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The investigation of the impact of energy consumption patterns on peak emissions in China is pertinent and crucial for achieving global climate goals. The study aims to form a forecast of carbon emissions in China and diagnose the achievement of established decarbonization goals based on structural changes in primary energy consumption until 2030. To achieve the goal, the study formed a Dynamic stochastic general equilibrium (DSGE) model with the implementation of Markov chains. The obtained research results yield important insights into the influence of various scenarios on carbon emissions in China by the year 2030. Specifically, considering structural transformations, an increase in emissions by 15 % can be anticipated, which is 7 % less than predicted by the classical scenario. Among the proposed scenarios, the Net Zero scenario is deemed the most efficient, where emissions could reach a level of 8509.5 million tons of CO₂. Conversely, the New Momentum scenario proves to be the most precarious, as it entails an escalation in coal usage, leading to a 4 % increase in emissions, amounting to 13264.84 million tons of CO₂. The contribution of this study is the approach to conducting carbon emission projections, which is based on the DSGE model with the implementation of Markov chains. This makes it possible to assess the adequacy of the set targets for carbon reduction based on alternative scenarios, which in crisis conditions can overlap and thus complement each other. • A forecast of carbon emissions in China. • The Net Zero scenario is assumed to be the most effective. • The approach to conducting carbon emission projections. • The potential threats to slow the rate of reduction of carbon emissions in China.
Cai et al. (Sat,) studied this question.