Author(s)

Jia Ziruo ¹, Gao Bo ¹

Affiliation(s)

¹ College of Transportation Science and Engineering, Civil Aviation University of China, Tianjin, China

Corresponding Author

Jia Ziruo

ABSTRACT

As a major greenhouse gas emitter, the aviation industry faces the dual challenge of meeting the rapidly growing flight demand while reducing carbon emissions. Using Tianjin's civil aviation carbon emissions as a case study, this research compares the predictive performance of partial least squares regression (PLSR)-enhanced STIRPAT and WOA–BP models. Results indicate that the WOA–BP model achieved superior prediction accuracy, with an average absolute relative error of 1.971%. Three scenarios—low-carbon, standard, and high-carbon—were established to predict the carbon peak year for Tianjin's civil aviation. Results indicate that only the low-carbon scenario shows a peak of 928,800 tons in  2040, whereas the other two scenarios are not expected to reach the carbon peak before 2050. Thus, relevant departments  should strengthen technological innovation and management coordination in the civil aviation field, rationally plan the development and emission reduction path of civil aviation, and promote the high-quality and sustainable development of Tianjin civil aviation to achieve early carbon peak.

KEYWORDS

Carbon emission prediction; Carbon peak; WOA–BP model; Green civil aviation

CITE THIS PAPER

Jia Ziruo, Gao Bo, WOA–BP-based Carbon Emission and Peak Carbon Prediction for Tianjin Civil Aviation. Environment and Climate Protection (2025) Vol. 4: 26-30. DOI: http://dx.doi.org/10.23977/envcp.2025.040104.

REFERENCES

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[4] Wang Anne, Bellet, Gao Yuwen, Yang Wu, Wang Ben, Liu Haitao, Sun Lushi. Analysis of factors affecting carbon emissions in Guangxi based on STIRPAT model[J]. Environmental Protection Science, 2024, 50 (04): 99-104+123.
[5] H. Li, X. Lin and Z. Li, "Intelligent Prediction Model of Emissions Based on Whale Algorithm Optimized BP Neural Network," 2022 International Conference on Electronics and Devices, Computational Science (ICEDCS), Marseille, France, 2022 
[6] Liu, B., Chang, H., Li, Y. et al. Carbon emissions predicting and decoupling analysis based on the PSO-ELM combined prediction model: evidence from Chongqing Municipality, China. Environ Sci Pollut Res 30, 78849–78864 2023. 

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