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Qi Li
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Li, Q.; Li, X.; Ren, A. Exploring Patterns of Transportation-Related CO2 Emissions. Encyclopedia. Available online: https://encyclopedia.pub/entry/22184 (accessed on 04 August 2024).
Li Q, Li X, Ren A. Exploring Patterns of Transportation-Related CO2 Emissions. Encyclopedia. Available at: https://encyclopedia.pub/entry/22184. Accessed August 04, 2024.
Li, Qi, Xiaodong Li, Ai Ren. "Exploring Patterns of Transportation-Related CO2 Emissions" Encyclopedia, https://encyclopedia.pub/entry/22184 (accessed August 04, 2024).
Li, Q., Li, X., & Ren, A. (2022, April 22). Exploring Patterns of Transportation-Related CO2 Emissions. In Encyclopedia. https://encyclopedia.pub/entry/22184
Li, Qi, et al. "Exploring Patterns of Transportation-Related CO2 Emissions." Encyclopedia. Web. 22 April, 2022.
Exploring Patterns of Transportation-Related CO2 Emissions
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This entry is adapted from the peer-reviewed paper 10.3390/su14084588

Carbon dioxide (CO2) emissions are one of the direct results of a transportation sector powered by burning fossil-based fuels.

carbon dioxide emission prediction transportation sector socioeconomic factors

1. Introduction

Global climate change has been recognized as the biggest threat to all living beings in the sea, on land, and in the atmosphere [1]. Unprecedented challenges, such as extreme weather, loss of species, shifting rainfall patterns, glaciers melting, and rising global mean sea level have affected the survival and growth of humanity globally [2]. According to the IPCC Fourth Assessment Report 2014, since 1750, the concentration of carbon dioxide (CO2) in the atmosphere has increased by 40%, while the same measure was 31% in 2001 [2][3]. As of 2010, the transportation sector accounted for 14% of global greenhouse gas emissions [3].
The transportation sector plays an essential role in humanity’s activities and affects the global economy. For example, the transportation of people and goods provides people with mobility, sustainable daily lives, local and international merchandise trade, and economic development [4][5][6][7]. However, most activities in the transportation sector are fueled by fossil-based energy sources, which are not renewable [8]. This implies that, while contributing to the global economy, the transportation sector has negative impacts on global climate change.
Considering its scale and the growing speed in energy consumption, the transportation sector has become the second largest CO2 emitter in the world [9]. The transportation of people and goods accounts for about 25% of total world energy consumption [10], and about 25% of greenhouse emissions in the European Union (EU) [11]. From 1990 to 2015, the share of CO2 emissions from the transportation sector in EU countries increased from 32% to 45% [12]. Given the continued growth in fossil-based energy usage and transportation-based CO2 emissions, building a sustainable transportation sector and reducing its CO2 emissions have become critical, especially for the 196 parties that adopted the Paris Agreement on December 12, 2015 [13].

2. Exploring Patterns of Transportation-Related CO2 Emissions

Existing studies have examined the impact of transportation activities on economic development. The causal relationship between logistics development and economic growth in both the short and long term was studied using a dynamic structural model [14][15][16]. The causal relationship between transportation and income was investigated using a panel dataset [17] that included the data of 15 EU countries from 1970 to 2008, and the researchers found an endogenous relationship between income and transportation. The impact of roadway and railway infrastructure on India’s economic growth was studied using the vector error correction model [18] and weak short-term effects were found. By examining data from India from 1970 to 2010, these researchers found unidirectional causality from railway transportation to economic growth. Another study investigating data from Turkey from 1970 to 2005 showed the impact of highway infrastructure on Turkey’s economic growth [19]. Similarly, the causal relationships between transportation infrastructure investment and economic growth were also studied in China using time series data from 1978 to 2008 [20]. However, unlike previous findings from other countries, these researchers found unidirectional Granger causality from economic growth to transportation sector infrastructure development at the national level. By grouping the 107 countries in the dataset into high-income, middle-income, and low-income countries, Liddle and Lung found Granger causality runs from GDP per capita to transportation energy consumption per capita by analyzing International Energy Agency data from 1971 to 2009 using panel methods [21]. These researchers also found sufficient evidence that many countries exhibited significant Granger causality running from transportation sector energy consumption to GDP. Although these results were not exactly consistent [22][23], the existing literature suggests casual relationships between transportation sector infrastructure development and economic growth, and researchers included selected economic features in the models. Therefore, researchers considered using socioeconomic features (including GDP, income-level, and GDP from different sectors) in the prediction model.
Another stream of existing literature has studied the connections between transportation sector activities and the related CO2 emissions. Lakshmanan and Han suggested that the growth in people’s propensity to travel drove up U.S. transportation energy use and related CO2 emissions from 1970 to 1991. Using a decomposition scheme analysis, the researchers also revealed that freight transportation played a more important role than passenger transportation in U.S. transportation energy use and CO2 emissions [24]. Similarly, Scholl et al. used a comparative analysis approach and studied the changes in CO2 emissions from passenger transportation activities in nine OECD countries [25]. By analyzing the data from 1973 to 1992, the researchers observed a sharp increase in travel-related energy use and CO2 emissions from travel-related activities and discussed the impact of fuel shifts within the transportation sector on the increase of CO2 emissions. In a study conducted by Lu et al., highway vehicle activity was identified as the major driving factor that increased transportation CO2 emissions from 1990 to 2002 in Germany, Taiwan, South Korea, and Japan [26]. Similar studies of transportation sector activity in selected Asian countries or regions suggested that travel-related activity was one of the major potential factors increasing CO2 emissions [27][28][29][30][31]. These studies all suggested that the transportation sector has a direct impact on CO2 emissions and listed it as the key explanatory variable for CO2 emissions at the national level. In this research, the researchers included transportation related features (air, railroad, and vehicle transportation) in the prediction models.
To forecast CO2 emissions, existing studies have adopted different approaches. Some have used time series analysis methods such as exponential smoothing models and ARIMA [32][33][34]. Similar studies used grey models to predict CO2 emissions in China, Iran, and Turkey [35][36]. Many other studies used time series models to predict CO2 emissions in China, the U.S., Malaysia, Iran, and Zimbabwe [37][38]. Some studies used neural network methods for CO2 emission prediction [39]. The gradient boosting decision tree (GBDT) algorithm was also used in predicting CO2 from envelope renovation projects in Taiwan [40]. The support vector machine model was also used in CO2 emission prediction in the Chengdu area [41]. All these studies used a dataset from one single nation and did not employ cross-validation using another nation’s dataset to evaluate the model. To fill this gap, this research aims to predict transportation-related CO2 emissions using socioeconomic features and transportation sector features. Researchers deploy the support vector machine (SVM) model and the gradient boosting regression (GBR) model to compare to the baseline model, the ordinary least squares (OLS) model, in order to find the best model.

References

  1. United Nation. Climate Change, ‘Biggest Threat Modern Humans Have Ever Faced’, World-Renowned Naturalist Tells Security Council, Calls for Greater Global Cooperation. 2021. Available online: https://www.un.org/press/en/2021/sc14445.doc.htm (accessed on 10 January 2022).
  2. IPCC 2001. Climate Change 2001 Synthesis Report: Mitigation. In Contribution of Working Group III to the Third Assessment Report of the Intergovernmental Panel on Climate Change, 2001; Watson, R.T., Ed.; Cambridge University Press: Cambridge, UK, 2001.
  3. Pachauri, R.K.; Meyer, L.A. (Eds.) IPCC 2014 Climate Change 2014: Synthesis Report. In Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; IPCC: Geneva, Switzerland, 2014; p. 151.
  4. Nallapaneni, M.K.; Dash, A. Internet of things: An opportunity for transportation and logistics. In Proceedings of the International Conference on Inventive Computing and Informatics, ICICI, Coimbatore, India, 23–24 November 2017; pp. 194–197.
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  6. Garcia-Lopez, M.-L.; Pasidis, I.; Viladecans-Marsal, E. Express delivery to the suburbs: The effects of transportation in Europe’s heterogeneous cities. SSRN Electron. J. 2015.
  7. Danish, T.H.; Baloch, M.A.; Suad, S. Modeling the impact of transport energy consumption on CO2 emission in Pakistan: Evidence from ARDL approach. Environ. Sci. Pollut. Res. 2018, 25, 9461–9473.
  8. Tarhan, C.; Çil, M.A. A study on hydrogen, the clean energy of the future: Hydrogen storage methods. J. Energy Storage 2021, 40, 102676.
  9. Giannakis, E.; Serghides, D.; Dimitriou, S.; Zittis, G. Land transport CO2 emissions and climate change: Evidence from Cyprus. Int. J. Sustain. Energy 2020, 39, 634–647.
  10. U.S. Energy Information Administration. International Energy Outlook 2016; U.S. Energy Information Administration: Washington, DC, USA, 2016. Available online: https://www.eia.gov/outlooks/ieo/pdf/transportation.pdf (accessed on 2 January 2022).
  11. Sajida, M.J.; Cao, Q.; Kanga, W. Transport sector carbon linkages of EU’s top seven emitters. Transp. Policy 2019, 80, 24–38.
  12. Gonzalez, R.M.; Marrero, G.; Rodriguez-Lopes, J.; Marrero, A. Analyzing CO2 emissions from passenger cars in Europe: A dynamic panel data approach. Energy Policy 2019, 129, 1271–1281.
  13. United Nations Treaty Collection. Paris Agreement; United Nations Treaty Collection: New York, NY, USA, 2015; Available online: https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement (accessed on 2 January 2022).
  14. Lean, H.H.; Huang, W.; Hong, J. Logistics and economic development: Experience from China. Transp. Policy 2014, 32, 96–104.
  15. Yaacob, N.F.F.; Mat Yazid, M.R.; Abdul Maulud, K.N.; Ahmad Basri, N.E. A Review of the Measurement Method, Analysis and Implementation Policy of Carbon Dioxide Emission from Transportation. Sustainability 2020, 12, 5873.
  16. Song, W.; Ren, A.; Li, X.; Li, Q. The Orchestrating Role of Carbon Subsidies in a Capital-Constrained Supply Chain. Math. Probl. Eng. 2021, 2021, 8920624.
  17. Beyzatlar, M.A.; Karacal, M.; Yetkiner, H. Granger-causality between transportation and GDP: A panel data approach. Transp. Res. 2014, 63, 43–55.
  18. Pradhan, R.P.; Bagchi, T.P. Effect of transportation infrastructure on economic growth in India: The VECM approach. Res. Transp. Econ. 2013, 38, 139–148.
  19. Kustepeli, Y.; Gulcan, Y.; Akgungor, S. Transportation infrastructure investment, growth and international trade in Turkey. Appl. Econ. 2012, 44, 2619–2629.
  20. Yu, N.; De Jong, M.; Storm, S.; Mi, J. Transport infrastructure, spatial clusters and regional economic growth in China. Transp. Rev. 2012, 32, 3–28.
  21. Liddle, B.; Lung, S. The long-run causal relationship between transport energy consumption and GDP: Evidence from heterogeneous panel methods robust to cross-sectional dependence. Econ. Lett. 2013, 121, 524–527.
  22. Lean, C.S. Empirical tests to discern linkages between construction and other economic sectors in Singapore. Constr. Manag. Econ. 2001, 19, 355–363.
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  26. Lu, I.J.; Lin, S.J.; Lewis, C. Decomposition and decoupling effects of carbon dioxide emission from highway transportation in Taiwan, Germany, Japan and South Korea. Energy Policy 2007, 35, 3226–3235.
  27. Timilsina, G.R.; Shrestha, A. Transport sector CO2 emissions growth in Asia: Underlying factors and policy options. Energy Policy 2009, 37, 4523–4539.
  28. Zhu, X.; Li, R. An Analysis of Decoupling and Influencing Factors of Carbon Emissions from the Transportation Sector in the Beijing-Tianjin-Hebei Area, China. Sustainability 2017, 9, 722.
  29. Liang, Y.; Niu, D.; Wang, H.; Li, Y. Factors Affecting Transportation Sector CO2 Emissions Growth in China: An LMDI Decomposition Analysis. Sustainability 2017, 9, 1730.
  30. Kim, S. Decomposition Analysis of Greenhouse Gas Emissions in Korea’s Transportation Sector. Sustainability 2019, 11, 1986.
  31. Yuan, Y.; Wang, Y.; Chi, Y.; Jin, F. Identification of Key Carbon Emission Sectors and Analysis of Emission Effects in China. Sustainability 2020, 12, 8673.
  32. Hassouna, F.; Al-Sahili, K. Environmental impact assessment of the transportation sector and hybrid vehicle implications in Palestine. Sustainability 2020, 12, 7878.
  33. Lotfalipour, M.; Falahi, M.; Bastam, M. Prediction of CO2 emissions in Iran using Grey and ARIMA models. Int. J. Energy Econ. Policy 2013, 3, 229–237.
  34. Chigora, F.; Thabani, N.; Mutambara, E. Forecasting 2 emission for Zimbabwe’s tourism destination vibrancy: A univariate approach using box-Jenkins ARIMA model. Afr. J. Hosp. Tour. Leis. 2019, 8.
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  36. Ofosu-Adarkwa, J.; Xie, N.; Javed, S.A. Forecasting CO2 emissions of China’s cement industry using a hybrid Verhulst-GM (1, N) model and emissions’ technical conversion. Renew. Sustain. Energy Rev. 2020, 130, 109945.
  37. Yang, H.; O’Connell, J.F. Short-term carbon emissions forecast for aviation industry in Shanghai. J. Clean. Prod. 2020, 275, 122734.
  38. Ang, C.; Morad, N.; Ismail, M.; Ismail, N. Projection of carbon dioxide emissions by energy consumption and transportation in Malaysia: A time series approach. J. Energy Technol. Policy 2013, 3, 63–75.
  39. Rezaei, M.H.; Sadeghzadeh, M.; Alhuyi Nazari, M.; Ahmadi, M.H.; Astaraei, F.R. Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries. Int. J. Low Carbon Technol. 2018, 13, 266–271.
  40. Tsay, Y.-S.; Yeh, C.-Y.; Chen, Y.-H.; Lu, M.-C.; Lin, Y.-C. A Machine Learning-Based Prediction Model of LCCO2 for Building Envelope Renovation in Taiwan. Sustainability 2021, 13, 8209.
  41. Zeng, H.; Shao, B.; Bian, G.; Dai, H.; Zhou, F. Analysis of Influencing Factors and Trend Forecast of CO2 Emission in Chengdu-Chongqing Urban Agglomeration. Sustainability 2022, 14, 1167.
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Subjects: Green & Sustainable Science & Technology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Qi Li
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Xiaodong Li
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Ai Ren
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Update Date: 24 Apr 2022
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