E3S Web of Conferences (Jan 2023)

Scenario Prediction of Carbon Peak in Fujian Electric Power Industry Based on STIRPAT Model

  • Chen Wanqing,
  • Xiang Kangli,
  • Guo Xiaodong,
  • Wu Yuan,
  • Ma Lianrui,
  • Chen Zihan,
  • Lin Hanxing,
  • Zheng Nan,
  • Cai Qiyuan

DOI
https://doi.org/10.1051/e3sconf/202340604043
Journal volume & issue
Vol. 406
p. 04043

Abstract

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The power industry plays a crucial role in achieving the carbon reduction objectives and facilitating the transition towards a low-carbon economy and society. This study employed the IPCC carbon emission coefficient method to calculate the carbon emissions of the power industry in Fujian Province from 2001 to 2021. To predict the carbon emissions of the power industry in Fujian Province from 2022 to 2030, this article established a STIRPAT model based on ridge regression. Empirical research was carried out in this study to investigate the timing of carbon peaking and peak carbon emissions in the power industry of Fujian Province, considering various scenarios. The calculation of carbon emissions indicates that the overall carbon emissions in the electricity industry in Fujian Province showed an upward trend from 2001 to 2021. By 2021, the emissions reached 9.646×107 tons, and the carbon emissions peak has not been reached. Scenario simulation analysis shows that under the energy-saving scenario, the electricity industry in Fujian Province is projected to reach its carbon emissions peak in 2025, with a peak value of 9.687×107 tons. However, in the baseline and ideal scenarios, the carbon emissions in the electricity industry in Fujian Province are projected to not peak before 2030. By 2030, the emissions are estimated to be 9.853×107 tons and 1.067×108 tons, respectively. The article concludes by presenting a comprehensive analysis of the most effective approach towards achieving carbon peaking in the power industry within Fujian Province. This is accomplished by examining the issue from various angles, including government planning, power generation structure, industrial structure, and public awareness.