IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v338y2023ics0306261923001940.html
   My bibliography  Save this article

Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector

Author

Listed:
  • Emami Javanmard, M.
  • Tang, Y.
  • Wang, Z.
  • Tontiwachwuthikul, P.

Abstract

Managing energy demand and reducing greenhouse gas emissions are among the most significant challenges ahead for many countries. Accurate prediction of energy demand and CO2 emissions is an important method to tackle the challenge. In addition, as a major source of energy consumption and CO2 emissions, the transportation sector is at the center of the problem. This study employs a hybrid approach integrating a multi-objective mathematical model with data-driven machine learning algorithms to predict energy demand and CO2 emissions in the transportation sector with improved accuracy. Sensitivity analyses are conducted to evaluate the impact of individual and joint consumptions of different energy resources on CO2 emissions. Case studies are conducted with countrywide statistics in Canada where the prediction indicates that energy demand and CO2 emissions will increase by 34.72% and 50.02% from 2019 to 2048 in the Canadian transportation sector. Results also indicated the varied impacts of different energy resources such that a 5% increase in energy demand in oil, gas, electricity, and renewable energy will result in + 4.8%, +0.315%, +0.28%, and −0.51% changes in CO2 emissions respectively. The proposed framework with integrated prediction model and the sensitivity analyses can help identify critical factors impacting CO2 emissions in the transportation sector and provide quantitative references for energy demand management and CO2 emissions reduction.

Suggested Citation

  • Emami Javanmard, M. & Tang, Y. & Wang, Z. & Tontiwachwuthikul, P., 2023. "Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector," Applied Energy, Elsevier, vol. 338(C).
    • Handle: RePEc:eee:appene:v:338:y:2023:i:c:s0306261923001940
    DOI: 10.1016/j.apenergy.2023.120830
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923001940
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.120830?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pao, Hsiao-Tien & Yu, Hsiao-Cheng & Yang, Yeou-Herng, 2011. "Modeling the CO2 emissions, energy use, and economic growth in Russia," Energy, Elsevier, vol. 36(8), pages 5094-5100.
    2. Benz, Eva & Trück, Stefan, 2009. "Modeling the price dynamics of CO2 emission allowances," Energy Economics, Elsevier, vol. 31(1), pages 4-15, January.
    3. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    4. Klemm, Christian & Vennemann, Peter, 2021. "Modeling and optimization of multi-energy systems in mixed-use districts: A review of existing methods and approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    5. Ren, Fei & Tian, Chenlu & Zhang, Guiqing & Li, Chengdong & Zhai, Yuan, 2022. "A hybrid method for power demand prediction of electric vehicles based on SARIMA and deep learning with integration of periodic features," Energy, Elsevier, vol. 250(C).
    6. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
    7. Ang, James B., 2007. "CO2 emissions, energy consumption, and output in France," Energy Policy, Elsevier, vol. 35(10), pages 4772-4778, October.
    8. Hao, Han & Geng, Yong & Li, Weiqi & Guo, Bin, 2015. "Energy consumption and GHG emissions from China's freight transport sector: Scenarios through 2050," Energy Policy, Elsevier, vol. 85(C), pages 94-101.
    9. He, Kebin & Huo, Hong & Zhang, Qiang & He, Dongquan & An, Feng & Wang, Michael & Walsh, Michael P., 2005. "Oil consumption and CO2 emissions in China's road transport: current status, future trends, and policy implications," Energy Policy, Elsevier, vol. 33(12), pages 1499-1507, August.
    10. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    11. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    12. Kumar Jadoun, Vinay & Rahul Prashanth, G & Suhas Joshi, Siddharth & Narayanan, K. & Malik, Hasmat & García Márquez, Fausto Pedro, 2022. "Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled Whale Optimization Algorithm," Applied Energy, Elsevier, vol. 315(C).
    13. Pao, Hsiao-Tien & Tsai, Chung-Ming, 2011. "Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil," Energy, Elsevier, vol. 36(5), pages 2450-2458.
    14. Piecyk, Maja I. & McKinnon, Alan C., 2010. "Forecasting the carbon footprint of road freight transport in 2020," International Journal of Production Economics, Elsevier, vol. 128(1), pages 31-42, November.
    15. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    16. Leerbeck, Kenneth & Bacher, Peder & Junker, Rune Grønborg & Goranović, Goran & Corradi, Olivier & Ebrahimy, Razgar & Tveit, Anna & Madsen, Henrik, 2020. "Short-term forecasting of CO2 emission intensity in power grids by machine learning," Applied Energy, Elsevier, vol. 277(C).
    17. Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).
    18. Chaturvedi, Shobhit & Rajasekar, Elangovan & Natarajan, Sukumar & McCullen, Nick, 2022. "A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India," Energy Policy, Elsevier, vol. 168(C).
    19. Xu, Shuojiang & Chan, Hing Kai & Zhang, Tiantian, 2019. "Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 169-180.
    20. Auffhammer, Maximilian & Carson, Richard T., 2008. "Forecasting the path of China's CO2 emissions using province-level information," Journal of Environmental Economics and Management, Elsevier, vol. 55(3), pages 229-247, May.
    21. Velasquez, Carlos E. & Zocatelli, Matheus & Estanislau, Fidellis B.G.L. & Castro, Victor F., 2022. "Analysis of time series models for Brazilian electricity demand forecasting," Energy, Elsevier, vol. 247(C).
    22. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    23. Zheng, Bo & Zhang, Qiang & Borken-Kleefeld, Jens & Huo, Hong & Guan, Dabo & Klimont, Zbigniew & Peters, Glen P. & He, Kebin, 2015. "How will greenhouse gas emissions from motor vehicles be constrained in China around 2030?," Applied Energy, Elsevier, vol. 156(C), pages 230-240.
    24. Sonmez, Mustafa & Akgüngör, Ali Payıdar & Bektaş, Salih, 2017. "Estimating transportation energy demand in Turkey using the artificial bee colony algorithm," Energy, Elsevier, vol. 122(C), pages 301-310.
    25. Yang, Christopher & McCollum, David L & McCarthy, Ryan & Leighty, Wayne, 2009. "Meeting an 80% Reduction in Greenhouse Gas Emissions from Transportation by 2050: A Case Study in California," Institute of Transportation Studies, Working Paper Series qt2ns1q98f, Institute of Transportation Studies, UC Davis.
    26. Tian, Wei, 2013. "A review of sensitivity analysis methods in building energy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 411-419.
    27. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
    28. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).
    29. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
    30. Sahraei, Mohammad Ali & Duman, Hakan & Çodur, Muhammed Yasin & Eyduran, Ecevit, 2021. "Prediction of transportation energy demand: Multivariate Adaptive Regression Splines," Energy, Elsevier, vol. 224(C).
    31. Ofosu-Adarkwa, Jeffrey & Xie, Naiming & Javed, Saad Ahmed, 2020. "Forecasting CO2 emissions of China's cement industry using a hybrid Verhulst-GM(1,N) model and emissions' technical conversion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    32. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
    33. Sahraei, Mohammad Ali & Çodur, Merve Kayaci, 2022. "Prediction of transportation energy demand by novel hybrid meta-heuristic ANN," Energy, Elsevier, vol. 249(C).
    34. Soytas, Ugur & Sari, Ramazan & Ewing, Bradley T., 2007. "Energy consumption, income, and carbon emissions in the United States," Ecological Economics, Elsevier, vol. 62(3-4), pages 482-489, May.
    35. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    36. Wu, Lifeng & Liu, Sifeng & Liu, Dinglin & Fang, Zhigeng & Xu, Haiyan, 2015. "Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model," Energy, Elsevier, vol. 79(C), pages 489-495.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ting Chen & Maochun Wang, 2024. "Deep Learning-Based Carbon Emission Forecasting and Peak Carbon Pathways in China’s Logistics Industry," Sustainability, MDPI, vol. 16(5), pages 1-23, February.
    2. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    2. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    3. Cowan, Wendy N. & Chang, Tsangyao & Inglesi-Lotz, Roula & Gupta, Rangan, 2014. "The nexus of electricity consumption, economic growth and CO2 emissions in the BRICS countries," Energy Policy, Elsevier, vol. 66(C), pages 359-368.
    4. P. Srinivasan & Inder Siddanth Ravindra, 2015. "Causality among Energy Consumption, CO2 Emission, Economic Growth and Trade," Foreign Trade Review, , vol. 50(3), pages 168-189, August.
    5. Tiba, Sofien & Omri, Anis, 2017. "Literature survey on the relationships between energy, environment and economic growth," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1129-1146.
    6. Shahbaz, Muhammad & Hye, Qazi Muhammad Adnan & Tiwari, Aviral Kumar & Leitão, Nuno Carlos, 2013. "Economic growth, energy consumption, financial development, international trade and CO2 emissions in Indonesia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 109-121.
    7. Bölük, Gülden & Mert, Mehmet, 2014. "Fossil & renewable energy consumption, GHGs (greenhouse gases) and economic growth: Evidence from a panel of EU (European Union) countries," Energy, Elsevier, vol. 74(C), pages 439-446.
    8. Sofien, Tiba & Omri, Anis, 2016. "Literature survey on the relationships between energy variables, environment and economic growth," MPRA Paper 82555, University Library of Munich, Germany, revised 14 Sep 2016.
    9. Ahmad, Najid & Du, Liangsheng, 2017. "Effects of energy production and CO2 emissions on economic growth in Iran: ARDL approach," Energy, Elsevier, vol. 123(C), pages 521-537.
    10. Pao, Hsiao-Tien & Fu, Hsin-Chia, 2013. "Renewable energy, non-renewable energy and economic growth in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 381-392.
    11. Kivyiro, Pendo & Arminen, Heli, 2014. "Carbon dioxide emissions, energy consumption, economic growth, and foreign direct investment: Causality analysis for Sub-Saharan Africa," Energy, Elsevier, vol. 74(C), pages 595-606.
    12. Dogan, Eyup & Seker, Fahri, 2016. "The influence of real output, renewable and non-renewable energy, trade and financial development on carbon emissions in the top renewable energy countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1074-1085.
    13. Muhammad, Shahbaz, 2012. "Multivariate granger causality between CO2 Emissions, energy intensity, financial development and economic growth: evidence from Portugal," MPRA Paper 37774, University Library of Munich, Germany, revised 31 Mar 2012.
    14. Itkonen, Juha V.A., 2012. "Problems estimating the carbon Kuznets curve," Energy, Elsevier, vol. 39(1), pages 274-280.
    15. Saboori, Behnaz & Sulaiman, Jamalludin, 2013. "CO2 emissions, energy consumption and economic growth in Association of Southeast Asian Nations (ASEAN) countries: A cointegration approach," Energy, Elsevier, vol. 55(C), pages 813-822.
    16. Saboori, Behnaz & Sulaiman, Jamalludin, 2013. "Environmental degradation, economic growth and energy consumption: Evidence of the environmental Kuznets curve in Malaysia," Energy Policy, Elsevier, vol. 60(C), pages 892-905.
    17. Sahraei, Mohammad Ali & Çodur, Merve Kayaci, 2022. "Prediction of transportation energy demand by novel hybrid meta-heuristic ANN," Energy, Elsevier, vol. 249(C).
    18. Mounir Ben Mbarek & Nadia Ben Ali & Rochdi Feki, 2014. "Causality relationship between CO 2 emissions, GDP and energy intensity in Tunisia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 16(6), pages 1253-1262, December.
    19. Chen, Ping-Yu & Chen, Sheng-Tung & Hsu, Chia-Sheng & Chen, Chi-Chung, 2016. "Modeling the global relationships among economic growth, energy consumption and CO2 emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 420-431.
    20. Mohammad Mafizur Rahman & Xuan-Binh (Benjamin) Vu, 2021. "Are Energy Consumption, Population Density and Exports Causing Environmental Damage in China? Autoregressive Distributed Lag and Vector Error Correction Model Approaches," Sustainability, MDPI, vol. 13(7), pages 1-19, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:338:y:2023:i:c:s0306261923001940. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.