The main objective of this study is to identify which mathematical and statistical models are used to forecast greenhouse gas emissions and other atmospheric pollutants from transportation systems. The research demonstrates how mathematical and statistical tools are used to identify and anticipate future scenarios from an environmental perspective, thus contributing to better solutions for mitigating increased emissions. The research concludes that mathematical and statistical models can more accurately anticipate and predict future challenges regarding the predictability of emissions from the transportation sector, promoting strategies that make urban mobility more sustainable. By identifying patterns and trends in emission data, these models contribute to developing proactive measures aimed at mitigating the negative impacts of transportation on the environment. The findings underscore that mathematical and statistical models play a crucial role in accurately predicting and managing future challenges associated with transportation-related emissions, thereby facilitating the development of policies and technological interventions that promote environmentally friendly, efficient, and sustainable urban mobility solutions. In addition, the study emphasizes the importance of integrating real-time data from traffic monitoring systems, fuel consumption reports, and meteorological inputs into forecasting models to improve their precision and adaptability. The growing availability of big data, combined with advancements in computational power, has allowed for more robust and dynamic emission models that can account for temporal and spatial variations in traffic patterns. However, challenges remain in terms of data quality, uncertainty in input variables, and the complexity of human behavior affecting transportation trends. Addressing these challenges requires a multidisciplinary approach that combines expertise from environmental science, transportation engineering, data analytics, and public policy.Furthermore, the adoption of such predictive tools can lead to the implementation of smarter transportation infrastructures, optimized traffic management systems, and targeted interventions aimed at reducing peak emissions. Encouraging the use of cleaner fuels, promoting electric vehicles, and enhancing public transport networks can be guided by insights from these models. Overall, this research reinforces that mathematical and statistical modeling is not only vital for forecasting emissions but also for driving systemic changes that align with global sustainability goals and foster healthier urban environments.

Keywords:greenhouse gas emissions, transportation systems, statistical models, mathematical modeling, air pollution forecasting, sustainable mobility, machine learning, time-series analysis, emission control, urban planning

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1. Araújo, A. R. M., Lobato, T. C. (2020). Analysis and forecast of CO₂ emissions from fuels in the road transportation sector in Brazil: 1990–2020. ANP.
2. CACHOLA, Celso da Silveira; ANDRADE, Ana Clara Antunes Costa de; PEYERL, Drielli. Trends and prospects for fuel consumption in Brazilian road transportation between 2020 and 2030. 2022. Available at: https://www.researchgate.net/publication/358516844_Tendencias_e_Perspectivas_para_o_Consumo_de_Combustiveis_no_Transporte_Rodoviario_Brasileiro_entre_2020_e_2030.
3. DJORDJEVIĆ, B.; KRMAC, E. Evaluation of energy-environment efficiency of European transport sectors. Energies, v. 12, n. 15, p. 2907, 2019.
4. Georgatzi, Vasiliki V. Stamboulis, Yeoryios, Vetsikas, Apostolos, 2020. "Examining the determinants of CO₂ emissions caused by the transport sector: Empirical evidence from 12 European countries," Economic Analysis and Policy, Elsevier, vol. 65(C), pages 11-20.
5. Murta, A. L. S., Freitas, M. A. V., Murta, M. P. A. (2023). CO₂ emissions reduction through biodiesel use in the urban bus fleet of Rio de Janeiro. Revista Valore.
6. SOLAYMANI, S. Biodiesel and its potential to mitigate transport-related CO₂ Carbon Research, v. 2, n. 38, 2023. DOI: 10.1007/s44246-023-00067-z.
7. Oliveira, A. V. M., Pamplona, ​​D. A., Paulo Filho, D. P. (2015). Study and forecast of urban public transportation demand in the São Paulo Metropolitan Region. Public Transportation Journal - ANTP, 37(1)
8. Wang, Q., Zhou, D., & Li, R. (2021). Modeling the impact of traffic flow and vehicle emissions on urban air quality: A case study of Beijing. Environmental Modeling & Assessment, 26(3), 325–338. DOI: 10.1007/s10666-021-09745-2.
9. Smith, A., Johnson, P., & Lee, K. (2022). Integrating machine learning techniques to predict greenhouse gas emissions from metropolitan transportation networks. Journal of Cleaner Transportation, 14(2), 145–162.
10. Chen, L., Zhang, Y., & Xu, H. (2020). A comparative analysis of emission forecasting models in emerging economies: Evidence from India’s road transport sector. Sustainable Cities and Society, 55, 102042. DOI: 10.1016/j.scs.2020.102042.
11. Kumar, S., & Verma, S. (2023). Application of ARIMA and LSTM models in forecasting vehicle emissions in urban areas. International Journal of Environmental Research and Public Health, 20(5), 3574.
12. García, R., Pérez, A., & Torres, J. (2021). Multi-criteria decision-making methods for evaluating transportation energy efficiency and emission reduction strategies. Transportation Research Part D: Transport and Environment, 93, 102774.
13. Lee, S. Y., & Park, H. J. (2019). Estimating the effects of electric vehicle adoption on urban air pollution using statistical and econometric models. Energy Policy, 132, 909–918.
14. Fernández, J., & Costa, M. (2020). Assessing the role of public transport investments in reducing CO₂ emissions: A longitudinal analysis across European cities. Transport Policy, 95, 12–24.
15. Zhang, X., Huang, J., & Chen, Y. (2022). Forecasting traffic-related emissions using real-time data and hybrid modeling approaches: A case study of Shanghai. Atmosphere, 13(8), 1235.
16. Martínez, P., & Santos, J. (2021). Use of DEA and TOPSIS methods for sustainable transport planning and environmental performance evaluation. Journal of Transport Geography, 90, 102873.
17. Almeida, F., Rodrigues, A., & Silva, T. (2023). Exploring the impact of alternative fuels on reducing urban transportation emissions: A case study in Lisbon. Renewable and Sustainable Energy Reviews, 162, 112345.

The main objective of this study is to identify which mathematical and statistical models are used to forecast greenhouse gas emissions and other atmospheric pollutants from transportation systems. The research demonstrates how mathematical and statistical tools are used to identify and anticipate future scenarios from an environmental perspective, thus contributing to better solutions for mitigating increased emissions. The research concludes that mathematical and statistical models can more accurately anticipate and predict future challenges regarding the predictability of emissions from the transportation sector, promoting strategies that make urban mobility more sustainable. By identifying patterns and trends in emission data, these models contribute to developing proactive measures aimed at mitigating the negative impacts of transportation on the environment. The findings underscore that mathematical and statistical models play a crucial role in accurately predicting and managing future challenges associated with transportation-related emissions, thereby facilitating the development of policies and technological interventions that promote environmentally friendly, efficient, and sustainable urban mobility solutions. In addition, the study emphasizes the importance of integrating real-time data from traffic monitoring systems, fuel consumption reports, and meteorological inputs into forecasting models to improve their precision and adaptability. The growing availability of big data, combined with advancements in computational power, has allowed for more robust and dynamic emission models that can account for temporal and spatial variations in traffic patterns. However, challenges remain in terms of data quality, uncertainty in input variables, and the complexity of human behavior affecting transportation trends. Addressing these challenges requires a multidisciplinary approach that combines expertise from environmental science, transportation engineering, data analytics, and public policy.Furthermore, the adoption of such predictive tools can lead to the implementation of smarter transportation infrastructures, optimized traffic management systems, and targeted interventions aimed at reducing peak emissions. Encouraging the use of cleaner fuels, promoting electric vehicles, and enhancing public transport networks can be guided by insights from these models. Overall, this research reinforces that mathematical and statistical modeling is not only vital for forecasting emissions but also for driving systemic changes that align with global sustainability goals and foster healthier urban environments.

Keywords:greenhouse gas emissions, transportation systems, statistical models, mathematical modeling, air pollution forecasting, sustainable mobility, machine learning, time-series analysis, emission control, urban planning

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Araújo, A. R. M., Lobato, T. C. (2020). Analysis and forecast of CO₂ emissions from fuels in the road transportation sector in Brazil: 1990–2020. ANP.
2. CACHOLA, Celso da Silveira; ANDRADE, Ana Clara Antunes Costa de; PEYERL, Drielli. Trends and prospects for fuel consumption in Brazilian road transportation between 2020 and 2030. 2022. Available at: https://www.researchgate.net/publication/358516844_Tendencias_e_Perspectivas_para_o_Consumo_de_Combustiveis_no_Transporte_Rodoviario_Brasileiro_entre_2020_e_2030.
3. DJORDJEVIĆ, B.; KRMAC, E. Evaluation of energy-environment efficiency of European transport sectors. Energies, v. 12, n. 15, p. 2907, 2019.
4. Georgatzi, Vasiliki V. Stamboulis, Yeoryios, Vetsikas, Apostolos, 2020. "Examining the determinants of CO₂ emissions caused by the transport sector: Empirical evidence from 12 European countries," Economic Analysis and Policy, Elsevier, vol. 65(C), pages 11-20.
5. Murta, A. L. S., Freitas, M. A. V., Murta, M. P. A. (2023). CO₂ emissions reduction through biodiesel use in the urban bus fleet of Rio de Janeiro. Revista Valore.
6. SOLAYMANI, S. Biodiesel and its potential to mitigate transport-related CO₂ Carbon Research, v. 2, n. 38, 2023. DOI: 10.1007/s44246-023-00067-z.
7. Oliveira, A. V. M., Pamplona, ​​D. A., Paulo Filho, D. P. (2015). Study and forecast of urban public transportation demand in the São Paulo Metropolitan Region. Public Transportation Journal - ANTP, 37(1)
8. Wang, Q., Zhou, D., & Li, R. (2021). Modeling the impact of traffic flow and vehicle emissions on urban air quality: A case study of Beijing. Environmental Modeling & Assessment, 26(3), 325–338. DOI: 10.1007/s10666-021-09745-2.
9. Smith, A., Johnson, P., & Lee, K. (2022). Integrating machine learning techniques to predict greenhouse gas emissions from metropolitan transportation networks. Journal of Cleaner Transportation, 14(2), 145–162.
10. Chen, L., Zhang, Y., & Xu, H. (2020). A comparative analysis of emission forecasting models in emerging economies: Evidence from India’s road transport sector. Sustainable Cities and Society, 55, 102042. DOI: 10.1016/j.scs.2020.102042.
11. Kumar, S., & Verma, S. (2023). Application of ARIMA and LSTM models in forecasting vehicle emissions in urban areas. International Journal of Environmental Research and Public Health, 20(5), 3574.
12. García, R., Pérez, A., & Torres, J. (2021). Multi-criteria decision-making methods for evaluating transportation energy efficiency and emission reduction strategies. Transportation Research Part D: Transport and Environment, 93, 102774.
13. Lee, S. Y., & Park, H. J. (2019). Estimating the effects of electric vehicle adoption on urban air pollution using statistical and econometric models. Energy Policy, 132, 909–918.
14. Fernández, J., & Costa, M. (2020). Assessing the role of public transport investments in reducing CO₂ emissions: A longitudinal analysis across European cities. Transport Policy, 95, 12–24.
15. Zhang, X., Huang, J., & Chen, Y. (2022). Forecasting traffic-related emissions using real-time data and hybrid modeling approaches: A case study of Shanghai. Atmosphere, 13(8), 1235.
16. Martínez, P., & Santos, J. (2021). Use of DEA and TOPSIS methods for sustainable transport planning and environmental performance evaluation. Journal of Transport Geography, 90, 102873.
17. Almeida, F., Rodrigues, A., & Silva, T. (2023). Exploring the impact of alternative fuels on reducing urban transportation emissions: A case study in Lisbon. Renewable and Sustainable Energy Reviews, 162, 112345.