricity consumption. This paper takes G province, which ranks first in total energy consumption and economic aggregate in China, as an example and calculates its CO2 emissions and achievement of peak CO2 emissions by multi-scenario analysis. The case study results show that the low carbon scenario(LC) is the best route for G province to peak CO2 emissions from energy consumption. The method proposed in this paper can set an achievable goal of 2030 carbon peaking for the government and industry policymakers, and find a feasible implementation path.