Has China’s coal consumption really peaked?—Prediction and scenario analysis of China’s coal consumption peak under the double-carbon target

Abstract

After China’s “Double Carbon” strategy was proposed to reduce energy and consumption, the optimization of China’s energy consumption structure and whether China’s coal consumption have already reached its peak once again attracted attention. This paper uses the theory of system dynamics to build a system dynamics model of coal consumption. Based on data analysis and the grey prediction model, three categories and nine sub-scenarios are established and the peak coal consumption is analyzed under each scenario. The results show that: The peak of coal consumption in China can echo the strategic goal of peaking carbon in 2030, which is in line with the laws of reality. The economic growth rate has the greatest influence on the coal consumption peak, followed by industrial structure and coal consumption intensity, and the population growth rate has the least influence. The SD3 scenario is the optimal path for China’s future coal consumption development. Under this scenario, coal consumption will peak at 437.8 million tons in 2027. If the future energy consumption trend is close to the SD3 scenario, the socioeconomic growth rate will slow down, the population growth rate will slow down, the industrial structure optimization degree will be higher, and the intensity of coal consumption will be lower. The coal consumption peak varies greatly according to different development goals and transformation dynamics. Under the constraints of the established peak time, the scenario of coal consumption peak has a variety of combinations.

Chinese electricity-focused input-output dataset with detailed coal power and alternative energy for 2018

“…This is valuable for unofficial methods of compiling IOTs. The use of micro data of coal power firms 31 not only depicts this sector’s heterogeneity, but also checks the macro data used to improve data quality.…”

“…Considering the advantages of the two data sources, macro data are preferred in this method, and data from the CEC are used for disaggregation when the macro data cannot provide more detailed structural information. The third was the unit-level cost database for China’s coal power that we previously constructed 31 . This database integrates data on the operational status, financial cost information, and profitability of coal power projects at different scales, technologies, and regions.…”

“…This step further disaggregated the gross output and value-added of coal power (a third-level sector) into six units (new sectors). Based on our unit-level cost database 31 , we selected the proxies required in this step, as listed in Table 3 . Notably, the proxy for gross output included heat sales revenue, which was consistent with our treatment of cogeneration.…”

“…To better understand the relationship between China’s electricity and economic sectors, we built the Chinese electricity-focused input-output (CEFIO) dataset for 2018 based on the official IOT 30 . In the CEFIO dataset, we disaggregated coal power from electricity as closely as possible by referring to the previously collected unit-level cost information of China’s coal power 31 . To trace the technical characteristics of transmission and distribution (T&D) and renewable power, we used macro data from official governments and industrial associations in China on the electricity mix (such as abandonment rate) and business activities (such as sales revenue) 32 – 35 .…”

Simulation analysis of carbon peak path in China from a multi-scenario perspective: evidence from random forest and back propagation neural network models

“…Li and Qin ( 2019 ) simulated the challenges that China may face in achieving the carbon peak path in 2030. Wang et al ( 2022b ) also put forward corresponding suggestions for China to achieve carbon peak. The time scope of the above literature research on carbon peaking is relatively short.…”