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Chinese Journal of Applied Ecology ›› 2023, Vol. 34 ›› Issue (2): 499-509.doi: 10.13287/j.1001-9332.202302.001

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Influencing factors and scenario forecasting of carbon emissions in Liaoning Province, China

NIU Le1,2, ZHAGN Lixia3, XI Fengming1, WANG Jiaoyue1*   

  1. 1Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China;
    2University of Chinese Academy of Sciences, Beijng 100049, China;
    3Liaoning Provincial Big Data Management Center, Shenyang 110002, Chin
  • Received:2022-07-20 Accepted:2022-10-11 Online:2023-02-15 Published:2023-08-15

Abstract: Liaoning is a province with large energy consumption and carbon emissions. Management of carbon emissions in Liaoning Province is crucial to realizing China's carbon peaking and carbon neutrality goals. To clarify the driving factors and trends of carbon emissions in Liaoning Province, we analyzed the impacts of six factors on carbon emissions in Liaoning Province through STIRPAT model based on carbon emission data from 1999 to 2019. The impact factors included population, urbanization rate, per-capita GDP, secondary industry ratio, energy consumption per unit GDP, and coal consumption ratio. Nine forecasting scenarios with three economic and population growth models and three emission reduction models were set up, and their carbon emission trends under the above nine forecasting scenarios were predicted. The results showed that the main driving factor of carbon emissions in Liaoning Province was per-capita GDP, and that the main inhibitor was energy consumption per unit GDP. The carbon peak year in Liaoning Province would fluctuate between 2020 and 2055 under the nine forecasting scenarios, with peak values ranging from 544 to 1088 million tons CO2. The medium economic development growth and high carbon emission reduction scenario would be the optimal carbon emission scenario in Liaoning Province. Under this forecasting scenario, Liaoning Province could achieve carbon peak (611 million tons CO2) by 2030 without affec-ting economic development through optimizing energy structure and controlling the intensity of energy consumption. Our results would be helpful for seeking the best path for carbon emission reduction in Liaoning Province and providing a reference for its realization of carbon peaking and carbon neutrality goals.

Key words: carbon emission, carbon peaking, scenario forecasting, Liaoning Province