Journal Home > Just Accepted

The China Regional Energy Model (C-REM) is a recursive-dynamic, multi-sector, multi-regional computable general equilibrium model that has been widely used in studies of the impact of energy and climate policies, with a focus on the distribution of effects across China’s provinces. Here we summarize its historical applications, describe the modeling methods used in the C-REM Version 4.0 (its newly updated version), and illustrate its features by showing sample simulation results for China’s carbon neutrality target. We use the latest regional input–output tables for China and the Global Trade Analysis Project 11 database to update the base year economic data to the year 2017. We match and reconcile provincial energy data with economic data to ensure consistency between physical and monetized values. We introduce carbon capture and storage technologies as a backstop to achieve net-zero emissions in the model simulations. Our simulations indicate that the 2060 carbon neutrality target will lead to a lower and earlier peak in total primary energy consumption with a transition towards non-fossil energy sources compared to the prior target, which focused only on the timing of the carbon peak. Our scenarios further suggest that the electricity and metal smelting sectors are the main contributors to CO2 reduction between 2025 and 2060. Assuming the current effort-sharing principle continues to be used for emissions reduction target allocation among provinces, more developed provinces and provinces that rely more on fossil-based energy will bear higher costs in a net zero energy transition. Certain northwest provinces are projected to experience positive impacts due to industry relocation, driven by abundant renewable resources and carbon storage capacity. The paper concludes with a discussion of anticipated directions for the future development of the C-REM.

File
ECM-2024-9400006_ESM.pdf (293.3 KB)
Publication history
Copyright
Rights and permissions

Publication history

Received: 23 November 2023
Revised: 26 January 2024
Accepted: 29 March 2024
Available online: 07 May 2024

Copyright

© The Author(s) 2024.

Rights and permissions

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0, http://creativecommons.org/licenses/by/4.0/).

Return