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ORIGINAL RESEARCH
Research on Carbon Emission Prediction
of the Transportation Industry in Shaanxi
Province Based on DFE-IPOA-KELM Model
1
School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Submission date: 2023-12-21
Final revision date: 2024-04-07
Acceptance date: 2024-06-05
Online publication date: 2024-09-24
Publication date: 2025-05-09
Corresponding author
Xusheng Lei
School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China, China
School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China, China
Pol. J. Environ. Stud. 2025;34(4):4289-4309
KEYWORDS
carbon emission predictiontransportation industrydual feature extractionPelican optimization algorithmkernel extreme learning machine
TOPICS
ABSTRACT
To assess whether Shaanxi Province’s transportation industry can achieve the carbon peak target
by 2030 and to determine the total carbon emissions of the industry by that year. A model based
on dual feature extraction (DFE) and an improved Pelican algorithm (IPOA) to optimize the kernel
extreme learning machine (KELM) is proposed in this study and used to predict transportation industry
carbon emissions from 2022 to 2040. First, the influencing factors of carbon emissions are extracted
through Spearman and gradient boosting decision trees (GBDT), and the extracted factors are
used as the input set of the carbon emission prediction model. Secondly, the IPOA is used to optimize
the parameters of the KELM to overcome its shortcomings of easily falling into local optimal solutions.
Finally, the combined model is used to predict the future carbon emissions of the transportation
industry in Shaanxi Province. Comparing the prediction results and error indicators with other optimal
benchmark models, the model improved by 19.98%, 30.72%, and 21.33% in the three indicators
of MAPE, RMSE, and MAE, respectively. It is confirmed that the carbon emission prediction model
proposed in this study is more effective and can more accurately reflect the future carbon emission trend
of the transportation industry in Shaanxi Province, China.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
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