Understanding Transportation Carbon Emission Prediction: Methods, Trends, and Reflections

Research Article
Open access

Understanding Transportation Carbon Emission Prediction: Methods, Trends, and Reflections

Qiyang Fan 1*
  • 1 College of Transportation Engineering, Dalian Maritime University, Dalian, China, 116000    
  • *corresponding author fqy1274767644@dlmu.edu.cn
ACE Vol.162
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-157-0
ISBN (Online): 978-1-80590-158-7

Abstract

In the context of the global response to climate change, transportation has received increasing attention as an important source of carbon emissions. The prediction methods for transportation carbon emissions have continued to develop over the past decade, forming a variety of research paths. This paper reviews the primary research methods on transportation carbon emission prediction in the past decade. Based on the systematic sorting and analysis of the existing literature, this paper classifies the mainstream methods into three categories: traditional mathematical models, simulation methods represented by system dynamics, and intelligent models and their coupled models. This paper systematically summarizes the theoretical foundations, applicable scenarios, and technical characteristics of each type of method, points out the advantages and limitations of different methods. At the same time, this paper proposes that future modeling research can be directed toward model coupling, standardization of the construction process, and other development paths. By comparing the applicability of different prediction methods, the results of this paper can help scholars quickly identify and compare different methods for solving specific research problems.

Keywords:

Transportation carbon emissions, prediction methods, multi-model coupling, carbon reduction

Fan,Q. (2025). Understanding Transportation Carbon Emission Prediction: Methods, Trends, and Reflections. Applied and Computational Engineering,162,54-62.
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References

[1]. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the International Conference on Climate Change. Shukla, P.R., Skea, J., Slaade, R., Al Khourdajie, A., van Diemen, R., McCollum, D., Pathak, M., Some, S., Vyas, P., Fradera, R., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2022; ISBN 9789291691609.

[2]. Jaramillo, Paulina, et al. "Transport (Chapter 10)." (2022): 1049–1160.

[3]. Ehrlich, Paul R., and John P. Holdren. "Critique." Bulletin of the Atomic Scientists 28.5 (1972): 16-27.

[4]. Gao H, Wang X, Wu K, et al. A review of building carbon emission accounting and prediction models[J]. Buildings, 2023, 13(7): 1617.

[5]. York, Richard, Eugene A. Rosa, and Thomas Dietz. "STIRPAT, IPAT, and ImPACT: analytic tools for unpacking the driving forces of environmental impacts." Ecological economics 46.3 (2003): 351–365.

[6]. Wang, Jingtian, and Xiaoming Ma. "Influencing factors of carbon emissions from transportation in China: Empirical analysis based on two-level econometrics method." Beijing Da Xue Xue Bao 57.6 (2021): 1133-1142. econometrics method." Beijing Da Xue Xue Bao 57.6 (2021): 1133-1142.

[7]. ZHANG Lanyi,LU Qiuping,ZHANG Yuanyuan,et al. Study on Influencing Factors of Transportation Carbon Emission and Carbon Reduction Trend in Fujian Province[J]. Journal of Harbin University of Commerce (Natural Science Edition),2022,38(03): 360- 366+375.DOI:10.19492/j.cnki.1672-0946.2022.03.016.

[8]. Zhong Jin, Li Zonghang. Research on national carbon emission forecast based on trend time series[J]. Operations Research and Fuzziology, 2023, 13: 3870.

[9]. Lv Yanqin, Fan Tianzheng, Zhang Jinning. Research on the spatial and temporal heterogeneity and influencing factors of carbon emission efficiency of transportation in China[J]. Ecological Economy,2023,39(03):13-22.

[10]. Huang Y, Zhu H, Zhang Z. The heterogeneous effect of driving factors on carbon emission intensity in the Chinese transport sector: Evidence from dynamic panel quantile regression[J]. Science of the Total Environment, 2020, 727: 138578.

[11]. Bolea L, Espinosa-Gracia A, Jimenez S. So close, no matter how far: a spatial analysis of CO2 emissions considering geographic and economic distances[J]. The World Economy, 2024, 47(2): 544–566.

[12]. Wang Y, Zhou Y, Zhu L, et al. Influencing factors and decoupling elasticity of China's transportation carbon emissions[J]. Energies, 2018, 11(5): 1157.

[13]. Jain S, Rankavat S. Analysing driving factors of India's transportation sector CO2 emissions: based on LMDI decomposition method[J]. Heliyon, 2023, 9(9).

[14]. Shui B, Cai Z, Luo X. Towards customized mitigation strategy in the transportation sector: an integrated analysis framework combining LMDI and hierarchical clustering method[J]. Sustainable Cities and Society, 2024, 107: 105340.

[15]. YANG Shaohua, ZHANG Yuquan, GENG Yong. Analysis of transportation carbon emission changes in the Yangtze River Economic Belt based on LMDI[J]. China Environmental Science,2022,42(10):4817-4826.DOI:10.19674/j.cnki.issn1000-6923.2022.0170.

[16]. WANG Chao, WU Limin. Analysis of transportation carbon emission reduction driving factors in the Silk Road Economic Belt based on the perspective of "double carbon"[J]. Arid Zone Resources and Environment, 2024, 38(02): 9-19. DOI:10.13448/j.cnki.jalre.2024.024.

[17]. Zhang, Lina, et al. "Carbon emissions in the transportation sector of Yangtze River Economic Belt: decoupling drivers and inequality." Environmental Science and Pollution Research 27 (2020): 21098-21108.

[18]. Cheng, Shulei, et al. "Potential role of fiscal decentralization on interprovincial differences in CO2 emissions in China." Environmental Science & amp; Technology 55.2 (2020): 813-822.

[19]. Köhler, Jonathan, et al. "Modelling sustainability transitions: an assessment of approaches and challenges." (2018).

[20]. Wang H, Cao R, Zeng W. Multi-agent based and system dynamics models integrated simulation of urban commuting relevant carbon dioxide emission reduction policy in China[J]. Journal of Cleaner Production, 2020, 272: 122620.

[21]. Wang H, Cao R, Zeng W. Multi-agent based and system dynamics models integrated simulation of urban commuting relevant carbon dioxide emission reduction policy in China[J]. Journal of Cleaner Production, 2020, 272: 122620.

[22]. Zolfagharian M, Romme A G L, Walrave B. Why, when, and how to combine system dynamics with other methods: towards an evidence-based framework[J]. Journal of Simulation, 2018, 12(2): 98-114.

[23]. Wang H, Shi W, He W, et al. Simulation of urban transportation carbon dioxide emission reduction environment economic policy in China: An integrated approach using agent-based modelling and system dynamics[J]. Journal of Cleaner Production, 2023, 392: 136221.

[24]. Ren Yanjuan. Research on low carbon strategy of transportation system based on system dynamics[D]. Chongqing Jiaotong University,2020.DOI:10.27671/d.cnki.gcjtc.2020.000067.

[25]. Çınarer G, Yeşilyurt M K, Ağbulut Ü, et al. Application of various machine learning algorithms in view of predicting the CO2 emissions in the transportation sector[J]. Science and Technology for Energy Transition, 2024, 79: 15.

[26]. Ji T, Li K, Sun Q, et al. Urban transportation emission prediction analysis through machine learning and deep learning techniques[J]. Transportation Research Part D: Transport and Environment, 2024, 135: 104389.

[27]. GAO Jinhe, HUANG Weiling,JIANG Haopeng. Multi-model comparative analysis of urban transportation carbon emission prediction[J]. Journal of Chongqing Jiaotong University (Natural Science Edition), 2020, 39(07): 33-39.

[28]. Huo Z, Zha X, Lu M, et al. Prediction of carbon emission of the transportation sector in Jiangsu province-regression prediction model based on GA-SVM[J]. Sustainability, 2023, 15(4): 3631.

[29]. Zhao Y, Liu R, Liu Z, et al. A review of macroscopic carbon emission prediction model based on machine learning[J]. Sustainability, 2023, 15(8): 6876.

Cite this article

Fan,Q. (2025). Understanding Transportation Carbon Emission Prediction: Methods, Trends, and Reflections. Applied and Computational Engineering,162,54-62.

Data availability

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Volume title: Proceedings of CONF-FMCE 2025 Symposium: Semantic Communication for Media Compression and Transmission

ISBN：978-1-80590-157-0(Print) / 978-1-80590-158-7(Online)
Editor：Anil Fernando
Conference website: https://2025.conffmce.org/glasgow.html
Conference date: 24 October 2025
Series: Applied and Computational Engineering
Volume number: Vol.162
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the International Conference on Climate Change. Shukla, P.R., Skea, J., Slaade, R., Al Khourdajie, A., van Diemen, R., McCollum, D., Pathak, M., Some, S., Vyas, P., Fradera, R., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2022; ISBN 9789291691609.

[2]. Jaramillo, Paulina, et al. "Transport (Chapter 10)." (2022): 1049–1160.

[3]. Ehrlich, Paul R., and John P. Holdren. "Critique." Bulletin of the Atomic Scientists 28.5 (1972): 16-27.

[4]. Gao H, Wang X, Wu K, et al. A review of building carbon emission accounting and prediction models[J]. Buildings, 2023, 13(7): 1617.

[5]. York, Richard, Eugene A. Rosa, and Thomas Dietz. "STIRPAT, IPAT, and ImPACT: analytic tools for unpacking the driving forces of environmental impacts." Ecological economics 46.3 (2003): 351–365.

[6]. Wang, Jingtian, and Xiaoming Ma. "Influencing factors of carbon emissions from transportation in China: Empirical analysis based on two-level econometrics method." Beijing Da Xue Xue Bao 57.6 (2021): 1133-1142. econometrics method." Beijing Da Xue Xue Bao 57.6 (2021): 1133-1142.

[7]. ZHANG Lanyi,LU Qiuping,ZHANG Yuanyuan,et al. Study on Influencing Factors of Transportation Carbon Emission and Carbon Reduction Trend in Fujian Province[J]. Journal of Harbin University of Commerce (Natural Science Edition),2022,38(03): 360- 366+375.DOI:10.19492/j.cnki.1672-0946.2022.03.016.

[8]. Zhong Jin, Li Zonghang. Research on national carbon emission forecast based on trend time series[J]. Operations Research and Fuzziology, 2023, 13: 3870.

[9]. Lv Yanqin, Fan Tianzheng, Zhang Jinning. Research on the spatial and temporal heterogeneity and influencing factors of carbon emission efficiency of transportation in China[J]. Ecological Economy,2023,39(03):13-22.

[10]. Huang Y, Zhu H, Zhang Z. The heterogeneous effect of driving factors on carbon emission intensity in the Chinese transport sector: Evidence from dynamic panel quantile regression[J]. Science of the Total Environment, 2020, 727: 138578.

[11]. Bolea L, Espinosa-Gracia A, Jimenez S. So close, no matter how far: a spatial analysis of CO2 emissions considering geographic and economic distances[J]. The World Economy, 2024, 47(2): 544–566.

[12]. Wang Y, Zhou Y, Zhu L, et al. Influencing factors and decoupling elasticity of China's transportation carbon emissions[J]. Energies, 2018, 11(5): 1157.

[13]. Jain S, Rankavat S. Analysing driving factors of India's transportation sector CO2 emissions: based on LMDI decomposition method[J]. Heliyon, 2023, 9(9).

[14]. Shui B, Cai Z, Luo X. Towards customized mitigation strategy in the transportation sector: an integrated analysis framework combining LMDI and hierarchical clustering method[J]. Sustainable Cities and Society, 2024, 107: 105340.

[15]. YANG Shaohua, ZHANG Yuquan, GENG Yong. Analysis of transportation carbon emission changes in the Yangtze River Economic Belt based on LMDI[J]. China Environmental Science,2022,42(10):4817-4826.DOI:10.19674/j.cnki.issn1000-6923.2022.0170.

[16]. WANG Chao, WU Limin. Analysis of transportation carbon emission reduction driving factors in the Silk Road Economic Belt based on the perspective of "double carbon"[J]. Arid Zone Resources and Environment, 2024, 38(02): 9-19. DOI:10.13448/j.cnki.jalre.2024.024.

[17]. Zhang, Lina, et al. "Carbon emissions in the transportation sector of Yangtze River Economic Belt: decoupling drivers and inequality." Environmental Science and Pollution Research 27 (2020): 21098-21108.

[18]. Cheng, Shulei, et al. "Potential role of fiscal decentralization on interprovincial differences in CO2 emissions in China." Environmental Science & amp; Technology 55.2 (2020): 813-822.

[19]. Köhler, Jonathan, et al. "Modelling sustainability transitions: an assessment of approaches and challenges." (2018).

[20]. Wang H, Cao R, Zeng W. Multi-agent based and system dynamics models integrated simulation of urban commuting relevant carbon dioxide emission reduction policy in China[J]. Journal of Cleaner Production, 2020, 272: 122620.

[21]. Wang H, Cao R, Zeng W. Multi-agent based and system dynamics models integrated simulation of urban commuting relevant carbon dioxide emission reduction policy in China[J]. Journal of Cleaner Production, 2020, 272: 122620.

[22]. Zolfagharian M, Romme A G L, Walrave B. Why, when, and how to combine system dynamics with other methods: towards an evidence-based framework[J]. Journal of Simulation, 2018, 12(2): 98-114.

[23]. Wang H, Shi W, He W, et al. Simulation of urban transportation carbon dioxide emission reduction environment economic policy in China: An integrated approach using agent-based modelling and system dynamics[J]. Journal of Cleaner Production, 2023, 392: 136221.

[24]. Ren Yanjuan. Research on low carbon strategy of transportation system based on system dynamics[D]. Chongqing Jiaotong University,2020.DOI:10.27671/d.cnki.gcjtc.2020.000067.

[25]. Çınarer G, Yeşilyurt M K, Ağbulut Ü, et al. Application of various machine learning algorithms in view of predicting the CO2 emissions in the transportation sector[J]. Science and Technology for Energy Transition, 2024, 79: 15.

[26]. Ji T, Li K, Sun Q, et al. Urban transportation emission prediction analysis through machine learning and deep learning techniques[J]. Transportation Research Part D: Transport and Environment, 2024, 135: 104389.

[27]. GAO Jinhe, HUANG Weiling,JIANG Haopeng. Multi-model comparative analysis of urban transportation carbon emission prediction[J]. Journal of Chongqing Jiaotong University (Natural Science Edition), 2020, 39(07): 33-39.

[28]. Huo Z, Zha X, Lu M, et al. Prediction of carbon emission of the transportation sector in Jiangsu province-regression prediction model based on GA-SVM[J]. Sustainability, 2023, 15(4): 3631.

[29]. Zhao Y, Liu R, Liu Z, et al. A review of macroscopic carbon emission prediction model based on machine learning[J]. Sustainability, 2023, 15(8): 6876.

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