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Keywords

Neural ODE model; CGE model; high-energy-consuming industries; carbon trading; carbon emissions

Abstract

High-energy-consuming industries represent the primary source of carbon emissions in China, which makes the reduction of their carbon emissions a critical priority for the country's carbon emission reduction efforts. However, due to the lack of robust carbon emission constraint mechanisms targeting these industries, their motivation for emission reduction remains significantly insufficient. To address this issue, this paper proposes a simulation method that integrates neural ordinary differential equations (Neural ODE) with a computable general equilibrium (CGE) model. Using input-output data from 2022 at the national and provincial levels, this method constructs a baseline scenario and three emission reduction scenarios to assess the impact of high-energy-consuming industries participating in carbon trading on provincial carbon emissions and carbon market. The results indicate that, compared to the absence of additional policies, the inclusion of high-energy-consuming industries in carbon market trading effectively reduces energy consumption and total carbon emissions, increases total carbon market trading volume and prices, and facilitates the province ’s achievement of carbon peaking by 2028.

DOI

10.19781/j.issn.1673-9140.2026.01.022

First Page

233

Last Page

242

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